%0 Journal Article %J PLoS Genet %D 2013 %T Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. %A Liu, Li %A Sabo, Aniko %A Neale, Benjamin M %A Nagaswamy, Uma %A Stevens, Christine %A Lim, Elaine %A Bodea, Corneliu A %A Muzny, Donna %A Reid, Jeffrey G %A Banks, Eric %A Coon, Hillary %A DePristo, Mark %A Dinh, Huyen %A Fennel, Tim %A Flannick, Jason %A Gabriel, Stacey %A Garimella, Kiran %A Gross, Shannon %A Hawes, Alicia %A Lewis, Lora %A Makarov, Vladimir %A Maguire, Jared %A Newsham, Irene %A Poplin, Ryan %A Ripke, Stephan %A Shakir, Khalid %A Samocha, Kaitlin E %A Wu, Yuanqing %A Boerwinkle, Eric %A Buxbaum, Joseph D %A Cook, Edwin H %A Devlin, Bernie %A Schellenberg, Gerard D %A Sutcliffe, James S %A Daly, Mark J %A Gibbs, Richard A %A Roeder, Kathryn %K Case-Control Studies %K Child %K Child Development Disorders, Pervasive %K Exome %K Genetic Predisposition to Disease %K Genetic Variation %K Genome-Wide Association Study %K Humans %K Population Control %K Sequence Analysis, DNA %K Software %X

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.

%B PLoS Genet %V 9 %P e1003443 %8 2013 Apr %G eng %N 4 %1 https://www.ncbi.nlm.nih.gov/pubmed/23593035?dopt=Abstract %R 10.1371/journal.pgen.1003443 %0 Journal Article %J Neuron %D 2013 %T Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. %A Lim, Elaine T %A Raychaudhuri, Soumya %A Sanders, Stephan J %A Stevens, Christine %A Sabo, Aniko %A MacArthur, Daniel G %A Neale, Benjamin M %A Kirby, Andrew %A Ruderfer, Douglas M %A Fromer, Menachem %A Lek, Monkol %A Liu, Li %A Flannick, Jason %A Ripke, Stephan %A Nagaswamy, Uma %A Muzny, Donna %A Reid, Jeffrey G %A Hawes, Alicia %A Newsham, Irene %A Wu, Yuanqing %A Lewis, Lora %A Dinh, Huyen %A Gross, Shannon %A Wang, Li-San %A Lin, Chiao-Feng %A Valladares, Otto %A Gabriel, Stacey B %A DePristo, Mark %A Altshuler, David M %A Purcell, Shaun M %A State, Matthew W %A Boerwinkle, Eric %A Buxbaum, Joseph D %A Cook, Edwin H %A Gibbs, Richard A %A Schellenberg, Gerard D %A Sutcliffe, James S %A Devlin, Bernie %A Roeder, Kathryn %A Daly, Mark J %K Case-Control Studies %K Child Development Disorders, Pervasive %K Child, Preschool %K Chromosomes, Human, X %K Demography %K Female %K Gene Deletion %K Genetic Variation %K Homozygote %K Humans %K Linkage Disequilibrium %K Loss of Heterozygosity %K Male %K Risk Factors %X

To characterize the role of rare complete human knockouts in autism spectrum disorders (ASDs), we identify genes with homozygous or compound heterozygous loss-of-function (LoF) variants (defined as nonsense and essential splice sites) from exome sequencing of 933 cases and 869 controls. We identify a 2-fold increase in complete knockouts of autosomal genes with low rates of LoF variation (≤ 5% frequency) in cases and estimate a 3% contribution to ASD risk by these events, confirming this observation in an independent set of 563 probands and 4,605 controls. Outside the pseudoautosomal regions on the X chromosome, we similarly observe a significant 1.5-fold increase in rare hemizygous knockouts in males, contributing to another 2% of ASDs in males. Taken together, these results provide compelling evidence that rare autosomal and X chromosome complete gene knockouts are important inherited risk factors for ASD.

%B Neuron %V 77 %P 235-42 %8 2013 Jan 23 %G eng %N 2 %1 https://www.ncbi.nlm.nih.gov/pubmed/23352160?dopt=Abstract %R 10.1016/j.neuron.2012.12.029 %0 Journal Article %J Nature %D 2012 %T Patterns and rates of exonic de novo mutations in autism spectrum disorders. %A Neale, Benjamin M %A Kou, Yan %A Liu, Li %A Ma'ayan, Avi %A Samocha, Kaitlin E %A Sabo, Aniko %A Lin, Chiao-Feng %A Stevens, Christine %A Wang, Li-San %A Makarov, Vladimir %A Polak, Paz %A Yoon, Seungtai %A Maguire, Jared %A Crawford, Emily L %A Campbell, Nicholas G %A Geller, Evan T %A Valladares, Otto %A Schafer, Chad %A Liu, Han %A Zhao, Tuo %A Cai, Guiqing %A Lihm, Jayon %A Dannenfelser, Ruth %A Jabado, Omar %A Peralta, Zuleyma %A Nagaswamy, Uma %A Muzny, Donna %A Reid, Jeffrey G %A Newsham, Irene %A Wu, Yuanqing %A Lewis, Lora %A Han, Yi %A Voight, Benjamin F %A Lim, Elaine %A Rossin, Elizabeth %A Kirby, Andrew %A Flannick, Jason %A Fromer, Menachem %A Shakir, Khalid %A Fennell, Tim %A Garimella, Kiran %A Banks, Eric %A Poplin, Ryan %A Gabriel, Stacey %A DePristo, Mark %A Wimbish, Jack R %A Boone, Braden E %A Levy, Shawn E %A Betancur, Catalina %A Sunyaev, Shamil %A Boerwinkle, Eric %A Buxbaum, Joseph D %A Cook, Edwin H %A Devlin, Bernie %A Gibbs, Richard A %A Roeder, Kathryn %A Schellenberg, Gerard D %A Sutcliffe, James S %A Daly, Mark J %K Autistic Disorder %K Case-Control Studies %K DNA-Binding Proteins %K Exome %K Exons %K Family Health %K Genetic Predisposition to Disease %K Humans %K Models, Genetic %K Multifactorial Inheritance %K Mutation %K Phenotype %K Poisson Distribution %K Protein Interaction Maps %K Transcription Factors %X

Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.

%B Nature %V 485 %P 242-5 %8 2012 Apr 04 %G eng %N 7397 %1 https://www.ncbi.nlm.nih.gov/pubmed/22495311?dopt=Abstract %R 10.1038/nature11011