Genetic analyses of diverse populations improves discovery for complex traits

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

Research output: Contribution to journalLetter

19 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)514-518
Number of pages5
JournalNature
Volume570
Issue number7762
DOIs
StatePublished - Jun 27 2019

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Population
Genome-Wide Association Study
Healthcare Disparities
Genetic Research
Precision Medicine
Population Genetics
Genomics
Epidemiology
Genome
Guidelines
Phenotype
Health
Pharmaceutical Preparations

All Science Journal Classification (ASJC) codes

  • General

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Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., ... Carlson, C. S. (2019). Genetic analyses of diverse populations improves discovery for complex traits. Nature, 570(7762), 514-518. https://doi.org/10.1038/s41586-019-1310-4
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 M. ; Bien, Stephanie A. ; Cheng, Iona ; Cullina, Sinead ; Hodonsky, Chani J. ; Hu, Yao ; Huckins, Laura M. ; Jeff, Janina ; 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, Veronica W. ; Sockell, Alexandra ; Vahi, Karan ; Verbanck, Marie ; Vishnu, Abhishek ; Walker, Ryan W. ; Young, Kristin L. ; Zubair, Niha ; Acuña-Alonso, Victor ; Ambite, Jose Luis ; Barnes, Kathleen C. ; Boerwinkle, Eric ; Bottinger, Erwin P. ; Bustamante, Carlos D. ; Caberto, Christian ; Canizales-Quinteros, Samuel ; Conomos, Matthew P. ; Deelman, Ewa ; Do, Ron ; Doheny, Kimberly ; Fernández-Rhodes, Lindsay ; Fornage, Myriam ; Hailu, Benyam ; Heiss, Gerardo ; 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, Sachi ; Buyske, Steven ; Haiman, Christopher A. ; Kooperberg, Charles ; Le Marchand, Loic ; Loos, Ruth J.F. ; Matise, Tara C. ; North, Kari E. ; Peters, Ulrike ; Kenny, Eimear E. ; Carlson, Christopher S. / Genetic analyses of diverse populations improves discovery for complex traits. In: Nature. 2019 ; Vol. 570, No. 7762. pp. 514-518.
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title = "Genetic analyses of diverse populations improves discovery for complex traits",
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.",
author = "Wojcik, {Genevieve L.} and Mariaelisa Graff and Nishimura, {Katherine K.} and Ran Tao and Jeffrey Haessler and Gignoux, {Christopher R.} and Highland, {Heather M.} and Patel, {Yesha M.} and Sorokin, {Elena P.} and Avery, {Christy L.} and Belbin, {Gillian M.} and Bien, {Stephanie A.} and Iona Cheng and Sinead Cullina and Hodonsky, {Chani J.} and Yao Hu and Huckins, {Laura M.} and Janina Jeff and Justice, {Anne E.} and Kocarnik, {Jonathan M.} and Unhee Lim and Lin, {Bridget M.