Guidelines for Large-Scale Sequence-Based Complex Trait Association Studies: Lessons Learned from the NHLBI Exome Sequencing Project.
Massively parallel whole-genome sequencing (WGS) data have ushered in a new era in human genetics. These data are now being used to understand the role of rare variants in complex traits and to advance the goals of precision medicine. The technological and computing advances that have enabled us to generate WGS data on thousands of individuals have also outpaced our ability to perform analyses in scientifically and statistically rigorous and thoughtful ways. The past several years have witnessed the application of whole-exome sequencing (WES) to complex traits and diseases. From our analysis of NHLBI Exome Sequencing Project (ESP) data, not only have a number of important disease and complex trait association findings emerged, but our collective experience offers some valuable lessons for WGS initiatives. These include caveats associated with generating automated pipelines for quality control and analysis of rare variants; the importance of studying minority populations; sample size requirements and efficient study designs for identifying rare-variant associations; and the significance of incidental findings in population-based genetic research. With the ESP as an example, we offer guidance and a framework on how to conduct a large-scale association study in the era of WGS.
Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53205, USA; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
Paul L
PL
Auer
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA 98195, USA.
Alex P
AP
Reiner
Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
Gao
G
Wang
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Hyun Min
HM
Kang
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Goncalo R
GR
Abecasis
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Vertex Pharmaceuticals, Boston, MA 02210, USA.
David
D
Altshuler
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Department of Pediatrics, University of Washington, Seattle, WA 98195, USA.
Michael J
MJ
Bamshad
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
Deborah A
DA
Nickerson
Department of Pathology, University of Vermont, Colchester, VT 05405, USA; Department of Biochemistry, University of Vermont, Burlington, VT 05405, USA.
Russell P
RP
Tracy
Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
Stephen S
SS
Rich
Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address: sleal@bcm.edu.
Suzanne M
SM
Leal
D059472:Exome / Q000235:genetics*; D005260:Female; D056726:Genetic Association Studies / Q000379:methods* / Q000592:standards*; D014644:Genetic Variation; D015894:Genome, Human / Q000235:genetics; D017408:Guidelines as Topic; D006801:Humans; D008297:Male; D054555:National Heart, Lung, and Blood Institute (U.S.); D011786:Quality Control; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA; D014481:United States
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