Support for calcium channel gene defects in autism spectrum disorders
© Lu et al.; licensee BioMed Central Ltd. 2012
Received: 22 August 2012
Accepted: 31 October 2012
Published: 15 December 2012
Alternation of synaptic homeostasis is a biological process whose disruption might predispose children to autism spectrum disorders (ASD). Calcium channel genes (CCG) contribute to modulating neuronal function and evidence implicating CCG in ASD has been accumulating. We conducted a targeted association analysis of CCG using existing genome-wide association study (GWAS) data and imputation methods in a combined sample of parent/affected child trios from two ASD family collections to explore this hypothesis.
A total of 2,176 single-nucleotide polymorphisms (SNP) (703 genotyped and 1,473 imputed) covering the genes that encode the α1 subunit proteins of 10 calcium channels were tested for association with ASD in a combined sample of 2,781 parent/affected child trios from 543 multiplex Caucasian ASD families from the Autism Genetics Resource Exchange (AGRE) and 1,651 multiplex and simplex Caucasian ASD families from the Autism Genome Project (AGP). SNP imputation using IMPUTE2 and a combined reference panel from the HapMap3 and the 1,000 Genomes Project increased coverage density of the CCG. Family-based association was tested using the FBAT software which controls for population stratification and accounts for the non-independence of siblings within multiplex families. The level of significance for association was set at 2.3E-05, providing a Bonferroni correction for this targeted 10-gene panel.
Four SNPs in three CCGs were associated with ASD. One, rs10848653, is located in CACNA1C, a gene in which rare de novo mutations are responsible for Timothy syndrome, a Mendelian disorder that features ASD. Two others, rs198538 and rs198545, located in CACN1G, and a fourth, rs5750860, located in CACNA1I, are in CCGs that encode T-type calcium channels, genes with previous ASD associations.
These associations support a role for common CCG SNPs in ASD.
KeywordsAutism spectrum disorders Calcium channel genes Common variants Imputed SNPs Association studies
Autism spectrum disorders (ASD) are a group of neurodevelopmental traits characterized by behavioral symptoms in three domains: deficits in communication skills, deficits in social skills, and the presence of restricted repetitive behaviors . ASD prevalence is currently estimated at 1/88 children (http://www.cdc.gov/media/releases/2012/p0329_autism_disorder.html), with a 4:1 ratio of boys to girls . A recent twin study indicates that ASD is heritable, but its etiology is likely to include both genetic and environmental factors and their interactions . All studies indicate that the etiology of ASD is likely to be very heterogeneous, and most predisposing genetic and environmental risk factors are currently unidentified. Recent whole-genome exon sequencing studies of ASD samples estimate that as many as 100 to 1,000 genes may be involved .
The current hunt for ASD genes is focused on whole exome sequencing to identify rare de novo mutations in simplex families [5, 6]. In contrast, we hypothesize that targeted association analyses of common variants in ASD candidate genes can provide complementary information that is valuable. We report herein an association study that examines the family of calcium channel genes (CCG) that is supported by: (1) biologic insights into the roles of these genes in the brain ; (2) evidence derived from a Mendelian disorder that features ASD ; and (3) several previous more limited linkage and association studies of ASD.
10 α 1 subunit calcium channel genes tested for association with ASD
Gene symbol (Chrom band)
Most dominant brain expressed regiona
Cells and tissues with gene expression in addition to brain
Ca v 1 subfamily
Skeletal muscle; transverse tubules
Cardiac myocytes; smooth muscle myocytes; endocrine cells; neurons
Endocrine cells; neurons; cardiac cells and pacemaker cells; cochlear hair cells
Retina; spinal cord; adrenal gland; mast cells
Ca v 2 subfamily
Ca v 3 subfamily
Neurons; smooth muscle myocytes
Neurons; cardiac and smooth muscle myocytes
The most salient prior genetic evidence implicating CCG in ASD comes from a CACNA1C gene mutation that results in Timothy syndrome (TS), a Mendelian disorder with delayed repolarization of the heart following a heartbeat . TS features ASD along with deficits in language and social development . It is caused by a de novo missense mutation in the eighth exon of CACNA1C that encodes the alpha 1C subunit proteins of an L-type voltage-gated calcium (Ca2+) channel (high voltage activation and slow voltage-dependent inactivation with long-lasting currents). Additional genetic support of a role for CCG in ASD comes from the association with ASD of a single-nucleotide polymorphism (SNP) in the CACNA1G gene encoding a T-type Ca2+ channel subunit  (transient duration of opening) detected in an analysis of parent/child ASD affected trios from 284 nuclear multiplex families with only affected boys from the Autism Genetics Research Exchange (AGRE) collection. CACNA1G is located within a chromosome 17-linked region (17q11-21) that has been identified and formally replicated in families with only affected boys [15, 16]. Activities of T-type Ca2+ channels are associated with neuronal firing in the brain . An additional T-type CCG, CACNA1H, has been implicated in ASD through previous gene sequencing studies. Heterozygous missense mutations were identified in six out of 461 individuals with ASD from the AGRE panel . This gene is expressed in many regions of the brain that exhibit abnormal sizes in individuals with ASD . The mutations identified alter Cav3.2 channel function by decreasing voltage sensitivity, slowing channel activation, and disrupting channel inactivation, causing sustained large calcium currents .
