| Title: | Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship |
| Journal: | Cell Genomics |
| Published: | 25 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41297543/ |
| DOI: | https://doi.org/10.1016/j.xgen.2025.101075 |
| URL: | https://www.cell.com/cell-genomics/pdf/S2666-979X(25)00331-3.pdf |
Publication 18841
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Abstract
Polygenic scores (PGSs) that can predict response to interventions can facilitate precision medicine and are detectable in observational datasets as PGS-by-exposure (PGS×E) interactions. PGSs based on interactions (iPGSs) or variance effects (vPGSs) may be more powerful than standard PGSs for detecting PGS×E, but these have yet to be systematically compared. We describe a generalized pipeline for developing and comparing these PGS types and apply it to detect genetic modification of the relationship between adiposity (measured by BMI) and a broad set of cardiometabolic risk factors. Our applied analysis in the UK Biobank identified significant PGS×BMI for 16/20 risk factors, most consistently for the iPGS approach. Many interactions replicated in All of Us (AoU); for example, we observed a 72% larger BMI-alanine aminotransferase association in the top iPGS decile in AoU. Our study provides a framework for the comparison of PGS×E strategies and informs efforts toward clinically useful response-focused PGSs.</p>
12 Keywords
- Adiposity
- Body Mass Index
- Cardiometabolic Risk Factors
- Cardiovascular Diseases
- Female
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Male
- Middle Aged
- Multifactorial Inheritance
- Risk Factors
5 Authors
- Kenneth E. Westerman
- Julie E. Gervis
- Luke J. O'Connor
- Miriam S. Udler
- Alisa K. Manning
1 Application
| Application ID | Title |
|---|---|
| 27892 | Genetic studies of type 2 diabetes, related metabolic traits, and their complications |
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