Open Access

Bio-collections in autism research

  • Jamie Reilly1Email author,
  • Louise Gallagher2,
  • June L. Chen3,
  • Geraldine Leader4 and
  • Sanbing Shen1
Molecular AutismBrain, Cognition and Behavior20178:34

DOI: 10.1186/s13229-017-0154-8

Received: 16 March 2017

Accepted: 23 June 2017

Published: 10 July 2017

Abstract

Autism spectrum disorder (ASD) is a group of complex neurodevelopmental disorders with diverse clinical manifestations and symptoms. In the last 10 years, there have been significant advances in understanding the genetic basis for ASD, critically supported through the establishment of ASD bio-collections and application in research. Here, we summarise a selection of major ASD bio-collections and their associated findings. Collectively, these include mapping ASD candidate genes, assessing the nature and frequency of gene mutations and their association with ASD clinical subgroups, insights into related molecular pathways such as the synapses, chromatin remodelling, transcription and ASD-related brain regions. We also briefly review emerging studies on the use of induced pluripotent stem cells (iPSCs) to potentially model ASD in culture. These provide deeper insight into ASD progression during development and could generate human cell models for drug screening. Finally, we provide perspectives concerning the utilities of ASD bio-collections and limitations, and highlight considerations in setting up a new bio-collection for ASD research.

Background

Autism spectrum disorder (ASD) is a group of early onset and heterogeneous neurodevelopmental disorders affecting males (1/42) more often than females (1/189) [1]. The prevalence of ASD has risen rapidly; from 0.5/1000 people in early epidemiological studies of 1960–1970 [2, 3] to 1/68 children of school age according to recent data from the Centre for Disease Control [1].

ASD is characterised by atypical development of social behaviour, communication deficits and the presence of repetitive and stereotyped behaviours [4]. It is highly clinically heterogeneous and accompanied by commonly occurring comorbidities that are not core to the disorder but frequently disabling. Communication deficit also persists in social communication disorder (SCD), and the new diagnosis of SCD (DSM-5) makes it possible to distinguish ASD from SCD individuals. The severity may vary across a range of parameters including ASD symptoms, IQ and comorbid behaviours [4]. For example, 70% ASD patients will have at least 1 comorbid psychiatric disorder [5], such as social anxiety, depression and bipolar disorder [6]. In addition, ASD is frequently associated with epilepsy, gastrointestinal and immune disorders [7].

ASD is a highly heritable complex polygenic condition. Estimated heritability based on family and twin studies are 50–80% [8, 9]. It is strongly linked to genetic factors involving the development and function of the nervous system [10], mitochondrial function [11], the immune system [12] and epigenetic regulations [13]. Genetic risk is attributed to rare copy number variants (CNV) and single nucleotide variants (SNV) acting on the background of common genetic variation (reviewed by [14]). High throughput genome sequencing technologies have facilitated genomic discovery, and advanced bioinformatics methodologies have enabled investigation of protein-protein interactions [15, 16] and functionally related pathways [17, 18]. The pathway to gene discovery has required large-scale international collaborative efforts based on the assembly of large bio-collections that are now publicly available and the subject of this review. In parallel to bio-collections, large-scale patient registries have provided epidemiological data that illustrate the course and prognosis of ASD and are helping to identify environmental factors influencing the aetiology [1922]. Despite the advances, significant gaps in our knowledge of the aetiology remain and effective treatments for core ASD symptoms are elusive. The genetic and clinical heterogeneity of ASD means that further advancement will require larger bio-collections coupled with rich clinical data, ideally longitudinally to obtain a clear picture of the disorder both on the molecular and physiological levels.

Autism bio-collections

A bio-collection is a large set of biologically characterised samples, such as blood or tissue collected from a group of individuals who typically have a specific medical condition. Bio-collections are useful as a dedicated resource to generate clinical and scientific data for the analysis of medical conditions on a large scale [23], as well as to create functional disease models to explore the biology of clinical conditions. Large-scale bio-collections and associated comprehensive data that can aid the interrogation of the relationship between the genotype and phenotype effects at the individual and group levels can address the issue of heterogeneity. The purpose of this review is to provide a summary of the publicly available ASD bio-collections, to highlight the impact of these on ASD research and to identify new directions for ASD bio-collection for future research purposes.

Methods and search criteria

A literature search was conducted amongst published studies from Jan 2001 to Nov 2016 on electronic databases of Web of Science, EBSCO, PubMed, Science Direct, MEDLINE, Wiley Online Library. The search terms included “biobank”, “registry”, “collection”, “autism” and citation of bio-collections. A total of 263 studies from ASD bio-collections have been included in the tables and references of this review (Tables 2, 3, 4, 5 and 6).

Inclusion criteria

This review included (a) studies using original samples of human tissues in ASD bio-collections; (b) studies using bio-samples extracted from systematically collected bio-resources (i.e. DNA, RNA, protein) for investigating the risk or influence of ASD; (c) the population studies involving participants of autism, Asperger and pervasive developmental disorder not otherwise specified (PDD-NOS); (d) studies published in peer-reviewed journals and (e) in English.

Exclusion criteria

Studies were excluded (a) if they did not mention the collection(s) in the research data, references, acknowledgements or supplementary materials; (b) if the bio-samples were not derived from a systematic sample collection; and (d) if studies only concerned animal models of ASD without using ASD bio-collections or data.

We focus largely on studies from five bio-collections, four providing DNA, cell lines and metabolites, the Autism Genetic Resource Exchange (AGRE), Simons Simplex Collection (SSC), The Danish Newborn Screening Biobank (DNSB) and The Autism Simplex Collection (TASC) one providing brain tissue, Autism BrainNet (formerly the Autism Tissue Program (ATP)).We also included two emerging bio-collections that have fewer or no publications released yet, but could be of significant impact in the future. They are the Autism Inpatient Collection (AIC) [24] and the Autism Spectrum Stem Cell Resource [25]. An overview of the bio-collections and their website links can be found in Table 1.
Table 1

Information on the biobanks covered in the review

Name

Founded

Location

Type of sample collected and stored

Website/source paper

Autism Genetic Resource Exchange (AGRE)

1997

USA

Blood and immortalised cell lines

AGRE, www.agre.org

Simons Simplex Collection (SSC)

2010

USA

Blood and immortalised cell lines

https://sfari.org/resources/autism-cohorts/simons-simplex-collection

Danish Newborn Screening Bio-collection

1980’s

Denmark

Dried blood spot samples

http://www.ssi.dk/Diagnostik/Center%20for%20Neonatal%20Screening/Den%20Neonatale%20Screenings%20Biobank.aspx)

Autism Tissue Program

1998

USA

Post-mortem brain tissue

https://autismbrainnet.org/researchers/

Autism Spectrum Stem cell Resource

2014

USA

Skin fibroblasts, blood, induced pluripotent stem cells, neural stem cells, neuronal cells, glial cells

[25]

The Autism Simplex Collection

2010

USA and Europe

Blood

[97]

Autism Inpatient Collection

2014

USA

Blood and lymphoblasts

[24]

Results

Autism Genetic Resource Exchange (AGRE)

AGRE was established in 1997 by the Cure Autism Now (CAN) Foundation and the Human Biological Data Interchange (HBDI). Samples are provided by families with children affected by ASD and are coupled with anonymously coded clinical diagnostic data, such as Autism Diagnostic Interview–Revised (ADI–R) and Autism Diagnostic Observational Schedule (ADOS). Additional clinical data include photographic dysmorphology, neurological and physical examination, and family and medical history. AGRE is currently managed by Autism Speaks. It contains over 2500 families and the resource has contributed to high profile genetic discoveries relating to ASD (Table 2). Samples are housed at the National Institute of Mental Health repository at Rutgers’ University in the form of immortalised cell lines, DNA and serum samples which can be accessed by researchers through applications [20].
Table 2

Overview of studies using the AGRE collection

Reference

Bio-collection

Samples

Number

Study

Findings

[35]

AGRE

Genomic data (AGRE), brain tissue (mouse)

4327 samples (AGRE)

8789 samples (total)

Genotype-phenotype study

HMGN1 found to be a negative regulator of MECP2 expression. Dysregulation alters behaviour in mice, and AGRE collection contains genotypes linked to altered expression

[180]

AGRE

Blood

152 subjects

Quantitative trait analysis

Chromosome region 7q found to be a risk region for Autism Symptoms

[181]

AGRE

Lymphoblasts

1438 subjects

Association study

CNTNAP2 identified as an ASD susceptibility gene

[182]

AGRE

Blood

1794 subjects

Linkage analysis

Chromosome 7q35 may harbour a gene that could contribute to variability in spoken language

[183]

AGRE

Genomic data

455 subjects

Pedigree study

Association found with chromosome X region Xp22.11-P21.2, where gene IL1RAPL1 is located and also implicated in ASD

[184]

AGRE

Blood and lymphoblasts

252 families

Gene expression analysis and association

ROBO1-4 found to be associated with ASD. Low expression levels of ROBO1-2 found in ASD patients

[185]

AGRE

Blood and lymphoblasts

3211 subjects

Gene association study

Analysis of SNP polymorphisms in PCDHA suggest it as a potential candidate gene for ASD

[186]

AGRE and ATP

Lymphoblasts and brain tissue

3211 subjects (AGRE)

21 subjects (ATP)

Gene association study

ZNF804A found to be associated with ASD and verbal deficits, where knockdown of this gene reduced expression of SNAP25, and both are reduced in the anterior cingulate gyrus in ASD brains.

[187]

AGRE

Blood and lymphoblasts

72 families

Association study

Common variant found in CNTNAP2 that is linked to ASD susceptibility

[43]

AGRE

Blood

470 families (total) 224(AGRE)

Association study

GABRB3 and GABRG3 found to be associated with ASD

[188]

AGRE

Blood and lymphoblasts

975 subjects

CNV analysis

Analysis of 15q13.1-3 region revealed APBA2 as an ASD candidate gene

[189]

AGRE

Blood and lymphoblasts

1577 subjects (total)

1526 subjects (AGRE)

CNV analysis

CNTNAP2 detected in ASD patients suggested to have a contribution to the disorder

[74]

AGRE

Lymphoblasts

6 subjects

Proof of principle

48 genes showed differential expression between patients and controls. Many genes involved in signalling, focal adhesion and metabolism

[190]

AGRE

Lymphoblasts

18 subjects* (controls provided by AGRE)

Profiling study

Altered levels of UBE3A (1.5–2 fold increase) expression found in ASD patients with 15q11-14 duplications. APP and SUMO found to be decreased, and are involved in apoptosis

[40]

AGRE

Blood and lymphoblasts

334 families

Reanalysis of data set using different analysis method

Association found in chromosome 1, which was previously overlooked. Further evidence that 17q11 is associated with ASD

[191]

AGRE

Genomic data

12 families

Method paper

Description of parent of origin method to detect mosaic chromosomal abnormalities.

[192]

AGRE

Blood and lymphoblasts

518 families

Replication study and functional study

The gene EN2 suggested to act as ASD susceptibility locus, and mutations could alter brain development

[41]

AGRE

Blood and lymphoblasts

389 families (AGRE) 518 families (total)

Association study

Haplotypes found in ASD families found to affect regulation of EN2 gene expression

[75]

AGRE

Blood and lymphoblasts

954 subjects

Gene-gene interaction study

Glutathione pathway is implicated in autism

[28]

AGRE

Blood and lymphoblasts

6056 subjects (TOTAL)

4444 subjects (AGRE)

GWAS

UBE3A, NRXN1, BZRAP, and MDGA2 found to have disruptive CNVs amongst ASD patients, some only occurring once amongst patients

[83]

AGRE

Genomic data

830 subjects

Methods paper

Use of disease symptoms improves detection of linkage in genetic data. Useful when heterogeneity is involved

[38]

AGRE

Blood

18 subjects

Genotype-phenotype study

3 out of 18 patients with ASD and macrocephaly had mutations in PTEN gene. Considered as ASD gene to be explored

[39]

AGRE

Blood and lymphoblasts

88 subjects (total)

39 subjects (AGRE)

Mutation screening

De novo missense mutation found in one patient with ASD and macrocephaly.

[193]

AGRE

Blood and lymphoblasts

95 families

Gene linkage study

Chromosome region 2q suggested to contain an autism susceptibility gene

[53]

AGRE

Blood

88 families(total)

62 families (AGRE)

Linkage analysis

GABRB3 polymorphism found to be associated with ASD

[194]

AGRE

Blood

115 families

Linkage analysis

Analysis carried out for a ASD family subset with obsessive compulsive behaviours (n = 35) found evidence of linkage to chromosome 1 and further evidence on chromosome 6 and 19

[82]

AGRE

Blood and lymphoblasts

279 subjects

Method paper

Multiplex ligation-dependent probe amplification shown to be effective at detecting microduplications and deletions

[50]

AGRE

Genomic data

748 subjects

Association study

MET variants associated with social and communication phenotypes amongst people ASD

[49]

AGRE

Blood and lymphoblasts

2712 subjects (total)

631 subjects (AGRE)

Association study

Multiple genes implicated in the MET pathway with ASD, such as PLAUR and SERPINE1

[48]

AGRE

Blood

743 families (total)

283(AGRE)

Association study

MET promoter variant that decreases expression found to be associated with ASD

[195]

AGRE

Blood and lymphoblasts

109 subjects

Replication study

Independent sample from the same cohort showed same linkage association to chromosome region 17q21

[196]

AGRE

Blood

480 families

Genetic score study

3 risk SNPs (ATP2B2, PITX1, HOXA1) had high reproducibility in males, 2 in females (MARK1, ITGB3), and 3 across both genders (CTNAP2, JARID2, EN2).

[197]

AGRE

Blood

381 subjects

Association study

Association between ASD in males and ATP2B2

[198]

AGRE

Blood

2569 subjects

Functional genomics study

Combining functional genomics and statistical analysis helped identify common variants in ASD

[199]

AGRE

Blood

2837 subjects

Association study

Rare haplotype affecting promoter of DLX1 found to be associated with ASD. No common variants found for DLX genes and GAD1

[200]

AGRE

Blood

2261 subjects

GWAS

The chromosome regions Xp22.33/Yp11.31 suggested to harbour male specific variants for ASD

[201]

AGRE

Blood

1132 subjects

QTL analysis

Chromosome regions 16p12-13 and 8q23-24 linked to harbour genes contributing to deficits in non-verbal communication in autistic patients

[202]

AGRE

Blood

993 subjects

Association study

Glu27 allele of ADRB2 gene suggested to confer increased risk of autism, with pregnancy related stressors having an increased effect

[203]

AGRE

Blood and brain tissue

90 subjects

Gene identification

Identification of the gene CORTBP2 from autism candidate region 7q31

[54]

AGRE

Blood

611 families

Association study

Reinforced evidence that GABRA4 and GABRB1 are implicated in ASD. Other ethnic groups found to have SNPs in these genes

[204]

AGRE

Blood

228 families (total)

38 (AGRE)

Association study

HOXG1 polymorphism A218G found to be associated in increased head circumference amongst ASD patients

[205]

AGRE

Genomic data

2165 subjects + 1165 families (total)

2165 subjects (AGRE)

GWAS

Associations found in the following genes with ASD and linked co-morbidities; KCND2, NOS2A and NELL1

[206]

AGRE

DNA

37 twin sets (total)

15 twin sets (AGRE)

Association study

Terbutaline exposure for two or more weeks associated with increased concordance for ASD. 2 polymorphisms for ADRB2 associated with ASD

[207]

AGRE

Blood

284 families (total)

38 families (AGRE)

Linkage/association study

Variants of PON1 found to be associated with ASD families in North America, but not in Italian families

[208]

AGRE

Blood

38 subjects

CNV study

Microdeletions and duplications on chromosome regions 3p26.3, 6q24, 22q11.2, 4q34.2 and 1q24 linked to ASD with physical anomalies. Genes STXBP5 and LRRN1 identified as candidate genes

[209]

AGRE

SSC

Genomic data

2294 subjects (SSC)

579 subjects (AGRE)

35663

CNV analysis

Exploration of evolution of human specific SRGAP2 genes. Rare duplications observed in SSC cohort for SRGAP2C.

[210]

AGRE

Genomic data from [211]

121 families

QTL-analysis

2 loci were identified in chromosomes 11 and 17 associated with social responsiveness in ASD families

[81]

AGRE

Blood

411 families (total)

371 families (AGRE)

Method paper

Detection of amplicons using mismatch repair. More amplicon variants were found in patients compared to controls

[212]

AGRE

Blood

66 subjects

Metabolite analysis

ASD families have lower levels of unprocessed Reelin protein in blood than controls

[213]

AGRE

Blood

90 subjects

Gene characterisation

CADPS and CADPS2 characterised and cloned. Found to be activators of protein secretion. No disease specific variants found amongst ASD patients

[214]

AGRE

Genomic data

1146 subjects

Linkage analysis

Linkage peaks found for language—speech phenotypes consistent with potential motor speed disorder in following chromosome regions; 1q24.2, 3q25.31, 4q22.3, 5p12, 5q33.1, 17p12, 17q11.2, 17q22, 4p15.2 and 21q22.2. multiple candidate genes were also identified

[215]

AGRE

Blood

2140 subjects

Linkage analysis

Parental origin effect significantly linked to chromosomes 4, 15 and 20

[42]

AGRE

Blood

167 families

Association study

EN2 found to be associated with ASD susceptibly

[216]

AGRE

Blood and lymphoblasts

537 subjects (total)

34 subjects (AGRE)

CNV analysis

Proposal that increased CNV load, particularly duplication of base pairs, predisposes to ASD. Negative correlation found with CNV load and social and communication skills. Applied to both common and rare CNVs

[73]

AGRE

Blood and lymphoblasts

4714 subjects (total)

1336 subjects (AGRE)

CNV analysis

Genes involved in Neuronal adhesion (NLGN1, ASTN2) and ubiquitin pathways (UBE3, PARK2, RFWD2, FBXO40) were found in ASD patients. Further evidence of NRXN1 and CNTN4 involved with ASD.

[217]

AGRE

Blood

147 subjects

Genotype phenotype

Suggested relationship between polymorphism MTFR 677C → T and autism-related behaviours

[218]

AGRE

Blood and lymphoblasts

693 subjects (AGRE)

5878 subjects (total)

CNV analysis

Microduplications and microdeletions in chromosome 16p11.2 associated with psychiatric disorders; duplications associated with schizophrenia, bipolar disorder and ASD, and deletions with ASD and other neurodevelopmental disorders

[219]

AGRE

Blood

219 subjects

Variant analysis

DLX1/2 and DLX5/6 gene analysis may not contribute to ASD but functional analysis of variants still worth investigation

[36]

AGRE

Blood

1410 (total)

401 (AGRE)

Association study

No association found for a sequence variant in mental retardation found in exon 1 of MECP gene in autism cohort

[220]

AGRE

Blood and lymphoblasts

112 families(total)

79 families (AGRE)

Association study

A haplotype for DRD1 is found to be associated with ASD risk amongst males

[221]

AGRE

Data from [222]

551 subjects (AGRE)

SNP analysis

Analysis of SNPs revealed variants of CD38 associated with ASD. Variants of CD38 linked to control of OXT secretion.

[223]

AGRE

Lymphoblastoid cell

14 subjects

Gene expression analysis

First study to show differential expression between lymphoblastoid cell lines. Genes affected implicated in cell death and development, nervous system development and immune development and function

[224]

AGRE

Lymphoblasts

116 subjects

Gene expression analysis

Patients with severe ASD showed altered expression of genes involved in Circadian rhythm. 20 novel genes found putative non-coding regions associated with androgen sensitivity

[225]

AGRE

Genomic data

1295 families (total)

696 families (AGRE)

GWAS

Noise reduction filter for GWAS leads to list of 830 candidate genes, where they impact dendrite and axon outgrowth and guidance

[29]

AGRE ATP

Blood and brain

133 sib pairs (total) 77 Sib pairs (AGRE) 17 brain tissue (ATP)

Ogliogenic hypothesis study

Evidence of epigenetic and genetic factors possibly contributing to ASD and UBE3 having a possible role in ASD

[179]

AGRE

Blood and lymphoblasts

192 subjects (AGRE)

483 subjects (total)

Association study

Disruptions in NRXN1 gene found to be associated with ASD

[226]

AGRE

Genomic data

476 subjects (total)

290 subjects (AGRE)

Association study

Suggestive association of parent and maternal origin effect on SLC6A4 promoter variant and ASD. Further testing required on biological model or larger cohort

[26]

AGRE

Blood and lymphoblasts

1549 subjects

410 subjects (AGRE)

Mutation screening

Recurrent microdeletions in chromosome region 16p11.2 were observed in ASD patients and not in controls

[227]

AGRE

Blood and lymphoblasts

974 subjects (total)

512 subjects (AGRE)

Mutation screening

RIMS3 identified as a possible ASD susceptibility gene

[228]

AGRE

Blood

33 families (AGRE)

49 families (total)

Association study

Association found for HLA-DR4 gene in higher frequency in geographically defined subtype, but not in controls or AGRE sample

[229]

AGRE

Blood

508 families (total)

139 families (AGRE)

Association study

Analysis of 2p15-16.1 microdeletions region identified two candidate genes; XPO1 and OXT1

[230]

AGRE

Blood and lymphoblasts

407 families (total)

138 families

Association analysis

Polymorphisms found in or near DLX1 and DLX2 found to be associated with ASD

[231]

AGRE

Blood and lymphoblasts

512 families (total) 138 families (AGRE)

Association study

Association found between ASD and MTHFR gene in simplex families but not in multiplex families

[37]

AGRE

Blood and lymphoblasts

219 families (total)

98 families (AGRE)

Association study

Polymorphisms in MECP2 found to be associated with ASD

[232]

AGRE

Genomic data

990 families

Association study

2 genes found to be associated with ASD; RYR2 and UPP2

[233]

AGRE

Genomic data

2194 families (total)

543 families (AGRE)

Association study

Association found between the calcium channel genes (CACNA1L, CACNA1C and CACNA1) with ASD

[32]

AGRE

Blood

470 families (total)

224 families (AGRE)

Gene association studies

GABRA4 and GABRB1 found to be associated with ASD

[234]

AGRE

Genomic data

680 families (AGRE) 1167 families (total)

GWAS

Identification of a common novel risk locus as chromosome region 5p14.1. Common and rare variants identified. AGRE used as validation dataset

[75]

AGRE

Lymphoblasts

12 subjects

Cell necrosis

Cells from autistic patients were more susceptible to oxidative and nitrosative stress

[235]

AGRE

Blood and lymphoblasts

1142 subjects (total)

139 families (AGRE)

Association study

GTF2i found to be associated with ASD

[236]

AGRE

Blood and lymphoblasts

207 families

CNV analysis

Translocation between short arms of chromosome 16 and 15 reported in 1 female patient. Nominal association of A2BP1/FOX1 observed in ASD cohort.

[237]

AGRE

Serum

21 human subjects

13 rhesus monkeys

Exposure study

Monkeys exposed to antibodies from human mothers of autistic children displayed stereotypies and hyperactive behaviour. Autoimmune component suggested to contribute to ASD

[238]

AGRE

ATP

Blood, lymphoblasts and brain tissue

276 families (AGRE)

17 subjects (ATP)

Association study

MARK1 gene found to be associated with ASD. Overexpression of gene also found in prefrontal cortex (BA46) but not cerebellum in human post-mortem tissue. Mouse model showed abnormalities in dendrites.

[55]

AGRE

Blood

123 families (total)

75 Families (AGRE)

Linkage disequilibrium study

Nominal evidence found for ASD risk alleles in GABAa Receptor subunits

[52]

AGRE

Blood

137 families (total)

80 families (AGRE)

Linkage and association study

SLC6A4 found not to be associated to rigid-compulsive subset of ASD patients.

[239]

AGRE

Blood

158 families (total)

Linkage analysis

Increased support that chromosome regions 19p13 and 17q11.2 harbour ASD susceptibility loci

[240]

AGRE

Blood and lymphoblasts

1336 subjects (AGRE)

1509 subjects (total)

CNV analysis

Large-scale survey of 15q24 microdeletion syndrome identifies atypical deletion that narrows critical region and (776 kb versus 1.75mb) and number of genes (15 versus 38) sequencing of genes recommended

[241]

AGRE

Genomic data

4278 subjects (total)

1518 subjects (AGRE)

Transmission disequilibrium testing

AGRE dataset found to have a genome wide signals at chromosome region 10q26.13 in both sexes and paternal signals in 6p21.1

[30]

AGRE

Blood and lymphoblasts

2886 subjects (total)

1441 subjects (AGRE)

CNV analysis

Microdeletions and duplications at chromosome region 15q13.2q13.3 found to be associated with ASD symptoms and other psychiatric disorders

[242]

AGRE

Blood and Lymphoblasts

34 subjects

Linkage analysis study

Chromosomes 7q and 21q are associated with a subset of ASD patients with developmental regression

[222]

AGRE

Blood and brain tissue

1221 subjects (total)

263 subjects (AGRE)

Association study

Two genetic variants of CD38 found to be associated with ASD

[243]

 

Blood

233 subjects

Association study

HOXA1 A218G alleles found to significantly influence head growth rates.

[244]

AGRE

Blood

196 families

Association study

Association not found between SNPs in DLX6 and PLCO on chromosome 7q21-22 and ASD

[245]

AGRE

Blood

196 families

Association study

Presence of a susceptibility mutation found in TDO2 or nearby gene

[246]

AGRE

Blood and lymphoblasts

249 families

Association study

Elevated levels of STX1A found to be associated with ASD

[47]

AGRE and ATP

Lymphoblasts

14 subjects (AGRE)

84 subjects (ATP)

Methylation study

Different methylation patterns found for genes involved in cell death/survival, neurodevelopment and gene transcription. Decreased expression of RORA and BCL2 was found in brain samples of ASD patients

[247]

AGRE

Blood and lymphoblasts

110 subjects

Genetic association study

Association found between PER1 and NPAS2 and ASD

[248]

AGRE

Blood and lymphoblasts

104 families

Genetic association study

BDNF associated with ASD; significantly higher expression in ASD subjects

[249]

AGRE

Blood and lymphoblasts

13,205 subjects (total)

80 subjects (AGRE)

CNV analysis

Disruption of the PTCHD1 locus on Xp22.11 identified in families with ASD and in families with Intellectual disability. Novel CNVs identified in DPYD and DPP6.

