Genetic analyses across cardiovascular traits: leveraging genetic correlations to empower locus discovery and prediction in common cardiovascular diseases

Common genetic variation detected by genome-wide association studies (GWAS) partially explains variability in the spectrum of cardiac phenotypes. In this work, we explore genetic correlations among 58 cardiac-related traits/diseases, detecting novel ones. We subsequently employ multi-trait analysis of GWAS (MTAG), which meta-analyzes genetically correlated traits, to improve genomic loci discovery and prediction in atrial fibrillation (AF), coronary artery disease (CAD), and heart failure (HF). We identify 19 novel loci specific for AF, 131 for CAD, and 141 for HF. Polygenic scores (PGS) in 15,177 Canadian individuals show similar results when PGS are derived from conventional GWAS versus MTAG summary statistics, although MTAG-PGS improve prediction and discrimination of CAD in females [∆R<sup>2</sup> 1.735% (95% Confidence Interval (CI): 0.609-2.856); Net reclassification index 0.208 (95%CI: 0.139-0.277)]. This work describes new relevant genetic correlations among cardiac-related traits/diseases and supports MTAG to improve loci discovery in common cardiovascular diseases and potentially improve the prediction of CAD in females.

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Publication Details

Journal
npj Genomic Medicine
Published
2025-09-29
DOI
https://doi.org/10.1038/s41525-025-00515-2
Citations
1
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
1.82

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article

Genetic analyses across cardiovascular traits: leveraging genetic correlations to empower locus discovery and prediction in common cardiovascular diseases

Yiwei Lai, Anna Nozza, Elisabeth Goulet, Michael W.T. Tanck et al.
1 citations
npj Genomic Medicine
Genetic Associations and Epidemiology
1.82
article

Genetic analyses across cardiovascular traits: leveraging genetic correlations to empower locus discovery and prediction in common cardiovascular diseases

Yiwei Lai, Anna Nozza, Elisabeth Goulet, Michael W.T. Tanck, Connie R. Bezzina, Marie‐Pierre Dubé, Rafik Tadros, Jean‐Claude Tardif, Simon de Denus, Louis‐Philippe Lemieux Perreault, Amélie Jeuken, Najim Lahrouchi, Julia Cadrin‐Tourigny, Peter Guerra, Guillaume Lettre, David Busseuil, Paloma Jordà
article en
1 citations

Abstract

Common genetic variation detected by genome-wide association studies (GWAS) partially explains variability in the spectrum of cardiac phenotypes. In this work, we explore genetic correlations among 58 cardiac-related traits/diseases, detecting novel ones. We subsequently employ multi-trait analysis of GWAS (MTAG), which meta-analyzes genetically correlated traits, to improve genomic loci discovery and prediction in atrial fibrillation (AF), coronary artery disease (CAD), and heart failure (HF). We identify 19 novel loci specific for AF, 131 for CAD, and 141 for HF. Polygenic scores (PGS) in 15,177 Canadian individuals show similar results when PGS are derived from conventional GWAS versus MTAG summary statistics, although MTAG-PGS improve prediction and discrimination of CAD in females [∆R<sup>2</sup> 1.735% (95% Confidence Interval (CI): 0.609-2.856); Net reclassification index 0.208 (95%CI: 0.139-0.277)]. This work describes new relevant genetic correlations among cardiac-related traits/diseases and supports MTAG to improve loci discovery in common cardiovascular diseases and potentially improve the prediction of CAD in females.

npj Genomic MedicineVol. 10(1)
Montreal Heart Institute (CA), Instituto de Salud Carlos III (ES), Centro de Investigación en Red en Enfermedades Cardiovasculares (ES), Amsterdam Neuroscience (NL), Institut d'Investigació Biomèdica de Girona (ES), ERN GUARD-Heart (NL), Amsterdam University Medical Centers (NL), Hospital Universitari de Girona Doctor Josep Trueta (ES), University of Amsterdam (NL)
Université de Montréal, Fondation Institut de Cardiologie de Montréal, Institut de Cardiologie de Montréal, Takeda Canada, Canadian Institutes of Health Research, Institute of Genetics
Openalex Percentile: Top 12%
Genetic Associations and Epidemiology
1.82
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