| Title: | Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank. |
| Journal: | PLOS Genetics |
| Published: | 24 Apr 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38656964/ |
| DOI: | https://doi.org/10.1371/journal.pgen.1011221 |
| URL: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1011221&type=printable |
Publication 9843
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Abstract
Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation.</p>
3 Authors
- Boxi Lin
- Andrew D Paterson
- Lei Sun
1 Application
| Application ID | Title |
|---|---|
| 64875 | Novel Statistical Methods for Disease Prediction, Causal Inference and Discovery in Genetic and Genomic Studies |
Enabling scientific discoveries that improve human health