Recently, exon sequencing has identified de novo mutations in two other CCGs, CACNA1D and CACNA1E, in a sample of 209 sporadic ASD families that have no previous history of ASD or its related phenotypes . CACNA1I was previously implicated in ASD by a GWAS analysis that applied a noise reduction approach to boost statistical power (GWAS-NR) in a combined sample of 597 Caucasian ASD families collected by the Hussman Institute for Human Genomics (HIHG) and 696 AGRE multiplex families . A haplotype block in CACNA1I was associated with a P value of 1.8E-05.
In the current study we assess the potential role of CCG in ASD by focusing on 10 genes that encode α1 subunits. A dense panel of SNPs is tested for association with ASD in the combination of two study samples ascertained for families with ASD with genotypes available through ongoing studies of accumulated and public GWAS data: 543 multiplex ASD families from the AGRE repository  and 1,651 families from the Autism Genome Project (AGP) . SNP coverage of the 10 CCGs has been extended by SNP imputation, and association has been tested using the Family Based Association Test (FBAT) software. Using this approach, the association analysis is not vulnerable to the effects of population stratification and is corrected for the non-independence of sibling pairs. Four SNPs in CACN1C, CACNA1I, and CACN1G meeting the Bonferroni corrected level of significance were considered to be associated with ASD, further supporting the relevance of CCG to ASD.
Overall study design
Using GWAS data available to interested researchers and methods of imputation, a dense panel of 2,176 pruned common SNPs (703 called and 1,473 imputed) in 10 CCGs was tested for association with ASD using the FBAT software that corrects for population stratification and non-independence of parent/child trios within nuclear families. With this approach, each SNP is tested for a transmission ratio that differs significantly from its expected 50%. The study sample consisted of 2,781 Caucasian parent/child trios, where 1,103 are from 543 AGRE families and 1,678 are from 1,651 AGP families. Prior to analysis, 10% to 15% of the families where one or both parents were not Caucasian were identified using multidimensional scaling analysis and removed to reduce genetic heterogeneity that could be attributable to race. SNP imputation was conducted separately in the AGRE and AGP panels using the IMPUTE2 software and a combined reference panel from the HapMap3 and the 1,000 Genomes Project. Allele frequencies and patterns of linkage disequilibrium were estimated within each sample for the imputed SNPs. These were compared for consistency. A Bonferroni correction for the 2,176 SNPs tested was used to define a level of significance of P <2.3E-05 that was used for association, as we had not examined the association of any of the genotyped SNPs in either sample prior to the analyses reported herein.
Study sample and genotyping: AGRE
The AGRE repository is described fully in . It is composed of nuclear families ascertained for two or more children with ASD identified by the Autism Diagnostic Instrument Revised (ADIR) . Monozygotic twins and children with non-idiopathic ASD such as fragile X, abnormal brain imaging, abnormal karyotype, neurogenetic disorder, and perinatal insults were excluded from the panel for the current analyses. Both parents and the children of 731 families had GWAS genotyping performed on two platforms: 86% on the Illumina HumanHap 550 BeadChip platform and 14% on the HumanOmni1-Quad BeadChip platform. Quality control (QC) of SNPs included removing those with Hardy-Weinberg Equilibrium P <0.0001, a Mendelian error rate >0.1, or a minor allele frequency (MAF) <0.01. Multidimensional scaling analysis  was performed to identify the founder ancestry of the parents, resulting in 543 multiplex Caucasian families (1,103 trios) for association analysis. Regulatory review, approval, and oversight of AGRE's human subject research is provided by Western IRB, an AAHRPP-accredited Independent Review Board located in Olympia, WA (AGRE website).
Study sample: AGP
AGP families were ascertained for children with ASD and collected from more than 50 centers in North America and Europe and combined into a single panel for genotyping and GWAS analysis. The individual study samples that comprise the AGP each comply with the current laws of the country in which the samples were collected. The AGRE families that are part of AGP panel have been excluded from the AGP for the work reported here. Children with ASD are identified by positive results on ADI-R  and the Autism Diagnostic Observation Schedule (ADOS) . A total of 1,884 AGP trios and nuclear families recruited from Phase 2-Stage1 were genotyped using the Illumina Human 1M-single Infinium BeadChip platform. Similar to the AGRE panel, non-idiopathic ASD children have been excluded from analysis. SNPs were filtered using the same QC criteria. Multidimensional scaling analysis (MDS), equivalent to that performed for the AGRE families, identified 1,651 Caucasian families (1,678 trios) to be combined with the 543 AGRE families for association analysis.
Multidimensional scaling: identifying Caucasian families within AGRE and the AGP
MDS was conducted using the PLINK software to identify the Caucasian founders separately in the AGRE and AGP samples. The genetic similarity between N founders was estimated based on identity by state (IBS) marker concordance at every fifth marker on the autosomal chromosomes. This yielded an N by N similarity matrix. Principal components analysis was performed to transform the estimated matrix in order to project the greatest amount of IBS information in a two dimensional plot, geometrically clustering the founders with the same genetic ancestry. Eighty percent of the AGRE founders were identified as Caucasians and 93% of the AGP founders were categorized into the AGRE cluster classified as Caucasian. Both parents had to be classified into this cluster for the family to be retained in the analysis. A total of 543 AGRE (1,103 trios) families and 1,651 AGP (1,678 trios) were classified as Caucasian.
