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Genetic Analyses of Diverse Populations Improves Discovery for Complex Traits

Citation

Wojcik, Genevieve L.; Graff, Mariaelisa; Nishimura, Katherine K.; Tao, Ran; Haessler, Jeffrey; Gignoux, Christopher R.; Highland, Heather M.; Patel, Yesha M.; Sorokin, Elena P.; & Avery, Christy L., et al. (2019). Genetic Analyses of Diverse Populations Improves Discovery for Complex Traits. Nature, 570, 514-519. PMCID: PMC6785182

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 conditions(13)-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.

URL

http://dx.doi.org/10.1038/s41586-019-1310-4

Reference Type

Journal Article

Year Published

2019

Journal Title

Nature

Author(s)

Wojcik, Genevieve L.
Graff, Mariaelisa
Nishimura, Katherine K.
Tao, Ran
Haessler, Jeffrey
Gignoux, Christopher R.
Highland, Heather M.
Patel, Yesha M.
Sorokin, Elena P.
Avery, Christy L.
Belbin, Gillian Morven
Bien, Stephanie A.
Cheng, Iona
Cullina, Sinead
Hodonsky, Chani J.
Hu, Yao
Huckins, Laura M.
Jeff, Janina M.
Justice, Anne E.
Kocarnik, Jonathan M.
Lim, Unhee
Lin, Bridget M.
Lu, Yingchang
Nelson, Sarah C.
Park, Sung-Shim L.
Poisner, Hannah
Preuss, Michael H.
Richard, Melissa A.
Schurmann, Claudia
Setiawan, V. Wendy
Sockell, Alexandra
Vahi, Karan
Verbanck, Marie
Vishnu, Abhishek
Walker, Ryan W.
Young, Kristin L.
Zubair, Niha
Acuna-Alonso, Victor
Ambite, Jose Luis
Barnes, Kathleen C.
Boerwinkle, Eric A.
Bottinger, Erwin P.
Bustamante, Carlos D.
Caberto, Christian P.
Canizales-Quinteros, Samuel
Conomos, Matthew P.
Deelman, Ewa
Do, Ron
Doheny, Kimberly
Fernandez-Rhodes, Lindsay
Fornage, Myriam
Hailu, Benyam
Heiss, Gerardo M.
Henn, Brenna M.
Hindorff, Lucia A.
Jackson, Rebecca D.
Laurie, Cecelia A.
Laurie, Cathy C.
Li, Yuqing
Lin, Dan-Yu
Moreno-Estrada, Andres
Nadkarni, Girish
Norman, Paul J.
Pooler, Loreall C.
Reiner, Alexander P.
Romm, Jane
Sabatti, Chiara
Sandoval, Karla
Sheng, Xin
Stahl, Eli A.
Stram, Daniel O.
Thornton, Timothy A.
Wassel, Christina L.
Wilkens, Lynne R.
Winkler, Cheryl A.
Yoneyama, Sachiko
Buyske, Steven G.
Haiman, Christopher A.
Kooperberg, Charles L.
Le Marchand, Loic
Loos, Ruth J. F.
Matise, Tara C.
North, Kari E.
Peters, Ulrike
Kenny, Eimear E.
Carlson, Christopher S.

PMCID

PMC6785182