| Title: | Genetic insights into the age-specific biological mechanisms governing human ovarian aging |
| Journal: | American Journal of Human Genetics |
| Published: | 4 Aug 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37543033/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2023.07.006 |
| URL: | http://www.cell.com/article/S000292972300246X/pdf |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 10197
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Abstract
There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.</p>
8 Authors
- Sven E. Ojavee
- Liza Darrous
- Marion Patxot
- Kristi Läll
- Krista Fischer
- Reedik Mägi
- Zoltan Kutalik
- Matthew R. Robinson
1 Application
| Application ID | Title |
|---|---|
| 35520 | Improving estimation and prediction of common complex disease risk |
Enabling scientific discoveries that improve human health