Genetic analyses of diverse populations improves discovery for complex traits

doi:10.1038/s41586-019-1310-4

. 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

Genevieve L Wojcik¹, Mariaelisa Graff², Katherine K Nishimura³, Ran Tao^{4

5}, Jeffrey Haessler³, Christopher R Gignoux^{1

6}, Heather M Highland², Yesha M Patel⁷, Elena P Sorokin¹, Christy L Avery², Gillian M Belbin^{8

9}, Stephanie A Bien³, Iona Cheng¹⁰, Sinead Cullina^{8

9}, Chani J Hodonsky², Yao Hu³, Laura M Huckins¹¹, Janina Jeff^{8

9}, Anne E Justice², Jonathan M Kocarnik³, Unhee Lim¹², Bridget M Lin², Yingchang Lu⁹, Sarah C Nelson¹³, Sung-Shim L Park⁷, Hannah Poisner^{8

9}, Michael H Preuss⁹, Melissa A Richard¹⁴, Claudia Schurmann^{9

15

16}, Veronica W Setiawan⁷, Alexandra Sockell¹, Karan Vahi¹⁷, Marie Verbanck⁹, Abhishek Vishnu⁹, Ryan W Walker⁹, Kristin L Young², Niha Zubair³, Victor Acuña-Alonso¹⁸, Jose Luis Ambite¹⁷, Kathleen C Barnes⁶, Eric Boerwinkle¹⁹, Erwin P Bottinger^{9

15

16}, Carlos D Bustamante¹, Christian Caberto¹², Samuel Canizales-Quinteros²⁰, Matthew P Conomos¹³, Ewa Deelman¹⁷, Ron Do^{9

11}, Kimberly Doheny²¹, Lindsay Fernández-Rhodes^{2

22}, Myriam Fornage¹⁴, Benyam Hailu²³, Gerardo Heiss², Brenna M Henn²⁴, Lucia A Hindorff²⁵, Rebecca D Jackson²⁶, Cecelia A Laurie¹³, Cathy C Laurie¹³, Yuqing Li^{10

27}, Dan-Yu Lin², Andres Moreno-Estrada²⁸, Girish Nadkarni⁹, Paul J Norman⁶, Loreall C Pooler⁷, Alexander P Reiner¹³, Jane Romm²¹, Chiara Sabatti¹, Karla Sandoval²⁸, Xin Sheng⁷, Eli A Stahl¹¹, Daniel O Stram⁷, Timothy A Thornton¹³, Christina L Wassel²⁹, Lynne R Wilkens¹², Cheryl A Winkler³⁰, Sachi Yoneyama², Steven Buyske³¹, Christopher A Haiman³², Charles Kooperberg³, Loic Le Marchand¹², Ruth J F Loos^{9

11}, Tara C Matise³³, Kari E North², Ulrike Peters³, Eimear E Kenny^{34

35

36

37}, Christopher S Carlson³⁸

Affiliations

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.

PMID: 31217584
PMCID: PMC6785182
DOI: 10.1038/s41586-019-1310-4

Genetic analyses of diverse populations improves discovery for complex traits

Genevieve L Wojcik et al. Nature. 2019 Jun.

. 2019 Jun;570(7762):514-518.

doi: 10.1038/s41586-019-1310-4. Epub 2019 Jun 19.

Authors

Affiliations

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.

PMID: 31217584
PMCID: PMC6785182
DOI: 10.1038/s41586-019-1310-4

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 ancestry^1-3. 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-specific^4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations^11,12. 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.

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Figures

**Extended Data Fig. 1 |. Number of unique participants in the GWAS Catalog from 2006 to 2017 (inclusive).**
We observed that—although the number of unique participants (in millions) in the GWAS Catalog has grown substantially over the past decade—the relative proportion of participants of non-European descent has remained constant, with the majority of progress within Asian populations.

**Extended Data Fig. 2 |. Correlation between SNP genotype and PC1-PC10.**
a, The correlation (r²) for novel and residual loci calculated by obtaining the individual level data for all PAGE participants and correlating the SNP genotype with each of the ten PCs. The correlation between each locus and each of the ten PCs was plotted on the y axis, novel loci are plotted in grey and residual loci are plotted in yellow. We observed an especially high correlation between a novel locus and PC4, which represents Native Hawaiian/Pacific Islander ancestry. b, The individual level data for all PAGE participants were obtained and plotted in a parallel coordinates plot, such that each PAGE individual is represented by a set of line segments connecting their eigenvalues. This allows us to see which race/ethnicity groups are differentiated at each PC. For example, we see predominantly green lines as outliers for PC4, which indicates that this vector represents a continuum of Native Hawaiian/Pacific Islander ancestry.