[80]

AGRE and ATP

Lymphoblasts and brain tissue

13 subjects (AGRE)

3 subjects (ATP)

Genotype-phenotype study

Increased dosage of the gene CYFIP1 results in altered cellular and dendritic morphology and dysregulates mTOR pathway in ASD patients with duplications in 15q11-13

[250]

AGRE

Blood and lymphoblasts

95 subjects (AGRE)

134 subjects (total)

Genomic and molecular study

No coding mutations or parental-specific expression found in ASD and Gilles de la tourettes syndrome (GTS) in the gene IMMP2L. Gene should not be written out as factor for both conditions

[251]

AGRE

Blood and lymphoblasts

283 families

Linkage mapping study

PRKCB1 shown to be associated with ASD

[252]

AGRE

Blood and lymphoblasts

1086 subjects

Candidate gene study

PITX1 shown to be associated with ASD

[253]

AGRE

Blood

406 families (total)

99 Families (AGRE)

Association and linkage disequilibrium study

GAD1 SNPs found not to be associated with ASD

[254]

AGRE

Blood

322 families (total)

86 families (AGRE)

Association study

No association found with APOE gene and ASD.

[255]

AGRE

Genomic data

4530 subjects

Association study

Immune function genes CD99L2, JARID2 and TPO show association with ASD

[256]

AGRE

Blood and lymphoblasts

334 families

Association study

Analysis of 2q24-q33 region found following genes associated with ASD; SLC25A12, STK39 and ITGA4

[257]

AGRE

Blood and lymphoblasts

411 families (total)

371 families (AGRE)

Linkage analysis

Linkage analysis of SNPs suggests SLC25A12 to be associated with ASD

[258]

AGRE

Blood and lymphoblasts

352 families

Association study

No association found between polymorphisms in TPH1 and TPH2 and ASD susceptibility or endophenotypes

[259]

AGRE

Blood and lymphoblasts

352 families (total)

295 families (AGRE)

Association study

No association found between SLC6A4 variants and susceptibility to ASD

[260]

AGRE

Blood and lymphoblasts

1011 subjects

Association study

AHI1, a gene associated with Joubert Syndrome, is also implicated in ASD

[261]

AGRE

Genomic data

2883 individuals

Methods paper

Tool that provides visualisation of SNP data

[262]

AGRE

Serum

34 subjects

Metabolite study

ASD patients had lower levels of the enzyme AAT in serum compared to controls. Difference is much more significant in ASD patients with regressive onset

[263]

AGRE

Blood and lymphoblasts

486 subjects (total)

252 subjects (AGRE)

Genotype-phenotype study

Mice with CADPS2 knockout display autistic-like behaviour and cellular features. Analysis of human Cadps2 mRNA revealed aberrant splicing that resulted in some patients lacking exon 3 of the transcribed gene

[264]

AGRE

Blood and genomic data

860 subjects (total)

468 subjects (AGRE)

GWAS

Regions in 5q21.1 and 15q22.1-q22.2 found to have most significant association in combined data for Asperger. 8 regions overlap with ASD linkage areas, and 3 overlapped with a Finnish cohort

[79]

AGRE

Lymphoblasts

14 subjects

MicroRNA analysis

Dysregulation of MicroRNA expression contributes to gene expression in ASD. Gene targets ID3 and PLK2 were validated by knockdown and overexpression assays

[265]

AGRE

Genomic data

289 families

Method paper

SNPs involved in three-way epistatic interactions found and all located in gene GLRX3

[58]

AGRE

Blood and lymphoblasts

264 families

CNV analysis

De novo CNVs were found to be strongly associated with Autism

[266]

AGRE

Blood and lymphoblasts

248 subjects (total)

146 subjects (AGRE)

Association study

Results suggestive that a y-chromosome haplotype effect is associated with ASD

[267]

AGRE

Blood and lymphoblasts

196 families

Transmission analysis

Polymorphisms in INPP1, PIK3G and TSC2 found to have linkage disequilibrium in ASD subjects

[268]

AGRE

Blood and lymphoblasts

196 families

Transmission analysis

Suggestive evidence that GRM8 is a susceptibility gene in ASD

[269]

AGRE

Blood and lymphoblasts

196 families

Association study

Suggestive but tentative evidence for MTF1 and SLC11A3 as ASD suspectability genes

[270]

AGRE

Blood and lymphoblasts

10 subjects

Whole genome sequencing

59 candidate genes suggested to be associated with ASD susceptibility, with ANK3 being the top result. 33 non-coding variants were also identified.

[271]

AGRE

Genomic data [73]

1336 subjects

Method paper

CNV analysis method that uses both B-allele frequency and log R ratio to find CNVs. Found all 21 validated short duplications in AGRE dataset. Analysis is much faster.

[272]

AGRE

Blood and lymphoblasts

Data taken from Ramoz, 2004

Association study

Suggestive association found for ASD-related routines and rituals with a polymorphism in SLC25A12

[273]

AGRE

Blood and lymphoblasts

144 subjects

Sequencing study

7 rare variants found in NLGN3 and NLGN4X. UTR found not to be significant. 2 intronic variants suggested to influence regulation of genes. Limited by throughput and cost

[274]

AGRE

Blood

351 families

Association study

Nominal significance found for 15 genes, top 3 being MYO1D, ACCN1 and LASP1 suggested for further study

[275]

AGRE

Genomic data

148 families

Linkage analysis

Male-specific linkage mapped to chromosome 17q11. Evidence of sex specific risk alleles in ASD

[56]

AGRE

Lymphoblasts

284 subjects

Association study

CACNAG identified as a candidate gene for ASD

[276]

AGRE

Lymphoblasts

267 subjects (AGRE)

Linkage and association study

SLC6A4 shown to contribute to ASD susceptibility

[78]

AGRE

Lymphoblasts

12 subjects

MicroRNA study

Lymphoblastoid cell lines from ASD patients can be used to assess microRNAs in ASD. Dysregulated MicroRNAs found to target genes linked to ASD

[277]

AGRE

Blood samples

100 subjects

Cholesterol metabolism

20% of the samples have shown hypercholestolemia, indicating that cholesterol metabolism could be perturbed in ASD

[278]

AGRE

Genomic DNA

756 subjects

Association study

EGF found to have significant association with ASD

[279]

AGRE and ATP

Data mining (AGRE) brain tissue (ATP) and blood

83 subjects

Linkage study

3p26.1, 3p26.3, 3q25-27 and 5p15 enriched for differentially expressed genes in blood and brain tissue. CNTN4, CADPS2, SUMF1, SLC9A9 and NTRK3 implicated in ASD and even more genes involved in neurological disorders that are co-morbid with ASD

[280]

AGRE

Blood

97 families

Expression profile analysis

RAY1/ST7 locus found to contain a multi-transcript system. Screening of ASD patients found rare variants not present in controls

[281]

AGRE

Blood and lymphoblasts

196 families (total)

95 families (AGRE)

Mutation screening

No mutations found in coding regions of X-chromosomal NLGN genes.

[282]

AGRE

Blood and lymphoblasts

136 families (total)

96 families (AGRE)

Association study

High association of FMR1 gene variant found amongst east Asian individuals, but not when whole sample was analysed, stratification confounded result

[283]

AGRE

Lymphoblasts

11 subjects

Neurotoxicity

Both ASD patients and controls showed upregulation of heat shock proteins when expressed to thimerosal

[33]

AGRE

Blood and lymphoblasts

3101 subjects (AGRE)

10796 subjects (total)

GWAS

Genome-wide SNPs found in CDH10 and CDH9 found to be associated with ASD

[284]

AGRE

ATP

Blood, lymphoblasts and brain tissue

1031 families (AGRE)

3104 families (total)

30 subjects (ATP)

GWAS

Analysis found association in chromosome region 5p15, where genes SEMA5A and TASR2 are located. Analysis of brain tissue showed reduced expression of SEMA5A in ASD subjects

[27]

AGRE

Lymphoblasts

5675 subjects (AGRE)

Association study

Micro deletion found in chromosome 16p11.2. amongst AGRE, Boston Children’s Hospital and Icelandic population data sets

[57]

AGRE

Blood

229 families

Association study

Sodium channel genes SCN1A1-3 contained SNPs of interest amongst ASD families for future studies

[285]

AGRE

Blood

564 families (total)

327 families (AGRE)

genetic analysis only

261 subjects (serotonin analysis)

Association study

ITGB3 genetic variation found to be associated with serotonin blood levels and ASD susceptibility

[286]

AGRE

Genomic data

5328 subjects

Recurrence rate study

Significant difference in recurrence rates between male only families and female carriers in regard to ASD. Female protective effect suggested to be at work in high genetic-risk families involving female carriers. Shorter interbirth intervals correlated to ASD risk.

[287]

AGRE

Blood lymphoblasts

1587 subjects

Linkage analysis

Replication of linkage on 20p13. Linkage found for chromosomes 6q27, 8q13.2, 1p31.3, 8p21.2 and v8p12

[288]

AGRE

Lymphoblasts

75 subjects (total)

50 subjects (AGRE)

Gene characterisation

Gene characterised and assessed for mutation amongst ASD patients. No concrete association found

[289]

AGRE

Genomic data

487 families

Method paper

Pathways of interest analysed using GWAS SNP data. 5 pathways shown to be of significance in regards to ASD

[290]

AGRE

Blood and lymphoblasts

383 subjects

Loci analysis

AGRE and Finnish ASD dataset both showed strong association with 3p24-26 locus containing the gene OXTR

[211]

AGRE

Blood and lymphoblasts

833 families

Genome-wide screen

Evidence of linkage to ASD found on chromosomes 17, 5, 11, 4 and 8, of which 17 having the highest association score in the group

[291]

AGRE

Blood and lymphoblasts

110 families

Genome-wide linkage analysis

Nominal evidence for linkage found in chromosomes 2–4,8, 10–12,15-16,18 and 20. significant linkage found for chromosomes 5 and 8 after reanalysis

[292]

AGRE

Blood and lymphoblasts

389 families

Association study

No evidence found that RH -ABO foetal-maternal incompatibility is associated with ASD

[46]

AGRE

Blood

126 families (total) 81 families (AGRE)

Association study

RELN alleles with large CGG repeats may play a role in aetiology of certain ASD cases

[293]

AGRE

Blood and lymphoblasts

165 subjects

Population genetics

Study suggested two groups: low risk families caused by spontaneous mutations, and high risk caused by female offspring that carry ASD-causing mutation that is passed onto their own offspring

[294]

AGRE

Blood and lymphoblasts

205 families

Gene association study

No association found between ASD and variant of the gene EN2

[295]

AGRE

Lymphoblasts

20 subjects

Intracellular redox study

Inbalance of glutathione redox in cell lines derived from patients with ASD

[76]

AGRE

Lymphoblasts

86 subjects

Transmethylation/transsulfuration study

Cell lines derived from parents of ASD children showed abnormal transmethylation/transsulfuration metabolism and DNA hypomethylation

Study numbers listed as families or subjects wherever applicable

The AGRE resource has been used extensively in genomics studies in ASD. Approaches have included gene-mapping such as genome-wide linkage and association studies in addition to studies of chromosomal structure, particularly the identification of copy number variants. Important ASD chromosomal regions identified include microdeletions and microduplications of 16p11.2 [26, 27], rearrangements and microdeletion/duplication of 15q13.2q13.3 [2831], common variants in the 5p14.1 region [32, 33], Neurexins and 11p12–p13 [34].

It has also helped in identification of recurrent candidate genes, such as MECP2 [3537], PTEN [38, 39], EN2 [4042], RELN [40, 4346], RORA [47], MET [4850], NGLN3-4 [51], BZRAP1 [28], SLC6A4 [40, 52] GABA receptors [32, 43, 5355], CACNA1G [56] and the sodium channel genes SCN1A, SCN2A and SCN3A [57].

These studies particularly highlighted an important role of de novo and large inherited copy number variations (CNVs), which are detected in 10% of sporadic ASD [58], which has been widely replicated in other bio-collections [5971]. The use of AGRE combined with other AGP resources have uncovered SHANK2, SYNGAP1, DLGAP2 and the X-linked DDX53-PTCHD1 locus as novel ASD genes, as well as pathways of cellular proliferation, signalling, neuronal projection and motility [72]. AGRE samples formed a replication set in a separate analysis highlighting CNVs of neuronal cell adhesion and ubiquitin pathway in ASD [73].

AGRE lymphoblastoid cells enabled studies into shared ubiquitin and neuronal gene expression in lymphoblastoid cells and brain [73, 74], gluthathione metabolism, oxidative stress [75, 76] and stress response [77], microRNAs and their use in ASD profiling [78, 79], CYFIP1 dosage effect on mTOR regulation [80], and changes in methylation patterns of RORA and BLC2 and their effects on apoptosis, cellular differentiation, inflammation and neural development [47].

The AGRE collection was also used to establish genetic methodologies and bioinformatic tools. This included using mismatch repair to detect amplicons in ASD [81], using multiplex ligation-dependent probe amplification (MPLA) to improve detection of microduplications and microdeletions [82], and incorporating disease symptoms to improve linkage detection in genetic data [83] and analysis of genetic loci to search for candidate genes [84].

Simons Simplex Collection (SSC)

The SSC is a genetic and clinical repository, which contains material derived from 2600 families. Whereas the AGRE contains multiplex families and trios, The SSC ascertained “simplex” ASD families defined as families where only one child has ASD and at least one other typically developing sibling. DNA is available for both parents, the affected child and an unaffected sibling. Thus the SSC samples are particularly valuable in evaluating parental inheritance. Samples were collected at multiple sites and were stored as immortalised cell lines at Rutgers University Cell and DNA Repository (RUCDR). Each sample was verified for parentage, gender and Fragile X mutation. In-depth clinical phenotypes were characterised for all participants to support genotype-phenotype analyses. These included data on diagnostic status, medical and psychiatric comorbidity, family history and medication use for the affected person. Broader ASD phenotype measures were collected for unaffected family members.

The SSC has become a vast resource of ASD and contributed significantly to numerous Whole exome sequencing studies of ASD in the past ~7 years (Table 3). The main findings showed that de novo mutations were frequently enriched in ASD patients [60]. Whole-genome sequencing results showed a significant enrichment of de novo and private disruptive mutations in putative regulatory regions of previously identified ASD risk genes. It also identified novel risk factors of CANX, SAE1 and PIK3CA with small CNVs and exon-specific SNPs, which were overlooked in previous CNV studies or exon sequencing [85]. It has also been observed that many de novo mutations were of paternal origin (4:1) and positively correlated with paternal age, [65]. The disruptive mutations were located in genes involve in transcription regulation, chromatin remodelling and synapse formation [86, 87].
Table 3

Overview of studies using the SSC collection

Reference

Bio-collection

Samples

Number

Study

Findings

[296]

SSC

Genomic data

2760 subjects

CNV analysis

No association found between conception-assisted reproduction and risk of ASD

[297]

SSC

Lymphoblasts

900 subjects

Sequencing study

Rare functional variants of TSC1/TSC2 did not show association with ASD

[298]

SSC

Genomic data

965 subjects (SSC)

Integrative analysis

Integrative analysis of data from 4 exome sequencing studies revealed enrichment of genes involved in chromatin remodelling and transcription in ASD patients

[88]

SSC

Blood

3730 subjects

Genotype-phenotype

Subtype of autism was caused by mutations to CHD8, of which 15 were found.

[299]

SSC

Blood

259 subjects

CNV analysis

Paired duplications mark cryptic inversions and other complex structural variations in CNV data.

[300]

SSC

Blood

552 subjects (total)

412 subjects (SSC)

Transcriptome analysis

Neuron development, nitric oxide signalling, neurogenesis and skeletal development were found outliers amongst ASD patients in TGEN cohort, whereas outliers were found in neurogenesis in ASD patients from SSC cohort

[301]

SSC

Blood and lymphoblasts

99 families

CNV analysis

55 potential pathogenic CNVs were identified and validated. 20% were considered rare when compared to the database of genomic variants. CNVs found in lymphoblast DNA but not in blood, suggesting pre-existing mutations may have been present in initial lymphoblast cells

[302]

SSC

AGRE

Blood urine

12600 subjects (total)

1887 subjects (SSC)

752 subjects (AGRE)

Association study

TMLHE found to have high levels of deletion in male-male multiplex families (1 in 190) and deficiency of this gene could be a susceptibility factor for ASD.

[303]

SSC

Genomic and exomic data

Taken from earlier studies [60, 61, 70]

Genotype-phenotype study

Mutations in ASD candidate genes have greatest impact on pyramidal neurons, cortical neurons and medium spiny neurons. Truncating de novo mutations play a small role in high-functioning cases. The greater the functional disruption of genes, the more severe the phenotypes are.

[304]

SSC

Blood

2575 subjects

GWAS

Reducing phenotypic heterogeneity within the cohort did not have a significant effect on increasing genetic homogeneity.

[305]

SSC

AGRE

Blood

14989 subjects (total)

5981 subjects (AGRE)

1815 subjects (SSC)

GWAS

CNVs found in SEMA5 regulated gene network found to be associated with ASD

[86]

SSC

Blood

13,804 subjects

WES

104 genes were implicated in 5% of ASD cases, where they are involved in transcription, chromatin remodelling and synapse formation.

[59]

SSC

Blood

2963 subjects

WES

De novo INDELS primarily originate from father, frameshift INDELS associated with ASD, Frameshift INDELS more frequent in females. RIMS1 and KMT2E found to be associated with ASD

[306]

SSC

Blood

8 subjects

Methods Paper

WGS data more effective than WES for detection of INDELS. x60 sequencing required to recover 95% of detected Indels

[307]

SSC

Genomic data

2066 subjects

Homozygosity study

In ASD simplex families, increased runs of homozygosity is associated with Intellectual disability

[308]

SSC

Blood

1227 subjects (total)

350 subjects (SSC)

CNV analysis

CNV burden correlates to certain disorders; high CNV burden to Intellectual disability and low CNV burden to dyslexia

[178]

SSC

AGRE

Blood

3168 subjects (total)

2478 subjects (SSC)

719 subjects (AGRE)

Rearrangement hotspot study

1q21 duplications found to be associated with Autism. CNVs identified in CHD1L, ACACA, DPP10, PLCB1, TRPM1, NRXN1, FHIT and HYDIN enriched in ASD. Duplications linked to decreased non-verbal IQ and duplications linked to severity of ASD.

[149]

SSC

IPSCs and lymphoblasts

1041 subjects

Disease modelling study

Disruption of TRPC6 causes disruption in human neurons and linked to a non-syndromic form of ASD. First Study to use Patent-derived IPSCs to model non-syndromic form of ASD

[309]

SSC

AGRE

Blood

2975 subjects (total)

1429 subjects (SSC)

14 subjects (AGRE

GWAS sequencing

Rare variants in synaptic genes associated with ASD. Loss of function in candidate genes a major risk factor for ASD.

[310]

SSC

TASC

Blood

932 families (total)

Method paper

Transmission and de novo association(TADA) is a method that incorporates WES data, as well as inherited variants, and variants identified between cases and controls

[311]

SSC

Exome data

597 subjects

Method description

Association was found between ASD and rare variants of the gene ABCA7 in exome data

[312]

SSC

Blood

15479 subjects (total)

9479 subjects (SSC)

Transmission analysis

Demonstration that high and low IQs could be distinguished by LGD load in respective gene targets. Transmission of rare variants with low LGD load occurs more often to affected offspring. Biased transmission towards children with low IQ

[61]

SSC

Blood

1478 subjects

WES

Gene disrupting mutations were twice as frequent in ASD subjects compared to controls. Genes disrupted were associated with Fragile X Protein FMRP.

[121]

SSC

Blood

762 subjects

CNV study

Female subjects showed a higher mutational burden before developing ASD.

[313]

SSC

Blood

720 subjects

Association study

Association was found between gene SLC25A12 and restricted and repetitive behaviour.

[314]

SSC

Blood

2106 families (TOTAL)

965 families(SSC)

Common variation study

Multiple common variants of genes additively contribute to ASD risk. Simplex families found to closely follow additive model compared to multiplex families

[315]

SSC

Blood

285 subjects

Transcriptomic study

Enriched genes found in long term potentiation/depression, Notch signalling and neurogenesis amongst ASD Patients. 55 gene prediction model performed well on male subjects, but not female subjects

[316]

SSC

Blood

58 subjects

Transcriptomic study

Upregulation of spliceosome, mitochondrial and ribosomal pathways and downregulation of neuroreceptor-ligand, immune response and calcium signalling pathways in ASD patients compared to controls

[317]

SSC

Genomic data

78349 subjects (total)

3080 subjects (SSC)

SNP study

17–29% of variance in liability explained by SNPS. Genetic correlation found between disorders;

High: Schizophrenia and bipolar disorder

Moderate: Schizophrenia and major depressive disorder, major depressive disorder and ADHD, major depressive disorder and bipolar disorder

Low: Schizophrenia and ASD

[60]

SSC

Genomic Data

1784 subjects

CNV study

De novo duplications and deletions are major contributors to ASD. Females shown to have a greater genetic resistance to autism.

[318]

SSC

AGRE

TASC

Blood

6970 subjects (total)

806 subjects (AGRE)

996 subjects (TASC)

563 subjects (AGRE)

WES

2-fold enrichment of complete knockout of autosomal genes with low LoF variation, and 1.5-fold enrichment for rare hemizygous knockout in males. Both contribute 3 and 2% to ASD risk, respectively.

[63]

SSC

Lymphoblasts

386 subjects

CNV study

Recurrent and rare de novo CNVs were discovered to alter gene expression in chromosome regions 3q27, 3p13, 3p26, 2p15, 16p11.2 and 7q11.23.

[129]

SSC

IPSCs

12 subjects

Disease modelling

Overexpression of FOXG1 was linked to increased head circumference and ASD severity in idiopathic autism subjects. An overabundance of inhibitory neurons in ASD cell lines was also found.

[319]

SSC

Genomic and clinical data

2478 subjects

Gene-environment study

Individuals with ASD-associated CNVs were more susceptible to effects of febrile episodes and maternal infection during pregnancy and have impact on behavioural outcomes

[320]

SSC

Blood

10118 (TOTAL)

1974 (SSC)

Genetic association

Higher prevalence of SLC12A5 variants containing altered CpG sites amongst ASD patients.

[321]

SSC

DNSB

Blood

2418 subjects (SSC)

1353 subjects (DSNB)

CNV analysis

17q12 deletion identified as a CNV variant that confers high risk of ASD and Schizophrenia

[322]

SSC

AGRE

Genomic data

49167 subjects (total)

1124 subjects (SSC)

1835 subjects (AGRE)

CNV analysis

More significant CNVs that could infer ASD risk were identified using combined large clinical datasets of neurodevelopmental disorders than with ASD cohorts alone

[323]

SSC

Lymphoblasts

5451 subjects

Association study

No association was found for heterozygous mutations in CNTNAP2 and contribution to ASD risk

[324]

SSC

Blood and lymphoblastoid cell lines

593 families

Method description

A novel method was used to detect de novo and transmitted insert-deletions(Intel’s) in exomic data

[325]

SSC

Blood

1315 subjects (total)

145 subjects (SSC)

CNV analysis

Duplication CNVs enriched in negative regulation categories, deletion CNVs enriched in positive regulation categories. Highly connected genes in network enriched in patients with a single gene CNV change

[65]

SSC

Blood

677 subjects (SSC)

WES

De novo mutations paternal in origin (4:1) and positive correlation with age. Recurrent mutations in genes CDH8 and NTNG1.

[64]

SSC

Blood

20 families (total)

19 families(SSC)

WES

21 de novo mutations identified. 11 of which found to be protein altering. Mutations identified in FOXP1, GRIN2B, SCN1A, LAMC3 and CNTNAP2.

[87]

SSC

Blood

2246 subjects (SSC)

WES

27 de novo events found in 16 genes, 59% predicted to truncate proteins. further support for genotype-phenotype relationship in CDH8 and DYRKA1

[326]

SSC

Blood

19 subjects (total)

4 subjects (SSC)

Genotype-phenotype

Overexpression/increased dosage of MECP2 related with core features of ASD

[133]

AGRE

SSC

Data taken from [327]

8816 subjects (total)

737 subjects (SSC)

4449 (AGRE)

Replication study

Findings could not be replicated from Skafidas paper

[328]

SSC

DNBS

Genomic data

38000 subjects (total)

4358 subjects (SSC)

19142 subjects (DNBS)

General population study

Genetic influences on ASD risk found to influence typical variation in social and communication ability in the general population

[67]

SSC

Blood

2256 subjects

De novo and familial influences

Familial influences were more significant in cases of high-functioning ASD conditions.

[327]

SSC

Lymphoblasts

1 subject

Clinical report

De novo microdeletion in chromosome 3q29 associated in person with ASD, childhood psychosis and intellectual disability

[68]

SSC

Genomic data

1024 families

De novo mutation analysis

Significant role for loss of function mutations in ASD cases.

[329]

AGRE

SSC

Blood

8816 subjects (total)

737 subjects (SSC)

4449 subjects (AGRE)

Predictive testing

Diagnostic classifier containing 237 SNPs and 146 genes

[330]

SSC

AGRE

Blood

975 subjects (total)

392 subjects (SSC)

585 subjects (AGRE)

Genotype-phenotype study

NPAS1 found to repress generation of specific subtypes of cortical interneurons

[85]

SSC

Blood

53 families

Whole genome sequencing

Enrichment of disruptive mutations in putative regulatory regions in ASD patients

[71]

SSC

Blood

9231 subjects

Genotype-phenotype study

Disrupting mutations in DYRK1A were linked to a subset of 15 patients with a syndromic form of ASD/ID.

