| Literature DB >> 29700473 |
Donna M Werling1, Harrison Brand2,3,4, Joon-Yong An1, Matthew R Stone2, Lingxue Zhu5, Joseph T Glessner2,3,4, Ryan L Collins2,3,6, Shan Dong1, Ryan M Layer7,8, Eirene Markenscoff-Papadimitriou1, Andrew Farrell7,8, Grace B Schwartz1, Harold Z Wang2, Benjamin B Currall2,3,4, Xuefang Zhao2,3,4, Jeanselle Dea1, Clif Duhn1, Carolyn A Erdman1, Michael C Gilson1, Rachita Yadav2,3,4, Robert E Handsaker4,9, Seva Kashin4,9, Lambertus Klei10, Jeffrey D Mandell1, Tomasz J Nowakowski1,11,12, Yuwen Liu13, Sirisha Pochareddy14, Louw Smith1, Michael F Walker1, Matthew J Waterman15, Xin He13, Arnold R Kriegstein16, John L Rubenstein1, Nenad Sestan14, Steven A McCarroll4,9, Benjamin M Neale4,17,18, Hilary Coon19,20, A Jeremy Willsey1,21, Joseph D Buxbaum22,23,24,25, Mark J Daly4,17,18, Matthew W State1, Aaron R Quinlan7,8,20, Gabor T Marth7,8, Kathryn Roeder5,26, Bernie Devlin27, Michael E Talkowski28,29,30,31, Stephan J Sanders32.
Abstract
Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.Entities:
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Year: 2018 PMID: 29700473 PMCID: PMC5961723 DOI: 10.1038/s41588-018-0107-y
Source DB: PubMed Journal: Nat Genet ISSN: 1061-4036 Impact factor: 38.330