Dataset Information

Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls.

ABSTRACT: We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD.

SUBMITTER: Liu L

PROVIDER: S-EPMC3623759 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls.

Liu Li L Sabo Aniko A Neale Benjamin M BM Nagaswamy Uma U Stevens Christine C Lim Elaine E Bodea Corneliu A CA Muzny Donna D Reid Jeffrey G JG Banks Eric E Coon Hillary H Depristo Mark M Dinh Huyen H Fennel Tim T Flannick Jason J Gabriel Stacey S Garimella Kiran K Gross Shannon S Hawes Alicia A Lewis Lora L Makarov Vladimir V Maguire Jared J Newsham Irene I Poplin Ryan R Ripke Stephan S Shakir Khalid K Samocha Kaitlin E KE Wu Yuanqing Y Boerwinkle Eric E Buxbaum Joseph D JD Cook Edwin H EH Devlin Bernie B Schellenberg Gerard D GD Sutcliffe James S JS Daly Mark J MJ Gibbs Richard A RA Roeder Kathryn K

PLoS genetics 20130411 4

We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fra ...[more]

PMID: 23593035

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Project description:Autism spectrum disorders (ASD) are neurodevelopmental disorders characterized by delayed/abnormal language development, deficits in social interaction, repetitive behaviors and restricted interests. The heterogeneity in clinical presentation of ASD, likely due to different etiologies, complicates genetic/biological analyses of these disorders. DNA microarray analyses were conducted on 116 lymphoblastoid cell lines (LCL) from individuals with idiopathic autism who are divided into 3 phenotypic subgroups according to severity scores from the commonly used Autism Diagnostic Interview-Revised questionnaire and age-matched, nonautistic controls. Statistical analyses of gene expression data from control LCL against that of LCL from ASD probands identify genes for which expression levels are either quantitatively or qualitatively associated with phenotypic severity. Comparison of the significant differentially expressed genes from each subgroup relative to the control group reveals differentially expressed genes unique to each subgroup as well as genes in common across subgroups. Among the findings unique to the most severely affected ASD group are genes that regulate circadian rhythm, which has been shown to have multiple effects on neurological as well as metabolic functions commonly dysregulated in autism. Among the genes common to all 3 subgroups of ASD are 5 novel genes which appear to associate with androgen sensitivity, which may underlie the strong 4:1 bias towards affected males. Gene expression profiling of 116 LCL from autistic (87) and nonautistic (29) individuals were obtained using a custom-printed DNA microarray containing 39,936 elements (TIGR 40K Human array, GPL3427) and a reference design in which each sample was compared to the Stratagene Universal Human RNA standard. The 87 autistic samples were divided into phenotypic subgroups (language, mild, savant) on the basis of cluster analyses of scores from an autism diagnostic questionnaire, the Autism Diagnostic Interview-Revised instrument. Differentially expressed genes were determined for all autistic vs. control groups, as well as for each of 3 phenotypic ASD groups and controls.

Dataset Information

Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls.

Publications

Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls.

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