ArticleJournal of the American Heart Association2024
Genome-Wide European Ancestry Study Identifies Coronary Artery Disease-Associated Loci Through Gene-Sex Hormone Interaction.
Article in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Adropin, S100A1, and SERCA2b Dysregulation in Coronary Artery Disease: Molecular and In Silico Insights into Calcium Signaling and Metabolic Dysfunction.Biomedicines · 2026Article
- A Genetic Risk Prediction Model for Coronary Artery Disease Integrating CYP17A1 Polymorphisms and Clinical Variables in a Chinese Population.International journal of general medicine · 2026Article
- Ancestry gaps in cardiovascular GWAS: a multi-database review of African representation in genomic studies.Frontiers in genetics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundAlthough sex differences in coronary artery disease (CAD) risk have been observed, little is known about the role of sex hormones in CAD genetics. Accounting for sex hormone levels may help identify CAD-risk loci and extend our knowledge of its genetic architecture. METHODS AND
resultsA total of 365 662 individuals of European ancestry enrolled in the UK Biobank were considered. Genetic interaction of total testosterone, bioavailable testosterone, and SHBG (sex hormone-binding globulin) were evaluated. Gene-environment interactions in millions of samples software was used to conduct sex-stratified genome-wide interaction analysis with prevalent CAD as the outcome. Participant age at enrollment and principal components 1 to 10 were adjusted as covariates. We identified 45 loci in men and 8 loci in women that reached genome-wide significance (
conclusionsThis genome-wide gene-sex hormone interaction study identified genomic-risk loci that may contribute to the differential CAD risk between men and women, which otherwise would not have been discovered in a traditional genome-wide association study solely including marginal genetic effects.
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