[331]

SSC

Blood

903 families

WES

Enrichment of non-synonymous and potentially pathogenic mutations in mitochondrial DNA in ASD patients compared to controls. Transmission of potential pathogenic mutations differed between mother-ASD pairs and mother-sibling pairs

[332]

SSC

Lymphoblasts

1 family

Mutation analysis

PKA found to be an upstream regulator of UBE3A, where mutation in phosphorylation site results in hyperactivity of UBE3A

[333]

SSC

Blood

686 subjects +

612 families (SSC)

WES

Bi-allelic mutations found in genes enriched in inherited ASD cases (AMT, PEX7, SYNE1, VPS13B, PAH, POMGNT1)

[333]

SSC

Blood

928 subjects

WES

Strong evidence that de novo mutations are associated with ASD

[69]

SSC

Blood, lymphoblasts and saliva

1174 families

CNV analysis

Significant associations found between ASD and de novo duplications of chromosome 7q11.23. de novo CNVs identified in 5 other regions, including 16p13.2

[334]

SSC

Blood and lymphoblasts

2591 families

CNV analysis

De novo CNVs associated with ASD. 6 loci and 65 genes identified, many targeting the chromatin or synapse

[335]

SSC

Genomic data

2337 families

Transmission disequilibrium

Excess of truncating inherited mutations associated with ASD. RIMS1, CUL7, LTZR1 identified as candidate genes

[336]

SSC

Genomic data

411 families

Transmission disequilibrium

Affected ASD patients inherited more CNVs than their unaffected siblings, and these CNVs of ASD patients affected more genes. Enrichment of brain-specific genes in inherited CNVs amongst ASD patients

[312]

SSC

Genomic data

10,942 subjects (total)

4942 subjects (SSC)

Biased transmission study

Frequent biased transmission of disruptive mutations to Low IQ ASD patients. Low and high IQ subjects can be distinguished by mutational load.

Study numbers listed as families or subjects wherever applicable

The SSC has enabled detection of the ultra-rare “recurrent” CNVs. This included duplications of 7q11.23, 15q11.2 (NIPA) and 16p13.11, and deletion/duplication of 16p11.2, 16p13.2 (USP7), 1q21.1, 2p16.3, 7q31.1, 15q13.2–q13.3, 16p13.3, 20q13.33 and 22q11.21 [60]. The SSC also helped identify recurrent gene mutations in ASD include CHD8, NTNG1, GRIN2B, SCN1A and LAMC3, which are important for transcriptional regulation, neuronal differentiation and function [87].

CHD8 was further evaluated as an ASD candidate gene in children with developmental delay or ASD, and 15 independent mutations were identified and enriched in a subset of ASD with altered brain size, distinct facial features and gastrointestinal complaints. Disruption of CDH8 in zebra fish recapitulated some of the patient phenotypes including increased head size and impaired gastrointestinal motility [88]. CHD8 is shown to control expression of other high-confidence de novo ASD risk genes such as DYRK1A, GRIN2B and POGZ [89]. Mutation of DYRK1A was strongly linked to a subset of ASD patients with seizures at infancy, hypertonia, intellectual disability, microencephaly, dysmorphic facial features and impaired speech [71, 89]. POGZ gene which plays a role in cell cycle progression is also found to contribute to a subset of ASD with varying developmental delay, vision problems, motor coordination impairment, tendency of obesity, microcephaly, hyperactivity and feeding problems [90].

Danish Newborn Screening (NBS) Biobank

The NBS Biobank has a large collection of dried blood spot samples (DBSS), which are taken from new-borns 5–7 days after birth. They are sent to the New-born Screening lab at the Statens Serum Institute for analysis, and stored at −20 °C in a separate freezing facility at the NBS Biobank. Prior to collection, parents are informed via leaflets about the biobank, with focus on what the samples will be used for (documentation, testing and retesting, research, etc.). Participants can opt out of storage at any time via a letter to the department. For security, both the clinical data and biological samples are linked via a unique number, kept in separate buildings, and are accessible by authorised personnel only [91]. The advantage of the NBS resources is that it provides a large amount of non-ASD controls as well as Danish ASD samples.

In the past 30 years the NBS Bio-collection has accumulated samples from 2.2 million individuals, around 65,000–70,000 samples per year from Denmark, Greenland and the Faroe Islands. Most recently this resource has been included under the Danish iPsych consortium with the Psychiatric Genomics Consortium (PGC), added 8–12 k samples to the PGC analysis and significantly increased its power to detect common genetic effects for ASD, which have been recently published [92].

DBSS were also used to examine metabolites. A group led by Abdallah carried out a series of studies on Danish collections (Table 4) to examine the potential role of cytokines and chemokines involved in signalling and immune response of ASD. Initially using amniotic fluid from the Danish Birth Cohort (DBC) collection [93, 94], they followed up with DBSS from new-borns crossed referenced from that cohort [95, 96]; they detected an imbalance of cytokines amongst ASD subjects compared to the controls. Most of the chemicals were lower than normal, such as Th-1 and Th-2 like cytokines involved in proliferation, priming and activation of these cell types, whereas a small number of cytokines displayed increased expression in ASD. The abnormal levels of these chemicals could lead to a hypoactive or “inactive” immune system in the brain, making it more susceptible to infection-related ASD. However, when chemokine levels were examined in amniotic fluid, no concrete relationship could be established.

The Autism Simplex Collection (TASC)

TASC is a trio-based international bio-collection that was assembled in collaboration with the Autism Genome Project and funded by Autism Speaks [97]. Trios, comprised of both parents and a child affected with ASD with no known medical or genetic cause. Collection of samples took place between 2008 and 2010 across 13 sites; 9 in North America and 4 in Europe. Management, storage and distribution of TASC data are handled by the Centre for Collaborative Genetic Studies on Mental Disorders (CCGSMD) [97]. Samples are housed at the NIMH and AGRE repositories both of which are located at Rutgers University.

So far, TASC has been used for GWAS studies [66] and CNV studies [72, 98, 99] and WES Studies [16, 100, 101]. In addition, TASC has also been used in WGS as part of the MSSNG project, which is discussed below

Autism Inpatient Collection (AIC)

The AIC is a bio-collection for ASD research based on those on the serve end of the spectrum with severe language impairment, intellectual disability and self-injurious behaviour. This collection was founded on the basis that this segment of ASD patients are largely unrepresented in current studies. Bio-samples are initially recruited from 147 patients, and ongoing recruitment is estimated at 400 per year. Psychiatric, clinical and phenotypic data are collected in addition to blood samples for the creation of lymphoblastoid cell lines by RUCDR. Amongst this collection, over half are non-verbal, over 40% have intellectual disability and a quarter exhibit self-injurious behaviour [24]. This collection has yet to be used in any genetics-based studies. The fact that many patients are on the severe end of the spectrum makes it a welcome addition, and it opens opportunities to explore this under-represented group.

Autism Tissue Program (ATP)/Autism BrainNet

The Autism Tissue Program, now the Autism Brain Network, is a post-mortem ASD brain collection coordinated by a network of parents, caregivers, physicians and pathologists. Brain samples are preserved in formalin and/or in −80 °C freezers to maximise the potential studies. In some cases, both hemispheres are fixed in formalin when there is freezing capacity or if the post-mortem interval exceeds 24 h. Corresponding clinical data include age, sex, ethnicity, diagnosis, brain size, cause of death, post-mortem interval and preservation method for the left and right hemisphere of the brain. Due to the rarity of the sample, a thorough application procedure assesses scope, scale and feasibility of proposed projects prior to access of tissue, with the expectation that data, images and presentations generated by research on the samples are provided back to the Autism Brain Network 3 months after formal release of publications [102].

Brain pathology and molecular mechanisms have been the focus of studies using the ATP resource (Table 5) although many studies looking at brain anatomy and cell morphology employed samples from this collection, molecular and genetic studies are the primary focus of this review. Such studies included transcriptomics [103105], epigenetics [29, 106115] and alternative splicing [116, 117]. A key discovery was the identification of convergent molecular pathology linking to neuronal, glial and immune genes [105] in a transcriptomics study that investigated the gene co-expression network between autistic and control brains. This led to the proposal of abnormal cortical patterning as an underlying mechanism due to attenuated differential expression in frontal and temporal cortices in ASD brains.

A recent study showed reduced Vitamin B12 in ASD brains [118] where the ATP made a very large contribution. Post-mortem examination of brain tissue ranging from foetal to the elderly subjects also showed a marked decline of the brain vitamin B12 with age, together with lower activity of methionine synthase in the elderly, but the differences were more pronounced in ASD and schizophrenia subjects when compared to controls. Acetylation is an important post-translational modification in the field of epigenetics. ATP also made a significant contribution to a large-scale histone acetylome-wide association study (HAWAS) using the prefrontal cortex, cerebellum and temporal cortex in ASD patients and controls. Despite their heterogeneity, 68% of syndromic and idiopathic ASD cases shared a common acetylome signature at >5000 cis-regulatory elements in the prefrontal cortex and temporal cortex. Aberrant acetylome was found to be associated with synaptic transmission, ion transport, epilepsy, behavioural abnormality, chemokinesis, histone deacetylation and immunity [113].

The ATP sample was used in a methylation study that investigated differential methylation in CpG loci in three brain regions: temporal cortex, dorsolateral prefrontal cortex and cerebellum. Differential methylation of four genes (PRRT1, C11orf21/TSPAN32, ZFP57 and SDHAP3) was detected. PRRT1, C11orf21/TSPAN32 were hypomethylated while the latter two were hypermethylated [109]. A further investigation in Brodmann’s area also found a pattern of hypomethylation of a number of genes including C11orf21/TSPAN32 that are implicated in immune function and synaptic pruning [111]. These hypomethylated genes correlated with those showing overexpression by Voineagu.

The methylation studies have further uncovered dysregulation of OXTR and SHANK3 genes in ASD. OXTR gene encoding oxytoxcin receptor was significantly hypermethylated in the peripheral blood cells and temporal cortex of ASD, highlighting a reduced oxytocin signalling in the aetiology of ASD [108] and a therapeutic target of ASD. Differential methylation of the SHANK3 gene was detected between ASD and control brains. They found that when three 5′ CpG islands of the gene were examined, they observed altered methylation also changed SHANK3 splicing, with specific SHANK3 isoforms expressed in ASD [114].

This is echoed by a recent study, which reveals a dynamic microexon regulation associated with the remodelling of protein-interaction networks during neurogenesis. The neural microexons are frequently dysregulated in the brains of ASD, which is associated with reduced expression of SRRM4 [116]. The neuronal-specific splicing factor A2BP1/FOX1 and A2BP1-dependent splicing of alternative exons are also dysregulated in ASD brain [105].

Replication studies and pooling resources

Research data from one bio-collection is not always replicable in another sample set. Therefore, cross-validation between different bio-collections will not only minimise false positive, but also identify the common risk factors and subset-specific factors. For example, a genome-wide survey was carried out to test trans-generational effects of mother-child interactions, and the AGRE and SSC samples were used to replicate the original findings of 16 ASD risk genes (PCDH9, FOXP1, GABRB3, NRXN1, RELN, MACROD2, FHIT, RORA, CNTN4, CNTNAP2, FAM135B, LAMA1, NFIA, NLGN4X, RAPGEF4 and SDK1) involving urea transport and neural development. The results from the AGRE and SSC cohorts did not match the original study and showed fewer associations. When post-correction of the statistics was applied, the results lost their significance [119]. This could partially be due to the differences in the array design with different coverage of SNPs and/or different methodologies.

The meta-analysis of five data sets including the AGRE and SSC demonstrates that females have a greater tolerance to CNV burden. This leads to a speculation that the maternal tolerance of the CNVs can result in decreased foetal loss amongst females compared to males, and that ASD-specific CNV burden contributes to high sibling occurrence. What is interesting about this study is that the results for high CNV burden in females are consistent throughout each data set. This is an example showing how multiple bio-collections can give a clearer picture in a combined study where individual studies may be ambiguous [120, 121].

Many major studies on the genetics of ASD have also been accomplished as a result of the collaborations amongst the institutions (Tables 2, 3, 4, 5 and 6). An effort was made to evaluate the association of Fragile X Mental Retardation 2 locus (AFF2) with ASD using joint resources from AGRE (127 males) and SSC (75 males). AFF2 encodes an RNA-binding protein, which is silenced in Fragile X. The study found that 2.5% of ASD males carry highly conserved missense mutations on AFF2 gene which was significantly enriched in ASD patients, when compared to >5000 unaffected controls [122]. A WES was published recently, which sequenced the exomes of over 20,000 individuals, including those from the SSC and Swedish registries. The study identified 107 candidate genes, and reinforced ASD pathways of synaptic formation, chromatin remodelling and gene transcription. This study detected mutations in genes involved in calcium- (CACNA2D3, CACNA1D) and sodium-gated channels (SCN2A) which were related to neuronal function, and in genes involved in post-translational methylation (SUV420H1, KMT2C, ASH1L, SETD5, WHSC1) and demethylation (KDM4B, KDM3A, KDM5B, KDM6B) of lysine residues on histones which provided molecular basis linking to neuronal excitation and epigenetic changes in ASD [86].
Table 4

Overview of studies using the DNSB collection

Reference

Bio-collection

Samples

Number

Study

Findings

[136]

DNSB

DBSS

1100 subjects

Chemokine analysis

Analysis of crude estimates showed decreased levels of RANTES. Adjusted estimates showed no significance amongst 3 chemokines studied (RANTES, MCP-1, MIP-1A). Cautious suggestion of altered immunity in neonatal period amongst ASD patients

[96]

DNSB

DBSS

1200 subjects

Cytokine analysis

Suggestive evidence of decreased levels of certain th-1 and th-2 like cytokines in newborns later diagnosed with ASD.

[136]

DNSB

DBSS

1029 subjects

Neurotropic factor analysis

Decreased level of neurotropic factors found in ASD patients during Neonatal period

Study numbers listed as families or subjects wherever applicable

Table 5

Overview of studies using the ATP/Autism BrainNet collection

Reference

Bio-collection

Samples

Number

Study

Findings

[337]

ATP and AGRE

Brain tissue, blood and lymphoblasts

18 subjects (ATP)

841 families (AGRE)

1029 families (total)

Gene expression and association analysis

Altered expression of mitochondrial genes in anterior cingulate gyrus, motor cortex and thalamus of ASD patients. Polymorphisms in MTX2, NEFL and SLC25A27 found to be associated with ASD.

[338]

ATP

Brain tissue

18 subjects

Gene expression analysis

Reduced expression of several genes related to electron transport in anterior cingulate gyrus, motor cortex and thalamus of ASD patients

[339]

ATP

Brain tissue

57 subjects

Functional genomic study

Analysis of CNVs showed differences of what pathways are altered between children and adults; cell number, cortical patterning and differentiation in the former, and signalling and repair pathways in the latter. Prefrontal cortex samples were used

[106]

ATP

Brain tissue

33 subjects

GWAS

Patients with ASD had more genes that were up- or down-regulated in an- individual specific manner when prefrontal cortex tissue was examined

[340]

ATP

Brain tissue

126 subjects (total)

42 subjects (ATP)

Sequencing study

Recurrent deleterious mutations found in ARID1B, SCN1A, SCN2A and SETD2. Higher proportion of mutations that are deleterious, protein-altering or cause loss-of-function in ASD patients compared to controls. Cortical and cerebellar tissue was used.

[107]

ATP

Brain tissue

25 subjects

Deep sequencing study

Altered adenosine to inosine editing found in cerebella tissue from ASD patients. Dysfunctional for of editing enzyme ADARB1 more frequently in ASD Cerebella

[341]

ATP

Brain tissue

28 subjects (ATP)

43 subjects (total)

Gene expression analysis

Signalling partners of FMRP and GRM5 (HOMER1, APP, RAC1, STEP) shown to have altered expression in the cerebellar vermis and superior frontal cortex in ASD patients compared to controls.

[342]

ATP

Brain tissue

19 subjects

mRNA analysis

Reduction of multiple GABA receptor subtypes (A6, B2, D, E, G2, T and P2) detected in cerebella vermis and superior frontal cortex ASD patients

[343]

ATP

Brain tissue

25 subjects

Assay study

Imbalance in isoforms of precursor BDNF protein found in fusiform gryrus of ASD patients

[103]

ATP

Brain tissue

18 subjects

Transcriptional and epigenetic association analysis

Downregulation of genes related to oxidative phosphorylation and protein translation. Associations were found between specific behaviour domains of ASD and gene expression modules related to myelination, immune response and purinergic signalling. Cerebral and Brodmann area 19 tissue was used

[108]

ATP

Brain tissue

16 subjects

Methylation study

Increased methylation was found for the gene OTXR in ASD patients in blood and DNA from the temporal cortex

[104]

ATP

Brain tissue

107 subjects

Transcriptome analysis

Dysregulated gene expression associated with glial cells shown to have negative correlation with gene expression relating to synaptic transmission in ASD patients when Brodmann areas 10, 19 and 44 were analysed

[344]

ATP

Brain tissue

32 subjects

Transcription analysis

RORA may have dimorphic effects on gene expression in certain areas of cortical tissue between genders, and deficiency appears to cause greater gene dysregulation amongst males in both mice and humans

[345]

ATP

Brain tissue

30 subjects

Transcription analysis

RPP25 expression is decreased in the prefrontal cortex of ASD patients

[116]

ATP

Brain tissue

23 subjects

Alternate splicing analysis and discovery

A conserved group of microexons involved in modulation of interaction domains of proteins and neurogenesis is disrupted in patients with ASD

[29]

ATP

Brain tissue

17 subjects

Methylation study

UBE3 implicated as a contributing gene to autism and Angelman syndrome

[346]

ATP

Brain tissue

20 subjects

Anti-sense RNA study

Discovery of anti-sense non-coding RNA that binds to moesin at 5p14.1 in ASD cerebral cortex tissue

[109]

ATP

Brain tissue

40 subjects

Methylation study

4 differentially methylated regions; 3 in temporal cortex and 1 in cerebellum. 3/4 regions were again found in different samples and brain regions.

[117]

ATP

Brain tissue and lymphoblasts

36 subjects (total)

Transcription and alternative splicing study

Accelerated decrease of MS gene transcription across ageing found in ASD patient cerebral cortex samples

[347]

ATP

Brain tissue

73 subjects

Methylation study

Correlation found between reduced expression of MECP2 and increased methylation on the promoter region

[110]

ATP

Brain tissue

24 subjects

Methylation study

Hypomethylation of mir142 and upregulation of mi-RNAs targeting OXTR gene in prefrontal cortex of ASD brains

[348]

ATP

Brain tissue

24 subjects

Signal transduction study

Downregulation of PI3K-Akt genes observed in fusiform gyrus tissue of ASD patients. Similar effects noted in rat brain tissue exposed to valproic acid

[349]

ATP

Brain tissue (ATP)

neuronal cells

6 subjects

CHiP study

RORA found to regulate 2BP1, CYP19A1, HSD17B10, ITPR1,

NLGN1 and NTRK2 via transcription. Low levels of RORA causes dysregulation of these genes and associated pathways. Prefrontal cortex and cerebellum tissue was used.

[350]

ATP

Brain tissue (ATP)

153 families (other)

54 subjects (ATP)

Functional characterisation study

Variant of the HTR2A gene rs6311 in ASD patients has lower level of expression and contains extended 5′untranslated region. Speculation that this variant could be a risk factor in ASD. Frontopolar cortex tissue was used.

[351]

ATP

Brain tissue

28 subjects

Micro-RNA study

Difference in pattern of micro-RNA expression between ASD superior temporal gyrus samples and controls. Further evidence that Mir-320, Mir-132 and Mir-302 are involved in ASD.

[113]

ATP

Brain tissue

94 subjects (total)

51 subjects (ATP)

Acetylome study

Common acetylome signatures found amongst 68% of ASD cases in 5000 regulatory regions in the prefrontal and temporal cortex. Acetylome profiles were not affected by SNPs at these regulatory regions.

[352]

ATP and AGRE

Brain tissue, blood and lymphoblasts

21 subjects (ATP)

252 families (AGRE)

Association study

Variants of LMX1B show modest association with ASD. Analysis of mRNA from anterior cingulate gyrus is much lower in ASD patients compared to controls.

[105]

ATP

Brain tissue

36 subjects

Gene co-expression network analysis

Transcriptional and splicing dysfunction implicated in disorder. Enrichment for genes in glial, immune and neuronal modules. Gene A2BP1 linked to alterations in splicing. Studies based on using temporal cortex, frontal cortex and cerebellum

[353]

ATP

Brain tissue

28 subjects (total)

8 subjects (ATP)

Gene expression analysis

Genes expressed at higher levels in males enriched in upregulated genes in post-mortem neocortical tissue in ASD patients, including astrocyte and microglia markers

[118]

ATP

Brain tissue (ATP)

and placenta

12 subjects (ATP)

64 subjects (total)

Vitamin B12 study

Reduced levels of B12 found in ASD, aged and Schizophrenic patients compared to controls. oxidative stress found in ASD and Schizophrenia patients. Frontal cortex tissue was used

[114]

ATP

Brain tissue

98 subjects

Methylation study

Altered methylation patterns discovered in SHANK3 gene in cerebella tissue of ASD patients

[115]

ATP

Brain tissue

20 subjects

Epigenetic study

Enrichment of 5-hmc in cerebella tissue may be associated with increased binding by MECP2 to RELN and GAD1 promotors

Study numbers listed as families or subjects wherever applicable

Table 6

Overview of studies using Multipe collections

Reference

Biobank

Sample type

Number

Study

Findings

[119]

SSC and AGRE

Blood and genomic data

8044 subjects (AGRE)

4348 subjects (SSC)

Genome-wide survey on translational effects

Investigation of maternal genetic effects in ASD. Validation using other data sets (SSC and AGRE) did not reproduce similar results).

[354]

SSC and AGRE

Genomic and clinical data

Subjects (AGRE) 941

1048 subjects (SSC)

Gene association study

ATP2C2 and MRPL19 found to be associated with language impairment and dyslexia, respectively

[122]

SSC and AGRE

Blood

AGRE–127 subjects

SSC–75 subjects

Parallel sequencing study

Rare variants of the AFF2 gene found to associated with ASD susceptibility in males

[355]

SSC and AGRE

Genomic data

359 subjects (AGRE)

SS–885 subjects

GWAS

Female protective effect in ASD is not mediated by a single genetic locus.

[356]

AGRE and SSC

Genomic data

13 subjects (AGRE)

3 subjects (SSC)

WES

Loss of CTNND2 function linked to severe ASD

[86]

SSC and TASC

Blood and lymphoblasts

15480 (total)

2475(SSC)

601(TASC)

WES

107 genes implicated in ASD. These genes are responsible for synaptic formation, chromatin remodelling and transcriptional regulation

[123]

SSC and AGRE

Blood and lymphoblasts, genomic and clinical data

5657 subjects (total)

1555 subjects (AGRE)

872 subjects (SSC)

WES

Mutations in SHANK1, 2 and 3 accounts for 1 in 50 ASD cases. SHANK1 mutations linked to mild effects, SHANK2 for moderate and SHANK2 for severe.

Study numbers listed as families or subjects wherever applicable

Multiple bio-collections were employed to investigate SHANK1, 2 and 3, which are scaffolding proteins implicated in ASD. They devised a genetic screen and meta-analysis on patients and controls including cohorts from the AGRE, SSC and Swedish twin registry. In total, ~1% of all patients in the study had a mutation in this group of genes. The mutations in SHANK3 had the highest frequency (0.69%) in patients with ASD and profound intellectual disability. SHANK1 (0.04%) and SHANK2 (0.17%) mutations occurred less frequently and were present in individuals with ASD and normal IQ, and ASD with moderate intellectual disability [123].

Recently Autism, Speaks, in coordination with Google and Genome Canada, have launched another initiative; MSSNG (https://www.mss.ng/). The objective of the MSSNG project is whole genome sequencing of 10000 genomes of families affected by ASD. This incorporates AGRE along with other bio-collections to sequence the entire genomes of families with autistic children, and as of the summer of 2016, it has reached the halfway goal of 5000 genomes out of 10000, with the contribution of the AGRE (1746) and TASC (458). Two studies have been published from this initiative. In the first study, genomes from 200 families were sequenced [124]. The findings revealed many of the de novo mutations (75%) from fathers, which increased dramatically with paternal age. Clustered de novo mutations however were mostly maternal origin, and located near CNV regions subject to high mutation. The ASD genomes were enriched with damaging de novo mutations, of which 15.6% were non-coding and 22.5% genic non-coding, respectively. Many of the mutations affected regulatory regions that are targeted by DNase 1 or involved in exon skipping [124]. The second study [125] featured 5205 sequenced genomes with clinical data, where an average of 73.8 de novo single nucleotide variants and 12.6 insertions/deletions/CNVs were detected per ASD patient. Eighteen new genes were also discovered (CIC, CNOT3, DIP2C, MED13, PAX5, PHF3, SMARCC2, SRSF11, UBN2, DYNC1H1, AGAP2, ADCY3, CLASP1, MYO5A, TAF6, PCDH11X, KIAA2022 and FAM47A) that were not reported in ASD previously. These data clearly demonstrate that ASD is associated with multiple risk factors, and within an ASD individual, and multiple genetic alterations may be present. The Whole genome sequencing is therefore a powerful tool to detect genetic changes at all levels. Resources like MSSNG are valuable, and pooling of ASD bio-collections are essential for identification of the common and subgroup-specific pathways and drug targets of such a multi-factorial disease of ASD which involves hundreds of risk factors.

