A Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization

Johanna L Smith; Catherine Tcheandjieu; Ozan Dikilitas; Kruthika Lyer; Kazuo Miyazawa; Austin Hilliard; Julie Lynch; Jerome I Rotter; Yii-Der Ida Chen; Wayne Huey-Herng Sheu; Kyong-Mi Chang; Stavroula Kanoni; Phil Tsao; Kaoru Ito; Matthew Kosel; Shoa L Clarke; Daniel J Schaid; Themistocles L Assimes; Iftikhar J Kullo

doi:10.1101/2023.06.02.23290896

A Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization

medRxiv [Preprint]. 2023 Jun 6:2023.06.02.23290896. doi: 10.1101/2023.06.02.23290896.

Authors

Johanna L Smith¹, Catherine Tcheandjieu^{2

3

4

5}, Ozan Dikilitas¹, Kruthika Lyer⁶, Kazuo Miyazawa⁷, Austin Hilliard^{4

6}, Julie Lynch⁸, Jerome I Rotter⁹, Yii-Der Ida Chen⁹, Wayne Huey-Herng Sheu^{10

11

12}, Kyong-Mi Chang¹³, Stavroula Kanoni¹⁴, Phil Tsao^{4

15}, Kaoru Ito⁷, Matthew Kosel¹⁶, Shoa L Clarke^{4

15}, Daniel J Schaid¹⁶, Themistocles L Assimes¹⁵, Iftikhar J Kullo¹

Affiliations

¹ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
² Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
³ Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA.
⁴ VA Palo Alto Health Care System, Palo Alto, CA, USA.
⁵ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁶ Stanford University School of Medicine, Palo Alto, CA, USA.
⁷ Riken Ctr. for Integrative Medical Sciences, Yokohama City, Japan.
⁸ Salt Lake City VA Met CTR., Salt Lake City, UT, USA.
⁹ Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.
¹⁰ Institute of Molecular and Genomic Medicine, National Health Research Institutes, Taiwan.
¹¹ Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
¹² Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
¹³ Corporal Michael J Crescenz VA Medical Ctr. Philadelphia, PA, USA.
¹⁴ Queen Mary University of London, Cambridge, UK.
¹⁵ Stanford University, Stanford, CA, USA.
¹⁶ Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Abstract

Background: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (PRS_CHD) for 5 genetic ancestry groups.

Methods: We derived ancestry-specific and multi-ancestry PRS_CHD based on pruning and thresholding (PRS_P+T) and continuous shrinkage priors (PRS_CSx) applied on summary statistics from the largest multi-ancestry genome-wide meta-analysis for CHD to date, including 1.1 million participants from 5 continental populations. Following training and optimization of PRS_CHD in the Million Veteran Program, we evaluated predictive performance of the best performing PRS_CHD in 176,988 individuals across 9 cohorts of diverse genetic ancestry.

Results: Multi-ancestry PRS_P+T outperformed ancestry specific PRS_P+T across a range of tuning values. In training stage, for all ancestry groups, PRS_CSx performed better than PRS_P+T and multi-ancestry PRS outperformed ancestry-specific PRS. In independent validation cohorts, the selected multi-ancestry PRS_P+T demonstrated the strongest association with CHD in individuals of South Asian (SAS) and European (EUR) ancestry (OR per 1SD[95% CI]; 2.75[2.41-3.14], 1.65[1.59-1.72]), followed by East Asian (EAS) (1.56[1.50-1.61]), Hispanic/Latino (HIS) (1.38[1.24-1.54]), and weakest in African (AFR) ancestry (1.16[1.11-1.21]). The selected multi-ancestry PRSCSx showed stronger associacion with CHD in comparison within each ancestry group where the association was strongest in SAS (2.67[2.38-3.00]) and EUR (1.65[1.59-1.71]), progressively decreasing in EAS (1.59[1.54-1.64]), HIS (1.51[1.35-1.69]), and lowest in AFR (1.20[1.15-1.26]).

Conclusions: Utilizing diverse summary statistics from a large multi-ancestry genome-wide meta-analysis led to improved performance of PRS_CHD in most ancestry groups compared to single-ancestry methods. Improvement of predictive performance was limited, specifically in AFR and HIS, despite use of one of the largest and most diverse set of training and validation cohorts to date. This highlights the need for larger GWAS datasets of AFR and HIS individuals to enhance performance of PRS_CHD.

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Abstract

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