| Title: | Gene-based association study reveals a distinct female genetic signal in primary hypertension |
| Journal: | Human Genetics |
| Published: | 3 May 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37133573/ |
| DOI: | https://doi.org/10.1007/s00439-023-02567-9 |
| Citations: | 6 (6 in last 2 years) as of 8 Aug 2024 |
Publication 7846
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Abstract
Hypertension is a polygenic disease that affects over 1.2 billion adults aged 30-79 worldwide. It is a major risk factor for renal, cerebrovascular, and cardiovascular diseases. The heritability of hypertension is estimated to be high; nevertheless, our understanding of its underlying mechanisms remains scarce and incomplete. This study covered the entries from European ancestry from the UK-Biobank (UKB), with 74,090 cases diagnosed with essential (primary) hypertension and 200,734 controls. We compared the findings from large-scale genome-wide association studies (GWAS) to the gene-based method of proteome-wide association studies (PWAS). We focused on 70 statistically significant associated genes, most of which failed to reach significance in variant-based GWAS. A total of 30% of the PWAS-associated genes were validated against independent cohorts, including the Finnish Biobank. Furthermore, gene-based analyses that were performed on both sexes revealed sex-dependent genetics with a stronger genetic component associated with females. Analysis of systolic and diastolic blood pressure measurements confirms a strong genetic effect associated with females. We demonstrated that gene-based approaches provide insight into the underlying biology of hypertension. Specifically, the expression profiles of the identified genes exposed the enrichment of endothelial cells from multiple organs. Furthermore, females' top-ranked significant genes are involved in cellular immunity. We conclude that studying hypertension and blood pressure via gene-based association methods improves interpretability and exposes sex-dependent genetic effects, which enhances clinical utility.3 Authors
- Roei Zucker
- Michael Kovalerchik
- Michal Linial
1 Application
| Application ID | Title |
|---|---|
| 26664 | Novel machine-learning framework for improved inference in GWAS |
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