Abstract MPWE43: Multi-trait Polygenic Risk Scores Improve Prediction of Heart Failure
Abstract
Background: While clinical risk factors (CRF) have been used to predict heart failure (HF), an end-stage syndrome with high morbidity and mortality, the role of polygenic risk score (PRS) in identifying HF risk prediction remains unclear. Method: Using genome-wide summary statistics, we constructed 52 PRSs, including 1 for HF, 43 for echocardiographic(echo) traits, and 8 for CRF. Associations with incident HF were evaluated in 31,650 trans-ancestry participants (55% non-White; mean follow-up 16 years) from seven cohorts from the Trans-Omics for Precision Medicine (TOPMed) program, randomly divided into training (80%) and testing (20%) sets. Cox regression models adjusted for demographic and CRF were used to relate PRSs and to incident HF. Least absolute shrinkage and selection operator (LASSO) feature selection was performed on HF-related PRSs to build a multi-trait PRS (mPRS) in the training set. Associations of mPRS with incident HF, HF with preserved and reduced left ventricular (LV) ejection fraction (HFpEF and HFrEF, respectively), were examined in the testing set, with predictive performance evaluated by the C-statistic. Replication was conducted in 510,074 participants from the Million Veteran Program (MVP). Results: In the training set (n=25,320, HF cases=3,380), 17 out of 52 PRSs were associated with incident HF, where PRSs for HF and LV ejection fraction showed the largest effects (Figure 1a, HR HF-PRS : 2.02,95% CI 1.97-2.08, HR LVEF-PRS :0.93, 95% CI 0.89-0.97, p-value <0.05). Nine HF-related PRSs (1 HF, 6 echo, 2 CRF) were selected by LASSO to construct a multi-trait PRS (mPRS). In the testing set (n=6330, HF cases=856), per SD increase of mPRS was associated with about twofold increase in the risk of incident HF and its subtypes (Figure 1b, HR HF : 1.81, 95% CI 1.68–1.96; HR HFpEF : 2.08, 95% CI 1.78-2.43; HR HFrEF :1.97, 95% CI 1.68-2.31), and the associations were stronger in White vs Non-white participants. The associations of mPRS with incident HF and its subtypes were replicated in MVP (Figure 1b, HR HF : 1.13, 95% CI 1.11–1.15; HR HFpEF : 1.09, 95% CI 1.07-1.11; HR HFrEF :1.17, 95% CI 1.15-1.19). Adding mPRS over CRF modestly improved HF and HFrEF prediction in the trans-ancestral and white group (Figure 1c, HF trans-ancestral : estimated C: 0.89, delta C=1%, all p-values<0.05). Conclusion: The component PRS and mPRS were associated with HF risk, and mPRS can improve the prediction of incident HF, highlighting the potential for identifying at-risk populations.
Article Details
Authors (32)
Shinhye Chung
University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas, United States
Brian Spitzer
Department of Medicine, Harvard Medical School;CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
Qin Hui
Ngoc Quynh Nguyen
UTHealth School of Public Health, Houston, Texas, United States
Taryn Alkis
The University of Texas Health Science Center at Houston, Houston, Texas, United States
Chang Liu
Traci Bartz
University of Washington, Seattle, Washington, United States
Eric Boerwinkle
Jennifer Brody
University of Washington, Seattle, WA, USA.
Susan Cheng
Myriam Fornage
Michael Hall
University of Mississippi, Jackson, MS, USA.
Bertha Hidalgo
Robert Kaplan
Donald Lloyd-Jones
Framingham Center for Population and Prevention Science, Framingham, MA
Alanna Morrison
University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas, United States
Lawrence Phillips
Atlanta VA Medical Center, Decatur, Georgia, United States
Bruce Psaty
University of Washington, Seattle, WA, USA.
Laura Raffield
Maryam Rahafrooz
Brown University, Providence, Rhode Island, United States
Carlos Rodriguez
Jerome Rotter
The Lundquist Institute, Torrance, California, United States
Sanjiv Shah
Northwestern University Feinberg School of Medicine, Chicago
Amil Shah
University of Texas Southwestern Medical Center, Dallas (A.S.).
Kent Taylor
The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, United States
Peter Wilson
Vanessa Xanthakis
Jacob Joseph
Yan Sun
Tamar Sofer
Vasan Ramachandran
The University of Texas San Antonio School of Public Health, San Antonio, Texas, United States
Bing Yu
College of Chemistry and Materials Science, Guangdong Provincial Key Laboratory of Supramolecular Coordination Chemistry