| Title: | Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis |
| Journal: | PLOS Genetics |
| Published: | 15 Dec 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33320851/ |
| DOI: | https://doi.org/10.1371/journal.pgen.1009060 |
| URL: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009060&type=printable |
Publication 5248
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Abstract
Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.</p>
5 Authors
- Corbin Quick
- Xiaoquan Wen
- Gonçalo Abecasis
- Michael Boehnke
- Hyun Min Kang
Enabling scientific discoveries that improve human health