| Title: | Multitrait analyses identify genetic variants associated with aortic valve function and aortic stenosis risk |
| Journal: | Nature Genetics |
| Published: | 19 Dec 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41419685/ |
| DOI: | https://doi.org/10.1038/s41588-025-02397-7 |
| URL: | https://www.nature.com/articles/s41588-025-02397-7.pdf |
Publication 17083
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Abstract
The genetic influences on normal aortic valve function and their impact on aortic stenosis risk are of substantial interest. We used deep learning to measure peak velocity, mean gradient and aortic valve area from magnetic resonance imaging and conducted genome-wide association studies (GWAS) in 59,571 participants in the UK Biobank. Incorporating the aortic valve measurement GWAS with aortic stenosis GWAS using multitrait analysis of GWAS (MTAG), we identified 166 distinct loci (134 with aortic valve traits, 134 with aortic stenosis and 166 unique loci across all GWAS), including PCSK9 and LDLR. The MTAG aortic stenosis PGS was associated with aortic stenosis in All of Us (hazard ratio (HR) = 3.32 for top 5% versus all others, P = 8.8 × 10−22) and Mass General Brigham Biobank (HR = 2.76, P = 7.8 × 10−15). Using Mendelian randomization, we found evidence supporting a potential causal role for Lp(a) and LDL on aortic valve function. These findings have implications for the early pathogenesis of aortic stenosis and suggest modifiable pathways as targets for preventive therapy.</p>
17 Keywords
- Aged
- Aortic Valve
- Aortic Valve Stenosis
- Female
- Genetic Predisposition to Disease
- Genetic Variation
- Genome-Wide Association Study
- Humans
- Lipoprotein(a)
- Magnetic Resonance Imaging
- Male
- Mendelian Randomization Analysis
- Middle Aged
- Polymorphism, Single Nucleotide
- Proprotein Convertase 9
- Receptors, LDL
- Risk Factors
16 Authors
- Shinwan Kany
- Joel T. Rämö
- Cody Hou
- Sean J. Jurgens
- Shaan Khurshid
- Victor Nauffal
- Jonathan W. Cunningham
- Emily S. Lau
- Satoshi Koyama
- Jennifer E. Ho
- Jeffrey E. Olgin
- Sammy Elmariah
- Aarno Palotie
- Mark E. Lindsay
- Patrick T. Ellinor
- James P. Pirruccello
1 Application
| Application ID | Title |
|---|---|
| 41664 | Machine learning from complex and genetic data |
Enabling scientific discoveries that improve human health