A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits

The development of polygenic risk scores (PRSs) has proved useful to stratify the general European population into different risk groups. However, PRSs are less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-Eur...

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Bibliographic Details
Published in:American journal of human genetics 2021-04, Vol.108 (4), p.632-655
Main Authors: Cai, Mingxuan, Xiao, Jiashun, Zhang, Shunkang, Wan, Xiang, Zhao, Hongyu, Chen, Gang, Yang, Can
Format: Article
Language:English
Subjects:
Africa - ethnology
ancestry
Asian People - genetics
Black People - genetics
Body Height - genetics
Body Mass Index
China - ethnology
Computer Simulation
cross-population
Databases, Factual
Female
Genome-Wide Association Study
GWAS
Humans
Male
Models, Genetic
Multifactorial Inheritance - genetics
polygenic risk score
Principal Component Analysis
Sample Size
UK Biobankcross-population
United Kingdom
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Summary:The development of polygenic risk scores (PRSs) has proved useful to stratify the general European population into different risk groups. However, PRSs are less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ancestry genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. Our framework can also incorporate population-specific effects to further improve construction of PRS. With innovations in data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient, and robust PRS construction across a range of genetic architectures. In a Chinese cohort, our methods achieved 7.3%–198.0% accuracy gain for height and 19.5%–313.3% accuracy gain for body mass index (BMI) in terms of predictive R2 compared to existing PRS approaches. We also show that XPA and XPASS can achieve substantial improvement for construction of height PRSs in the African population, suggesting the generality of our framework across global populations.
ISSN:0002-9297
1537-6605
DOI:10.1016/j.ajhg.2021.03.002