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Genet Epidemiol. 2019 Feb;43(1):50-62. doi: 10.1002/gepi.22166. Epub 2018 Oct 15.

Generalizing polygenic risk scores from Europeans to Hispanics/Latinos.

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Department of Biostatistics, University of Washington, Seattle, Washington.
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York.
Department of Epidemiology, Brown University, Providence, Rhode Island.
Department of Family Medicine and Public Health, University of California San Diego, San Diego, California.
Department of Biomedical Sciences, City University of Hong Kong, HKSAR.
Department of Electronic Engineering, City University of Hong Kong, HKSAR.
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts.
Department of Medicine, Harvard Medical School, Boston, Massachusetts.


Polygenic risk scores (PRSs) are weighted sums of risk allele counts of single-nucleotide polymorphisms (SNPs) associated with a disease or trait. PRSs are typically constructed based on published results from Genome-Wide Association Studies (GWASs), and the majority of which has been performed in large populations of European ancestry (EA) individuals. Although many genotype-trait associations have generalized across populations, the optimal choice of SNPs and weights for PRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns. We compare various approaches for PRS construction, using GWAS results from both large EA studies and a smaller study in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL, <mml:math xmlns:mml=""><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>12</mml:mn> <mml:mo>,</mml:mo> <mml:mn>803</mml:mn></mml:math> ). We consider multiple approaches for selecting SNPs and for computing SNP weights. We study the performance of the resulting PRSs in an independent study of Hispanics/Latinos from the Women's Health Initiative (WHI, <mml:math xmlns:mml=""><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> <mml:mo>,</mml:mo> <mml:mn>582</mml:mn></mml:math> ). We support our investigation with simulation studies of potential genetic architectures in a single locus. We observed that selecting variants based on EA GWASs generally performs well, except for blood pressure trait. However, the use of EA GWASs for weight estimation was suboptimal. Using non-EA GWAS results to estimate weights improved results.


admixed populations; genetic diversity; linkage disequilibrium

[Available on 2020-02-01]
[Indexed for MEDLINE]

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