Multiethnic polygenic risk scores improve risk prediction in diverse populations

ABSTRACT 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 E...

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Bibliographic Details
Published in:Genetic epidemiology 2017-12, Vol.41 (8), p.811-823
Main Authors: Márquez‐Luna, Carla, Loh, Po‐Ru, Price, Alkes L.
Format: Article
Language:English
Subjects:
Accuracy
Alleles
Cohort Studies
Data processing
Diabetes mellitus
Diabetes Mellitus, Type 2 - genetics
Diabetes Mellitus, Type 2 - pathology
Ethnicity - genetics
Feature extraction
Genome-Wide Association Study
Genotype
height
Hispanic or Latino - genetics
Humans
Insurance claims
Minority & ethnic groups
Models, Genetic
Multifactorial Inheritance
Phenotype
polygenic prediction
Polymorphism, Single Nucleotide
Population genetics
Population studies
Risk Factors
type 2 diabetes
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Summary:ABSTRACT 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 R2 = 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.
ISSN:0741-0395
1098-2272
DOI:10.1002/gepi.22083