An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder

Donna M. Werling, Harrison Brand, Joon-Yong An, Matthew R. Stone, Lingxue Zhu, Joseph T. Glessner, Ryan L. Collins, Shan Dong, Ryan M. Layer, Eirene Markenscoff-Papadimitriou, Andrew Farrell, Grace B. Schwartz, Harold Z. Wang, Benjamin B. Currall, Xuefang Zhao, Jeanselle Dea, Clif Duhn, Carolyn A. Erdman, Michael C. Gilson, Rachita YadavRobert E. Handsaker, Seva Kashin, Lambertus Klei, Jeffrey D. Mandell, Tomasz J. Nowakowski, Yuwen Liu, Sirisha Pochareddy, Louw Smith, Michael F. Walker, Matthew J. Waterman, Xin He, Arnold R. Kriegstein, John L. Rubenstein, Nenad Sestan, Steven A. McCarroll, Benjamin M. Neale, Hilary Coon, A. Jeremy Willsey, Joseph D. Buxbaum, Mark J. Daly, Matthew W. State, Aaron R. Quinlan, Gabor T. Marth, Kathryn Roeder, Bernie Devlin, Michael E. Talkowski, Stephan J. Sanders

Research output: Contribution to journal › Article

38 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)727-736
Number of pages10
JournalNature Genetics
Volume50
Issue number5
DOIs
Publication statusPublished - 2018 May 1
Externally publishedYes

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Genome-Wide Association Study
Genomic Structural Variation
Nucleotides
Nucleic Acid Regulatory Sequences
Autistic Disorder
Genes
Genome
Mutation
Proteins
Autism Spectrum Disorder

ASJC Scopus subject areas

  • Genetics

Cite this

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. / Werling, Donna M.; Brand, Harrison; An, Joon-Yong; Stone, Matthew R.; Zhu, Lingxue; Glessner, Joseph T.; Collins, Ryan L.; Dong, Shan; Layer, Ryan M.; Markenscoff-Papadimitriou, Eirene; Farrell, Andrew; Schwartz, Grace B.; Wang, Harold Z.; Currall, Benjamin B.; Zhao, Xuefang; Dea, Jeanselle; Duhn, Clif; Erdman, Carolyn A.; Gilson, Michael C.; Yadav, Rachita; Handsaker, Robert E.; Kashin, Seva; Klei, Lambertus; Mandell, Jeffrey D.; Nowakowski, Tomasz J.; Liu, Yuwen; Pochareddy, Sirisha; Smith, Louw; Walker, Michael F.; Waterman, Matthew J.; He, Xin; Kriegstein, Arnold R.; Rubenstein, John L.; Sestan, Nenad; McCarroll, Steven A.; Neale, Benjamin M.; Coon, Hilary; Willsey, A. Jeremy; Buxbaum, Joseph D.; Daly, Mark J.; State, Matthew W.; Quinlan, Aaron R.; Marth, Gabor T.; Roeder, Kathryn; Devlin, Bernie; Talkowski, Michael E.; Sanders, Stephan J.

In: Nature Genetics, Vol. 50, No. 5, 01.05.2018, p. 727-736.

Research output: Contribution to journal › Article

Werling, DM, Brand, H, An, J-Y, Stone, MR, Zhu, L, Glessner, JT, Collins, RL, Dong, S, Layer, RM, Markenscoff-Papadimitriou, E, Farrell, A, Schwartz, GB, Wang, HZ, Currall, BB, Zhao, X, Dea, J, Duhn, C, Erdman, CA, Gilson, MC, Yadav, R, Handsaker, RE, Kashin, S, Klei, L, Mandell, JD, Nowakowski, TJ, Liu, Y, Pochareddy, S, Smith, L, Walker, MF, Waterman, MJ, He, X, Kriegstein, AR, Rubenstein, JL, Sestan, N, McCarroll, SA, Neale, BM, Coon, H, Willsey, AJ, Buxbaum, JD, Daly, MJ, State, MW, Quinlan, AR, Marth, GT, Roeder, K, Devlin, B, Talkowski, ME & Sanders, SJ 2018, 'An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder', Nature Genetics, vol. 50, no. 5, pp. 727-736. https://doi.org/10.1038/s41588-018-0107-y
Werling DM, Brand H, An J-Y, Stone MR, Zhu L, Glessner JT et al. An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. Nature Genetics. 2018 May 1;50(5):727-736. https://doi.org/10.1038/s41588-018-0107-y
Werling, Donna M. ; Brand, Harrison ; An, Joon-Yong ; Stone, Matthew R. ; Zhu, Lingxue ; Glessner, Joseph T. ; Collins, Ryan L. ; Dong, Shan ; Layer, Ryan M. ; Markenscoff-Papadimitriou, Eirene ; Farrell, Andrew ; Schwartz, Grace B. ; Wang, Harold Z. ; Currall, Benjamin B. ; Zhao, Xuefang ; Dea, Jeanselle ; Duhn, Clif ; Erdman, Carolyn A. ; Gilson, Michael C. ; Yadav, Rachita ; Handsaker, Robert E. ; Kashin, Seva ; Klei, Lambertus ; Mandell, Jeffrey D. ; Nowakowski, Tomasz J. ; Liu, Yuwen ; Pochareddy, Sirisha ; Smith, Louw ; Walker, Michael F. ; Waterman, Matthew J. ; He, Xin ; Kriegstein, Arnold R. ; Rubenstein, John L. ; Sestan, Nenad ; McCarroll, Steven A. ; Neale, Benjamin M. ; Coon, Hilary ; Willsey, A. Jeremy ; Buxbaum, Joseph D. ; Daly, Mark J. ; State, Matthew W. ; Quinlan, Aaron R. ; Marth, Gabor T. ; Roeder, Kathryn ; Devlin, Bernie ; Talkowski, Michael E. ; Sanders, Stephan J. / An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. In: Nature Genetics. 2018 ; Vol. 50, No. 5. pp. 727-736.
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AU - Werling, Donna M.

AU - Brand, Harrison

AU - An, Joon-Yong

AU - Stone, Matthew R.

AU - Zhu, Lingxue

AU - Glessner, Joseph T.

AU - Collins, Ryan L.

AU - Dong, Shan

AU - Layer, Ryan M.

AU - Markenscoff-Papadimitriou, Eirene

AU - Farrell, Andrew

AU - Schwartz, Grace B.

AU - Wang, Harold Z.

AU - Currall, Benjamin B.

AU - Zhao, Xuefang

AU - Dea, Jeanselle

AU - Duhn, Clif

AU - Erdman, Carolyn A.

AU - Gilson, Michael C.

AU - Yadav, Rachita

AU - Handsaker, Robert E.

AU - Kashin, Seva

AU - Klei, Lambertus

AU - Mandell, Jeffrey D.

AU - Nowakowski, Tomasz J.

AU - Liu, Yuwen

AU - Pochareddy, Sirisha

AU - Smith, Louw

AU - Walker, Michael F.

AU - Waterman, Matthew J.

AU - He, Xin

AU - Kriegstein, Arnold R.

AU - Rubenstein, John L.

AU - Sestan, Nenad

AU - McCarroll, Steven A.

AU - Neale, Benjamin M.

AU - Coon, Hilary

AU - Willsey, A. Jeremy

AU - Buxbaum, Joseph D.

AU - Daly, Mark J.

AU - State, Matthew W.

AU - Quinlan, Aaron R.

AU - Marth, Gabor T.

AU - Roeder, Kathryn

AU - Devlin, Bernie

AU - Talkowski, Michael E.

AU - Sanders, Stephan J.

PY - 2018/5/1

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N2 - 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.

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