Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology

Ying Wang; Masahiro Kanai; Taotao Tan; Mireille Kamariza; Kristin Tsuo; Kai Yuan; Wei Zhou; Yukinori Okada; BioBank Japan Project; Hailiang Huang; Patrick Turley; Elizabeth G Atkinson; Alicia R Martin

doi:10.1016/j.xgen.2023.100408

Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology

Cell Genom. 2023 Sep 14;3(10):100408. doi: 10.1016/j.xgen.2023.100408. eCollection 2023 Oct 11.

Authors

Ying Wang^{1

2}, Masahiro Kanai^{1

2

3

4}, Taotao Tan⁵, Mireille Kamariza⁶, Kristin Tsuo^{1

2}, Kai Yuan^{1

2}, Wei Zhou^{1

2}, Yukinori Okada^{4

7

8

9}; BioBank Japan Project; Hailiang Huang^{1

2}, Patrick Turley¹⁰, Elizabeth G Atkinson⁵, Alicia R Martin^{1

2}

Affiliations

¹ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
² Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
³ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
⁴ Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
⁵ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
⁶ Society of Fellows, Harvard University, Cambridge, MA 02138, USA.
⁷ Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
⁸ Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan.
⁹ Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan.
¹⁰ Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA.

Abstract

Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRS_multi, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRS_multi compared with PRSs constructed from single-ancestry GWASs (PRS_single). Through extensive simulations and empirical analyses, we showed that PRS_multi overall outperformed PRS_single in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.

Keywords: genetic architecture; genome-wide association studies; multi-ancestry; polygenic risk scores.

Abstract

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