| Title: | Moderating the Heritability of Body Mass Index by Age and Sex With Genomic Data |
| Journal: | Genetic Epidemiology |
| Published: | 6 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41493071/ |
| DOI: | https://doi.org/10.1002/gepi.70026 |
Publication 16868
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Abstract
Environmental contexts may increase or decrease the heritability of a phenotype. Or equivalently, people with some genotypes may be more (or less) sensitive to environmental differences. Such genetic sensitivity to environmental contexts is called gene-environment interaction (G × E). While G × E has been robustly detected in twin and model organism studies for numerous phenotypes, there is a lingering perception that existing genome-wide G × E methods struggle to identify substantively significant and replicable interactions. We propose a novel method for examining G × E heritability using genetic marginal effects from genome-wide G × E analyses and Linkage Disequilibrium Score Regression (LDSC). We demonstrate the effectiveness of our method for body mass index (BMI) using biological sex (binary) and age (continuous) as moderators. Using the same procedures for both binary and continuous moderators, we detect robust evidence for G × E. Our results are consistent with findings from twin G × E studies of BMI and are more sensitive to environmental moderation than other LDSC-based methods. We conclude that BMI heritability is substantially more sensitive to variation in sex and age than is currently appreciated. Extending this method to other phenotype-moderator combinations has the potential to reveal G × E across numerous outcomes and moderators.</p>
15 Keywords
- Adult
- Age Factors
- Aged
- Body Mass Index
- Female
- Gene-Environment Interaction
- Genome-Wide Association Study
- Humans
- Linkage Disequilibrium
- Male
- Middle Aged
- Models, Genetic
- Phenotype
- Polymorphism, Single Nucleotide
- Sex Factors
3 Authors
- Sarah E. Benstock
- Elizabeth Prom-Wormley
- Brad Verhulst
1 Application
| Application ID | Title |
|---|---|
| 57923 | Using genetic and brain imaging data from the UK Biobank to develop novel methods for understanding mental health problems |
Enabling scientific discoveries that improve human health