Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Ying Wang; Shinichi Namba; Esteban Lopera; Sini Kerminen; Kristin Tsuo; Kristi Läll; Masahiro Kanai; Wei Zhou; Kuan-Han Wu; Marie-Julie Favé; Laxmi Bhatta; Philip Awadalla; Ben Brumpton; Patrick Deelen; Kristian Hveem; Valeria Lo Faro; Reedik Mägi; Yoshinori Murakami; Serena Sanna; Jordan W Smoller; Jasmina Uzunovic; Brooke N Wolford; Global Biobank Meta-analysis Initiative; Cristen Willer; Eric R Gamazon; Nancy J Cox; Ida Surakka; Yukinori Okada; Alicia R Martin; Jibril Hirbo

doi:10.1016/j.xgen.2022.100241

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts

Cell Genom. 2023 Jan 4;3(1):100241. doi: 10.1016/j.xgen.2022.100241. eCollection 2023 Jan 11.

Authors

Ying Wang^{1

2}, Shinichi Namba³, Esteban Lopera⁴, Sini Kerminen⁵, Kristin Tsuo^{1

2}, Kristi Läll⁶, Masahiro Kanai^{1

2

3

7}, Wei Zhou^{1

2}, Kuan-Han Wu⁸, Marie-Julie Favé⁹, Laxmi Bhatta¹⁰, Philip Awadalla^{9

11}, Ben Brumpton^{10

12

13}, Patrick Deelen^{4

14}, Kristian Hveem^{10

12}, Valeria Lo Faro^{15

16

17}, Reedik Mägi⁶, Yoshinori Murakami¹⁸, Serena Sanna^{4

19}, Jordan W Smoller²⁰, Jasmina Uzunovic⁹, Brooke N Wolford^{8

10}; Global Biobank Meta-analysis Initiative; Cristen Willer^{10

21

22}, Eric R Gamazon^{23

24

25}, Nancy J Cox^{23

25}, Ida Surakka²¹, Yukinori Okada^{3

26

27

28}, Alicia R Martin^{1

2}, Jibril Hirbo^{23

25}

Collaborators

Global Biobank Meta-analysis Initiative:
Wei Zhou, Masahiro Kanai, Kuan-Han H Wu, Humaira Rasheed, Kristin Tsuo, Jibril B Hirbo, Ying Wang, Arjun Bhattacharya, Huiling Zhao, Shinichi Namba, Ida Surakka, Brooke N Wolford, Valeria Lo Faro, Esteban A Lopera-Maya, Kristi Läll, Marie-Julie Favé, Sinéad B Chapman, Juha Karjalainen, Mitja Kurki, Maasha Mutaamba, Juulia J Partanen, Ben M Brumpton, Sameer Chavan, Tzu-Ting Chen, Michelle Daya, Yi Ding, Yen-Chen A Feng, Christopher R Gignoux, Sarah E Graham, Whitney E Hornsby, Nathan Ingold, Ruth Johnson, Triin Laisk, Kuang Lin, Jun Lv, Iona Y Millwood, Priit Palta, Anita Pandit, Michael H Preuss, Unnur Thorsteinsdottir, Jasmina Uzunovic, Matthew Zawistowski, Xue Zhong, Archie Campbell, Kristy Crooks, Geertruida H de Bock, Nicholas J Douville, Sarah Finer, Lars G Fritsche, Christopher J Griffiths, Yu Guo, Karen A Hunt, Takahiro Konuma, Riccardo E Marioni, Jansonius Nomdo, Snehal Patil, Nicholas Rafaels, Anne Richmond, Jonathan A Shortt, Peter Straub, Ran Tao, Brett Vanderwerff, Kathleen C Barnes, Marike Boezen, Zhengming Chen, Chia-Yen Chen, Judy Cho, George Davey Smith, Hilary K Finucane, Lude Franke, Eric R Gamazon, Andrea Ganna, Tom R Gaunt, Tian Ge, Hailiang Huang, Jennifer Huffman, Jukka T Koskela, Clara Lajonchere, Matthew H Law, Liming Li, Cecilia M Lindgren, Ruth Jf Loos, Stuart MacGregor, Koichi Matsuda, Catherine M Olsen, David J Porteous, Jordan A Shavit, Harold Snieder, Richard C Trembath, Judith M Vonk, David Whiteman, Stephen J Wicks, Cisca Wijmenga, John Wright, Jie Zheng, Xiang Zhou, Philip Awadalla, Michael Boehnke, Nancy J Cox, Daniel H Geschwind, Caroline Hayward, Kristian Hveem, Eimear E Kenny, Yen-Feng Lin, Reedik Mägi, Hilary C Martin, Sarah E Medland, Yukinori Okada, Aarno V Palotie, Bogdan Pasaniuc, Serena Sanna, Jordan W Smoller, Kari Stefansson, David A van Heel, Robin G Walters, Sebastian Zöllner, Alicia R Martin, Cristen J Willer, Mark J Daly, Benjamin M Neale

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 Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
⁴ Department of Genetics, UMCG, University of Groningen, Groningen, the Netherlands.
⁵ Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
⁶ Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
⁷ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
⁸ Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48103, USA.
⁹ Ontario Institute for Cancer Research, Toronto, ON, Canada.
¹⁰ K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway.
¹¹ Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
¹² HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway.
¹³ Clinic of Medicine, St. Olav's Hospital, Trondheim University Hospital, 7030 Trondheim, Norway.
¹⁴ Oncode Institute, Utrecht, the Netherlands.
¹⁵ Department of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
¹⁶ Department of Clinical Genetics, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands.
¹⁷ Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
¹⁸ Division of Molecular Pathology, Institute of Medical Science, the University of Tokyo, Tokyo, Japan.
¹⁹ Institute for Genetics and Biomedical Research (IRGB), National Research Council (CNR), 09100 Cagliari, Italy.
²⁰ Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
²¹ Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
²² Department of Biostatistics and Center for Statistical Genetics, and Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
²³ Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
²⁴ MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
²⁵ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
²⁶ Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
²⁷ Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC) and Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita 565-0871, Japan.
²⁸ Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan.

Abstract

Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Keywords: Global-Biobank Meta-analysis Initiative; accuracy heterogeneity; multi-ancestry genetic prediction; polygenic risk scores.

Abstract

Grants and funding