Stem cell research and autism spectrum stem cell resource

A major impediment to recent drug discovery particularly in the field of neuroscience is the lack of human cell models. The iPSC technology developed by Nobel Laureate Shinya Yamanaka has provided an excellent opportunity [126]. Fibroblasts from patients’ biopsy can be converted into iPSCs with defined transcription factors, which resemble embryonic stem cells and can become most cell types in our body. Therefore, patient-derived iPSCs may be used to investigate disease pathology, progression and mechanisms to create human disease models for drug screening and testing [127, 128].

The SSC has also commenced efforts to create iPSC lines from idiopathic ASD patients who have large head circumference but unknown gene association [129]. The iPSCs were grown into organoids to mimic cortical development, and ASD organoids were shown to display a disproportionate ratio of inhibitory: excitatory neurons. The cortical gene FOXG1 was overexpressed in ASD organoids, and this overexpression correlated with the severity of ASD and their head size [129]. This study has demonstrated a proof-of-concept to model ASD in culture stem cells.

The Children’s Hospital in Orange County California has set up a bio-collection dedicated to this task, the ASD Stem Cell Resource. ASD patients were screened and accepted based on the following criteria: ASD patients if they have no other conditions (i.e. trauma, stroke, seizure disorders) affecting the central nervous system other than ASD; if they have no features of other known genetic conditions (e.g. tuberous sclerosis); Fragile X patients if they are genotypically confirmed for the CGG repeat number of the FMR1 mutation; idiopathic autism patients who are negative for FMR1 mutation and chromosomal abnormality; if they possess an IQ of 40 or greater, and if they are 8-year-old or above. Skin punches and blood were collected in one location (MIND Institute), and fibroblasts were cultured and stored at the Hospital. The collection has been organised into seven groups; unaffected controls, Fragile X without ASD, Fragile X with ASD, permutations without ASD, permutations with ASD, ASD (not meeting full criteria for idiopathic status) and idiopathic ASD.

As of 2014, this resource was composed of iPSCs from 200 unaffected donors and patients. The collection includes fibroblasts, blood, iPSCs, iPSC-derived neuronal and glial cells. The first study published using this bio-collection was the iPSC models of Fragile X syndrome [130]. The Fragile X patient fibroblasts were used to derive iPSCs and differentiate into neurons for transcriptomic analysis. The neuronal differentiation genes (WNT1, BMP4, POU3F4, TFAP2C, PAX3) were shown to be upregulated, whereas potassium channel genes (KCNA1, KCNC3, KCNG2, KCNIP4, KCNJ3, KCNK9, KCNT1) were downregulated in Fragile X iPSC-derived neurons. The temporal regulation of SHANK1 and NNAT genes were also altered, with reduced SHANK1 mRNA and increased NNAT mRNA in patient cells. While the stem cell collection is relatively new, it has great potential to facilitate brain cell culture in vitro, which would otherwise not be feasible by using post mortem brain tissue.

Discussion

It is clear from the studies reviewed here that large ASD bio-collections have had an undisputable impact on progressing genomic discovery in ASD, leading to enhanced understanding of ASD neurobiology. While many studies used private collections as sources for tissue and data, large and well characterised samples from the collections reviewed have supported the discovery of small genetic effects, e.g. in GWAS and rare genetic mutations such as pathogenic CNV and SNV but it is clear, as highlighted for other neurodevelopmental disorders such as Schizophrenia that larger samples are required. Both genetic and phenotypic heterogeneity are impediments to gene discovery. Large bio-collections aim to reduce these effects but challenges remain. Each of the bio-collections reviewed has its own strengths and limitations.

Phenotypic and genotypic heterogeneity

Some of the bio-collections, e.g. SSC, AGRE, TASC, reduced phenotypic heterogeneity through the use of research gold standards for ASD diagnosis, ADI-R and ADOS. Different versions of these instruments based on the timeline when these data have been collected have been used. IQ measurement is more complex to calculate due to the broad range of IQ commonly included within bio-collections. Differences also exist in the clinical profile of subjects included in the different collections with some samples, e.g. SSC, comprised of more individuals with higher cognitive functioning relative to AGRE, TASC or AIC. Medical and psychiatric comorbidities [7] have greater recognition but are not as systematically evaluated in each of the collections. Differences in ascertainment are also relevant. The SSC focused on simplex autism, i.e. families where only one child was affected to maximise the detection of rare variants. Consequently, the relative contribution of common genetic risk within the SSC sample appears reduced. In contrast to autism specific bio-collections, the DNSB, provides a large population-based sample with clinical diagnosis that can maximise power within GWAS studies to detect common genetic variation but does not provide in-depth clinical data for phenotype-genotype analyses. This was evident in the studies on amniotic fluid and DBSS where different diagnostic criteria would have been applied at the time of the subjects’ diagnoses, meaning one criteria would have excluded subjects(ICD8) whereas another would not (ICD10) [95, 96] [93, 94].

Throughout the studies listed here, there is an imbalance of ethnicities of bio-collections, as many of the studies rely heavily on Caucasian/European descent, which has been pointed out in some journals [131] and should consider diverse family structures [132], which can otherwise lead to population stratification [133]. Fortunately, efforts are underway to explore genetics of ASD in other countries such as China [134] and Brazil [135], which will reinforce many of the earlier findings covered in this review.

Samples

Large collections providing DNA for genomics studies have been advantageous; however, as studies move beyond the scope of genetics into transcriptomics, epigenomics and proteomics, a wider variety of sample types will be required. Serum will be valuable for investigating circulating metabolites and proteins that are expressed peripherally, including chemokines [93, 95], cytokines [94, 96], neurotropins [136], MMPs [137] and hormones [138]; however, this may not be the most appropriate tissue to investigate brain relevant ASD genes and proteins. DBSS, which can be useful for WES [139, 140], methylation [141] and gene expression [142], would not be as useful as fresh drawn blood for WGS, as DBSS-derived DNA would need to be amplified prior to use for analysis, potentially causing bias.

However, human brain tissue is a rare resource; brain tissue is very difficult to access due to its scarcity, and the preservation methods used may limit studies being carried out. Also, the types of brain cells are dependent on brain tissue being used; neuronal tissue in grey matter or glial tissue in white matter. Many of the studies listed in the Autism BrainNet, for example, utilised certain sections of the brain; and the most commonly used sections are the prefrontal cortex, temporal cortex, Brodmann’s area, cerebellum and cingulate gyrus. While findings from these sections have been of crucial importance, a capacity to model the entire brain and to observe progression of ASD development would be ideal, and patient’s somatic cells can now be converted to iPSCs and then into disease cell types.

IPSCs have been used as disease models for Fragile X syndrome [143145] and Rett syndrome [146], and iPSCs have been generated from patents with deletions in SHANK3 [147] which are implicated in a number of neurodevelopmental disorders. The three-dimensional culture is developed and iPSCs can also be used to create mini-organoids, which can come very close to mimicking aspects of brain development [129, 148]. In addition to the brain cell types discussed earlier [129, 149], the iPSCs could be used to generate other cell types implicated in ASD co-morbidities, such as the gut [88, 150] and the blood brain barrier [151, 152].

Fibroblasts are the first cell type used to make iPSCs from mice [126] and humans [153] and remain as the most popular cell type for generating neural stem cells, neurons or iPSCs. Fibroblasts are easier to reprogram than many other somatic cells, and the reprogramming efficiency is between 0.1–1% depending on the reprogramming method [154]. They require basic culture media and proliferate rapidly, so large numbers of fibroblasts can be generated in a short period. Unlike keratinocytes they require trained medical personnel to obtain skin biopsies, which could be distressing to some ASD patients. Low passages of fibroblasts are required for reprogramming as higher passages dramatically reduce reprogramming efficiency and increase genomic instability [155].In addition to their use for IPSCs, fibroblasts can be used to investigate amino acid transport, and ASD fibroblasts were found to have greater affinity for transporting alanine, but less affinity for tyrosine—a key component for the synthesis of the neurotransmitter dopamine [156]. Fibroblasts can be used as a proxy to investigate transport across the blood-brain barrier [156, 157] and to investigate calcium signalling [158, 159].

Keratinocytes can also be used for generating IPSCs [160]. Collection is less invasive than skin biopsy and can be carried out by non-medical personnel. The hair samples are easy to transport and culture and transformed cells are easier to identify and isolate. Similar to fibroblasts, keratinocytes are reprogrammed at low passages and fewer methods have been employed to reprogram keratinocytes than fibroblasts. The lentiviral, retroviral and episomal reprogramming were tried successfully [155, 161, 162], and keratinocytes were shown to have high reprogramming efficiency of 1–2%. The major challenge is the reproducibility of keratinocyte growth, and it often requires repeated rounds of hair plucking from a same donor.

Organization

There are many generic articles and white papers for biobanks available, including consensus best-practice recommendations. For those who may wish to start their own bio-collections, we have listed a few articles in Table 7 for further reading on topics pertaining to collection, management, sustainability and quality control. In addition, links to international guidelines can be found here (http://www.oecd.org/sti/biotech/guidelinesforhumanbiobanksandgeneticresearchdatabaseshbgrds.htm; http://www.isber.org/?page=BPR; https://biospecimens.cancer.gov/practices/). However, even when using best practice guidelines, the storage and use of bio samples will be subject to the laws where the facilities are located, and will vary from country to country [163].
Table 7

Description of papers relating to aspects of biobanking

Reference

Subject of paper

[357]

Introduces concept of adding value to stakeholders (patient donors/funders/research customers) and to find balance between aspects of sustainability (acceptability/efficiency/accomplishment)

[358]

Feasibility of simplified consent form for biobanking. Result indicates simplified forms combined with supplemental information for further reading effective in minimising form length and complexity

[359]

Review paper detailing best practice guidelines for sample collection and storage, management of data and infrastructure. In addition, ethical, legal and social issues are explored

[360]

Paper discussing aspects of embryonic stem cell banking that can be applied to iPSCS

[361]

Key issues relating to delivery and safety testing of iPSC stocks for use in research and therapy. Importance of international and national coordinated banking systems are also discussed

[362]

Description of enclosed culture system for iPSCS and neural precursors for use in preclinical and basic research

Participation and ethics

Stakeholders can have a considerable influence on how a bio-collection operates and how a bio-collection can be set up, managed and monitored [164]. In addition to researchers, clinicians and parents in bio-collections of ASD research, autistic stakeholders should be included as part of the stakeholder group, which could help guide and inform how research is carried out. A recent survey [165] was carried out amongst researcher-community engagement on ASD research in the UK. A high dissatisfaction and level of disengagement was expressed by parents and patients, who felt that research outcomes made little or no difference to their day-to-day lives and that they were not communicated, not involved or valued. Patients also felt that they did not receive follow-up and researchers were unapproachable and driven by data collection. Establishment and sustainability of a good stakeholder engagement are essential in ASD research and in biobanking. This will not only help guide research to subjects that matter to the community, but also the future of the biobank. One initiative, such as SPARK (Simons Foundation Powering Autism Research for Knowledge) is underway to encourage ASD communities in the USA to participate in ASD research. While such a goal is laudable, it is crucial that participants are engaged in the entire process. They are not just the suppliers of bio-collections for research and data collection, but also make an input into research areas, which directly impinge on the quality of their life. Meanwhile, regular public events to update research progress and challenges to the stakeholder community may help win their understanding, appreciation and continuous support.

The ethics and obtainment of consent are significant factors for bio-collection research. The main considerations include what information shall be given to potential donors regarding the protocol and its implications of the research, how consent should be obtained [166] or what shall be done if consent was not clearly given [167]. It is also a matter of debate whether the consent should be “broad” and if the patient shall consent to a framework of research; if ethical review of each project shall be carried out by independent committees, and what are the strategies to inform and renew consent if there is significant deviation of framework; where shall the consent be revisited and renewed for every new study [168]; how the data will be protected and accessed [169, 170]; and how the findings will be communicated [171]. The latter is especially important if findings are of clinical significance to certain donors or it may affect their health or well-being [167]. These are the issues that each ethical application faces in making the application.

For people with ASD, it can be very complicated. Parents will give consent for their children if they want to donate samples for the bio-collection, but there is a question of adults who may not have the ability to give consent or to fully understand the implications. It is also important to clearly communicate what this research will mean for the patient and the family, including findings that may be of pathological as well as clinical significance. Liu and Scott have commented on how the discoveries made in ASD research can be distorted by media. If parents/patients are misled to believe that a cure will come out a few years down the road, this may lead to disappointment and make them reluctant to participate in further research. Liu and Scott pointed out that the Neurodiversity Movement group (high-functioning autists) would have issues with certain research. They will not participate in research if they feel it may threaten or undermine people with ASD [128]. They prefer investment on services and therapies, rather than on genetic studies which may result in prevention of autistics being born [172174], and the idea of curing autism is a complicated topic of debate [175].

For iPSC research, it was suggested to educate participants on the current state of research, to clearly explain the benefits and risks of biopsy donation and to consult the ASD community on research focus of an ASD bio-collection and on distribution of the cell lines [128]. For clinical trials of stem cells, stem cell counsellors shall inform participants the benefits and risks of enrolling in stem cell trials and to safeguard them from the dangers of stem cell tourism. Such an approach should also be considered for ASD-related studies [176].

Conclusions

In conclusion, bio-collections have been shown as valuable resources and enabled large-scale studies on ASD. The recent genetic studies have begun to reveal de novo mutations on major cellular pathways [17, 177]. There is also emerging evidence that ASD continuum contains subgroups with discrete mutations in specific genes such as CDH8 [88], DYRK1A [71] and POGZ [90] and gene mutations like NRXN1 [28, 60, 73, 178, 179] and SHANKs [72, 98, 114, 123] recurring in broad populations. There is a vast amount of clinical and biological information available in these bio-collections, and the data are in the need for concrete guidelines on ethics and governance. The communication and trust shall be maintained between the researchers and families who have given biological and personal information. Finally, the availability of iPSC resources dedicated to idiopathic and syndromic forms of ASD could be a tremendous boon to the research community and such models are anticipated to be complementary with animal models and to speed up the development of therapeutic interventions for ASD. They could open up the possibilities of functional studies of ASD on a large scale and could become a future model for other iPSC bio-collections to be set up worldwide.

Abbreviations

ADI-R: 

Autism Diagnostic Interview–Revised

ADOS: 

Autistic Diagnostic Observation Schedule

AGP: 

Autism Genome Project

AGRE: 

Autism Genetic Resource Exchange

AIC: 

Autism Inpatient Collection

ASD: 

Autism spectrum disorders

ATP: 

Autism tissue program

CAN: 

Cure Autism Now Foundation

CCGSMD: 

Centre for Collaborative Genetic Studies on Mental Disorders

CNV: 

Copy number variation

DBC: 

Danish Birth Cohort

DBSS: 

Dried blood spot samples

DNSB: 

Danish Newborn Screening Biobank

DSM-5: 

Diagnostic and Statistical Manual of Mental Disorders

GSH: 

Glutathione

GWAS: 

Genome-wide association study

HAWAS: 

Histone acetylome-wide association study

HBDI: 

Human Biological Data Interchange

ICD: 

International Statistical Classification of Diseases and Related Health Problems

iPSC: 

Induced pluripotent stem cells

MMP: 

Matrix metalloproteinase

MPLA: 

Multiplex ligation-dependent probe amplification

PDD-NOS: 

Pervasive developmental disorder not otherwise specified

RUCDR: 

Rutgers University Cell and DNA Repository

SCD: 

Social (pragmatic) communication disorder

SNP: 

Single nucleotide polymorphism

SNV: 

Single nucleotide variation

SSC: 

Simons Simplex Collection

TASC: 

Autism Simplex Collection

WES: 

Whole exome sequencing study

Declarations

Acknowledgements

I would like to thank all authors who contributed to this manuscript

Funding

Not applicable.

Availability of data and materials

Not applicable.

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Authors’ contributions

JR collected the literature and wrote the manuscript and tables. JLC authored the search criteria section. SS, LG and GL gave feedback and suggested changes which were incorporated into the manuscript. All authors read and approved the final manuscript.

Authors’ information

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Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Regenerative Medicine Institute, School of Medicine, BioMedical Sciences Building, National University of Ireland (NUI)
(2)
Trinity Translational Medicine Institute and Department of Psychiatry, Trinity Centre for Health Sciences
(3)
Department of Special Education, Faculty of Education, East China Normal University
(4)
Irish Centre for Autism and Neurodevelopmental Research (ICAN), Department of Psychology, National University of Ireland Galway