SNP imputation in the 10 CCG: methods and quality control
Imputation of SNP genotypes is based on genotyped SNPs that are IBS where two unrelated individuals share short stretches of their haplotypes from their common ancestors. Current algorithms for genotype imputation are based on hidden Markov models (HMM) and include Beagle , MaCH , IMPUTE, and IMPUTE2 [29–31]. For the work reported here, imputation was conducted using IMPUTE2 [30, 31] with the CEU (Utah residents with northern and western European ancestry) reference panel. For the reference data, a combined panel from the HapMap 3 and 1,000 Genomes projects were used [32, 33]. All have very high accuracy as indicated by concordance rates of between 94% and 97% for called and imputed genotypes when masking those that were called . For haplotype reconstruction, HMM are used to infer the haplotype phase and impute the missing genotypes. The models include a matrix of transition probabilities to allow for the occurrence of recombination between adjacent markers and a matrix of emission probabilities to mimic the effect of mutation.
To insure the availability of only high quality imputed genotypes for analyses with IMPUTE2, the developers of this program filtered the reference Hapmap3 and 1,000 Genomes SNP data with quality control measures and removed those SNPs with problems as described on their website (http://mathgen.stats.ox.ac.uk/impute/data_download_1000G_pilot_plus_hapmap3.html). The effect size was set at 2,000 (−Ne option) to scale the recombination rates in the HMM. Each imputed marker was assessed by an information measure that ranged from 0 to 1. The value of one is achieved when the information from imputed genotypes equals the information one would expect if the alleles are genotyped and sampled from the population. To assign the imputed genotypes for each individual, IMPUTE2 outputs three posterior probability scores corresponding to three possible genotypes and assigns the one with the highest score. Imputed SNPs and their genotypes were accepted at a measure >0.4 and a probability score >0.9. SNP pruning was implemented under PLINK to decrease the redundancy by using the linkage disequilibrium threshold of R2 ≥0.99. MaCH was used to validate the imputed SNPs that were found to be associated with ASD. Imputation of genotypes performed well in both the AGRE and AGP samples with an overall 95% concordance rate. After SNP pruning of 3,675 markers, 2,176 SNPs (703 called, 1,473 imputed) remained for association analysis.
For the associated SNPs that were imputed, an additional quality check was used. The SNP allele frequencies were estimated separately in the AGP and AGRE samples. The frequency estimates were the same for each SNP indicating that if there were an error in imputation it was the same in both samples. Then linkage disequilibrium was estimate for adjacent genotyped SNPs for the two imputed associated SNPs in AGP and AGRE separately. These estimates are each very close in both samples.
Association analysis: family based association test (FBAT) in the combined AGRE and AGP sample
The FBAT software was used to test for association of ASD and the panel of 2,176 SNPs. Genetic effects were assumed to be additive, and the -e option was used to adjust for the correlation between sibling marker genotypes in multiplex families . Markers on X-chromosome for the CACNA1F gene were also tested using FBAT . The level of significance, P <2.3E-05, was based on a Bonferroni correction for testing 2,176 SNPs.
Copy number variation, transcription factor binding site, and brain expression pattern analysis
The University of California Santa Cruz Genome browser (hg19) was used to identify chromatin immunoprecipitation sites and copy number variations and their associated phenotypic findings reported to the DECIPHER and ISCA consortia including the loci identified in this study. Transcription factor binding site consensus sequences were determined using Tfsitescan (http://www.ifti.org/cgi-bin/ifti/Tfsitescan.pl). Brain expression patterns were assessed for each gene using the Human Brain Transcriptome Atlas (HBT; http://hbatlas.org/pages/hbtd) and GENSAT databases (http://www.gensat.org/search.jsp). All of the 10 CCG were expressed in brain regions based on the spatiotemporal gene expression data provided by human brain transcriptome (HBT, http://hbatlas.org/). Their most dominant expression regions in brain (Table 1) were based on the data at the age 3 time-point. Eight of them, except CACNA1S and CACNA1F, were determined as highly expressed in specific brain regions by considering a log2 of signal intensity >6 . The regions of enriched gene expression were confirmed with the mouse in situ expression in GENSAT.
Calcium channel genes associated with ASD
Ca v 1.2 CACNA1C
Ca v 3.1 CACNA1G
Ca v 3.3 CACNA1I
The ratio of transmissions to non-transmissions of the major allele in this sample of more than 2,700 parent/child trios, with 5,400 potentially heterozygous and informative parents is 825:643. This SNP has been imputed, and since its information measure of nearly 0.9 indicates there is little uncertainty in the imputed genotypes, we are convinced that the imputed genotypes and the resulting association are quite accurate. Supporting this is a detectable Mendelian error rate of <2% in the AGRE sample and 0 in the AGP sample. The minor allele frequency and the pattern of linkage disequilibrium with its neighboring SNPs are consistent across the AGRE and AGP study samples where it was imputed separately, further supporting the accuracy of its imputed genotypes. This SNP is not within the eighth exon of CACN1C, where the Timothy syndrome mutation resides, but is within an intron of the gene.
The next two associated SNPs reported in Table 2 are in CACNA1G. They have been genotyped in this study sample, and the genotypes have passed stringent quality control criteria and the allele frequencies and patterns of linkage disequilibrium with neighboring SNPs are consistent across the AGRE and AGP samples, providing us with confidence in this association. Although their allele frequencies differ, 0.20 and 0.06, they exhibit some degree of linkage disequilibrium, with each other (D’ = 0.83). Neither was associated in the previous study that implicated this gene, and the SNP implicated in that study is not associated here , However, this is a much larger study having a different design and method of analysis. CACNA1G encodes the Cav3.1 Ca2+ channel subunit in the third CCG subfamily, a T-type Ca2+ channel, listed in Table 1. Interestingly, the rs198538 SNP is located within a well-conserved portion of the genome that can be pulled down by Egr-1 chromatin immunoprecipitation [39, 40].
