Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Buu Truong; Leland E Hull; Yunfeng Ruan; Qin Qin Huang; Whitney Hornsby; Hilary Martin; David A van Heel; Ying Wang; Alicia R Martin; S Hong Lee; Pradeep Natarajan

doi:10.1016/j.xgen.2024.100523

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Cell Genom. 2024 Apr 10;4(4):100523. doi: 10.1016/j.xgen.2024.100523. Epub 2024 Mar 19.

Authors

Affiliations

¹ Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA.
² Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
³ Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK.
⁴ Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
⁵ Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
⁶ Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
⁷ Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia.
⁸ Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA. Electronic address: pnatarajan@mgh.harvard.edu.

Abstract

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10^-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10^-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10^-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10^-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10^-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Keywords: PRS; South Asian; clinical utility; combination; cross ancestry; integrative.

MeSH terms

Benchmarking
Coronary Artery Disease* / diagnosis
Genetic Risk Score
Humans
Manipulation, Osteopathic*
Multifactorial Inheritance / genetics

Abstract

MeSH terms

Grants and funding