References

  1. Christensen DL, Baio J, Van Naarden Braun K, Bilder D, Charles J, Constantino JN, et al. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. MMWR Surveill Summ. 2016;65:1–23. doi:10.15585/mmwr.ss6503a1.PubMedView ArticleGoogle Scholar
  2. Charman T. The prevalence of autism spectrum disorders. Recent evidence and future challenges. Eur Child Adolesc Psychiatry. 2002;11:249–56. doi:10.1007/s00787-002-0297-8.PubMedView ArticleGoogle Scholar
  3. Newschaffer CJ, Croen LA, Daniels J, Giarelli E, Grether JK, Levy SE, et al. The epidemiology of autism spectrum disorders. Annu Rev Public Health. 2007;28:235–58. doi:10.1146/annurev.publhealth.28.021406.144007.PubMedView ArticleGoogle Scholar
  4. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington: American Psychiatric Association; 2013. doi:10.1176/appi.books.9780890425596.View ArticleGoogle Scholar
  5. Simonoff E, Pickles A, Charman T, Chandler S, Loucas T, Baird G. Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample. J Am Acad Child Adolesc Psychiatry. 2008;47:921–9.PubMedView ArticleGoogle Scholar
  6. Mazzone L, Ruta L, Reale L. Psychiatric comorbidities in asperger syndrome and high functioning autism: diagnostic challenges. Ann Gen Psychiatry. 2012;11:16. doi:10.1186/1744-859X-11-16.PubMedPubMed CentralView ArticleGoogle Scholar
  7. Chen M-H, Su T-P, Chen Y-S, Hsu J-W, Huang K-L, Chang W-H, et al. Comorbidity of allergic and autoimmune diseases in patients with autism spectrum disorder: A nationwide population-based study. Res Autism Spectr Disord. 2013;7:205–12. doi:10.1016/j.rasd.2012.08.008.View ArticleGoogle Scholar
  8. Abrahams BS, Geschwind DH. Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet. 2008;9:341–55. doi:10.1038/nrg2346.PubMedPubMed CentralView ArticleGoogle Scholar
  9. Tick B, Bolton P, Happé F, Rutter M, Rijsdijk F. Heritability of autism spectrum disorders: a meta-analysis of twin studies. J Child Psychol Psychiatry. 2016;57:585–95. doi:10.1111/jcpp.12499.PubMedView ArticleGoogle Scholar
  10. Persico AM, Napolioni V. Autism genetics. Behav Brain Res. 2013;251:95–112. doi:10.1016/j.bbr.2013.06.012.PubMedView ArticleGoogle Scholar
  11. Rossignol DA, Frye RE. Mitochondrial dysfunction in autism spectrum disorders: a systematic review and meta-analysis. Mol Psychiatry. 2012;17:290–314. doi:10.1038/mp.2010.136.PubMedView ArticleGoogle Scholar
  12. Onore C, Careaga M, Ashwood P. The role of immune dysfunction in the pathophysiology of autism. Brain Behav Immun. 2012;26:383–92. doi:10.1016/j.bbi.2011.08.007.PubMedView ArticleGoogle Scholar
  13. Schanen NC. Epigenetics of autism spectrum disorders. Hum Mol Genet. 2006;15 Spec No 2:R138–50. doi:10.1093/hmg/ddl213.PubMedView ArticleGoogle Scholar
  14. Bourgeron T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nat Rev Neurosci. 2015;16:551–63.PubMedView ArticleGoogle Scholar
  15. Sakai Y, Shaw CA, Dawson BC, Dugas DV, Al-Mohtaseb Z, Hill DE, et al. Protein interactome reveals converging molecular pathways among autism disorders. Sci Transl Med. 2011;3:86ra49. doi:10.1126/scitranslmed.3002166.PubMedPubMed CentralView ArticleGoogle Scholar
  16. Liu L, Lei J, Sanders SJ, Willsey AJ, Kou Y, Cicek AE, et al. DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics. Mol Autism. 2014;5:22. doi:10.1186/2040-2392-5-22.PubMedPubMed CentralView ArticleGoogle Scholar
  17. Oron O, Elliott E. Delineating the Common Biological Pathways Perturbed by ASD’s Genetic Etiology: Lessons from Network-Based Studies. Int J Mol Sci. 2017;18:828.PubMed CentralView ArticleGoogle Scholar
  18. Wen Y, Alshikho MJ, Herbert MR. Pathway Network Analyses for Autism Reveal Multisystem Involvement, Major Overlaps with Other Diseases and Convergence upon MAPK and Calcium Signaling. PLoS ONE. 2016;11:e0153329. doi:10.1371/journal.pone.0153329.PubMedPubMed CentralView ArticleGoogle Scholar
  19. Fischbach GD, Lord C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron. 2010;68:192–5. doi:10.1016/j.neuron.2010.10.006.PubMedView ArticleGoogle Scholar
  20. Geschwind DH, Sowinski J, Lord C, Iversen P, Shestack J, Jones P, et al. The Autism Genetic Resource Exchange: A Resource for the Study of Autism and Related Neuropsychiatric Conditions. Am J Hum Genet. 2001;69:463–6. doi:10.1086/321292.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Stoltenberg C, Schjølberg S, Bresnahan M, Hornig M, Hirtz D, Dahl C, et al. The Autism Birth Cohort: a paradigm for gene-environment-timing research. Mol Psychiatry. 2010;15:676–80. doi:10.1038/mp.2009.143.PubMedPubMed CentralView ArticleGoogle Scholar
  22. Al-Jawahiri R, Milne E. Resources available for autism research in the big data era: a systematic review. PeerJ. 2017;5:e2880. doi:10.7717/peerj.2880.PubMedPubMed CentralView ArticleGoogle Scholar
  23. Lochmüller H, Schneiderat P. Biobanking in rare disorders. Adv Exp Med Biol. 2010;686:105–13. doi:10.1007/978-90-481-9485-8_7.PubMedView ArticleGoogle Scholar
  24. Siegel M, Smith KA, Mazefsky C, Gabriels RL, Erickson C, Kaplan D, et al. The autism inpatient collection: methods and preliminary sample description. Mol Autism. 2015;6:61. doi:10.1186/s13229-015-0054-8.PubMedPubMed CentralView ArticleGoogle Scholar
  25. Brick DJ, Nethercott HE, Montesano S, Banuelos MG, Stover AE, Schutte SS, et al. The autism spectrum disorders stem cell resource at children’s hospital of orange county: implications for disease modeling and drug discovery. Stem Cells Transl Med. 2014;3:1275–86. doi:10.5966/sctm.2014-0073.PubMedPubMed CentralView ArticleGoogle Scholar
  26. Kumar RA, KaraMohamed S, Sudi J, Conrad DF, Brune C, Badner JA, et al. Recurrent 16p11.2 microdeletions in autism. Hum Mol Genet. 2008;17:628–38. doi:10.1093/hmg/ddm376.PubMedView ArticleGoogle Scholar
  27. Weiss LA, Shen Y, Korn JM, Arking DE, Miller DT, Fossdal R, et al. Association between microdeletion and microduplication at 16p11.2 and autism. N Engl J Med. 2008;358:667–75. doi:10.1056/NEJMoa075974.PubMedView ArticleGoogle Scholar
  28. Bucan M, Abrahams BS, Wang K, Glessner JT, Herman EI, Sonnenblick LI, et al. Genome-wide analyses of exonic copy number variants in a family-based study point to novel autism susceptibility genes. PLoS Genet. 2009;5:e1000536. doi:10.1371/journal.pgen.1000536.PubMedPubMed CentralView ArticleGoogle Scholar
  29. Jiang Y-H, Sahoo T, Michaelis RC, Bercovich D, Bressler J, Kashork CD, et al. A mixed epigenetic/genetic model for oligogenic inheritance of autism with a limited role for UBE3A. Am J Med Genet A. 2004;131:1–10. doi:10.1002/ajmg.a.30297.PubMedView ArticleGoogle Scholar
  30. Miller DT, Shen Y, Weiss LA, Korn J, Anselm I, Bridgemohan C, et al. Microdeletion/duplication at 15q13.2q13.3 among individuals with features of autism and other neuropsychiatric disorders. J Med Genet. 2009;46:242–8. doi:10.1136/jmg.2008.059907.PubMedView ArticleGoogle Scholar
  31. Sahoo T, Shaw CA, Young AS, Whitehouse NL, Schroer RJ, Stevenson RE, et al. Array-based comparative genomic hybridization analysis of recurrent chromosome 15q rearrangements. Am J Med Genet A. 2005;139A:106–13. doi:10.1002/ajmg.a.31000.PubMedView ArticleGoogle Scholar
  32. Ma DQ, Whitehead PL, Menold MM, Martin ER, Ashley-Koch AE, Mei H, et al. Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. Am J Hum Genet. 2005;77:377–88. doi:10.1086/433195.PubMedPubMed CentralView ArticleGoogle Scholar
  33. Wang K, Zhang H, Ma D, Bucan M, Glessner JT, Abrahams BS, et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature. 2009;459:528–33. doi:10.1038/nature07999.PubMedPubMed CentralView ArticleGoogle Scholar
  34. Autism Genome Project Consortium, Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, et al. Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet. 2007;39:319–28. doi:10.1038/ng1985.View ArticleGoogle Scholar
  35. Abuhatzira L, Shamir A, Schones DE, Schäffer AA, Bustin M. The chromatin-binding protein HMGN1 regulates the expression of methyl CpG-binding protein 2 (MECP2) and affects the behavior of mice. J Biol Chem. 2011;286:42051–62. doi:10.1074/jbc.M111.300541.PubMedPubMed CentralView ArticleGoogle Scholar
  36. Harvey CG, Menon SD, Stachowiak B, Noor A, Proctor A, Mensah AK, et al. Sequence variants within exon 1 of MECP2 occur in females with mental retardation. Am J Med Genet B Neuropsychiatr Genet. 2007;144B:355–60. doi:10.1002/ajmg.b.30425.PubMedView ArticleGoogle Scholar
  37. Loat CS, Curran S, Lewis CM, Duvall J, Geschwind D, Bolton P, et al. Methyl-CpG-binding protein 2 polymorphisms and vulnerability to autism. Genes Brain Behav. 2008;7:754–60. doi:10.1111/j.1601-183X.2008.00414.x.PubMedPubMed CentralView ArticleGoogle Scholar
  38. Butler MG, Dasouki MJ, Zhou XP, Talebizadeh Z, Brown M, Takahashi TN, et al. Subset of individuals with autism spectrum disorders and extreme macrocephaly associated with germline PTEN tumour suppressor gene mutations. J Med Genet. 2005;42:318–21. doi:10.1136/jmg.2004.024646.PubMedPubMed CentralView ArticleGoogle Scholar
  39. Buxbaum JD, Cai G, Chaste P, Nygren G, Goldsmith J, Reichert J, et al. Mutation screening of the PTEN gene in patients with autism spectrum disorders and macrocephaly. Am J Med Genet B Neuropsychiatr Genet. 2007;144B:484–91. doi:10.1002/ajmg.b.30493.PubMedPubMed CentralView ArticleGoogle Scholar
  40. Bartlett CW, Gharani N, Millonig JH, Brzustowicz LM. Three autism candidate genes: a synthesis of human genetic analysis with other disciplines. Int J Dev Neurosci. 2005;23:221–34. doi:10.1016/j.ijdevneu.2004.10.004.PubMedView ArticleGoogle Scholar
  41. Benayed R, Choi J, Matteson PG, Gharani N, Kamdar S, Brzustowicz LM, et al. Autism-associated haplotype affects the regulation of the homeobox gene, ENGRAILED 2. Biol Psychiatry. 2009;66:911–7. doi:10.1016/j.biopsych.2009.05.027.PubMedPubMed CentralView ArticleGoogle Scholar
  42. Gharani N, Benayed R, Mancuso V, Brzustowicz LM, Millonig JH. Association of the homeobox transcription factor, ENGRAILED 2, 3, with autism spectrum disorder. Mol Psychiatry. 2004;9:474–84. doi:10.1038/sj.mp.4001498.PubMedView ArticleGoogle Scholar
  43. Ashley-Koch AE, Mei H, Jaworski J, Ma DQ, Ritchie MD, Menold MM, et al. An analysis paradigm for investigating multi-locus effects in complex disease: examination of three GABA receptor subunit genes on 15q11-q13 as risk factors for autistic disorder. Ann Hum Genet. 2006;70(Pt 3):281–92. doi:10.1111/j.1469-1809.2006.00253.x.PubMedView ArticleGoogle Scholar
  44. Serajee FJ, Zhong H, Mahbubul Huq AHM. Association of Reelin gene polymorphisms with autism. Genomics. 2006;87:75–83. doi:10.1016/j.ygeno.2005.09.008.PubMedView ArticleGoogle Scholar
  45. Skaar DA, Shao Y, Haines JL, Stenger JE, Jaworski J, Martin ER, et al. Analysis of the RELN gene as a genetic risk factor for autism. Mol Psychiatry. 2005;10:563–71. doi:10.1038/sj.mp.4001614.PubMedView ArticleGoogle Scholar
  46. Zhang H, Liu X, Zhang C, Mundo E, Macciardi F, Grayson DR, et al. Reelin gene alleles and susceptibility to autism spectrum disorders. Mol Psychiatry. 2002;7:1012–7. doi:10.1038/sj.mp.4001124.PubMedView ArticleGoogle Scholar
  47. Nguyen A, Rauch TA, Pfeifer GP, Hu VW. Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, RORA, whose protein product is reduced in autistic brain. FASEB J. 2010;24:3036–51. doi:10.1096/fj.10-154484.PubMedPubMed CentralView ArticleGoogle Scholar
  48. Campbell DB, Sutcliffe JS, Ebert PJ, Militerni R, Bravaccio C, Trillo S, et al. A genetic variant that disrupts MET transcription is associated with autism. Proc Natl Acad Sci U S A. 2006;103:16834–9. doi:10.1073/pnas.0605296103.PubMedPubMed CentralView ArticleGoogle Scholar
  49. Campbell DB, Li C, Sutcliffe JS, Persico AM, Levitt P. Genetic evidence implicating multiple genes in the MET receptor tyrosine kinase pathway in autism spectrum disorder. Autism Res. 2008;1:159–68. doi:10.1002/aur.27.PubMedPubMed CentralView ArticleGoogle Scholar
  50. Campbell DB, Warren D, Sutcliffe JS, Lee EB, Levitt P. Association of MET with social and communication phenotypes in individuals with autism spectrum disorder. Am J Med Genet B Neuropsychiatr Genet. 2010;153B:438–46. doi:10.1002/ajmg.b.30998.PubMedView ArticleGoogle Scholar
  51. Talebizadeh Z, Lam DY, Theodoro MF, Bittel DC, Lushington GH, Butler MG. Novel splice isoforms for NLGN3 and NLGN4 with possible implications in autism. J Med Genet. 2006;43:e21. doi:10.1136/jmg.2005.036897.PubMedPubMed CentralView ArticleGoogle Scholar
  52. McCauley JL, Olson LM, Dowd M, Amin T, Steele A, Blakely RD, et al. Linkage and association analysis at the serotonin transporter (SLC6A4) locus in a rigid-compulsive subset of autism. Am J Med Genet B Neuropsychiatr Genet. 2004;127B:104–12. doi:10.1002/ajmg.b.20151.PubMedView ArticleGoogle Scholar
  53. Buxbaum JD, Silverman JM, Smith CJ, Greenberg DA, Kilifarski M, Reichert J, et al. Association between a GABRB3 polymorphism and autism. Mol Psychiatry. 2002;7:311–6. doi:10.1038/sj.mp.4001011.PubMedView ArticleGoogle Scholar
  54. Collins AL, Ma D, Whitehead PL, Martin ER, Wright HH, Abramson RK, et al. Investigation of autism and GABA receptor subunit genes in multiple ethnic groups. Neurogenetics. 2006;7:167–74. doi:10.1007/s10048-006-0045-1.PubMedPubMed CentralView ArticleGoogle Scholar
  55. McCauley JL, Olson LM, Delahanty R, Amin T, Nurmi EL, Organ EL, et al. A linkage disequilibrium map of the 1-Mb 15q12 GABA(A) receptor subunit cluster and association to autism. Am J Med Genet B Neuropsychiatr Genet. 2004;131B:51–9. doi:10.1002/ajmg.b.30038.PubMedView ArticleGoogle Scholar
  56. Strom SP, Stone JL, Ten Bosch JR, Merriman B, Cantor RM, Geschwind DH, et al. High-density SNP association study of the 17q21 chromosomal region linked to autism identifies CACNA1G as a novel candidate gene. Mol Psychiatry. 2010;15:996–1005. doi:10.1038/mp.2009.41.PubMedView ArticleGoogle Scholar
  57. Weiss LA, Escayg A, Kearney JA, Trudeau M, MacDonald BT, Mori M, et al. Sodium channels SCN1A, SCN2A and SCN3A in familial autism. Mol Psychiatry. 2003;8:186–94. doi:10.1038/sj.mp.4001241.PubMedView ArticleGoogle Scholar
  58. Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, et al. Strong association of de novo copy number mutations with autism. Science. 2007;316:445–9. doi:10.1126/science.1138659.PubMedPubMed CentralView ArticleGoogle Scholar
  59. Dong S, Walker MF, Carriero NJ, DiCola M, Willsey AJ, Ye AY, et al. De novo insertions and deletions of predominantly paternal origin are associated with autism spectrum disorder. Cell Rep. 2014;9:16–23. doi:10.1016/j.celrep.2014.08.068.PubMedPubMed CentralView ArticleGoogle Scholar
  60. Levy D, Ronemus M, Yamrom B, Lee Y, Leotta A, Kendall J, et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron. 2011;70:886–97. doi:10.1016/j.neuron.2011.05.015.PubMedView ArticleGoogle Scholar
  61. Iossifov I, Ronemus M, Levy D, Wang Z, Hakker I, Rosenbaum J, et al. De novo gene disruptions in children on the autistic spectrum. Neuron. 2012;74:285–99. doi:10.1016/j.neuron.2012.04.009.PubMedPubMed CentralView ArticleGoogle Scholar
  62. Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515:216–21. doi:10.1038/nature13908.PubMedPubMed CentralView ArticleGoogle Scholar
  63. Luo R, Sanders SJ, Tian Y, Voineagu I, Huang N, Chu SH, et al. Genome-wide transcriptome profiling reveals the functional impact of rare de novo and recurrent CNVs in autism spectrum disorders. Am J Hum Genet. 2012;91:38–55. doi:10.1016/j.ajhg.2012.05.011.PubMedPubMed CentralView ArticleGoogle Scholar
  64. O’Roak BJ, Deriziotis P, Lee C, Vives L, Schwartz JJ, Girirajan S, et al. Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat Genet. 2011;43:585–9. doi:10.1038/ng.835.PubMedPubMed CentralView ArticleGoogle Scholar
  65. O’Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature. 2012;485:246–50. doi:10.1038/nature10989.PubMedPubMed CentralView ArticleGoogle Scholar
  66. O’Roak BJ, Stessman HA, Boyle EA, Witherspoon KT, Martin B, Lee C, et al. Recurrent de novo mutations implicate novel genes underlying simplex autism risk. Nat Commun. 2014;5:5595. doi:10.1038/ncomms6595.PubMedPubMed CentralView ArticleGoogle Scholar
  67. Robinson EB, Samocha KE, Kosmicki JA, McGrath L, Neale BM, Perlis RH, et al. Autism spectrum disorder severity reflects the average contribution of de novo and familial influences. Proc Natl Acad Sci U S A. 2014;111:15161–5. doi:10.1073/pnas.1409204111.PubMedPubMed CentralView ArticleGoogle Scholar
  68. Samocha KE, Robinson EB, Sanders SJ, Stevens C, Sabo A, McGrath LM, et al. A framework for the interpretation of de novo mutation in human disease. Nat Genet. 2014;46:944–50. doi:10.1038/ng.3050.PubMedPubMed CentralView ArticleGoogle Scholar
  69. Sanders SJ, Ercan-Sencicek AG, Hus V, Luo R, Murtha MT, Moreno-De-Luca D, et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron. 2011;70:863–85. doi:10.1016/j.neuron.2011.05.002.PubMedPubMed CentralView ArticleGoogle Scholar
  70. Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012;485:237–41. doi:10.1038/nature10945.PubMedPubMed CentralView ArticleGoogle Scholar
  71. Van Bon BWM, Coe BP, Bernier R, Green C, Gerdts J, Witherspoon K, et al. Disruptive de novo mutations of DYRK1A lead to a syndromic form of autism and ID. Mol Psychiatry. 2016;21:126–32. doi:10.1038/mp.2015.5.PubMedView ArticleGoogle Scholar
  72. Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature. 2010;466:368–72. doi:10.1038/nature09146.PubMedPubMed CentralView ArticleGoogle Scholar
  73. Glessner JT, Wang K, Cai G, Korvatska O, Kim CE, Wood S, et al. Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature. 2009;459:569–73. doi:10.1038/nature07953.PubMedPubMed CentralView ArticleGoogle Scholar
  74. Baron CA, Liu SY, Hicks C, Gregg JP. Utilization of lymphoblastoid cell lines as a system for the molecular modeling of autism. J Autism Dev Disord. 2006;36:973–82. doi:10.1007/s10803-006-0134-x.PubMedView ArticleGoogle Scholar
  75. Bowers K, Li Q, Bressler J, Avramopoulos D, Newschaffer C, Fallin MD. Glutathione pathway gene variation and risk of autism spectrum disorders. J Neurodev Disord. 2011;3:132–43. doi:10.1007/s11689-011-9077-4.PubMedPubMed CentralView ArticleGoogle Scholar
  76. James SJ, Rose S, Melnyk S, Jernigan S, Blossom S, Pavliv O, et al. Cellular and mitochondrial glutathione redox imbalance in lymphoblastoid cells derived from children with autism. FASEB J. 2009;23:2374–83. doi:10.1096/fj.08-128926.PubMedPubMed CentralView ArticleGoogle Scholar
  77. Main PAE, Thomas P, Esterman A, Fenech MF. Necrosis is increased in lymphoblastoid cell lines from children with autism compared with their non-autistic siblings under conditions of oxidative and nitrosative stress. Mutagenesis. 2013;28:475–84. doi:10.1093/mutage/get025.PubMedPubMed CentralView ArticleGoogle Scholar
  78. Talebizadeh Z, Butler MG, Theodoro MF. Feasibility and relevance of examining lymphoblastoid cell lines to study role of microRNAs in autism. Autism Res. 2008;1:240–50. doi:10.1002/aur.33.PubMedPubMed CentralView ArticleGoogle Scholar
  79. Sarachana T, Zhou R, Chen G, Manji HK, Hu VW. Investigation of post-transcriptional gene regulatory networks associated with autism spectrum disorders by microRNA expression profiling of lymphoblastoid cell lines. Genome Med. 2010;2:23. doi:10.1186/gm144.PubMedPubMed CentralView ArticleGoogle Scholar
  80. Oguro-Ando A, Rosensweig C, Herman E, Nishimura Y, Werling D, Bill BR, et al. Increased CYFIP1 dosage alters cellular and dendritic morphology and dysregulates mTOR. Mol Psychiatry. 2015;20:1069–78. doi:10.1038/mp.2014.124.PubMedView ArticleGoogle Scholar
  81. Faham M, Zheng J, Moorhead M, Fakhrai-Rad H, Namsaraev E, Wong K, et al. Multiplexed variation scanning for 1,000 amplicons in hundreds of patients using mismatch repair detection (MRD) on tag arrays. Proc Natl Acad Sci U S A. 2005;102:14717–22. doi:10.1073/pnas.0506677102.PubMedPubMed CentralView ArticleGoogle Scholar
  82. Cai G, Edelmann L, Goldsmith JE, Cohen N, Nakamine A, Reichert JG, et al. Multiplex ligation-dependent probe amplification for genetic screening in autism spectrum disorders: efficient identification of known microduplications and identification of a novel microduplication in ASMT. BMC Med Genomics. 2008;1:50. doi:10.1186/1755-8794-1-50.PubMedPubMed CentralView ArticleGoogle Scholar
  83. Bureau A, Labbe A, Croteau J, Mérette C. Using disease symptoms to improve detection of linkage under genetic heterogeneity. Genet Epidemiol. 2008;32:476–86. doi:10.1002/gepi.20320.PubMedView ArticleGoogle Scholar
  84. Yonan AL, Palmer AA, Smith KC, Feldman I, Lee HK, Yonan JM, et al. Bioinformatic analysis of autism positional candidate genes using biological databases and computational gene network prediction. Genes Brain Behav. 2003;2:303–20.PubMedView ArticleGoogle Scholar
  85. Turner TN, Hormozdiari F, Duyzend MH, McClymont SA, Hook PW, Iossifov I, et al. Genome Sequencing of Autism-Affected Families Reveals Disruption of Putative Noncoding Regulatory DNA. Am J Hum Genet. 2016;98:58–74. doi:10.1016/j.ajhg.2015.11.023.PubMedView ArticleGoogle Scholar
  86. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014;515:209–15. doi:10.1038/nature13772.PubMedPubMed CentralView ArticleGoogle Scholar
  87. O’Roak BJ, Vives L, Fu W, Egertson JD, Stanaway IB, Phelps IG, et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science. 2012;338:1619–22. doi:10.1126/science.1227764.PubMedPubMed CentralView ArticleGoogle Scholar
  88. Bernier R, Golzio C, Xiong B, Stessman HA, Coe BP, Penn O, et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell. 2014;158:263–76. doi:10.1016/j.cell.2014.06.017.PubMedPubMed CentralView ArticleGoogle Scholar
  89. Cotney J, Muhle RA, Sanders SJ, Liu L, Willsey AJ, Niu W, et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat Commun. 2015;6:6404. doi:10.1038/ncomms7404.PubMedPubMed CentralView ArticleGoogle Scholar
  90. Stessman HAF, Willemsen MH, Fenckova M, Penn O, Hoischen A, Xiong B, et al. Disruption of POGZ Is Associated with Intellectual Disability and Autism Spectrum Disorders. Am J Hum Genet. 2016;98:541–52. doi:10.1016/j.ajhg.2016.02.004.PubMedPubMed CentralView ArticleGoogle Scholar
  91. Nørgaard-Pedersen B, Hougaard DM. Storage policies and use of the Danish Newborn Screening Biobank. J Inherit Metab Dis. 2007;30:530–6. doi:10.1007/s10545-007-0631-x.PubMedView ArticleGoogle Scholar
  92. The Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol Autism. 2017;8:21. doi:10.1186/s13229-017-0137-9.View ArticleGoogle Scholar
  93. Abdallah MW, Larsen N, Grove J, Nørgaard-Pedersen B, Thorsen P, Mortensen EL, et al. Amniotic fluid chemokines and autism spectrum disorders: an exploratory study utilizing a Danish Historic Birth Cohort. Brain Behav Immun. 2012;26:170–6. doi:10.1016/j.bbi.2011.09.003.PubMedView ArticleGoogle Scholar
  94. Abdallah MW, Larsen N, Grove J, Nørgaard-Pedersen B, Thorsen P, Mortensen EL, et al. Amniotic fluid inflammatory cytokines: potential markers of immunologic dysfunction in autism spectrum disorders. World J Biol Psychiatry. 2013;14:528–38. doi:10.3109/15622975.2011.639803.PubMedView ArticleGoogle Scholar
  95. Abdallah MW, Larsen N, Grove J, Bonefeld-Jørgensen EC, Nørgaard-Pedersen B, Hougaard DM, et al. Neonatal chemokine levels and risk of autism spectrum disorders: findings from a Danish historic birth cohort follow-up study. Cytokine. 2013;61:370–6. doi:10.1016/j.cyto.2012.11.015.PubMedView ArticleGoogle Scholar