The fourth associated SNP in Table 2 is rs5750860 in CACNA1I, which is also a T-Type calcium channel gene with properties similar to those of CACNA1G. This SNP has been imputed, and again confidence in the genotypes arises from strong information measure, low rates of Mendelian errors of 2.3% in the AGRE sample and 0% in the AGP sample, and consistent allele frequencies and patterns of linkage disequilibrium with neighboring SNPs in these samples. Prior to this study, all three T-type CCG were implicated in ASD [14, 18, 21], and we thus provide additional supportive evidence. The unique features of this channel subtype may eventually help provide insight into the means by which the T-Type genes could predispose to ASD.
Prior to the availability and widespread adoption of GWAS technology, heritability estimates for complex traits and disorders were derived from phenotypic correlations in relative pairs (primarily twins) . Heritability estimates have not been explained by the additive combinations of SNPs associated at the genome-wide significance level of 5.0E-8 for most traits . In response, there has been a growing interest in incorporating GWAS SNPs that show some, but not a significant, association, into estimates of the genetic contributions to complex traits and disorders [21, 43]. That is, investigators are hypothesizing that the ‘missing heritabilities’ for complex disorders might be found in the polygenic contributions of SNPs with positive associations that do not achieve a genome-wide level of significance because their effects are too small to provide adequate statistical power in the available study samples for their detection . Studies applying the polygenic model now report evidence of missing heritability among the ‘non-significant associations’ . Here we consider the same possibility of smaller undetected effects among the non-significant GWAS SNPs, but take a different approach to their detection and application to ASD. We focus on a specific set of genes that exhibit biological plausibility and support along with prior evidence of association with ASD. Ten CCG are tested using the best study samples and analytic approaches available for these endeavors. Two existing GWAS are combined into one sample, providing more than 2,700 parent/affected child trios for analysis, and SNP imputation is conducted to insure adequate coverage of the genes. Four associated SNPs in three CCG exceeded the level of significance.
Prior evidence for our focus on CCG derived from the ASD component of TS, which results from a de novo mutation in the eighth exon of a CCG, CACNA1C. We hypothesized that this genetic cause of a Mendelian form of ASD may be part of a family of genes that could predispose to the genetically complex idiopathic forms of ASD. To pursue this we tested the 10 CCGs that encode the largest subunits, α1, of 10 distinct Ca2+ channels. Biologically, these channels couple depolarization to a vast number of intracellular neuronal functions modulated by calcium, including signaling, gene transcription, and neurotransmitter release . The α1 subunit contains both the voltage-sensing mechanism and forms the calcium-selective pores, mediating the calcium current [12, 47]. Mutations in CACNA1C cause a significant increase in the sustained intracellular calcium rise, leading to changes in gene expression and altered neuronal differentiation, partly through changes in early growth response protein (Egr1) transcription factor levels . The associated SNP we detected in CACNA1C may be tagging such a mutation or a variant with a lesser effect.
The rs198538 SNP in the CACNA1G gene identified in this study is located within a portion of the genome that can be pulled down by Egr1 chromatin immunoprecipitation . Its association with autism comes from its fellow family member, Egr2/krox20. Egr2 was identified as the most downregulated gene in a study that analyzed lymphoblastoid cell line gene expression among monozygotic autistic twin sets in the AGRE cohort . Most interestingly, the rs198538 SNP forms part of a consensus binding site for the transcription factor aryl hydrocarbon nuclear receptor translocator (Arnt), CAC GCW (Tfsitescan). Within this binding site, CAC GCACTG, the second C (underlined) is conserved across evolution and is polymorphic in the population, corresponding to the rs198538 SNP. A genetic association has been found between the ARNT2 gene and both autism and Asperger syndrome . In addition, rare variants in the ARNT2 gene have been identified in patients with ASD [6, 51]. A member of the basic-helix-loop-helix (bHLH-PAS) superfamily of transcription factors, ARNT2 is highly expressed in brain  and forms complexes with hypoxia inducible factor (HIF1alpha) and the arylhydrocarbon receptor (AHR) to mediate neuronal responses to oxygen and xenobiotics . While the ability of ARNT2 to participate in AHR-mediated responses to xenobiotics is still under debate, this SNP variant and those in ARNT2 may contribute to the environmental influences observed in ASD . Furthermore, as in Timothy syndrome, excessive calcium-mediated signaling may underlie the brain pathology in carriers of common SNP or rare single nucleotide variants in calcium channels. The recent findings of a de novo synonymous variant in CACNA1G in an ASD proband from the Simons Simplex Collection  and missense mutations in CACNA1H in six of 461 individuals with ASD  further supports the potential for the genes identified in this study and their variants in ASD.
