| Title: | Evaluating confounding in rare variant genome wide association studies |
| Journal: | Nature Communications |
| Published: | 29 May 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42215513/ |
| DOI: | https://doi.org/10.1038/s41467-026-73776-9 |
Publication 19865
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
The theorised risk that confounded rare variant associations will emerge from population based genetic studies has not been investigated empirically. Here, we use 306,991 sequenced exomes from the UK Biobank to demonstrate that recent demography is poorly captured by common and rare variant principal components, and accounting for haplotype sharing does not eliminate false-positive rare variant associations with non-heritable spatially structured traits. Through re-analysis of 155 phenotypes in siblings, we show a trend of higher effect estimates bias for non-uniformly distributed traits, suggesting population stratification is most pervasive in these settings. Despite its spatial structure, bias of rare variant associations with height appeared most strongly influenced by assortative mating. We explore the risk of elevated false discovery rates for recent variants private to extended families sharing polygenic liability to extreme phenotypes, as well as through local linkage with common causal variants. Overall, we consider the complex confounding mechanisms that can impact rare variant studies and demonstrate family-based approaches can enable important sensitivity analyses.</p>
7 Authors
- Aimee L. Hanson
- Gareth J. Griffith
- Si Fang
- Neil M. Davies
- George Davey Smith
- Daniel J. Lawson
- Gibran Hemani
1 Application
| Application ID | Title |
|---|---|
| 81499 | Using human genetics to develop insight into putative causal relationships between modifiable exposures and disease endpoints |
Enabling scientific discoveries that improve human health