  96. Abdallah MW, Larsen N, Mortensen EL, Atladóttir HÓ, Nørgaard-Pedersen B, Bonefeld-Jørgensen EC, et al. Neonatal levels of cytokines and risk of autism spectrum disorders: an exploratory register-based historic birth cohort study utilizing the Danish Newborn Screening Biobank. J Neuroimmunol. 2012;252:75–82. doi:10.1016/j.jneuroim.2012.07.013.PubMedView ArticleGoogle Scholar
  97. Buxbaum JD, Bolshakova N, Brownfeld JM, Anney RJ, Bender P, Bernier R, et al. The Autism Simplex Collection: an international, expertly phenotyped autism sample for genetic and phenotypic analyses. Mol Autism. 2014;5:34. doi:10.1186/2040-2392-5-34.PubMedPubMed CentralView ArticleGoogle Scholar
  98. Anney R, Klei L, Pinto D, Regan R, Conroy J, Magalhaes TR, et al. A genome-wide scan for common alleles affecting risk for autism. Hum Mol Genet. 2010;19:4072–82. doi:10.1093/hmg/ddq307.PubMedPubMed CentralView ArticleGoogle Scholar
  99. Pinto D, Delaby E, Merico D, Barbosa M, Merikangas A, Klei L, et al. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am J Hum Genet. 2014;94:677–94. doi:10.1016/j.ajhg.2014.03.018.PubMedPubMed CentralView ArticleGoogle Scholar
  100. Liu L, Sabo A, Neale BM, Nagaswamy U, Stevens C, Lim E, et al. Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet. 2013;9:e1003443. doi:10.1371/journal.pgen.1003443.PubMedPubMed CentralView ArticleGoogle Scholar
  101. Neale BM, Kou Y, Liu L, Ma’ayan A, Samocha KE, Sabo A, et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature. 2012;485:242–5. doi:10.1038/nature11011.PubMedPubMed CentralView ArticleGoogle Scholar
  102. Haroutunian V, Pickett J. Autism brain tissue banking. Brain Pathol. 2007;17:412–21. doi:10.1111/j.1750-3639.2007.00097.x.PubMedView ArticleGoogle Scholar
  103. Ginsberg MR, Rubin RA, Falcone T, Ting AH, Natowicz MR. Brain transcriptional and epigenetic associations with autism. PLoS ONE. 2012;7:e44736. doi:10.1371/journal.pone.0044736.PubMedPubMed CentralView ArticleGoogle Scholar
  104. Gupta S, Ellis SE, Ashar FN, Moes A, Bader JS, Zhan J, et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat Commun. 2014;5:5748. doi:10.1038/ncomms6748.PubMedPubMed CentralView ArticleGoogle Scholar
  105. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011;474:380–4. doi:10.1038/nature10110.PubMedPubMed CentralView ArticleGoogle Scholar
  106. Ben-David E, Shohat S, Shifman S. Allelic expression analysis in the brain suggests a role for heterogeneous insults affecting epigenetic processes in autism spectrum disorders. Hum Mol Genet. 2014;23:4111–24. doi:10.1093/hmg/ddu128.PubMedView ArticleGoogle Scholar
  107. Eran A, Li JB, Vatalaro K, McCarthy J, Rahimov F, Collins C, et al. Comparative RNA editing in autistic and neurotypical cerebella. Mol Psychiatry. 2013;18:1041–8. doi:10.1038/mp.2012.118.PubMedView ArticleGoogle Scholar
  108. Gregory SG, Connelly JJ, Towers AJ, Johnson J, Biscocho D, Markunas CA, et al. Genomic and epigenetic evidence for oxytocin receptor deficiency in autism. BMC Med. 2009;7:62. doi:10.1186/1741-7015-7-62.PubMedPubMed CentralView ArticleGoogle Scholar
  109. Ladd-Acosta C, Hansen KD, Briem E, Fallin MD, Kaufmann WE, Feinberg AP. Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry. 2014;19:862–71. doi:10.1038/mp.2013.114.PubMedView ArticleGoogle Scholar
  110. Mor M, Nardone S, Sams DS, Elliott E. Hypomethylation of miR-142 promoter and upregulation of microRNAs that target the oxytocin receptor gene in the autism prefrontal cortex. Mol Autism. 2015;6:46. doi:10.1186/s13229-015-0040-1.PubMedPubMed CentralView ArticleGoogle Scholar
  111. Nardone S, Sams DS, Reuveni E, Getselter D, Oron O, Karpuj M, et al. DNA methylation analysis of the autistic brain reveals multiple dysregulated biological pathways. Transl Psychiatry. 2014;4:e433. doi:10.1038/tp.2014.70.PubMedPubMed CentralView ArticleGoogle Scholar
  112. Shulha HP, Cheung I, Whittle C, Wang J, Virgil D, Lin CL, et al. Epigenetic signatures of autism: trimethylated H3K4 landscapes in prefrontal neurons. Arch Gen Psychiatry. 2012;69:314–24. doi:10.1001/archgenpsychiatry.2011.151.PubMedView ArticleGoogle Scholar
  113. Sun W, Poschmann J, Cruz-Herrera Del Rosario R, Parikshak NN, Hajan HS, Kumar V, et al. Histone Acetylome-wide Association Study of Autism Spectrum Disorder. Cell. 2016;167:1385–97. doi:10.1016/j.cell.2016.10.031. e11.PubMedView ArticleGoogle Scholar
  114. Zhu L, Wang X, Li X-L, Towers A, Cao X, Wang P, et al. Epigenetic dysregulation of SHANK3 in brain tissues from individuals with autism spectrum disorders. Hum Mol Genet. 2014;23:1563–78. doi:10.1093/hmg/ddt547.PubMedView ArticleGoogle Scholar
  115. Zhubi A, Chen Y, Dong E, Cook EH, Guidotti A, Grayson DR. Increased binding of MeCP2 to the GAD1 and RELN promoters may be mediated by an enrichment of 5-hmC in autism spectrum disorder (ASD) cerebellum. Transl Psychiatry. 2014;4:e349. doi:10.1038/tp.2013.123.PubMedPubMed CentralView ArticleGoogle Scholar
  116. Irimia M, Weatheritt RJ, Ellis JD, Parikshak NN, Gonatopoulos-Pournatzis T, Babor M, et al. A highly conserved program of neuronal microexons is misregulated in autistic brains. Cell. 2014;159:1511–23. doi:10.1016/j.cell.2014.11.035.PubMedPubMed CentralView ArticleGoogle Scholar
  117. Muratore CR, Hodgson NW, Trivedi MS, Abdolmaleky HM, Persico AM, Lintas C, et al. Age-dependent decrease and alternative splicing of methionine synthase mRNA in human cerebral cortex and an accelerated decrease in autism. PLoS ONE. 2013;8:e56927. doi:10.1371/journal.pone.0056927.PubMedPubMed CentralView ArticleGoogle Scholar
  118. Zhang Y, Hodgson NW, Trivedi MS, Abdolmaleky HM, Fournier M, Cuenod M, et al. Decreased brain levels of vitamin B12 in aging, autism and schizophrenia. PLoS ONE. 2016;11:e0146797. doi:10.1371/journal.pone.0146797.PubMedPubMed CentralView ArticleGoogle Scholar
  119. Tsang KM, Croen LA, Torres AR, Kharrazi M, Delorenze GN, Windham GC, et al. A genome-wide survey of transgenerational genetic effects in autism. PLoS ONE. 2013;8:e76978. doi:10.1371/journal.pone.0076978.PubMedPubMed CentralView ArticleGoogle Scholar
  120. Desachy G, Croen LA, Torres AR, Kharrazi M, Delorenze GN, Windham GC, et al. Increased female autosomal burden of rare copy number variants in human populations and in autism families. Mol Psychiatry. 2015;20:170–5. doi:10.1038/mp.2014.179.PubMedView ArticleGoogle Scholar
  121. Jacquemont S, Coe BP, Hersch M, Duyzend MH, Krumm N, Bergmann S, et al. A higher mutational burden in females supports a “female protective model” in neurodevelopmental disorders. Am J Hum Genet. 2014;94:415–25. doi:10.1016/j.ajhg.2014.02.001.PubMedPubMed CentralView ArticleGoogle Scholar
  122. Mondal K, Ramachandran D, Patel VC, Hagen KR, Bose P, Cutler DJ, et al. Excess variants in AFF2 detected by massively parallel sequencing of males with autism spectrum disorder. Hum Mol Genet. 2012;21:4356–64. doi:10.1093/hmg/dds267.PubMedPubMed CentralView ArticleGoogle Scholar
  123. Leblond CS, Nava C, Polge A, Gauthier J, Huguet G, Lumbroso S, et al. Meta-analysis of SHANK Mutations in Autism Spectrum Disorders: a gradient of severity in cognitive impairments. PLoS Genet. 2014;10:e1004580. doi:10.1371/journal.pgen.1004580.PubMedPubMed CentralView ArticleGoogle Scholar
  124. Yuen RKC, Merico D, Cao H, Pellecchia G, Alipanahi B, Thiruvahindrapuram B, et al. Genome-wide characteristics of de novo mutations in autism. npj. Genomic Med. 2016;1:160271–1602710. doi:10.1038/npjgenmed.2016.27.Google Scholar
  125. C Yuen RK, Merico D, Bookman M, L Howe J, Thiruvahindrapuram B, Patel RV, et al. Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci. 2017;20:602–11. doi:10.1038/nn.4524.PubMedView ArticleGoogle Scholar
  126. Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126:663–76. doi:10.1016/j.cell.2006.07.024.PubMedView ArticleGoogle Scholar
  127. Dolmetsch R, Geschwind DH. The human brain in a dish: the promise of iPSC-derived neurons. Cell. 2011;145:831–4. doi:10.1016/j.cell.2011.05.034.PubMedPubMed CentralView ArticleGoogle Scholar
  128. Liu EY, Scott CT. Great expectations: autism spectrum disorder and induced pluripotent stem cell technologies. Stem Cell Rev. 2014;10:145–50. doi:10.1007/s12015-014-9497-0.PubMedView ArticleGoogle Scholar
  129. Mariani J, Coppola G, Zhang P, Abyzov A, Provini L, Tomasini L, et al. FOXG1-Dependent Dysregulation of GABA/Glutamate Neuron Differentiation in Autism Spectrum Disorders. Cell. 2015;162:375–90. doi:10.1016/j.cell.2015.06.034.PubMedPubMed CentralView ArticleGoogle Scholar
  130. Lu P, Chen X, Feng Y, Zeng Q, Jiang C, Zhu X, et al. Integrated transcriptome analysis of human iPS cells derived from a fragile X syndrome patient during neuronal differentiation. Sci China Life Sci. 2016;59:1093–105. doi:10.1007/s11427-016-0194-6.PubMedView ArticleGoogle Scholar
  131. Pierce NP, O’Reilly MF, Sorrells AM, Fragale CL, White PJ, Aguilar JM, et al. Ethnicity reporting practices for empirical research in three autism-related journals. J Autism Dev Disord. 2014;44:1507–19. doi:10.1007/s10803-014-2041-x.PubMedView ArticleGoogle Scholar
  132. Hilton CL, Fitzgerald RT, Jackson KM, Maxim RA, Bosworth CC, Shattuck PT, et al. Brief report: Under-representation of African americans in autism genetic research: a rationale for inclusion of subjects representing diverse family structures. J Autism Dev Disord. 2010;40:633–9. doi:10.1007/s10803-009-0905-2.PubMedPubMed CentralView ArticleGoogle Scholar
  133. Robinson EB, Howrigan D, Yang J, Ripke S, Anttila V, Duncan LE, et al. Response to “Predicting the diagnosis of autism spectrum disorder using gene pathway analysis”. Mol Psychiatry. 2014;19:859–61. doi:10.1038/mp.2013.125.PubMedView ArticleGoogle Scholar
  134. Wang T, Guo H, Xiong B, Stessman HAF, Wu H, Coe BP, et al. De novo genic mutations among a Chinese autism spectrum disorder cohort. Nat Commun. 2016;7:13316. doi:10.1038/ncomms13316.PubMedPubMed CentralView ArticleGoogle Scholar
  135. Nascimento PP, Bossolani-Martins AL, Rosan DBA, Mattos LC, Brandão-Mattos C, Fett-Conte AC. Single nucleotide polymorphisms in the CNTNAP2 gene in Brazilian patients with autistic spectrum disorder. Genet Mol Res. 2016;15. doi:10.4238/gmr.15017422.
  136. Abdallah MW, Mortensen EL, Greaves-Lord K, Larsen N, Bonefeld-Jørgensen EC, Nørgaard-Pedersen B, et al. Neonatal levels of neurotrophic factors and risk of autism spectrum disorders. Acta Psychiatr Scand. 2013;128:61–9. doi:10.1111/acps.12020.PubMedView ArticleGoogle Scholar
  137. Abdallah MW, Pearce BD, Larsen N, Greaves-Lord K, Nørgaard-Pedersen B, Hougaard DM, et al. Amniotic fluid MMP-9 and neurotrophins in autism spectrum disorders: an exploratory study. Autism Res. 2012;5:428–33. doi:10.1002/aur.1254.PubMedView ArticleGoogle Scholar
  138. Baron-Cohen S, Auyeung B, Nørgaard-Pedersen B, Hougaard DM, Abdallah MW, Melgaard L, et al. Elevated fetal steroidogenic activity in autism. Mol Psychiatry. 2015;20:369–76. doi:10.1038/mp.2014.48.PubMedView ArticleGoogle Scholar
  139. Hollegaard MV, Grauholm J, Nielsen R, Grove J, Mandrup S, Hougaard DM. Archived neonatal dried blood spot samples can be used for accurate whole genome and exome-targeted next-generation sequencing. Mol Genet Metab. 2013;110:65–72. doi:10.1016/j.ymgme.2013.06.004.PubMedView ArticleGoogle Scholar
  140. Poulsen JB, Lescai F, Grove J, Bækvad-Hansen M, Christiansen M, Hagen CM, et al. High-Quality Exome Sequencing of Whole-Genome Amplified Neonatal Dried Blood Spot DNA. PLoS ONE. 2016;11:e0153253. doi:10.1371/journal.pone.0153253.PubMedPubMed CentralView ArticleGoogle Scholar
  141. Hollegaard MV, Grauholm J, Nørgaard-Pedersen B, Hougaard DM. DNA methylome profiling using neonatal dried blood spot samples: a proof-of-principle study. Mol Genet Metab. 2013;108:225–31. doi:10.1016/j.ymgme.2013.01.016.PubMedView ArticleGoogle Scholar
  142. Grauholm J, Khoo SK, Nickolov RZ, Poulsen JB, Bækvad-Hansen M, Hansen CS, et al. Gene expression profiling of archived dried blood spot samples from the Danish Neonatal Screening Biobank. Mol Genet Metab. 2015;116:119–24. doi:10.1016/j.ymgme.2015.06.011.PubMedView ArticleGoogle Scholar
  143. Urbach A, Bar-Nur O, Daley GQ, Benvenisty N. Differential modeling of fragile X syndrome by human embryonic stem cells and induced pluripotent stem cells. Cell Stem Cell. 2010;6:407–11. doi:10.1016/j.stem.2010.04.005.PubMedPubMed CentralView ArticleGoogle Scholar
  144. Bar-Nur O, Caspi I, Benvenisty N. Molecular analysis of FMR1 reactivation in fragile-X induced pluripotent stem cells and their neuronal derivatives. J Mol Cell Biol. 2012;4:180–3. doi:10.1093/jmcb/mjs007.PubMedView ArticleGoogle Scholar
  145. Halevy T, Czech C, Benvenisty N. Molecular mechanisms regulating the defects in fragile X syndrome neurons derived from human pluripotent stem cells. Stem Cell Reports. 2015;4:37–46. doi:10.1016/j.stemcr.2014.10.015.PubMedView ArticleGoogle Scholar
  146. Marchetto MCN, Carromeu C, Acab A, Yu D, Yeo GW, Mu Y, et al. A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell. 2010;143:527–39. doi:10.1016/j.cell.2010.10.016.PubMedPubMed CentralView ArticleGoogle Scholar
  147. Cocks G, Curran S, Gami P, Uwanogho D, Jeffries AR, Kathuria A, et al. The utility of patient specific induced pluripotent stem cells for the modelling of Autistic Spectrum Disorders. Psychopharmacology (Berl). 2014;231:1079–88. doi:10.1007/s00213-013-3196-4.View ArticleGoogle Scholar
  148. Kelava I, Lancaster MA. Stem cell models of human brain development. Cell Stem Cell. 2016;18:736–48. doi:10.1016/j.stem.2016.05.022.PubMedView ArticleGoogle Scholar
  149. Griesi-Oliveira K, Acab A, Gupta AR, Sunaga DY, Chailangkarn T, Nicol X, et al. Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons. Mol Psychiatry. 2015;20:1350–65. doi:10.1038/mp.2014.141.PubMedView ArticleGoogle Scholar
  150. Mahe MM, Workman M, Trisno S, Poling H, Watson CL, Sundaram N, et al. Functional enteric nervous system in human small intestine derived from pluripotent stem cells. Neurogastroenterol Motil. 2016;28:6–6.Google Scholar
  151. Brown JA, Pensabene V, Markov DA, Allwardt V, Neely MD, Shi M, et al. Recreating blood-brain barrier physiology and structure on chip: A novel neurovascular microfluidic bioreactor. Biomicrofluidics. 2015;9:054124. doi:10.1063/1.4934713.PubMedPubMed CentralView ArticleGoogle Scholar
  152. Fiorentino M, Sapone A, Senger S, Camhi SS, Kadzielski SM, Buie TM, et al. Blood-brain barrier and intestinal epithelial barrier alterations in autism spectrum disorders. Mol Autism. 2016;7:49. doi:10.1186/s13229-016-0110-z.PubMedPubMed CentralView ArticleGoogle Scholar
  153. Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, Tomoda K, et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell. 2007;131:861–72. doi:10.1016/j.cell.2007.11.019.PubMedView ArticleGoogle Scholar
  154. Rao MS, Malik N. Assessing iPSC reprogramming methods for their suitability in translational medicine. J Cell Biochem. 2012;113:3061–8.PubMedPubMed CentralView ArticleGoogle Scholar
  155. Raab S, Klingenstein M, Liebau S, Linta L. A Comparative View on Human Somatic Cell Sources for iPSC Generation. Stem Cells Int. 2014;2014:768391. doi:10.1155/2014/768391.PubMedPubMed CentralView ArticleGoogle Scholar
  156. Fernell E, Karagiannakis A, Edman G, Bjerkenstedt L, Wiesel F-A, Venizelos N. Aberrant amino acid transport in fibroblasts from children with autism. Neurosci Lett. 2007;418:82–6. doi:10.1016/j.neulet.2007.03.004.PubMedView ArticleGoogle Scholar
  157. Vumma R, Wiesel F-A, Flyckt L, Bjerkenstedt L, Venizelos N. Functional characterization of tyrosine transport in fibroblast cells from healthy controls. Neurosci Lett. 2008;434:56–60. doi:10.1016/j.neulet.2008.01.028.PubMedView ArticleGoogle Scholar
  158. Schmunk G, Boubion BJ, Smith IF, Parker I, Gargus JJ. Shared functional defect in IP3R-mediated calcium signaling in diverse monogenic autism syndromes. Transl Psychiatry. 2015;5:e643. doi:10.1038/tp.2015.123.PubMedPubMed CentralView ArticleGoogle Scholar
  159. Schmunk G, Nguyen RL, Ferguson DL, Kumar K, Parker I, Gargus JJ. High-throughput screen detects calcium signaling dysfunction in typical sporadic autism spectrum disorder. Sci Rep. 2017;7:40740. doi:10.1038/srep40740.PubMedPubMed CentralView ArticleGoogle Scholar
  160. Aasen T, Izpisúa Belmonte JC. Isolation and cultivation of human keratinocytes from skin or plucked hair for the generation of induced pluripotent stem cells. Nat Protoc. 2010;5:371–82. doi:10.1038/nprot.2009.241.PubMedView ArticleGoogle Scholar
  161. Streckfuss-Bömeke K, Wolf F, Azizian A, Stauske M, Tiburcy M, Wagner S, et al. Comparative study of human-induced pluripotent stem cells derived from bone marrow cells, hair keratinocytes, and skin fibroblasts. Eur Heart J. 2013;34:2618–29. doi:10.1093/eurheartj/ehs203.PubMedView ArticleGoogle Scholar
  162. Piao Y, Hung SS-C, Lim SY, Wong RC-B, Ko MSH. Efficient generation of integration-free human induced pluripotent stem cells from keratinocytes by simple transfection of episomal vectors. Stem Cells Transl Med. 2014;3:787–91. doi:10.5966/sctm.2013-0036.PubMedPubMed CentralView ArticleGoogle Scholar
  163. Chalmers D, Nicol D, Kaye J, Bell J, Campbell AV, Ho CWL, et al. Has the biobank bubble burst? Withstanding the challenges for sustainable biobanking in the digital era. BMC Med Ethics. 2016;17:39. doi:10.1186/s12910-016-0124-2.PubMedPubMed CentralView ArticleGoogle Scholar
  164. Bjugn R, Casati B. Stakeholder analysis: a useful tool for biobank planning. Biopreserv Biobank. 2012;10:239–44. doi:10.1089/bio.2011.0047.PubMedView ArticleGoogle Scholar
  165. Pellicano E, Dinsmore A, Charman T. Views on researcher-community engagement in autism research in the United Kingdom: a mixed-methods study. PLoS ONE. 2014;9:e109946. doi:10.1371/journal.pone.0109946.PubMedPubMed CentralView ArticleGoogle Scholar
  166. Hansson MG. Ethics and biobanks. British Journal of Cancer. 2009;100:8–12.PubMedView ArticleGoogle Scholar
  167. Helgesson G, Dillner J, Carlson J, Bartram CR, Hansson MG. Ethical framework for previously collected biobank samples. Nat Biotechnol. 2007;25:973–6. doi:10.1038/nbt0907-973b.PubMedView ArticleGoogle Scholar
  168. Steinsbekk KS, Kåre Myskja B, Solberg B. Broad consent versus dynamic consent in biobank research: is passive participation an ethical problem? Eur J Hum Genet. 2013;21:897–902. doi:10.1038/ejhg.2012.282.PubMedPubMed CentralView ArticleGoogle Scholar
  169. Knoppers BM, Isasi R. Stem cell banking: between traceability and identifiability. Genome Med. 2010;2:73. doi:10.1186/gm194.PubMedPubMed CentralView ArticleGoogle Scholar
  170. Auray-Blais C, Patenaude J. A biobank management model applicable to biomedical research. BMC Med Ethics. 2006;7:E4. doi:10.1186/1472-6939-7-4.PubMedView ArticleGoogle Scholar
  171. Lemke AA, Wolf WA, Hebert-Beirne J, Smith ME. Public and biobank participant attitudes toward genetic research participation and data sharing. Public Health Genomics. 2010;13:368–77. doi:10.1159/000276767.PubMedPubMed CentralView ArticleGoogle Scholar
  172. Ortega F. The Cerebral Subject and the Challenge of Neurodiversity. BioSocieties. 2009;4:425–45. doi:10.1017/S1745855209990287.View ArticleGoogle Scholar
  173. Pellicano E, Stears M. Bridging autism, science and society: moving toward an ethically informed approach to autism research. Autism Res. 2011;4:271–82. doi:10.1002/aur.201.PubMedView ArticleGoogle Scholar
  174. Kapp SK, Gillespie-Lynch K, Sherman LE, Hutman T. Deficit, difference, or both? Autism and neurodiversity. Dev Psychol. 2013;49:59–71. doi:10.1037/a0028353.PubMedView ArticleGoogle Scholar
  175. Barnes RE, McCabe H. Should we welcome a cure for autism? A survey of the arguments. Med Health Care Philos. 2012;15:255–69. doi:10.1007/s11019-011-9339-7.PubMedView ArticleGoogle Scholar
  176. Scott CT. The case for stem cell counselors. Stem Cell Rep. 2015;4:1–6. doi:10.1016/j.stemcr.2014.10.016.View ArticleGoogle Scholar
  177. Krumm N, O’Roak BJ, Shendure J, Eichler EE. A de novo convergence of autism genetics and molecular neuroscience. Trends Neurosci. 2014;37:95–105. doi:10.1016/j.tins.2013.11.005.PubMedView ArticleGoogle Scholar
  178. Girirajan S, Dennis MY, Baker C, Malig M, Coe BP, Campbell CD, et al. Refinement and discovery of new hotspots of copy-number variation associated with autism spectrum disorder. Am J Hum Genet. 2013;92:221–37. doi:10.1016/j.ajhg.2012.12.016.PubMedPubMed CentralView ArticleGoogle Scholar
  179. Kim H-G, Kishikawa S, Higgins AW, Seong I-S, Donovan DJ, Shen Y, et al. Disruption of neurexin 1 associated with autism spectrum disorder. Am J Hum Genet. 2008;82:199–207. doi:10.1016/j.ajhg.2007.09.011.PubMedPubMed CentralView ArticleGoogle Scholar
  180. Alarcón M, Cantor RM, Liu J, Gilliam TC, Geschwind DH. Autism Genetic Research Exchange Consortium. Evidence for a language quantitative trait locus on chromosome 7q in multiplex autism families. Am J Hum Genet. 2002;70:60–71. doi:10.1086/338241.PubMedView ArticleGoogle Scholar
  181. Alarcón M, Abrahams BS, Stone JL, Duvall JA, Perederiy JV, Bomar JM, et al. Linkage, association, and gene-expression analyses identify CNTNAP2 as an autism-susceptibility gene. Am J Hum Genet. 2008;82:150–9. doi:10.1016/j.ajhg.2007.09.005.PubMedPubMed CentralView ArticleGoogle Scholar
  182. Alarcón M, Yonan AL, Gilliam TC, Cantor RM, Geschwind DH. Quantitative genome scan and Ordered-Subsets Analysis of autism endophenotypes support language QTLs. Mol Psychiatry. 2005;10:747–57. doi:10.1038/sj.mp.4001666.PubMedView ArticleGoogle Scholar
  183. Allen-Brady K, Cannon D, Robison R, McMahon WM, Coon H. A unified theory of autism revisited: linkage evidence points to chromosome X using a high-risk subset of AGRE families. Autism Res. 2010;3:47–52. doi:10.1002/aur.119.PubMedGoogle Scholar
  184. Anitha A, Nakamura K, Yamada K, Suda S, Thanseem I, Tsujii M, et al. Genetic analyses of roundabout (ROBO) axon guidance receptors in autism. Am J Med Genet B Neuropsychiatr Genet. 2008;147B:1019–27. doi:10.1002/ajmg.b.30697.PubMedView ArticleGoogle Scholar
  185. Anitha A, Thanseem I, Nakamura K, Yamada K, Iwayama Y, Toyota T, et al. Protocadherin α (PCDHA) as a novel susceptibility gene for autism. J Psychiatry Neurosci. 2013;38:192–8. doi:10.1503/jpn.120058.PubMedPubMed CentralView ArticleGoogle Scholar
  186. Anitha A, Thanseem I, Nakamura K, Vasu MM, Yamada K, Ueki T, et al. Zinc finger protein 804A (ZNF804A) and verbal deficits in individuals with autism. J Psychiatry Neurosci. 2014;39:294–303. doi:10.1503/jpn.130126.PubMedPubMed CentralView ArticleGoogle Scholar
  187. Arking DE, Cutler DJ, Brune CW, Teslovich TM, West K, Ikeda M, et al. A common genetic variant in the neurexin superfamily member CNTNAP2 increases familial risk of autism. Am J Hum Genet. 2008;82:160–4. doi:10.1016/j.ajhg.2007.09.015.PubMedPubMed CentralView ArticleGoogle Scholar
  188. Babatz TD, Kumar RA, Sudi J, Dobyns WB, Christian SL. Copy number and sequence variants implicate APBA2 as an autism candidate gene. Autism Res. 2009;2:359–64. doi:10.1002/aur.107.PubMedView ArticleGoogle Scholar