Copy number variants encompassing associated SNPs
Numbers of deletions/duplicationsa
Relevant CNV phenotypes
Autism, developmental delay, speech delay
Microcephaly, mental retardation/developmental delay
Microcephaly, Mental retardation/developmental delay
Mental retardation/developmental delay
Biological plausibility and genetic support for the role of CCG in ASD led us to conduct the current targeted association study with GWAS data from AGP and AGRE. Four associations in three CCGs provide evidence of a role for common alleles in CCGs as predisposing risk factors for ASD. Follow-up studies of other candidate CCGs may reveal a more complete picture of the role of the CCG in ASD. CCG sequencing studies of ASD probands can be used to assess the role of rare variants in these genes.
AGRE, http://www.agre.org/index.cfm, AGP, http://www.autismgenome.org/, CDC news, http://www.cdc.gov/media/releases/2012/p0329_autism_disorder.html, FBAT software, http://www.biostat.harvard.edu/~fbat/default.html, GENSAT, http://www.gensat.org/search.jsp, HBT, http://hbatlas.org/pages/hbtd, HUGO Gene Nomenclature Committee, http://www.genenames.org/, IMPUTE2, http://mathgen.stats.ox.ac.uk/impute/impute_v2.html, MaCH, http://www.sph.umich.edu/csg/abecasis/MACH/tour/imputation.html, PLINK software, http://pngu.mgh.harvard.edu/~purcell/plink/dataman.shtml#extract, Tfsitescan, http://www.ifti.org/cgi-bin/ifti/Tfsitescan.pl, UCSC Genome Browser, http://genome.ucsc.edu/cgi-bin/hgGateway.
Autism spectrum disorders
Autism Diagnostic Interview
Calcium channel gene
Family Based Association Test
Genome-wide Association Study
These analyses were supported in part by NIH/NIMH Autism Center of Excellence network grant MH081754 to Daniel Geschwind (PI). JAMA is supported by the Today’s and Tomorrow’s Children Fund.
We gratefully acknowledge the resources provided by the Autism Genetic Resource Exchange (AGRE) Consortium and the participating AGRE families. The Autism Genetic Resource Exchange is a program of Autism Speaks and is supported, in part, by grant 1U24MH081810 from the National Institute of Mental Health to Clara M. Lajonchere (PI).
The AGRE Consortium: Dan Geschwind, M.D., Ph.D., UCLA, Los Angeles, CA; Maja Bucan, Ph.D., University of Pennsylvania, Philadelphia, PA; W.Ted Brown, M.D., Ph.D., F.A.C.M.G., N.Y.S. Institute for Basic Research in Developmental Disabilities, Staten Island, NY; Rita M. Cantor, Ph.D., UCLA School of Medicine, Los Angeles, CA; John N. Constantino, M.D., Washington University School of Medicine, St. Louis, MO; T.Conrad Gilliam, Ph.D., University of Chicago, Chicago, IL; Martha Herbert, M.D., Ph.D., Harvard Medical School, Boston, MA Clara Lajonchere, Ph.D, Cure Autism Now, Los Angeles, CA; David H. Ledbetter, Ph.D., Emory University, Atlanta, GA; Christa Lese-Martin, Ph.D., Emory University, Atlanta, GA; Janet Miller, J.D., Ph.D., Cure Autism Now, Los Angeles, CA; Stanley F. Nelson, M.D., UCLA School of Medicine, Los Angeles, CA; Gerard D. Schellenberg, Ph.D., University of Washington, Seattle, WA; Carol A. Samango-Sprouse, Ed.D., George Washington University, Washington, D.C.; Sarah Spence, M.D., Ph.D., UCLA, Los Angeles, CA; Matthew State, M.D., Ph.D., Yale University, New Haven, CT. Rudolph E. Tanzi, Ph.D., Massachusetts General Hospital, Boston, MA.
The authors gratefully acknowledge the families participating in the Autism Genome Project, (AGP) and the main funders: Autism Speaks (USA), the Health Research Board (HRB; Ireland), the Medical Research Council (MRC; UK), Genome Canada/Ontario Genomics Institute, and the Hilibrand Foundation (USA).
