| Title: | BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data |
| Journal: | Genome Biology |
| Published: | 13 Feb 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36782330/ |
| DOI: | https://doi.org/10.1186/s13059-023-02864-6 |
| URL: | https://genomebiology.biomedcentral.com/counter/pdf/10.1186/s13059-023-02864-6 |
Publication 11082
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Abstract
We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at ∼80%$$\sim 80\%$$ of the associated loci.</p>
8 Authors
- Shiyang Ma
- Chen Wang
- Atlas Khan
- Linxi Liu
- James Dalgleish
- Krzysztof Kiryluk
- Zihuai He
- Iuliana Ionita-Laza
1 Application
| Application ID | Title |
|---|---|
| 41849 | GWAS studies for kidney-related traits using UKBB data. |
Enabling scientific discoveries that improve human health