  189. Bakkaloglu B, O’Roak BJ, Louvi A, Gupta AR, Abelson JF, Morgan TM, et al. Molecular cytogenetic analysis and resequencing of contactin associated protein-like 2 in autism spectrum disorders. Am J Hum Genet. 2008;82:165–73. doi:10.1016/j.ajhg.2007.09.017.PubMedPubMed CentralView ArticleGoogle Scholar
  190. Baron CA, Tepper CG, Liu SY, Davis RR, Wang NJ, Schanen NC, et al. Genomic and functional profiling of duplicated chromosome 15 cell lines reveal regulatory alterations in UBE3A-associated ubiquitin-proteasome pathway processes. Hum Mol Genet. 2006;15:853–69. doi:10.1093/hmg/ddl004.PubMedView ArticleGoogle Scholar
  191. Baugher JD, Baugher BD, Shirley MD, Pevsner J. Sensitive and specific detection of mosaic chromosomal abnormalities using the Parent-of-Origin-based Detection (POD) method. BMC Genomics. 2013;14:367. doi:10.1186/1471-2164-14-367.PubMedPubMed CentralView ArticleGoogle Scholar
  192. Benayed R, Gharani N, Rossman I, Mancuso V, Lazar G, Kamdar S, et al. Support for the homeobox transcription factor gene ENGRAILED 2 as an autism spectrum disorder susceptibility locus. Am J Hum Genet. 2005;77:851–68. doi:10.1086/497705.PubMedPubMed CentralView ArticleGoogle Scholar
  193. Buxbaum JD, Silverman JM, Smith CJ, Kilifarski M, Reichert J, Hollander E, et al. Evidence for a susceptibility gene for autism on chromosome 2 and for genetic heterogeneity. Am J Hum Genet. 2001;68:1514–20. doi:10.1086/320588.PubMedPubMed CentralView ArticleGoogle Scholar
  194. Buxbaum JD, Silverman J, Keddache M, Smith CJ, Hollander E, Ramoz N, et al. Linkage analysis for autism in a subset families with obsessive-compulsive behaviors: evidence for an autism susceptibility gene on chromosome 1 and further support for susceptibility genes on chromosome 6 and 19. Mol Psychiatry. 2004;9:144–50. doi:10.1038/sj.mp.4001465.PubMedView ArticleGoogle Scholar
  195. Cantor RM, Kono N, Duvall JA, Alvarez-Retuerto A, Stone JL, Alarcón M, et al. Replication of autism linkage: fine-mapping peak at 17q21. Am J Hum Genet. 2005;76:1050–6. doi:10.1086/430278.PubMedPubMed CentralView ArticleGoogle Scholar
  196. Carayol J, Sacco R, Tores F, Rousseau F, Lewin P, Hager J, et al. Converging evidence for an association of ATP2B2 allelic variants with autism in male subjects. Biol Psychiatry. 2011;70:880–7. doi:10.1016/j.biopsych.2011.05.020.PubMedView ArticleGoogle Scholar
  197. Carayol J, Schellenberg GD, Dombroski B, Genin E, Rousseau F, Dawson G. Autism risk assessment in siblings of affected children using sex-specific genetic scores. Mol Autism. 2011;2:17. doi:10.1186/2040-2392-2-17.PubMedPubMed CentralView ArticleGoogle Scholar
  198. Carayol J, Schellenberg GD, Dombroski B, Amiet C, Génin B, Fontaine K, et al. A scoring strategy combining statistics and functional genomics supports a possible role for common polygenic variation in autism. Front Genet. 2014;5:33. doi:10.3389/fgene.2014.00033.PubMedPubMed CentralView ArticleGoogle Scholar
  199. Chang S-C, Pauls DL, Lange C, Sasanfar R, Santangelo SL. Common genetic variation in the GAD1 gene and the entire family of DLX homeobox genes and autism spectrum disorders. Am J Med Genet B Neuropsychiatr Genet. 2011;156:233–9. doi:10.1002/ajmg.b.31148.PubMedView ArticleGoogle Scholar
  200. Chang S-C, Pauls DL, Lange C, Sasanfar R, Santangelo SL. Sex-specific association of a common variant of the XG gene with autism spectrum disorders. Am J Med Genet B Neuropsychiatr Genet. 2013;162B:742–50. doi:10.1002/ajmg.b.32165.PubMedView ArticleGoogle Scholar
  201. Chen GK, Kono N, Geschwind DH, Cantor RM. Quantitative trait locus analysis of nonverbal communication in autism spectrum disorder. Mol Psychiatry. 2006;11:214–20. doi:10.1038/sj.mp.4001753.PubMedView ArticleGoogle Scholar
  202. Cheslack-Postava K, Fallin MD, Avramopoulos D, Connors SL, Zimmerman AW, Eberhart CG, et al. beta2-Adrenergic receptor gene variants and risk for autism in the AGRE cohort. Mol Psychiatry. 2007;12:283–91. doi:10.1038/sj.mp.4001940.PubMedGoogle Scholar
  203. Cheung J, Petek E, Nakabayashi K, Tsui LC, Vincent JB, Scherer SW. Identification of the human cortactin-binding protein-2 gene from the autism candidate region at 7q31. Genomics. 2001;78:7–11. doi:10.1006/geno.2001.6651.PubMedView ArticleGoogle Scholar
  204. Conciatori M, Stodgell CJ, Hyman SL, O’Bara M, Militerni R, Bravaccio C, et al. Association between the HOXA1 A218G polymorphism and increased head circumference in patients with autism. Biol Psychiatry. 2004;55:413–9. doi:10.1016/j.biopsych.2003.10.005.PubMedView ArticleGoogle Scholar
  205. Connolly JJ, Glessner JT, Hakonarson H. A genome-wide association study of autism incorporating autism diagnostic interview-revised, autism diagnostic observation schedule, and social responsiveness scale. Child Dev. 2013;84:17–33. doi:10.1111/j.1467-8624.2012.01838.x.PubMedView ArticleGoogle Scholar
  206. Connors SL, Crowell DE, Eberhart CG, Copeland J, Newschaffer CJ, Spence SJ, et al. beta2-adrenergic receptor activation and genetic polymorphisms in autism: data from dizygotic twins. J Child Neurol. 2005;20:876–84. doi:10.1177/08830738050200110401.PubMedView ArticleGoogle Scholar
  207. D’Amelio M, Ricci I, Sacco R, Liu X, D’Agruma L, Muscarella LA, et al. Paraoxonase gene variants are associated with autism in North America, but not in Italy: possible regional specificity in gene-environment interactions. Mol Psychiatry. 2005;10:1006–16. doi:10.1038/sj.mp.4001714.PubMedView ArticleGoogle Scholar
  208. Davis LK, Meyer KJ, Rudd DS, Librant AL, Epping EA, Sheffield VC, et al. Novel copy number variants in children with autism and additional developmental anomalies. J Neurodev Disord. 2009;1:292–301. doi:10.1007/s11689-009-9013-z.PubMedPubMed CentralView ArticleGoogle Scholar
  209. Dennis MY, Nuttle X, Sudmant PH, Antonacci F, Graves TA, Nefedov M, et al. Evolution of human-specific neural SRGAP2 genes by incomplete segmental duplication. Cell. 2012;149:912–22. doi:10.1016/j.cell.2012.03.033.PubMedPubMed CentralView ArticleGoogle Scholar
  210. Duvall J. A Quantitative Trait Locus Analysis of Social Responsiveness in Multiplex Autism Families. Am J Psychiatry. 2007;164:656. doi:10.1176/appi.ajp.164.4.656.PubMedView ArticleGoogle Scholar
  211. Yonan AL, Alarcón M, Cheng R, Magnusson PKE, Spence SJ, Palmer AA, et al. A Genomewide Screen of 345 Families for Autism-Susceptibility Loci. Am J Hum Genet. 2003;73:886–97. doi:10.1086/378778.PubMedPubMed CentralView ArticleGoogle Scholar
  212. Fatemi SH, Stary JM, Egan EA. Reduced blood levels of reelin as a vulnerability factor in pathophysiology of autistic disorder. Cell Mol Neurobiol. 2002;22:139–52.PubMedView ArticleGoogle Scholar
  213. Cisternas FA, Vincent JB, Scherer SW, Ray PN. Cloning and characterization of human CADPS and CADPS2, new members of the Ca2 + −dependent activator for secretion protein family. Genomics. 2003;81:279–91. doi:10.1016/S0888-7543(02)00040-X.PubMedView ArticleGoogle Scholar
  214. Flax JF, Hare A, Azaro MA, Vieland VJ, Brzustowicz LM. Combined linkage and linkage disequilibrium analysis of a motor speech phenotype within families ascertained for autism risk loci. J Neurodev Disord. 2010;2:210–23. doi:10.1007/s11689-010-9063-2.PubMedPubMed CentralView ArticleGoogle Scholar
  215. Fradin D, Cheslack-Postava K, Ladd-Acosta C, Newschaffer C, Chakravarti A, Arking DE, et al. Parent-of-origin effects in autism identified through genome-wide linkage analysis of 16,000 SNPs. PLoS ONE. 2010;5. doi:10.1371/journal.pone.0012513.
  216. Girirajan S, Johnson RL, Tassone F, Balciuniene J, Katiyar N, Fox K, et al. Global increases in both common and rare copy number load associated with autism. Hum Mol Genet. 2013;22:2870–80. doi:10.1093/hmg/ddt136.PubMedPubMed CentralView ArticleGoogle Scholar
  217. Goin-Kochel RP, Porter AE, Peters SU, Shinawi M, Sahoo T, Beaudet AL. The MTHFR 677C→T polymorphism and behaviors in children with autism: exploratory genotype-phenotype correlations. Autism Res. 2009;2:98–108. doi:10.1002/aur.70.PubMedView ArticleGoogle Scholar
  218. McCarthy SE, Makarov V, Kirov G, Addington AM, McClellan J, Yoon S, et al. Microduplications of 16p11.2 are associated with schizophrenia. Nat Genet. 2009;41:1223–7. doi:10.1038/ng.474.PubMedPubMed CentralView ArticleGoogle Scholar
  219. Hamilton SP, Woo JM, Carlson EJ, Ghanem N, Ekker M, Rubenstein JLR. Analysis of four DLX homeobox genes in autistic probands. BMC Genet. 2005;6:52. doi:10.1186/1471-2156-6-52.PubMedPubMed CentralView ArticleGoogle Scholar
  220. Hettinger JA, Liu X, Schwartz CE, Michaelis RC, Holden JJA. A DRD1 haplotype is associated with risk for autism spectrum disorders in male-only affected sib-pair families. Am J Med Genet B Neuropsychiatr Genet. 2008;147B:628–36. doi:10.1002/ajmg.b.30655.PubMedView ArticleGoogle Scholar
  221. Higashida H, Yokoyama S, Huang J-J, Liu L, Ma W-J, Akther S, et al. Social memory, amnesia, and autism: brain oxytocin secretion is regulated by NAD+ metabolites and single nucleotide polymorphisms of CD38. Neurochem Int. 2012;61:828–38. doi:10.1016/j.neuint.2012.01.030.PubMedView ArticleGoogle Scholar
  222. Munesue T, Yokoyama S, Nakamura K, Anitha A, Yamada K, Hayashi K, et al. Two genetic variants of CD38 in subjects with autism spectrum disorder and controls. Neurosci Res. 2010;67:181–91. doi:10.1016/j.neures.2010.03.004.PubMedView ArticleGoogle Scholar
  223. Hu VW, Frank BC, Heine S, Lee NH, Quackenbush J. Gene expression profiling of lymphoblastoid cell lines from monozygotic twins discordant in severity of autism reveals differential regulation of neurologically relevant genes. BMC Genomics. 2006;7:118. doi:10.1186/1471-2164-7-118.PubMedPubMed CentralView ArticleGoogle Scholar
  224. Hu VW, Sarachana T, Kim KS, Nguyen A, Kulkarni S, Steinberg ME, et al. Gene expression profiling differentiates autism case-controls and phenotypic variants of autism spectrum disorders: evidence for circadian rhythm dysfunction in severe autism. Autism Res. 2009;2:78–97. doi:10.1002/aur.73.PubMedPubMed CentralView ArticleGoogle Scholar
  225. Hussman JP, Chung R-H, Griswold AJ, Jaworski JM, Salyakina D, Ma D, et al. A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism. Mol Autism. 2011;2:1. doi:10.1186/2040-2392-2-1.PubMedPubMed CentralView ArticleGoogle Scholar
  226. Kistner-Griffin E, Brune CW, Davis LK, Sutcliffe JS, Cox NJ, Cook EH. Parent-of-origin effects of the serotonin transporter gene associated with autism. Am J Med Genet B Neuropsychiatr Genet. 2011;156:139–44. doi:10.1002/ajmg.b.31146.PubMedView ArticleGoogle Scholar
  227. Kumar RA, Sudi J, Babatz TD, Brune CW, Oswald D, Yen M, et al. A de novo 1p34.2 microdeletion identifies the synaptic vesicle gene RIMS3 as a novel candidate for autism. J Med Genet. 2010;47:81–90. doi:10.1136/jmg.2008.065821.PubMedView ArticleGoogle Scholar
  228. Lee L-C, Zachary AA, Leffell MS, Newschaffer CJ, Matteson KJ, Tyler JD, et al. HLA-DR4 in families with autism. Pediatr Neurol. 2006;35:303–7. doi:10.1016/j.pediatrneurol.2006.06.006.PubMedView ArticleGoogle Scholar
  229. Liu X, Malenfant P, Reesor C, Lee A, Hudson ML, Harvard C, et al. 2p15-p16.1 microdeletion syndrome: molecular characterization and association of the OTX1 and XPO1 genes with autism spectrum disorders. Eur J Hum Genet. 2011;19:1264–70. doi:10.1038/ejhg.2011.112.PubMedPubMed CentralView ArticleGoogle Scholar
  230. Liu X, Novosedlik N, Wang A, Hudson ML, Cohen IL, Chudley AE, et al. The DLX1and DLX2 genes and susceptibility to autism spectrum disorders. Eur J Hum Genet. 2009;17:228–35. doi:10.1038/ejhg.2008.148.PubMedView ArticleGoogle Scholar
  231. Liu X, Solehdin F, Cohen IL, Gonzalez MG, Jenkins EC, Lewis MES, et al. Population- and family-based studies associate the MTHFR gene with idiopathic autism in simplex families. J Autism Dev Disord. 2011;41:938–44. doi:10.1007/s10803-010-1120-x.PubMedView ArticleGoogle Scholar
  232. Lu ATH, Cantor RM. Allowing for sex differences increases power in a GWAS of multiplex Autism families. Mol Psychiatry. 2012;17:215–22. doi:10.1038/mp.2010.127.PubMedView ArticleGoogle Scholar
  233. Lu AT-H, Dai X, Martinez-Agosto JA, Cantor RM. Support for calcium channel gene defects in autism spectrum disorders. Mol Autism. 2012;3:18. doi:10.1186/2040-2392-3-18.PubMedPubMed CentralView ArticleGoogle Scholar
  234. Ma D, Salyakina D, Jaworski JM, Konidari I, Whitehead PL, Andersen AN, et al. A genome-wide association study of autism reveals a common novel risk locus at 5p14.1. Ann Hum Genet. 2009;73(Pt 3):263–73. doi:10.1111/j.1469-1809.2009.00523.x.PubMedPubMed CentralView ArticleGoogle Scholar
  235. Malenfant P, Liu X, Hudson ML, Qiao Y, Hrynchak M, Riendeau N, et al. Association of GTF2i in the Williams-Beuren syndrome critical region with autism spectrum disorders. J Autism Dev Disord. 2012;42:1459–69. doi:10.1007/s10803-011-1389-4.PubMedView ArticleGoogle Scholar
  236. Martin CL, Duvall JA, Ilkin Y, Simon JS, Arreaza MG, Wilkes K, et al. Cytogenetic and molecular characterization of A2BP1/FOX1 as a candidate gene for autism. Am J Med Genet B Neuropsychiatr Genet. 2007;144B:869–76. doi:10.1002/ajmg.b.30530.PubMedView ArticleGoogle Scholar
  237. Martin LA, Ashwood P, Braunschweig D, Cabanlit M, Van de Water J, Amaral DG. Stereotypies and hyperactivity in rhesus monkeys exposed to IgG from mothers of children with autism. Brain Behav Immun. 2008;22:806–16. doi:10.1016/j.bbi.2007.12.007.PubMedPubMed CentralView ArticleGoogle Scholar
  238. Maussion G, Carayol J, Lepagnol-Bestel A-M, Tores F, Loe-Mie Y, Milbreta U, et al. Convergent evidence identifying MAP/microtubule affinity-regulating kinase 1 (MARK1) as a susceptibility gene for autism. Hum Mol Genet. 2008;17:2541–51. doi:10.1093/hmg/ddn154.PubMedView ArticleGoogle Scholar
  239. McCauley JL, Li C, Jiang L, Olson LM, Crockett G, Gainer K, et al. Genome-wide and Ordered-Subset linkage analyses provide support for autism loci on 17q and 19p with evidence of phenotypic and interlocus genetic correlates. BMC Med Genet. 2005;6:1. doi:10.1186/1471-2350-6-1.PubMedPubMed CentralView ArticleGoogle Scholar
  240. McInnes LA, Nakamine A, Pilorge M, Brandt T, Jiménez González P, Fallas M, et al. A large-scale survey of the novel 15q24 microdeletion syndrome in autism spectrum disorders identifies an atypical deletion that narrows the critical region. Mol Autism. 2010;1:5. doi:10.1186/2040-2392-1-5.PubMedPubMed CentralView ArticleGoogle Scholar
  241. Meyer WK, Arbeithuber B, Ober C, Ebner T, Tiemann-Boege I, Hudson RR, et al. Evaluating the evidence for transmission distortion in human pedigrees. Genetics. 2012;191:215–32. doi:10.1534/genetics.112.139576.PubMedPubMed CentralView ArticleGoogle Scholar
  242. Molloy CA, Keddache M, Martin LJ. Evidence for linkage on 21q and 7q in a subset of autism characterized by developmental regression. Mol Psychiatry. 2005;10:741–6. doi:10.1038/sj.mp.4001691.PubMedView ArticleGoogle Scholar
  243. Muscarella LA, Guarnieri V, Sacco R, Militerni R, Bravaccio C, Trillo S, et al. HOXA1 gene variants influence head growth rates in humans. Am J Med Genet B Neuropsychiatr Genet. 2007;144B:388–90. doi:10.1002/ajmg.b.30469.PubMedView ArticleGoogle Scholar
  244. Nabi R, Zhong H, Serajee FJ, Huq AHMM. No association between single nucleotide polymorphisms in DLX6 and Piccolo genes at 7q21-q22 and autism. Am J Med Genet B Neuropsychiatr Genet. 2003;119B:98–101. doi:10.1002/ajmg.b.10012.PubMedView ArticleGoogle Scholar
  245. Nabi R, Serajee FJ, Chugani DC, Zhong H, Huq AHMM. Association of tryptophan 2,3 dioxygenase gene polymorphism with autism. Am J Med Genet B Neuropsychiatr Genet. 2004;125B:63–8. doi:10.1002/ajmg.b.20147.PubMedView ArticleGoogle Scholar
  246. Nakamura K, Anitha A, Yamada K, Tsujii M, Iwayama Y, Hattori E, et al. Genetic and expression analyses reveal elevated expression of syntaxin 1A (STX1A) in high functioning autism. Int J Neuropsychopharmacol. 2008;11:1073–84. doi:10.1017/S1461145708009036.PubMedView ArticleGoogle Scholar
  247. Nicholas B, Rudrasingham V, Nash S, Kirov G, Owen MJ, Wimpory DC. Association of Per1 and Npas2 with autistic disorder: support for the clock genes/social timing hypothesis. Mol Psychiatry. 2007;12:581–92. doi:10.1038/sj.mp.4001953.PubMedView ArticleGoogle Scholar
  248. Nishimura K, Nakamura K, Anitha A, Yamada K, Tsujii M, Iwayama Y, et al. Genetic analyses of the brain-derived neurotrophic factor (BDNF) gene in autism. Biochem Biophys Res Commun. 2007;356:200–6. doi:10.1016/j.bbrc.2007.02.135.PubMedView ArticleGoogle Scholar
  249. Noor A, Whibley A, Marshall CR, Gianakopoulos PJ, Piton A, Carson AR, et al. Disruption at the PTCHD1 Locus on Xp22.11 in Autism spectrum disorder and intellectual disability. Sci Transl Med. 2010;2:49ra68. doi:10.1126/scitranslmed.3001267.PubMedPubMed CentralView ArticleGoogle Scholar
  250. Petek E, Schwarzbraun T, Noor A, Patel M, Nakabayashi K, Choufani S, et al. Molecular and genomic studies of IMMP2L and mutation screening in autism and Tourette syndrome. Mol Genet Genomics. 2007;277:71–81. doi:10.1007/s00438-006-0173-1.PubMedView ArticleGoogle Scholar
  251. Philippi A, Roschmann E, Tores F, Lindenbaum P, Benajou A, Germain-Leclerc L, et al. Haplotypes in the gene encoding protein kinase c-beta (PRKCB1) on chromosome 16 are associated with autism. Mol Psychiatry. 2005;10:950–60. doi:10.1038/sj.mp.4001704.PubMedView ArticleGoogle Scholar
  252. Philippi A, Tores F, Carayol J, Rousseau F, Letexier M, Roschmann E, et al. Association of autism with polymorphisms in the paired-like homeodomain transcription factor 1 (PITX1) on chromosome 5q31: a candidate gene analysis. BMC Med Genet. 2007;8:74. doi:10.1186/1471-2350-8-74.PubMedPubMed CentralView ArticleGoogle Scholar
  253. Rabionet R, Jaworski JM, Ashley-Koch AE, Martin ER, Sutcliffe JS, Haines JL, et al. Analysis of the autism chromosome 2 linkage region: GAD1 and other candidate genes. Neurosci Lett. 2004;372:209–14. doi:10.1016/j.neulet.2004.09.037.PubMedView ArticleGoogle Scholar
  254. Raiford KL, Shao Y, Allen IC, Martin ER, Menold MM, Wright HH, et al. No association between the APOE gene and autism. Am J Med Genet B Neuropsychiatr Genet. 2004;125B:57–60. doi:10.1002/ajmg.b.20104.PubMedView ArticleGoogle Scholar
  255. Ramos PS, Sajuthi S, Langefeld CD, Walker SJ. Immune function genes CD99L2, JARID2 and TPO show association with autism spectrum disorder. Mol Autism. 2012;3:4. doi:10.1186/2040-2392-3-4.PubMedPubMed CentralView ArticleGoogle Scholar
  256. Ramoz N, Cai G, Reichert JG, Silverman JM, Buxbaum JD. An analysis of candidate autism loci on chromosome 2q24-q33: evidence for association to the STK39 gene. Am J Med Genet B Neuropsychiatr Genet. 2008;147B:1152–8. doi:10.1002/ajmg.b.30739.PubMedView ArticleGoogle Scholar
  257. Ramoz N, Reichert JG, Smith CJ, Silverman JM, Bespalova IN, Davis KL, et al. Linkage and association of the mitochondrial aspartate/glutamate carrier SLC25A12 gene with autism. Am J Psychiatry. 2004;161:662–9. doi:10.1176/appi.ajp.161.4.662.PubMedView ArticleGoogle Scholar
  258. Ramoz N, Cai G, Reichert JG, Corwin TE, Kryzak LA, Smith CJ, et al. Family-based association study of TPH1 and TPH2 polymorphisms in autism. Am J Med Genet B Neuropsychiatr Genet. 2006;141B:861–7. doi:10.1002/ajmg.b.30356.PubMedView ArticleGoogle Scholar
  259. Ramoz N, Reichert JG, Corwin TE, Smith CJ, Silverman JM, Hollander E, et al. Lack of evidence for association of the serotonin transporter gene SLC6A4 with autism. Biol Psychiatry. 2006;60:186–91. doi:10.1016/j.biopsych.2006.01.009.PubMedView ArticleGoogle Scholar
  260. Alvarez Retuerto AI, Cantor RM, Gleeson JG, Ustaszewska A, Schackwitz WS, Pennacchio LA, et al. Association of common variants in the Joubert syndrome gene (AHI1) with autism. Hum Mol Genet. 2008;17:3887–96. doi:10.1093/hmg/ddn291.PubMedPubMed CentralView ArticleGoogle Scholar
  261. Roberson EDO, Pevsner J. Visualization of shared genomic regions and meiotic recombination in high-density SNP data. PLoS ONE. 2009;4:e6711. doi:10.1371/journal.pone.0006711.PubMedPubMed CentralView ArticleGoogle Scholar
  262. Russo AJ, Neville L, Wroge C. Low Serum Alpha-1 Antitrypsin (AAT) in Family Members of Individuals with Autism Correlates with PiMZ Genotype. Biomark Insights. 2009;4:45–56.PubMedPubMed CentralGoogle Scholar
  263. Sadakata T, Washida M, Iwayama Y, Shoji S, Sato Y, Ohkura T, et al. Autistic-like phenotypes in Cadps2-knockout mice and aberrant CADPS2 splicing in autistic patients. J Clin Invest. 2007;117:931–43. doi:10.1172/JCI29031.PubMedPubMed CentralView ArticleGoogle Scholar
  264. Salyakina D, Ma DQ, Jaworski JM, Konidari I, Whitehead PL, Henson R, et al. Variants in several genomic regions associated with asperger disorder. Autism Res. 2010;3:303–10. doi:10.1002/aur.158.PubMedPubMed CentralView ArticleGoogle Scholar
  265. Schwender H, Bowers K, Fallin MD, Ruczinski I. Importance measures for epistatic interactions in case-parent trios. Ann Hum Genet. 2011;75:122–32. doi:10.1111/j.1469-1809.2010.00623.x.PubMedView ArticleGoogle Scholar
  266. Serajee FJ, Mahbubul Huq AHM. Association of Y chromosome haplotypes with autism. J Child Neurol. 2009;24:1258–61. doi:10.1177/0883073809333530.PubMedView ArticleGoogle Scholar
  267. Serajee FJ, Nabi R, Zhong H, Mahbubul Huq AHM. Association of INPP1, PIK3CG, and TSC2 gene variants with autistic disorder: implications for phosphatidylinositol signalling in autism. J Med Genet. 2003;40:e119. doi:10.1136/jmg.40.11.e119.PubMedPubMed CentralView ArticleGoogle Scholar
  268. Serajee FJ, Zhong H, Nabi R, Huq AHMM. The metabotropic glutamate receptor 8 gene at 7q31: partial duplication and possible association with autism. J Med Genet. 2003;40:e42. doi:10.1136/jmg.40.4.e42.PubMedPubMed CentralView ArticleGoogle Scholar
  269. Serajee FJ, Nabi R, Zhong H, Huq M. Polymorphisms in xenobiotic metabolism genes and autism. J Child Neurol. 2004;19:413–7. doi:10.1177/088307380401900603.PubMedView ArticleGoogle Scholar
  270. Shi L, Zhang X, Golhar R, Otieno FG, He M, Hou C, et al. Whole-genome sequencing in an autism multiplex family. Mol Autism. 2013;4:8. doi:10.1186/2040-2392-4-8.PubMedPubMed CentralView ArticleGoogle Scholar
  271. Shi J, Li P. An integrative segmentation method for detecting germline copy number variations in SNP arrays. Genet Epidemiol. 2012;36:373–83. doi:10.1002/gepi.21631.PubMedView ArticleGoogle Scholar
  272. Silverman JM, Buxbaum JD, Ramoz N, Schmeidler J, Reichenberg A, Hollander E, et al. Autism-related routines and rituals associated with a mitochondrial aspartate/glutamate carrier SLC25A12 polymorphism. Am J Med Genet B Neuropsychiatr Genet. 2008;147:408–10. doi:10.1002/ajmg.b.30614.PubMedView ArticleGoogle Scholar
  273. Steinberg KM, Ramachandran D, Patel VC, Shetty AC, Cutler DJ, Zwick ME. Identification of rare X-linked neuroligin variants by massively parallel sequencing in males with autism spectrum disorder. Mol Autism. 2012;3:8. doi:10.1186/2040-2392-3-8.PubMedPubMed CentralView ArticleGoogle Scholar
  274. Stone JL, Merriman B, Cantor RM, Geschwind DH, Nelson SF. High density SNP association study of a major autism linkage region on chromosome 17. Hum Mol Genet. 2007;16:704–15. doi:10.1093/hmg/ddm015.PubMedView ArticleGoogle Scholar
  275. Stone JL, Merriman B, Cantor RM, Yonan AL, Gilliam TC, Geschwind DH, et al. Evidence for sex-specific risk alleles in autism spectrum disorder. Am J Hum Genet. 2004;75:1117–23. doi:10.1086/426034.PubMedPubMed CentralView ArticleGoogle Scholar
