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Nature. 2019 Jun;570(7762):514-518. doi: 10.1038/s41586-019-1310-4. Epub 2019 Jun 19.

Genetic analyses of diverse populations improves discovery for complex traits.

Author information

1
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
2
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
3
Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
4
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
5
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
6
Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
7
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
8
The Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
9
The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
10
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
11
Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
12
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
13
Department of Biostatistics, University of Washington, Seattle, WA, USA.
14
Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA.
15
Hasso-Plattner-Institute for Digital Engineering, Digital Health Center, Potsdam, Germany.
16
Hasso-Plattner-Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
17
Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA.
18
Escuela Nacional de Antropologia e Historia, Mexico City, Mexico.
19
Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA.
20
Instituto Nacional de Medicina Genómica, Mexico City, Mexico.
21
Center for Inherited Disease Research, Johns Hopkins University, Baltimore, MD, USA.
22
Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA.
23
NIH National Institute on Minority Health and Health Disparities, Bethesda, MD, USA.
24
Department of Anthropology, University of California Davis, Davis, CA, USA.
25
NIH National Human Genome Research Institute, Bethesda, MD, USA.
26
Center for Clinical and Translational Science, Ohio State Medical Center, Columbus, OH, USA.
27
Cancer Prevention Institute of California, Fremont, CA, USA.
28
National Laboratory of Genomics for Biodiversity (UGA-LANGEBIO), Irapuato, Mexico.
29
College of Medicine, University of Vermont, Burlington, VT, USA.
30
Basic Science Program, Frederick National Laboratory, Frederick, MD, USA.
31
Department of Statistics, Rutgers University, New Brunswick, NJ, USA.
32
Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
33
Department of Genetics, Rutgers University, New Brunswick, NJ, USA.
34
The Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. eimear.kenny@mssm.edu.
35
The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. eimear.kenny@mssm.edu.
36
Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. eimear.kenny@mssm.edu.
37
Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. eimear.kenny@mssm.edu.
38
Division of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. ccarlson@fredhutch.org.

Abstract

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

PMID:
31217584
DOI:
10.1038/s41586-019-1310-4

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