- Gotham K, Bishop SL, Lord C: Autism Spectrum Disorders: Chapter 2 Diagnosis of Autism Spectrum Disorders. Edited by: Amaral DG, Dawson G, Geschwind DH. 2011, New York: Oxford University Press
- Autism and Developmental Disabilities Monitoring Network. 2007,http://www.cdc.gov/mmwr/pdf/ss/ss5601.pdf,
- Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, Miller J, Fedele A, Collins J, Smith K, Lotspeich L, Croen LA, Ozonoff S, Lajonchere C, Grether JK, Risch N: Genetic heritability and shared environmental factors among twin pairs with autism. Arch Gen Psychiatry. 2011, 68: 1095-1102. 10.1001/archgenpsychiatry.2011.76.PubMed CentralView ArticlePubMedGoogle Scholar
- Iossifov I, Ronemus M, Levy D, Wang Z, Hakker I, Rosenbaum J, Yamrom B, Lee YH, Narzisi G, Leotta A, Kendall J, Grabowska E, Ma B, Marks S, Rodgers L, Stepansky A, Troge J, Andrews P, Bekritsky M, Pradhan K, Ghiban E, Kramer M, Parla J, Demeter R, Fulton LL, Magrini VJ, Ye K, Darnell JC, Darnell RB: De novo gene disruptions in children on the autistic spectrum. Neuron. 2012, 74: 285-299. 10.1016/j.neuron.2012.04.009.PubMed CentralView ArticlePubMedGoogle Scholar
- Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, Ercan-Sencicek AG, DiLullo NM, Parikshak NN, Stein JL, Walker MF, Ober GT, Teran NA, Song Y, El-Fishawy P, Murtha RC, Choi M, Overton JD, Bjornson RD, Carriero NJ, Meyer KA, Bilguvar K, Mane SM, Sestan N, Lifton RP, Gunel M, Roeder K, Geschwind DH, Devlin B, State MW: De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012, 485: 237-241. 10.1038/nature10945.PubMed CentralView ArticlePubMedGoogle Scholar
- Neale BM, Kou Y, Liu L, Ma’ayan A, Samocha KE, Sabo A, Lin CF, Stevens C, Wang LS, Makarov V, Polak P, Yoon S, Maguire J, Crawford EL, Campbell NG, Geller ET, Valladares O, Schafer C, Liu H, Zhao T, Cai G, Lihm J, Dannenfelser R, Jabado O, Peralta Z, Nagaswamy U, Muzny D, Reid JG, Newsham I, Wu Y: Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature. 2012, 485: 242-245. 10.1038/nature11011.PubMed CentralView ArticlePubMedGoogle Scholar
- Gargus JJ: Genetic calcium signaling abnormalities in the central nervous system: seizures, migraine, and autism. Ann N Y Acad Sci. 2009, 1151: 133-156. 10.1111/j.1749-6632.2008.03572.x.View ArticlePubMedGoogle Scholar
- Splawski I, Timothy KW, Sharpe LM, Decher N, Kumar P, Bloise R, Napolitano C, Schwartz PJ, Joseph RM, Condouris K, Tager-Flusberg H, Priori SG, Sanguinetti MC, Keating MT: Ca(V)1.2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. Cell. 2004, 119: 19-31. 10.1016/j.cell.2004.09.011.View ArticlePubMedGoogle Scholar
- Catterall WA, Perez-Reyes E, Snutch TP, Striessnig J: International Union of Pharmacology. XLVIII. Nomenclature and structure-function relationships of voltage-gated calcium channels. Pharmacol Rev. 2005, 57: 411-425. 10.1124/pr.57.4.5.View ArticlePubMedGoogle Scholar
- Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, Sousa AM, Pletikos M, Meyer KA, Sedmak G, Guennel T, Shin Y, Johnson MB, Krsnik Z, Mayer S, Fertuzinhos S, Umlauf S, Lisgo SN, Vortmeyer A, Weinberger DR, Mane S, Hyde TM, Huttner A, Reimers M, Kleinman JE, Sestan N: Spatio-temporal transcriptome of the human brain. Nature. 2011, 478: 483-489. 10.1038/nature10523.PubMed CentralView ArticlePubMedGoogle Scholar
- Mermelstein PG, Foehring RC, Tkatch T, Song WJ, Baranauskas G, Surmeier DJ: Properties of Q-type calcium channels in neostriatal and cortical neurons are correlated with beta subunit expression. J Neurosci. 1999, 19: 7268-7277.PubMedGoogle Scholar
- Bidaud I, Mezghrani A, Swayne LA, Monteil A, Lory P: Voltage-gated calcium channels in genetic diseases. Biochim Biophys Acta. 2006, 1763: 1169-1174. 10.1016/j.bbamcr.2006.08.049.View ArticlePubMedGoogle Scholar
- Bader PL, Faizi M, Kim LH, Owen SF, Tadross MR, Alfa RW, Bett GC, Tsien RW, Rasmusson RL, Shamloo M: Mouse model of Timothy syndrome recapitulates triad of autistic traits. Proc Natl Acad Sci U S A. 2011, 108: 15432-15437. 10.1073/pnas.1112667108.PubMed CentralView ArticlePubMedGoogle Scholar
- Strom SP, Stone JL, Ten Bosch JR, Merriman B, Cantor RM, Geschwind DH, Nelson SF: 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. 10.1038/mp.2009.41.PubMed CentralView ArticlePubMedGoogle Scholar
- Stone JL, Merriman B, Cantor RM, Yonan AL, Gilliam TC, Geschwind DH, Nelson SF: Evidence for sex-specific risk alleles in autism spectrum disorder. Am J Hum Genet. 2004, 75: 1117-1123. 10.1086/426034.PubMed CentralView ArticlePubMedGoogle Scholar
- Cantor RM, Kono N, Duvall JA, Alvarez-Retuerto A, Stone JL, Alarcon M, Nelson SF, Geschwind DH: Replication of autism linkage: fine-mapping peak at 17q21. Am J Hum Genet. 2005, 76: 1050-1056. 10.1086/430278.PubMed CentralView ArticlePubMedGoogle Scholar
- Lory P, Monteil A, Chemin J, Leuranguer V, Bourinet E, Nargeot J: Molecular diversity of calcium channel activities by depolarization. Therapie. 2000, 55: 249-254.PubMedGoogle Scholar