  276. Sutcliffe JS, Delahanty RJ, Prasad HC, McCauley JL, Han Q, Jiang L, et al. Allelic heterogeneity at the serotonin transporter locus (SLC6A4) confers susceptibility to autism and rigid-compulsive behaviors. Am J Hum Genet. 2005;77:265–79. doi:10.1086/432648.PubMedPubMed CentralView ArticleGoogle Scholar
  277. Tierney E, Bukelis I, Thompson RE, Ahmed K, Aneja A, Kratz L, et al. Abnormalities of cholesterol metabolism in autism spectrum disorders. Am J Med Genet B Neuropsychiatr Genet. 2006;141B:666–8. doi:10.1002/ajmg.b.30368.PubMedPubMed CentralView ArticleGoogle Scholar
  278. Toyoda T, Nakamura K, Yamada K, Thanseem I, Anitha A, Suda S, et al. SNP analyses of growth factor genes EGF, TGFbeta-1, and HGF reveal haplotypic association of EGF with autism. Biochem Biophys Res Commun. 2007;360:715–20. doi:10.1016/j.bbrc.2007.06.051.PubMedView ArticleGoogle Scholar
  279. Vardarajan BN, Eran A, Jung JY, Kunkel LM, Wall DP. Haplotype structure enables prioritization of common markers and candidate genes in autism spectrum disorder. Transl Psychiatry. 2013;3:e262. doi:10.1038/tp.2013.38.PubMedPubMed CentralView ArticleGoogle Scholar
  280. Vincent JB, Petek E, Thevarkunnel S, Kolozsvari D, Cheung J, Patel M, et al. The RAY1/ST7 Tumor-Suppressor Locus on Chromosome 7q31 Represents a Complex Multi-transcript System. Genomics. 2002;80:283–94. doi:10.1006/geno.2002.6835.PubMedView ArticleGoogle Scholar
  281. Vincent JB, Kolozsvari D, Roberts WS, Bolton PF, Gurling HMD, Scherer SW. Mutation screening of X-chromosomal neuroligin genes: no mutations in 196 autism probands. Am J Med Genet B Neuropsychiatr Genet. 2004;129B:82–4. doi:10.1002/ajmg.b.30069.PubMedView ArticleGoogle Scholar
  282. Vincent JB, Thevarkunnel S, Kolozsvari D, Paterson AD, Roberts W, Scherer SW. Association and transmission analysis of the FMR1 IVS10 + 14C-T variant in autism. Am J Med Genet B Neuropsychiatr Genet. 2004;125B:54–6. doi:10.1002/ajmg.b.20088.PubMedView ArticleGoogle Scholar
  283. Walker SJ, Segal J, Aschner M. Cultured lymphocytes from autistic children and non-autistic siblings up-regulate heat shock protein RNA in response to thimerosal challenge. Neurotoxicology. 2006;27:685–92. doi:10.1016/j.neuro.2006.06.003.PubMedView ArticleGoogle Scholar
  284. Weiss LA, Arking DE, Gene Discovery Project of Johns Hopkins & the Autism Consortium, Daly MJ, Chakravarti A. A genome-wide linkage and association scan reveals novel loci for autism. Nature. 2009;461:802–8. doi:10.1038/nature08490.PubMedPubMed CentralView ArticleGoogle Scholar
  285. Weiss LA, Kosova G, Delahanty RJ, Jiang L, Cook EH, Ober C, et al. Variation in ITGB3 is associated with whole-blood serotonin level and autism susceptibility. Eur J Hum Genet. 2006;14:923–31. doi:10.1038/sj.ejhg.5201644.PubMedView ArticleGoogle Scholar
  286. Werling DM, Geschwind DH. Recurrence rates provide evidence for sex-differential, familial genetic liability for autism spectrum disorders in multiplex families and twins. Mol Autism. 2015;6:27. doi:10.1186/s13229-015-0004-5.PubMedPubMed CentralView ArticleGoogle Scholar
  287. Werling DM, Lowe JK, Luo R, Cantor RM, Geschwind DH. Replication of linkage at chromosome 20p13 and identification of suggestive sex-differential risk loci for autism spectrum disorder. Mol Autism. 2014;5:13. doi:10.1186/2040-2392-5-13.PubMedPubMed CentralView ArticleGoogle Scholar
  288. Yamagata T, Aradhya S, Mori M, Inoue K, Momoi MY, Nelson DL. The Human Secretin Gene: Fine Structure in 11p15.5 and Sequence Variation in Patients with Autism. Genomics. 2002;80:185–94. doi:10.1006/geno.2002.6814.PubMedView ArticleGoogle Scholar
  289. Yaspan BL, Bush WS, Torstenson ES, Ma D, Pericak-Vance MA, Ritchie MD, et al. Genetic analysis of biological pathway data through genomic randomization. Hum Genet. 2011;129:563–71. doi:10.1007/s00439-011-0956-2.PubMedPubMed CentralView ArticleGoogle Scholar
  290. Ylisaukko-oja T, Alarcón M, Cantor RM, Auranen M, Vanhala R, Kempas E, et al. Search for autism loci by combined analysis of Autism Genetic Resource Exchange and Finnish families. Ann Neurol. 2006;59:145–55. doi:10.1002/ana.20722.PubMedView ArticleGoogle Scholar
  291. Liu J, Nyholt DR, Magnussen P, Parano E, Pavone P, Geschwind D, et al. A genomewide screen for autism susceptibility loci. Am J Hum Genet. 2001;69:327–40. doi:10.1086/321980.PubMedPubMed CentralView ArticleGoogle Scholar
  292. Zandi PP, Kalaydjian A, Avramopoulos D, Shao H, Fallin MD, Newschaffer CJ. Rh and ABO maternal-fetal incompatibility and risk of autism. Am J Med Genet B Neuropsychiatr Genet. 2006;141B:643–7. doi:10.1002/ajmg.b.30391.PubMedView ArticleGoogle Scholar
  293. Zhao X, Leotta A, Kustanovich V, Lajonchere C, Geschwind DH, Law K, et al. A unified genetic theory for sporadic and inherited autism. Proc Natl Acad Sci U S A. 2007;104:12831–6. doi:10.1073/pnas.0705803104.PubMedPubMed CentralView ArticleGoogle Scholar
  294. Zhong H. No association between the EN2 gene and autistic disorder. J Med Genet. 2003;40:4e–4. doi:10.1136/jmg.40.1.e4.View ArticleGoogle Scholar
  295. James SJ, Jill James S, Melnyk S, Jernigan S, Hubanks A, Rose S, et al. Abnormal transmethylation/transsulfuration metabolism and DNA hypomethylation among parents of children with autism. J Autism Dev Disord. 2008;38:1966–75. doi:10.1007/s10803-008-0591-5.PubMedPubMed CentralView ArticleGoogle Scholar
  296. Ackerman S, Wenegrat J, Rettew D, Althoff R, Bernier R. No increase in autism-associated genetic events in children conceived by assisted reproduction. Fertil Steril. 2014;102:388–93. doi:10.1016/j.fertnstert.2014.04.020.PubMedView ArticleGoogle Scholar
  297. Bahl S, Chiang C, Beauchamp RL, Neale BM, Daly MJ, Gusella JF, et al. Lack of association of rare functional variants in TSC1/TSC2 genes with autism spectrum disorder. Mol Autism. 2013;4:5. doi:10.1186/2040-2392-4-5.PubMedPubMed CentralView ArticleGoogle Scholar
  298. Ben-David E, Shifman S. Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism. Mol Psychiatry. 2013;18:1054–6. doi:10.1038/mp.2012.148.PubMedView ArticleGoogle Scholar
  299. Brand H, Collins RL, Hanscom C, Rosenfeld JA, Pillalamarri V, Stone MR, et al. Paired-Duplication Signatures Mark Cryptic Inversions and Other Complex Structural Variation. Am J Hum Genet. 2015;97:170–6. doi:10.1016/j.ajhg.2015.05.012.PubMedPubMed CentralView ArticleGoogle Scholar
  300. Campbell MG, Kohane IS, Kong SW. Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome. BMC Med Genomics. 2013;6:34. doi:10.1186/1755-8794-6-34.PubMedPubMed CentralView ArticleGoogle Scholar
  301. Celestino-Soper PBS, Shaw CA, Sanders SJ, Li J, Murtha MT, Ercan-Sencicek AG, et al. Use of array CGH to detect exonic copy number variants throughout the genome in autism families detects a novel deletion in TMLHE. Hum Mol Genet. 2011;20:4360–70. doi:10.1093/hmg/ddr363.PubMedPubMed CentralView ArticleGoogle Scholar
  302. Celestino-Soper PBS, Violante S, Crawford EL, Luo R, Lionel AC, Delaby E, et al. A common X-linked inborn error of carnitine biosynthesis may be a risk factor for nondysmorphic autism. Proc Natl Acad Sci U S A. 2012;109:7974–81. doi:10.1073/pnas.1120210109.PubMedPubMed CentralView ArticleGoogle Scholar
  303. Chang J, Gilman SR, Chiang AH, Sanders SJ, Vitkup D. Genotype to phenotype relationships in autism spectrum disorders. Nat Neurosci. 2015;18:191–8. doi:10.1038/nn.3907.PubMedView ArticleGoogle Scholar
  304. Chaste P, Klei L, Sanders SJ, Hus V, Murtha MT, Lowe JK, et al. A genome-wide association study of autism using the Simons Simplex Collection: Does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol Psychiatry. 2015;77:775–84. doi:10.1016/j.biopsych.2014.09.017.PubMedView ArticleGoogle Scholar
  305. Cheng Y, Quinn JF, Weiss LA. An eQTL mapping approach reveals that rare variants in the SEMA5A regulatory network impact autism risk. Hum Mol Genet. 2013;22:2960–72. doi:10.1093/hmg/ddt150.PubMedPubMed CentralView ArticleGoogle Scholar
  306. Fang H, Wu Y, Narzisi G, O’Rawe JA, Barrón LTJ, Rosenbaum J, et al. Reducing INDEL calling errors in whole genome and exome sequencing data. Genome Med. 2014;6:89. doi:10.1186/s13073-014-0089-z.PubMedPubMed CentralView ArticleGoogle Scholar
  307. Gamsiz ED, Viscidi EW, Frederick AM, Nagpal S, Sanders SJ, Murtha MT, et al. Intellectual disability is associated with increased runs of homozygosity in simplex autism. Am J Hum Genet. 2013;93:103–9. doi:10.1016/j.ajhg.2013.06.004.PubMedPubMed CentralView ArticleGoogle Scholar
  308. Girirajan S, Brkanac Z, Coe BP, Baker C, Vives L, Vu TH, et al. Relative burden of large CNVs on a range of neurodevelopmental phenotypes. PLoS Genet. 2011;7:e1002334. doi:10.1371/journal.pgen.1002334.PubMedPubMed CentralView ArticleGoogle Scholar
  309. Griswold AJ, Dueker ND, Van Booven D, Rantus JA, Jaworski JM, Slifer SH, et al. Targeted massively parallel sequencing of autism spectrum disorder-associated genes in a case control cohort reveals rare loss-of-function risk variants. Mol Autism. 2015;6:43. doi:10.1186/s13229-015-0034-z.PubMedPubMed CentralView ArticleGoogle Scholar
  310. He X, Sanders SJ, Liu L, De Rubeis S, Lim ET, Sutcliffe JS, et al. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes. PLoS Genet. 2013;9:e1003671. doi:10.1371/journal.pgen.1003671.PubMedPubMed CentralView ArticleGoogle Scholar
  311. He Z, O’Roak BJ, Smith JD, Wang G, Hooker S, Santos-Cortez RLP, et al. Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data. Am J Hum Genet. 2014;94:33–46. doi:10.1016/j.ajhg.2013.11.021.PubMedPubMed CentralView ArticleGoogle Scholar
  312. Iossifov I, Levy D, Allen J, Ye K, Ronemus M, Lee Y-H, et al. Low load for disruptive mutations in autism genes and their biased transmission. Proc Natl Acad Sci U S A. 2015;112:E5600–7. doi:10.1073/pnas.1516376112.PubMedPubMed CentralView ArticleGoogle Scholar
  313. Kim S-J, Silva RM, Flores CG, Jacob S, Guter S, Valcante G, et al. A quantitative association study of SLC25A12 and restricted repetitive behavior traits in autism spectrum disorders. Mol Autism. 2011;2:8. doi:10.1186/2040-2392-2-8.PubMedPubMed CentralView ArticleGoogle Scholar
  314. Klei L, Sanders SJ, Murtha MT, Hus V, Lowe JK, Willsey AJ, et al. Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism. 2012;3:9. doi:10.1186/2040-2392-3-9.PubMedPubMed CentralView ArticleGoogle Scholar
  315. Kong SW, Collins CD, Shimizu-Motohashi Y, Holm IA, Campbell MG, Lee I-H, et al. Characteristics and predictive value of blood transcriptome signature in males with autism spectrum disorders. PLoS ONE. 2012;7:e49475. doi:10.1371/journal.pone.0049475.PubMedPubMed CentralView ArticleGoogle Scholar
  316. Kong SW, Shimizu-Motohashi Y, Campbell MG, Lee IH, Collins CD, Brewster SJ, et al. Peripheral blood gene expression signature differentiates children with autism from unaffected siblings. Neurogenetics. 2013;14:143–52. doi:10.1007/s10048-013-0363-z.PubMedPubMed CentralView ArticleGoogle Scholar
  317. Cross-Disorder Group of the Psychiatric Genomics Consortium, Lee SH, Ripke S, Neale BM, Faraone SV, Purcell SM, et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013;45:984–94. doi:10.1038/ng.2711.PubMed CentralView ArticleGoogle Scholar
  318. Lim ET, Raychaudhuri S, Sanders SJ, Stevens C, Sabo A, MacArthur DG, et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron. 2013;77:235–42. doi:10.1016/j.neuron.2012.12.029.PubMedPubMed CentralView ArticleGoogle Scholar
  319. Mazina V, Gerdts J, Trinh S, Ankenman K, Ward T, Dennis MY, et al. Epigenetics of autism-related impairment: copy number variation and maternal infection. J Dev Behav Pediatr. 2015;36:61–7. doi:10.1097/DBP.0000000000000126.PubMedPubMed CentralView ArticleGoogle Scholar
  320. Merner ND, Chandler MR, Bourassa C, Liang B, Khanna AR, Dion P, et al. Regulatory domain or CpG site variation in SLC12A5, encoding the chloride transporter KCC2, in human autism and schizophrenia. Front Cell Neurosci. 2015;9:386. doi:10.3389/fncel.2015.00386.PubMedPubMed CentralView ArticleGoogle Scholar
  321. Moreno-De-Luca D, SGENE Consortium, Mulle JG, Simons Simplex Collection Genetics Consortium, Kaminsky EB, Sanders SJ, et al. Deletion 17q12 is a recurrent copy number variant that confers high risk of autism and schizophrenia. Am J Hum Genet. 2010;87:618–30. doi:10.1016/j.ajhg.2010.10.004.PubMedPubMed CentralView ArticleGoogle Scholar
  322. Moreno-De-Luca D, Sanders SJ, Willsey AJ, Mulle JG, Lowe JK, Geschwind DH, et al. Using large clinical data sets to infer pathogenicity for rare copy number variants in autism cohorts. Mol Psychiatry. 2013;18:1090–5. doi:10.1038/mp.2012.138.PubMedView ArticleGoogle Scholar
  323. Murdoch JD, Gupta AR, Sanders SJ, Walker MF, Keaney J, Fernandez TV, et al. No evidence for association of autism with rare heterozygous point mutations in Contactin-Associated Protein-Like 2 (CNTNAP2), or in Other Contactin-Associated Proteins or Contactins. PLoS Genet. 2015;11:e1004852. doi:10.1371/journal.pgen.1004852.PubMedPubMed CentralView ArticleGoogle Scholar
  324. Narzisi G, O’Rawe JA, Iossifov I, Fang H, Lee Y-H, Wang Z, et al. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat Methods. 2014;11:1033–6. doi:10.1038/nmeth.3069.PubMedPubMed CentralView ArticleGoogle Scholar
  325. Noh HJ, Ponting CP, Boulding HC, Meader S, Betancur C, Buxbaum JD, et al. Network topologies and convergent aetiologies arising from deletions and duplications observed in individuals with autism. PLoS Genet. 2013;9:e1003523. doi:10.1371/journal.pgen.1003523.PubMedPubMed CentralView ArticleGoogle Scholar
  326. Peters SU, Hundley RJ, Wilson AK, Warren Z, Vehorn A, Carvalho CMB, et al. The behavioral phenotype in MECP2 duplication syndrome: a comparison with idiopathic autism. Autism Res. 2013;6:42–50. doi:10.1002/aur.1262.PubMedView ArticleGoogle Scholar
  327. Sagar A, Bishop JR, Tessman DC, Guter S, Martin CL, Cook EH. Co-occurrence of autism, childhood psychosis, and intellectual disability associated with a de novo 3q29 microdeletion. Am J Med Genet A. 2013;161A:845–9. doi:10.1002/ajmg.a.35754.PubMedView ArticleGoogle Scholar
  328. Robinson EB, St Pourcain B, Anttila V, Kosmicki JA, Bulik-Sullivan B, Grove J, et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat Genet. 2016;48:552–5. doi:10.1038/ng.3529.PubMedPubMed CentralView ArticleGoogle Scholar
  329. Skafidas E, Testa R, Zantomio D, Chana G, Everall IP, Pantelis C. Predicting the diagnosis of autism spectrum disorder using gene pathway analysis. Mol Psychiatry. 2014;19:504–10. doi:10.1038/mp.2012.126.PubMedView ArticleGoogle Scholar
  330. Stanco A, Pla R, Vogt D, Chen Y, Mandal S, Walker J, et al. NPAS1 represses the generation of specific subtypes of cortical interneurons. Neuron. 2014;84:940–53. doi:10.1016/j.neuron.2014.10.040.PubMedPubMed CentralView ArticleGoogle Scholar
  331. Wang Y, Picard M, Gu Z. Genetic evidence for elevated pathogenicity of mitochondrial DNA heteroplasmy in autism spectrum disorder. PLoS Genet. 2016;12:e1006391. doi:10.1371/journal.pgen.1006391.PubMedPubMed CentralView ArticleGoogle Scholar
  332. Yi JJ, Berrios J, Newbern JM, Snider WD, Philpot BD, Hahn KM, et al. An Autism-Linked Mutation Disables Phosphorylation Control of UBE3A. Cell. 2015;162:795–807. doi:10.1016/j.cell.2015.06.045.PubMedPubMed CentralView ArticleGoogle Scholar
  333. Yu TW, Chahrour MH, Coulter ME, Jiralerspong S, Okamura-Ikeda K, Ataman B, et al. Using whole-exome sequencing to identify inherited causes of autism. Neuron. 2013;77:259–73. doi:10.1016/j.neuron.2012.11.002.PubMedPubMed CentralView ArticleGoogle Scholar
  334. Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, et al. Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron. 2015;87:1215–33. doi:10.1016/j.neuron.2015.09.016.PubMedPubMed CentralView ArticleGoogle Scholar
  335. Krumm N, Turner TN, Baker C, Vives L, Mohajeri K, Witherspoon K, et al. Excess of rare, inherited truncating mutations in autism. Nat Genet. 2015;47:582–8. doi:10.1038/ng.3303.PubMedPubMed CentralView ArticleGoogle Scholar
  336. Krumm N, O’Roak BJ, Karakoc E, Mohajeri K, Nelson B, Vives L, et al. Transmission disequilibrium of small CNVs in simplex autism. Am J Hum Genet. 2013;93:595–606. doi:10.1016/j.ajhg.2013.07.024.PubMedPubMed CentralView ArticleGoogle Scholar
  337. Anitha A, Nakamura K, Thanseem I, Yamada K, Iwayama Y, Toyota T, et al. Brain region-specific altered expression and association of mitochondria-related genes in autism. Mol Autism. 2012;3:12. doi:10.1186/2040-2392-3-12.PubMedPubMed CentralView ArticleGoogle Scholar
  338. Anitha A, Nakamura K, Thanseem I, Matsuzaki H, Miyachi T, Tsujii M, et al. Downregulation of the expression of mitochondrial electron transport complex genes in autism brains. Brain Pathol. 2013;23:294–302. doi:10.1111/bpa.12002.PubMedView ArticleGoogle Scholar
  339. Chow ML, Pramparo T, Winn ME, Barnes CC, Li H-R, Weiss L, et al. Age-dependent brain gene expression and copy number anomalies in autism suggest distinct pathological processes at young versus mature ages. PLoS Genet. 2012;8:e1002592. doi:10.1371/journal.pgen.1002592.PubMedPubMed CentralView ArticleGoogle Scholar
  340. D’Gama AM, Pochareddy S, Li M, Jamuar SS, Reiff RE, Lam A-TN, et al. Targeted DNA Sequencing from Autism Spectrum Disorder Brains Implicates Multiple Genetic Mechanisms. Neuron. 2015;88:910–7. doi:10.1016/j.neuron.2015.11.009.PubMedPubMed CentralView ArticleGoogle Scholar
  341. Fatemi SH, Folsom TD, Kneeland RE, Yousefi MK, Liesch SB, Thuras PD. Impairment of fragile X mental retardation protein-metabotropic glutamate receptor 5 signaling and its downstream cognates ras-related C3 botulinum toxin substrate 1, amyloid beta A4 precursor protein, striatal-enriched protein tyrosine phosphatase, and homer 1, in autism: a postmortem study in cerebellar vermis and superior frontal cortex. Mol Autism. 2013;4:21. doi:10.1186/2040-2392-4-21.PubMedPubMed CentralView ArticleGoogle Scholar
  342. Fatemi SH, Reutiman TJ, Folsom TD, Rustan OG, Rooney RJ, Thuras PD. Downregulation of GABAA receptor protein subunits α6, β2, δ, ε, γ2, θ, and ρ2 in superior frontal cortex of subjects with autism. J Autism Dev Disord. 2014;44:1833–45. doi:10.1007/s10803-014-2078-x.PubMedView ArticleGoogle Scholar
  343. Garcia KLP, Yu G, Nicolini C, Michalski B, Garzon DJ, Chiu VS, et al. Altered balance of proteolytic isoforms of pro-brain-derived neurotrophic factor in autism. J Neuropathol Exp Neurol. 2012;71:289–97. doi:10.1097/NEN.0b013e31824b27e4.PubMedPubMed CentralView ArticleGoogle Scholar
  344. Hu VW, Sarachana T, Sherrard RM, Kocher KM. Investigation of sex differences in the expression of RORA and its transcriptional targets in the brain as a potential contributor to the sex bias in autism. Mol Autism. 2015;6:7. doi:10.1186/2040-2392-6-7.PubMedPubMed CentralView ArticleGoogle Scholar
  345. Huang H-S, Cheung I, Akbarian S. RPP25 is developmentally regulated in prefrontal cortex and expressed at decreased levels in autism spectrum disorder. Autism Res. 2010;3:153–61. doi:10.1002/aur.141.PubMedView ArticleGoogle Scholar
  346. Kerin T, Ramanathan A, Rivas K, Grepo N, Coetzee GA, Campbell DB. A noncoding RNA antisense to moesin at 5p14.1 in autism. Sci Transl Med. 2012;4:128ra40. doi:10.1126/scitranslmed.3003479.PubMedView ArticleGoogle Scholar
  347. Nagarajan RP, Hogart AR, Gwye Y, Martin MR, LaSalle JM. Reduced MeCP2 expression is frequent in autism frontal cortex and correlates with aberrant MECP2 promoter methylation. Epigenetics. 2006;1:e1–11.PubMedPubMed CentralView ArticleGoogle Scholar
  348. Nicolini C, Ahn Y, Michalski B, Rho JM, Fahnestock M. Decreased mTOR signaling pathway in human idiopathic autism and in rats exposed to valproic acid. Acta Neuropathol Commun. 2015;3:3. doi:10.1186/s40478-015-0184-4.PubMedPubMed CentralView ArticleGoogle Scholar
  349. Sarachana T, Hu VW. Genome-wide identification of transcriptional targets of RORA reveals direct regulation of multiple genes associated with autism spectrum disorder. Mol Autism. 2013;4:14. doi:10.1186/2040-2392-4-14.PubMedPubMed CentralView ArticleGoogle Scholar
  350. Smith RM, Banks W, Hansen E, Sadee W, Herman GE. Family-based clinical associations and functional characterization of the serotonin 2A receptor gene (HTR2A) in autism spectrum disorder. Autism Res. 2014;7:459–67. doi:10.1002/aur.1383.PubMedPubMed CentralView ArticleGoogle Scholar
  351. Stamova B, Ander BP, Barger N, Sharp FR, Schumann CM. Specific Regional and Age-Related Small Noncoding RNA Expression Patterns Within Superior Temporal Gyrus of Typical Human Brains Are Less Distinct in Autism Brains. J Child Neurol. 2015;30:1930–46. doi:10.1177/0883073815602067.PubMedPubMed CentralView ArticleGoogle Scholar
  352. Thanseem I, Nakamura K, Anitha A, Suda S, Yamada K, Iwayama Y, et al. Association of transcription factor gene LMX1B with autism. PLoS ONE. 2011;6:e23738. doi:10.1371/journal.pone.0023738.PubMedPubMed CentralView ArticleGoogle Scholar
  353. Werling DM, Parikshak NN, Geschwind DH. Gene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders. Nat Commun. 2016;7:10717. doi:10.1038/ncomms10717.PubMedPubMed CentralView ArticleGoogle Scholar
  354. Eicher JD, Gruen JR. Language impairment and dyslexia genes influence language skills in children with autism spectrum disorders. Autism Res. 2015;8:229–34. doi:10.1002/aur.1436.PubMedView ArticleGoogle Scholar
  355. Gockley J, Willsey AJ, Dong S, Dougherty JD, Constantino JN, Sanders SJ. The female protective effect in autism spectrum disorder is not mediated by a single genetic locus. Mol Autism. 2015;6:25. doi:10.1186/s13229-015-0014-3.PubMedPubMed CentralView ArticleGoogle Scholar
  356. Turner TN, Sharma K, Oh EC, Liu YP, Collins RL, Sosa MX, et al. Loss of δ-catenin function in severe autism. Nature. 2015;520:51–6. doi:10.1038/nature14186.PubMedPubMed CentralView ArticleGoogle Scholar
  357. Simeon-Dubach D, Watson P. Biobanking 3.0: evidence based and customer focused biobanking. Clin Biochem. 2014;47:300–8. doi:10.1016/j.clinbiochem.2013.12.018.PubMedView ArticleGoogle Scholar
  358. Beskow LM, Friedman JY, Hardy NC, Lin L, Weinfurt KP. Simplifying informed consent for biorepositories: stakeholder perspectives. Genet Med. 2010;12:567–72. doi:10.1097/GIM.0b013e3181ead64d.PubMedPubMed CentralView ArticleGoogle Scholar
  359. Vaught J, Lockhart NC. The evolution of biobanking best practices. Clin Chim Acta. 2012;413:1569–75. doi:10.1016/j.cca.2012.04.030.PubMedPubMed CentralView ArticleGoogle Scholar
  360. Stacey GN, Crook JM, Hei D, Ludwig T. Banking human induced pluripotent stem cells: lessons learned from embryonic stem cells? Cell Stem Cell. 2013;13:385–8. doi:10.1016/j.stem.2013.09.007.PubMedView ArticleGoogle Scholar
  361. Aoi T, Stacey G. Impact of National and International Stem Cell Banking Initiatives on progress in the field of cell therapy: IABS-JST Joint Workshop: Summary for Session 5. Biologicals. 2015;43:399–401. doi:10.1016/j.biologicals.2015.07.007.PubMedView ArticleGoogle Scholar
  362. Stover AE, Herculian S, Banuelos MG, Navarro SL, Jenkins MP, Schwartz PH. Culturing human pluripotent and neural stem cells in an enclosed cell culture system for basic and preclinical research. J Vis Exp. 2016. doi:10.3791/53685.PubMedPubMed CentralGoogle Scholar

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