**Fig. 1 |. Inclusion of multi-ethnic samples enables discovery and replication in GWAS.**
a, The population substructure present in the multi-ethnic sample of PAGE (n = 49,839) revealed complex patterns preventing meaningful stratification. Here we show that PC1 and PC2 show major patterns of variation, stratified by self-identified race/ethnicity. Individuals denoted by orange self-identified as ‘Other’. b, There are 8,979 previously reported trait–variant pairs, of which 1,444 replicated at a by-trait Bonferroni-adjusted significance level for P values estimated from a Wald test in SUGEN. In addition, we found 27 novel trait–variant pairs and 38 secondary signal pairs that remained after adjusting for known variants. BMI, body-mass index; eGFR, estimated glomerular filtration rate; HbA_1c, glycated haemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MCHC, mean corpuscular haemoglobin concentration; WHR, waist-to-hip ratio.

**Fig. 2 |. Weaker effect sizes of previously published trait–variant associations in non-European populations exacerbates disparity in PVE.**
a, Standardized effect sizes for the two largest self-reported subsets of the PAGE population show markedly weaker effect sizes in African Americans (z′_PAGE = 0.54 × z′_prior (yellow); z′ is the z-score from the trait–variant association standardized by the sample size in PAGE or the ‘prior’ publication from the NHGRI-EBI GWAS Catalog) than in Hispanic/Latino participants (z′_PAGE = 0.86 × z′_prior; red) compared to originally reported effect sizes from the NHGRI-EBI GWAS Catalog. Grey shading indicates the 95% confidence interval around the slope estimate. b, After identifying the SNP with the smallest P value in each locus, the PVE of height was calculated using the estimated effect size from this set of tag SNPs (left, GIANT-only GWAS; middle, UKB50k+GIANT meta-analysis; right, PAGE + GIANT meta-analysis). PVE was estimated independently in the UKB50k (White British) and PAGE (multi-ethnic) samples. The gap in PVE with previously reported loci from GIANT (8.14%) is exacerbated with the inclusion of 50,000 more individuals of European descent, to 11.19%. However, it narrows markedly with the inclusion of 50,000 multiethnic samples, to 3.91%.

**Fig. 3 |. Fine-mapping with multi-ethnic PAGE versus homogeneous UK Biobank samples for height.**
a, Comparison of 95% credible sets for height, comparing GIANT alone (n = 253,288) to UKB50k + GIANT (n = 303,288; paired-sample t-test P = 0.37) and PAGE + GIANT (n = 303,069; paired-sample t-test P = 0.01). Box plots show the median as the line in the notch, with the top and bottom of the box indicating the interquartile range. Whiskers extend to either the minimum value or 1.5 × the interquartile range. Notches indicate the 95% confidence interval of the medians. b, Top posterior probability from each 95% credible set for height, comparing GIANT (n = 253,288) to UKB50k + GIANT (n = 303,288) and PAGE + GIANT (n = 303,069). c, Example of results for a height locus from GWAS (rs11880992) in UKB50k + GIANT (n = 303,288) and PAGE + GIANT (n = 303,069), with linkage disequilibrium from weighted matrix from meta-analysis. d, Posterior probabilities for this signal with credible set in indicated by the diamond shapes. e, Linkage disequilibrium (r²) for the original 95% credible set from GIANT results stratified by populations. The index association SNP (rs11880992) with the highest posterior probability is denoted in bold.

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Comment in

Making the case for more inclusive GWAS.
Clyde D. Clyde D. Nat Rev Genet. 2019 Sep;20(9):500-501. doi: 10.1038/s41576-019-0160-0. Nat Rev Genet. 2019. PMID: 31292566 No abstract available.

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References

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[1] Need AC & Goldstein DB Next generation disparities in human genomics: concerns and remedies. Trends Genet. 25, 489–494 (2009). - PubMed

[2] Need AC & Goldstein DB Next generation disparities in human genomics: concerns and remedies. Trends Genet. 25, 489–494 (2009). - PubMed

[3] Bustamante CD, Burchard EG & De La Vega FM Genomics for the world. Nature 475, 163–165 (2011). - PMC - PubMed

[4] Bustamante CD, Burchard EG & De La Vega FM Genomics for the world. Nature 475, 163–165 (2011). - PMC - PubMed

[5] Popejoy AB & Fullerton SM Genomics is failing on diversity. Nature 538, 161–164 (2016). - PMC - PubMed

[6] Popejoy AB & Fullerton SM Genomics is failing on diversity. Nature 538, 161–164 (2016). - PMC - PubMed

[7] Gravel S et al. Demographic history and rare allele sharing among human populations. Proc. Natl Acad. Sci. USA 108, 11983–11988 (2011). - PMC - PubMed

[8] Gravel S et al. Demographic history and rare allele sharing among human populations. Proc. Natl Acad. Sci. USA 108, 11983–11988 (2011). - PMC - PubMed

[9] The SIGMA Type 2 Diabetes Consortium. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. J. Am. Med. Assoc 311, 2305–2314 (2014). - PMC - PubMed

[10] The SIGMA Type 2 Diabetes Consortium. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. J. Am. Med. Assoc 311, 2305–2314 (2014). - PMC - PubMed

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Genetic analyses of diverse populations improves discovery for complex traits

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