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Multiethnic polygenic risk scores improve risk prediction in diverse populations.

Publication ,  Journal Article
Márquez-Luna, C; Loh, P-R; South Asian Type 2 Diabetes (SAT2D) Consortium, ; SIGMA Type 2 Diabetes Consortium, ; Price, AL
Published in: Genetic epidemiology
December 2017

Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff  = 40k) and Latino training data in small sample size (Neff  = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff  = 40k) and South Asian (Neff  = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.

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Published In

Genetic epidemiology

DOI

EISSN

1098-2272

ISSN

0741-0395

Publication Date

December 2017

Volume

41

Issue

8

Start / End Page

811 / 823

Related Subject Headings

  • Risk Factors
  • Polymorphism, Single Nucleotide
  • Phenotype
  • Multifactorial Inheritance
  • Models, Genetic
  • Humans
  • Hispanic or Latino
  • Genotype
  • Genome-Wide Association Study
  • Ethnicity
 

Citation

APA
Chicago
ICMJE
MLA
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Márquez-Luna, C., Loh, P.-R., South Asian Type 2 Diabetes (SAT2D) Consortium, ., SIGMA Type 2 Diabetes Consortium, ., & Price, A. L. (2017). Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genetic Epidemiology, 41(8), 811–823. https://doi.org/10.1002/gepi.22083
Márquez-Luna, Carla, Po-Ru Loh, Po-Ru South Asian Type 2 Diabetes (SAT2D) Consortium, Po-Ru SIGMA Type 2 Diabetes Consortium, and Alkes L. Price. “Multiethnic polygenic risk scores improve risk prediction in diverse populations.Genetic Epidemiology 41, no. 8 (December 2017): 811–23. https://doi.org/10.1002/gepi.22083.
Márquez-Luna C, Loh P-R, South Asian Type 2 Diabetes (SAT2D) Consortium, SIGMA Type 2 Diabetes Consortium, Price AL. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genetic epidemiology. 2017 Dec;41(8):811–23.
Márquez-Luna, Carla, et al. “Multiethnic polygenic risk scores improve risk prediction in diverse populations.Genetic Epidemiology, vol. 41, no. 8, Dec. 2017, pp. 811–23. Epmc, doi:10.1002/gepi.22083.
Márquez-Luna C, Loh P-R, South Asian Type 2 Diabetes (SAT2D) Consortium, SIGMA Type 2 Diabetes Consortium, Price AL. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genetic epidemiology. 2017 Dec;41(8):811–823.
Journal cover image

Published In

Genetic epidemiology

DOI

EISSN

1098-2272

ISSN

0741-0395

Publication Date

December 2017

Volume

41

Issue

8

Start / End Page

811 / 823

Related Subject Headings

  • Risk Factors
  • Polymorphism, Single Nucleotide
  • Phenotype
  • Multifactorial Inheritance
  • Models, Genetic
  • Humans
  • Hispanic or Latino
  • Genotype
  • Genome-Wide Association Study
  • Ethnicity