Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls

• DOI: 10.1371/journal.pgen.1003443
• Publication Date: 2013-04-11T20:51:34Z
• Authors: Li Liu; Aniko Sabo; Benjamin M. Neale; Uma Nagaswamy; Christine Stevens; Elaine Lim; Corneliu A. Bodea; Donna Muzny; Jeffrey G. Reid; Eric Banks; Hillary Coon; Mark DePristo; Huyen Dinh; Tim Fennel; Jason Flannick; Stacey Gabriel; Kiran Garimella; Shannon Gross; Alicia Hawes; Lora Lewis; Vladimir Makarov; Jared Maguire; Irene Newsham; Ryan Poplin; Stephan Ripke; Khalid Shakir; Kaitlin E. Samocha; Yuanqing Wu; Eric Boerwinkle; Joseph D. Buxbaum; Edwin H. Cook; Bernie Devlin; Gerard D. Schelle...; James S. Sutcliffe; Mark J. Daly; Richard A. Gibbs; Kathryn Roeder
• Keywords: 0303 health sciences; Statistics; 610; Genetic Variation; Human Genetics; Sequence Analysis, DNA; QH426-470; 03 medical and health sciences; Child Development Disorders, Pervasive; Case-Control Studies; Genetics of Disease; Genetics; Humans; Exome; Genetic Predisposition to Disease; Population Control; Child; Biology; Genetic Association Studies; Mathematics; Software; Research Article; Genome-Wide Association Study

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.