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Nat Genet. 2018 Apr 26;50(5):727-736. doi: 10.1038/s41588-018-0107-y.

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

Author information

1
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
2
Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
3
Department of Neurology, Harvard Medical School, Boston, MA, USA.
4
Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA.
5
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA.
6
Program in Bioinformatics and Integrative Genomics, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
7
Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT, USA.
8
USTAR Center for Genetic Discovery, University of Utah School of Medicine, Salt Lake City, UT, USA.
9
Department of Genetics, Harvard Medical School, Boston, MA, USA.
10
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
11
Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA.
12
Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA.
13
Department of Human Genetics, University of Chicago, Chicago, IL, USA.
14
Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT, USA.
15
Department of Biology, Eastern Nazarene College, Quincy, MA, USA.
16
Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
17
Analytical and Translational Genetics Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
18
Department of Medicine, Harvard Medical School, Boston, MA, USA.
19
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA.
20
Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
21
Institute for Neurodegenerative Diseases, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
22
Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
23
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
24
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
25
Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
26
Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.
27
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. devlinbj@upmc.edu.
28
Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA. talkowski@chgr.mgh.harvard.edu.
29
Department of Neurology, Harvard Medical School, Boston, MA, USA. talkowski@chgr.mgh.harvard.edu.
30
Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA. talkowski@chgr.mgh.harvard.edu.
31
Departments of Pathology and Psychiatry, Massachusetts General Hospital, Boston, MA, USA. talkowski@chgr.mgh.harvard.edu.
32
Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA. stephan.sanders@ucsf.edu.

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.

PMID:
29700473
PMCID:
PMC5961723
DOI:
10.1038/s41588-018-0107-y
[Indexed for MEDLINE]
Free PMC Article

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