} and Yingchang Lu and Nelson, {Sarah C.} and Park, {Sung Shim L.} and Hannah Poisner and Preuss, {Michael H.} and Richard, {Melissa A.} and Claudia Schurmann and Setiawan, {Veronica W.} and Alexandra Sockell and Karan Vahi and Marie Verbanck and Abhishek Vishnu and Walker, {Ryan W.} and Young, {Kristin L.} and Niha Zubair and Victor Acu{\~n}a-Alonso and Ambite, {Jose Luis} and Barnes, {Kathleen C.} and Eric Boerwinkle and Bottinger, {Erwin P.} and Bustamante, {Carlos D.} and Christian Caberto and Samuel Canizales-Quinteros and Conomos, {Matthew P.} and Ewa Deelman and Ron Do and Kimberly Doheny and Lindsay Fern{\'a}ndez-Rhodes and Myriam Fornage and Benyam Hailu and Gerardo Heiss and Henn, {Brenna M.} and Hindorff, {Lucia A.} and Jackson, {Rebecca D.} and Laurie, {Cecelia A.} and Laurie, {Cathy C.} and Yuqing Li and Lin, {Dan Yu} and Andres Moreno-Estrada and Girish Nadkarni and Norman, {Paul J.} and Pooler, {Loreall C.} and Reiner, {Alexander P.} and Jane Romm and Chiara Sabatti and Karla Sandoval and Xin Sheng and Stahl, {Eli A.} and Stram, {Daniel O.} and Thornton, {Timothy A.} and Wassel, {Christina L.} and Wilkens, {Lynne R.} and Winkler, {Cheryl A.} and Sachi Yoneyama and Steven Buyske and Haiman, {Christopher A.} and Charles Kooperberg and {Le Marchand}, Loic and Loos, {Ruth J.F.} and Matise, {Tara C.} and North, {Kari E.} and Ulrike Peters and Kenny, {Eimear E.} and Carlson, {Christopher S.}",
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Wojcik, GL, Graff, M, Nishimura, KK, Tao, R, Haessler, J, Gignoux, CR, Highland, HM, Patel, YM, Sorokin, EP, Avery, CL, Belbin, GM, Bien, SA, Cheng, I, Cullina, S, Hodonsky, CJ, Hu, Y, Huckins, LM, Jeff, J, Justice, AE, Kocarnik, JM, Lim, U, Lin, BM, Lu, Y, Nelson, SC, Park, SSL, Poisner, H, Preuss, MH, Richard, MA, Schurmann, C, Setiawan, VW, Sockell, A, Vahi, K, Verbanck, M, Vishnu, A, Walker, RW, Young, KL, Zubair, N, Acuña-Alonso, V, Ambite, JL, Barnes, KC, Boerwinkle, E, Bottinger, EP, Bustamante, CD, Caberto, C, Canizales-Quinteros, S, Conomos, MP, Deelman, E, Do, R, Doheny, K, Fernández-Rhodes, L, Fornage, M, Hailu, B, Heiss, G, Henn, BM, Hindorff, LA, Jackson, RD, Laurie, CA, Laurie, CC, Li, Y, Lin, DY, Moreno-Estrada, A, Nadkarni, G, Norman, PJ, Pooler, LC, Reiner, AP, Romm, J, Sabatti, C, Sandoval, K, Sheng, X, Stahl, EA, Stram, DO, Thornton, TA, Wassel, CL, Wilkens, LR, Winkler, CA, Yoneyama, S, Buyske, S, Haiman, CA, Kooperberg, C, Le Marchand, L, Loos, RJF, Matise, TC, North, KE, Peters, U, Kenny, EE & Carlson, CS 2019, 'Genetic analyses of diverse populations improves discovery for complex traits', Nature, vol. 570, no. 7762, pp. 514-518. https://doi.org/10.1038/s41586-019-1310-4

