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Multiethnic polygenic risk prediction in diverse populations through transfer learning

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 the European population. The accuracy of PRS prediction in non-European populations is diminished due to m...

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Autores principales: Tian, Peixin, Chan, Tsai Hor, Wang, Yong-Fei, Yang, Wanling, Yin, Guosheng, Zhang, Yan Dora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9438789/
https://www.ncbi.nlm.nih.gov/pubmed/36061179
http://dx.doi.org/10.3389/fgene.2022.906965
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author Tian, Peixin
Chan, Tsai Hor
Wang, Yong-Fei
Yang, Wanling
Yin, Guosheng
Zhang, Yan Dora
author_facet Tian, Peixin
Chan, Tsai Hor
Wang, Yong-Fei
Yang, Wanling
Yin, Guosheng
Zhang, Yan Dora
author_sort Tian, Peixin
collection PubMed
description 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 the 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 a transfer learning framework to learn useful knowledge from the 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 the Asian population and the risk of asthma in the Indian population by borrowing information from the European population. TL-Multi achieved better prediction accuracy than the competing methods, including Lassosum and meta-analysis in both simulations and real applications.
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spelling pubmed-94387892022-09-03 Multiethnic polygenic risk prediction in diverse populations through transfer learning Tian, Peixin Chan, Tsai Hor Wang, Yong-Fei Yang, Wanling Yin, Guosheng Zhang, Yan Dora Front Genet Genetics 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 the 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 a transfer learning framework to learn useful knowledge from the 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 the Asian population and the risk of asthma in the Indian population by borrowing information from the European population. TL-Multi achieved better prediction accuracy than the competing methods, including Lassosum and meta-analysis in both simulations and real applications. Frontiers Media S.A. 2022-08-19 /pmc/articles/PMC9438789/ /pubmed/36061179 http://dx.doi.org/10.3389/fgene.2022.906965 Text en Copyright © 2022 Tian, Chan, Wang, Yang, Yin and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Tian, Peixin
Chan, Tsai Hor
Wang, Yong-Fei
Yang, Wanling
Yin, Guosheng
Zhang, Yan Dora
Multiethnic polygenic risk prediction in diverse populations through transfer learning
title Multiethnic polygenic risk prediction in diverse populations through transfer learning
title_full Multiethnic polygenic risk prediction in diverse populations through transfer learning
title_fullStr Multiethnic polygenic risk prediction in diverse populations through transfer learning
title_full_unstemmed Multiethnic polygenic risk prediction in diverse populations through transfer learning
title_short Multiethnic polygenic risk prediction in diverse populations through transfer learning
title_sort multiethnic polygenic risk prediction in diverse populations through transfer learning
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9438789/
https://www.ncbi.nlm.nih.gov/pubmed/36061179
http://dx.doi.org/10.3389/fgene.2022.906965
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