RT Journal Article
SR Electronic
T1 Multiethnic Polygenic Risk Prediction in Diverse Populations through Transfer Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.03.30.486333
DO 10.1101/2022.03.30.486333
A1 Tian, Peixin
A1 Chan, Tsai Hor
A1 Wang, Yong-Fei
A1 Yang, Wanling
A1 Yin, Guosheng
A1 Zhang, Yan Dora
YR 2022
UL http://biorxiv.org/content/early/2022/06/21/2022.03.30.486333.abstract
AB Polygenic risk scores (PRS) leverage the genetic contribution of an individual’s genotype to a complex trait by estimating disease risk. Traditional PRS prediction methods are predominantly for European population. The accuracy of PRS prediction in non-European populations is diminished due to much smaller sample size of genome-wide association studies (GWAS). In this article, we introduced a novel method to construct PRS for non-European populations, abbreviated as TL-Multi, by conducting transfer learning framework to learn useful knowledge from European population to correct the bias for non-European populations. We considered non-European GWAS data as the target data and European GWAS data as the informative auxiliary data. TL-Multi borrows useful information from the auxiliary data to improve the learning accuracy of the target data while preserving the efficiency and accuracy. To demonstrate the practical applicability of the proposed method, we applied TL-Multi to predict the risk of systemic lupus erythematosus (SLE) in Asian population and the risk of asthma in Indian population by borrowing information from European population. TL-Multi achieved better prediction accuracy than the competing methods including Lassosum and meta-analysis in both simulations and real applications.Competing Interest StatementThe authors have declared no competing interest.