Genetic analyses of diverse populations improves discovery for complex traits. / 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 M.; Bien, Stephanie A.; Cheng, Iona; Cullina, Sinead; Hodonsky, Chani J.; Hu, Yao; Huckins, Laura M.; Jeff, Janina; 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, Veronica W.; Sockell, Alexandra; Vahi, Karan; Verbanck, Marie; Vishnu, Abhishek; Walker, Ryan W.; Young, Kristin L.; Zubair, Niha; Acuña-Alonso, Victor; Ambite, Jose Luis; Barnes, Kathleen C.; Boerwinkle, Eric; Bottinger, Erwin P.; Bustamante, Carlos D.; Caberto, Christian; Canizales-Quinteros, Samuel; Conomos, Matthew P.; Deelman, Ewa; Do, Ron; Doheny, Kimberly; Fernández-Rhodes, Lindsay; Fornage, Myriam; Hailu, Benyam; Heiss, Gerardo; 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, Sachi; Buyske, Steven; Haiman, Christopher A.; Kooperberg, Charles; Le Marchand, Loic; Loos, Ruth J.F.; Matise, Tara C.; North, Kari E.; Peters, Ulrike; Kenny, Eimear E.; Carlson, Christopher S.

In: Nature, Vol. 570, No. 7762, 27.06.2019, p. 514-518.

Research output: Contribution to journalLetter

TY - JOUR

T1 - Genetic analyses of diverse populations improves discovery for complex traits

AU - Wojcik, Genevieve L.

AU - Graff, Mariaelisa

AU - Nishimura, Katherine K.

AU - Tao, Ran

AU - Haessler, Jeffrey

AU - Gignoux, Christopher R.

AU - Highland, Heather M.

AU - Patel, Yesha M.

AU - Sorokin, Elena P.

AU - Avery, Christy L.

AU - Belbin, Gillian M.

AU - Bien, Stephanie A.

AU - Cheng, Iona

AU - Cullina, Sinead

AU - Hodonsky, Chani J.

AU - Hu, Yao

AU - Huckins, Laura M.

AU - Jeff, Janina

AU - Justice, Anne E.

AU - Kocarnik, Jonathan M.

AU - Lim, Unhee

AU - Lin, Bridget M.

AU - Lu, Yingchang

AU - Nelson, Sarah C.

AU - Park, Sung Shim L.

AU - Poisner, Hannah

AU - Preuss, Michael H.

AU - Richard, Melissa A.

AU - Schurmann, Claudia

AU - Setiawan, Veronica W.

AU - Sockell, Alexandra

AU - Vahi, Karan

AU - Verbanck, Marie

AU - Vishnu, Abhishek

AU - Walker, Ryan W.

AU - Young, Kristin L.

AU - Zubair, Niha

AU - Acuña-Alonso, Victor

AU - Ambite, Jose Luis

AU - Barnes, Kathleen C.

AU - Boerwinkle, Eric

AU - Bottinger, Erwin P.

AU - Bustamante, Carlos D.

AU - Caberto, Christian

AU - Canizales-Quinteros, Samuel

AU - Conomos, Matthew P.

AU - Deelman, Ewa

AU - Do, Ron

AU - Doheny, Kimberly

AU - Fernández-Rhodes, Lindsay

AU - Fornage, Myriam

AU - Hailu, Benyam

AU - Heiss, Gerardo

AU - Henn, Brenna M.

AU - Hindorff, Lucia A.

AU - Jackson, Rebecca D.

AU - Laurie, Cecelia A.

AU - Laurie, Cathy C.

AU - Li, Yuqing

AU - Lin, Dan Yu

AU - Moreno-Estrada, Andres

AU - Nadkarni, Girish

AU - Norman, Paul J.

AU - Pooler, Loreall C.

AU - Reiner, Alexander P.

AU - Romm, Jane

AU - Sabatti, Chiara

AU - Sandoval, Karla

AU - Sheng, Xin

AU - Stahl, Eli A.

AU - Stram, Daniel O.

AU - Thornton, Timothy A.

AU - Wassel, Christina L.

AU - Wilkens, Lynne R.

AU - Winkler, Cheryl A.

AU - Yoneyama, Sachi

AU - Buyske, Steven

AU - Haiman, Christopher A.

AU - Kooperberg, Charles

AU - Le Marchand, Loic

AU - Loos, Ruth J.F.

AU - Matise, Tara C.

AU - North, Kari E.

AU - Peters, Ulrike

AU - Kenny, Eimear E.

AU - Carlson, Christopher S.

PY - 2019/6/27

Y1 - 2019/6/27

N2 - 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.

AB - 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.

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UR - http://www.scopus.com/inward/citedby.url?scp=85068067624&partnerID=8YFLogxK

U2 - 10.1038/s41586-019-1310-4

DO - 10.1038/s41586-019-1310-4

M3 - Letter

C2 - 31217584

AN - SCOPUS:85068067624

VL - 570

SP - 514

EP - 518

JO - Nature

JF - Nature

SN - 0028-0836

IS - 7762

ER -

Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J, Gignoux CR et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature. 2019 Jun 27;570(7762):514-518. https://doi.org/10.1038/s41586-019-1310-4