- Splawski I, Yoo DS, Stotz SC, Cherry A, Clapham DE, Keating MT: CACNA1H mutations in autism spectrum disorders. J Biol Chem. 2006, 281: 22085-22091. 10.1074/jbc.M603316200.View ArticlePubMedGoogle Scholar
- Brambilla P, Hardan A, di Nemi SU, Perez J, Soares JC, Barale F: Brain anatomy and development in autism: review of structural MRI studies. Brain Res Bull. 2003, 61: 557-569. 10.1016/j.brainresbull.2003.06.001.View ArticlePubMedGoogle Scholar
- O’Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, Levy R, Ko A, Lee C, Smith JD, Turner EH, Stanaway IB, Vernot B, Malig M, Baker C, Reilly B, Akey JM, Borenstein E, Rieder MJ, Nickerson DA, Bernier R, Shendure J, Eichler EE: Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature. 2012, 485: 246-250. 10.1038/nature10989.PubMed CentralView ArticlePubMedGoogle Scholar
- Hussman JP, Chung RH, Griswold AJ, Jaworski JM, Salyakina D, Ma D, Konidari I, Whitehead PL, Vance JM, Martin ER, Cuccaro ML, Gilbert JR, Haines JL, Pericak-Vance MA: A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism. Mol Autism. 2011, 2: 1-10.1186/2040-2392-2-1.PubMed CentralView ArticlePubMedGoogle Scholar
- Geschwind DH, Sowinski J, Lord C, Iversen P, Shestack J, Jones P, Ducat L, Spence SJ: The autism genetic resource exchange: a resource for the study of autism and related neuropsychiatric conditions. Am J Hum Genet. 2001, 69: 463-466. 10.1086/321292.PubMed CentralView ArticlePubMedGoogle Scholar
- Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, Liu XQ, Vincent JB, Skaug JL, Thompson AP, Senman L, Feuk L, Qian C, Bryson SE, Jones MB, Marshall CR, Scherer SW, Vieland VJ, Bartlett C, Mangin LV, Goedken R, Segre A, Pericak-Vance MA, Cuccaro ML, Gilbert JR, Wright HH, Abramson RK, Betancur C, Bourgeron T, Gillberg C, Autism Genome Project Consortium: Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nat Genet. 2007, 39: 319-328. 10.1038/ng1985.View ArticlePubMedGoogle Scholar
- Lord C, Rutter M, Le Couteur A: Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994, 24: 659-685. 10.1007/BF02172145.View ArticlePubMedGoogle Scholar
- Johnson RA, Wichern DW: Applied multivariate statistical analysis. 1992, Englewood Cliffs, NJ: Prentice-Hall, Inc., 3Google Scholar
- Risi S, Lord C, Gotham K, Corsello C, Chrysler C, Szatmari P, Cook EH, Leventhal BL, Pickles A: Combining information from multiple sources in the diagnosis of autism spectrum disorders. J Am Acad Child Adolesc Psychiatry. 2006, 45: 1094-1103. 10.1097/01.chi.0000227880.42780.0e.View ArticlePubMedGoogle Scholar
- Browning BL, Browning SR: A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009, 84: 210-223. 10.1016/j.ajhg.2009.01.005.PubMed CentralView ArticlePubMedGoogle Scholar
- Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR: MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010, 34: 816-834. 10.1002/gepi.20533.PubMed CentralView ArticlePubMedGoogle Scholar
- Marchini J, Howie B, Myers S, McVean G, Donnelly P: A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007, 39: 906-913. 10.1038/ng2088.View ArticlePubMedGoogle Scholar
- Howie BN, Donnelly P, Marchini J: A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009, 5: e1000529-10.1371/journal.pgen.1000529.PubMed CentralView ArticlePubMedGoogle Scholar
- Marchini J, Howie B: Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010, 11: 499-511. 10.1038/nrg2796.View ArticlePubMedGoogle Scholar
- International HapMap C, Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, Pasternak S, Wheeler DA, Willis TD, Yu F, Yang H, Zeng C, Gao Y, Hu H, Hu W, Li C, Lin W, Liu S, Pan H, Tang X, Wang J, Wang W, Yu J, Zhang Q: A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007, 449: 851-861. 10.1038/nature06258.View ArticleGoogle Scholar
- Via M, Gignoux C, Burchard EG: The 1000 Genomes Project: new opportunities for research and social challenges. Genome Med. 2010, 2: 3-10.1186/gm124.PubMed CentralView ArticlePubMedGoogle Scholar
- Lake SL, Blacker D, Laird NM: Family-based tests of association in the presence of linkage. Am J Hum Genet. 2000, 67: 1515-1525. 10.1086/316895.PubMed CentralView ArticlePubMedGoogle Scholar
- Horvath S, Laird NM, Knapp M: The transmission/disequilibrium test and parental-genotype reconstruction for X-chromosomal markers. Am J Hum Genet. 2000, 66: 1161-1167. 10.1086/302823.PubMed CentralView ArticlePubMedGoogle Scholar
- Carroll LS, Owen MJ: Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Med. 2009, 1: 102-10.1186/gm102.PubMed CentralView ArticlePubMedGoogle Scholar
- Ferreira MA, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L, Fan J, Kirov G, Perlis RH, Green EK, Smoller JW, Grozeva D, Stone J, Nikolov I, Chambert K, Hamshere ML, Nimgaonkar VL, Moskvina V, Thase ME, Caesar S, Sachs GS, Franklin J, Gordon-Smith K, Ardlie KG, Gabriel SB, Fraser C, Blumenstiel B, Defelice M, Breen G, Gill M: Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet. 2008, 40: 1056-1058. 10.1038/ng.209.PubMed CentralView ArticlePubMedGoogle Scholar
