| Title: | An enhanced framework for local genetic correlation analysis |
| Journal: | Nature Genetics |
| Published: | 10 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40065165/ |
| DOI: | https://doi.org/10.1038/s41588-025-02123-3 |
Publication 16224
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Abstract
Genetic correlation is a key parameter in the joint genetic model of complex traits, but it is usually estimated on a global genomic scale. Understanding local genetic correlations provides more detailed insight into the shared genetic architecture of complex traits. However, a state-of-the-art tool for local genetic correlation analysis, LAVA, is prone to false inference. Here we extend the high-definition likelihood (HDL) method to a local version, HDL-L, which performs genetic correlation analysis in small, approximately independent linkage disequilibrium blocks. HDL-L allows a more granular estimation of genetic variances and covariances. Simulations show that HDL-L offers more consistent heritability estimates and more efficient genetic correlation estimates compared with LAVA. HDL-L demonstrated robust performance across a wide range of simulations conducted under varying parameter settings. In the analysis of 30 phenotypes from the UK Biobank, HDL-L identified 109 significant local genetic correlations and showed a notable computational advantage. HDL-L proves to be a powerful tool for uncovering the detailed genetic landscape that underlies complex human traits, offering both accuracy and computational efficiency.</p>
10 Keywords
- Computer Simulation
- Genome-Wide Association Study
- Humans
- Likelihood Functions
- Linkage Disequilibrium
- Models, Genetic
- Multifactorial Inheritance
- Phenotype
- Polymorphism, Single Nucleotide
- Quantitative Trait Loci
3 Authors
- Yuying Li
- Yudi Pawitan
- Xia Shen
1 Application
| Application ID | Title |
|---|---|
| 19655 | Quantitative and population genetics in UK Biobank |
Enabling scientific discoveries that improve human health