Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups

Nuzulul Kurniansyah; Matthew O Goodman; Alyna T Khan; Jiongming Wang; Elena Feofanova; Joshua C Bis; Kerri L Wiggins; Jennifer E Huffman; Tanika Kelly; Tali Elfassy; Xiuqing Guo; Walter Palmas; Henry J Lin; Shih-Jen Hwang; Yan Gao; Kendra Young; Gregory L Kinney; Jennifer A Smith; Bing Yu; Simin Liu; Sylvia Wassertheil-Smoller; JoAnn E Manson; Xiaofeng Zhu; Yii-Der Ida Chen; I-Te Lee; C Charles Gu; Donald M Lloyd-Jones; Sebastian Zöllner; Myriam Fornage; Charles Kooperberg; Adolfo Correa; Bruce M Psaty; Donna K Arnett; Carmen R Isasi; Stephen S Rich; Robert C Kaplan; Susan Redline; Braxton D Mitchell; Nora Franceschini; Daniel Levy; Jerome I Rotter; Alanna C Morrison; Tamar Sofer

doi:10.1038/s41467-023-38990-9

Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups

Nat Commun. 2023 Jun 2;14(1):3202. doi: 10.1038/s41467-023-38990-9.

Authors

Nuzulul Kurniansyah^#¹, Matthew O Goodman^#^{1

2}, Alyna T Khan³, Jiongming Wang⁴, Elena Feofanova⁵, Joshua C Bis⁶, Kerri L Wiggins⁶, Jennifer E Huffman⁷, Tanika Kelly⁸, Tali Elfassy⁹, Xiuqing Guo¹⁰, Walter Palmas¹¹, Henry J Lin¹⁰, Shih-Jen Hwang^{12

13}, Yan Gao¹⁴, Kendra Young¹⁵, Gregory L Kinney¹⁵, Jennifer A Smith¹⁶, Bing Yu⁵, Simin Liu^{17

18}, Sylvia Wassertheil-Smoller^{19

20}, JoAnn E Manson^{2

21}, Xiaofeng Zhu²², Yii-Der Ida Chen¹⁰, I-Te Lee^{23

24}, C Charles Gu²⁵, Donald M Lloyd-Jones²⁶, Sebastian Zöllner^{4

27}, Myriam Fornage^{5

28}, Charles Kooperberg²⁹, Adolfo Correa³⁰, Bruce M Psaty^{31

32

33

34}, Donna K Arnett³⁵, Carmen R Isasi²⁰, Stephen S Rich³⁶, Robert C Kaplan^{20

29}, Susan Redline^{1

2}, Braxton D Mitchell³⁷, Nora Franceschini³⁸, Daniel Levy^{12

13}, Jerome I Rotter¹⁰, Alanna C Morrison⁵, Tamar Sofer^{39

40

41

42}

Affiliations

¹ Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
² Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
³ Department of Biostatistics, University of Washington, Seattle, WA, USA.
⁴ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
⁵ Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
⁶ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
⁷ Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
⁸ Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
⁹ Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA.
¹⁰ Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
¹¹ Department of Medicine, Columbia University Medical Center, New York, NY, USA.
¹² The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA.
¹³ The Framingham Heart Study, Framingham, MA, USA.
¹⁴ The Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA.
¹⁵ Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA.
¹⁶ Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
¹⁷ Center for Global Cardiometabolic Health, Department of Epidemiology, Brown University, Providence, RI, USA.
¹⁸ Department of Medicine, Brown University, Providence, RI, USA.
¹⁹ Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA.
²⁰ Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
²¹ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²² Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
²³ Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung City, 40705, Taiwan.
²⁴ School of Medicine, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan.
²⁵ Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
²⁶ Department of Preventive Medicine, Northwestern University, Chicago, IL, USA.
²⁷ Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
²⁸ Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
²⁹ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
³⁰ Departments of Medicine and Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA.
³¹ Department of Medicine, University of Washington, Seattle, WA, USA.
³² Department of Epidemiology, University of Washington, Seattle, WA, USA.
³³ Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.
³⁴ Health Systems and Population Health, University of Washington, Seattle, WA, USA.
³⁵ Office of the Provost, University of South Carolina, Columbia, SC, USA.
³⁶ Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA.
³⁷ Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
³⁸ Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
³⁹ Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA. [email protected].
⁴⁰ Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA. [email protected].
⁴¹ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. [email protected].
⁴² CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA. [email protected].

^# Contributed equally.

Abstract

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Blood Pressure / genetics
Ethnicity / genetics
Female
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Male
Multifactorial Inheritance / genetics
Population Health*
Risk Factors

Abstract

Publication types

MeSH terms

Grants and funding