- Sklar P, Smoller JW, Fan J, Ferreira MA, Perlis RH, Chambert K, Nimgaonkar VL, McQueen MB, Faraone SV, Kirby A, de Bakker PI, Ogdie MN, Thase ME, Sachs GS, Todd-Brown K, Gabriel SB, Sougnez C, Gates C, Blumenstiel B, Defelice M, Ardlie KG, Franklin J, Muir WJ, McGhee KA, MacIntyre DJ, McLean A, VanBeck M, McQuillin A, Bass NJ, Robinson M: Whole-genome association study of bipolar disorder. Mol Psychiatry. 2008, 13: 558-569. 10.1038/sj.mp.4002151.PubMed CentralView ArticlePubMedGoogle Scholar
- ENCODE Project Consortium: A user’s guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011, 9: e1001046-10.1371/journal.pbio.1001046.View ArticleGoogle Scholar
- Johnson CS, Thurlow SM, Furmanski P: Induction of leukemia regression in mice by immunotherapeutic transfer of T-lymphocytes. Cancer Res. 1986, 46: 183-189.View ArticlePubMedGoogle Scholar
- Van Dongen J, Slagboom PE, Draisma HH, Martin NG, Boomsma DI: The continuing value of twin studies in the omics era. Nat Rev Genet. 2012, 13: 640-653. 10.1038/nrg3243.View ArticlePubMedGoogle Scholar
- Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM: Finding the missing heritability of complex diseases. Nature. 2009, 461: 747-753. 10.1038/nature08494.PubMed CentralView ArticlePubMedGoogle Scholar
- Moskvina V, Craddock N, Holmans P, Nikolov I, Pahwa JS, Green E, Owen MJ, O’Donovan MC: Gene-wide analyses of genome-wide association data sets: evidence for multiple common risk alleles for schizophrenia and bipolar disorder and for overlap in genetic risk. Mol Psychiatry. 2009, 14: 252-260. 10.1038/mp.2008.133.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM: Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010, 42: 565-569. 10.1038/ng.608.PubMed CentralView ArticlePubMedGoogle Scholar
- Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, Sklar P: Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009, 460: 748-752.PubMedGoogle Scholar
- Strock J, Diverse-Pierluissi MA: Ca2+ channels as integrators of G protein-mediated signaling in neurons. Mol Pharmacol. 2004, 66: 1071-1076. 10.1124/mol.104.002261.View ArticlePubMedGoogle Scholar
- Adams PJ, Snutch TP: Calcium channelopathies: voltage-gated calcium channels. Subcell Biochem. 2007, 45: 215-251. 10.1007/978-1-4020-6191-2_8.View ArticlePubMedGoogle Scholar
- Pasca SP, Portmann T, Voineagu I, Yazawa M, Shcheglovitov A, Pasca AM, Cord B, Palmer TD, Chikahisa S, Nishino S, Bernstein JA, Hallmayer J, Geschwind DH, Dolmetsch RE: Using iPSC-derived neurons to uncover cellular phenotypes associated with Timothy syndrome. Nat Med. 2011, 17: 1657-1662. 10.1038/nm.2576.PubMed CentralView ArticlePubMedGoogle Scholar
- 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: 118-10.1186/1471-2164-7-118.PubMed CentralView ArticlePubMedGoogle Scholar
- Chakrabarti B, Dudbridge F, Kent L, Wheelwright S, Hill-Cawthorne G, Allison C, Banerjee-Basu S, Baron-Cohen S: Genes related to sex steroids, neural growth, and social-emotional behavior are associated with autistic traits, empathy, and Asperger syndrome. Autism Res. 2009, 2: 157-177. 10.1002/aur.80.View ArticlePubMedGoogle Scholar
- Vaags AK, Lionel AC, Sato D, Goodenberger M, Stein QP, Curran S, Ogilvie C, Ahn JW, Drmic I, Senman L, Chrysler C, Thompson A, Russell C, Prasad A, Walker S, Pinto D, Marshall CR, Stavropoulos DJ, Zwaigenbaum L, Fernandez BA, Fombonne E, Bolton PF, Collier DA, Hodge JC, Roberts W, Szatmari P, Scherer SW: Rare deletions at the neurexin 3 locus in autism spectrum disorder. Am J Hum Genet. 2012, 90: 133-141. 10.1016/j.ajhg.2011.11.025.PubMed CentralView ArticlePubMedGoogle Scholar
- Jain S, Maltepe E, Lu MM, Simon C, Bradfield CA: Expression of ARNT, ARNT2, HIF1 alpha, HIF2 alpha and Ah receptor mRNAs in the developing mouse. Mech Dev. 1998, 73: 117-123. 10.1016/S0925-4773(98)00038-0.View ArticlePubMedGoogle Scholar
- Keith B, Adelman DM, Simon MC: Targeted mutation of the murine arylhydrocarbon receptor nuclear translocator 2 (Arnt2) gene reveals partial redundancy with Arnt. Proc Natl Acad Sci U S A. 2001, 98: 6692-6697. 10.1073/pnas.121494298.PubMed CentralView ArticlePubMedGoogle Scholar
- Herbert MR: Contributions of the environment and environmentally vulnerable physiology to autism spectrum disorders. Curr Opin Neurol. 2010, 23: 103-110. 10.1097/WCO.0b013e328336a01f.View ArticlePubMedGoogle Scholar
- Betancur C, Sakurai T, Buxbaum JD: The emerging role of synaptic cell-adhesion pathways in the pathogenesis of autism spectrum disorders. Trends Neurosci. 2009, 32: 402-412. 10.1016/j.tins.2009.04.003.View ArticlePubMedGoogle Scholar
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