| Title: | Assessment of significance of conditionally independent GWAS signals |
| Journal: | Bioinformatics |
| Published: | 12 May 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33978749/ |
| DOI: | https://doi.org/10.1093/bioinformatics/btab332 |
Publication 9681
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Abstract
MOTIVATION: Multiple independently associated SNPs within a linkage disequilibrium region are a common phenomenon. Conditional analysis has been successful in identifying secondary signals. While conditional association tests are limited to specific genomic regions, they are benchmarked with genome-wide scale criterion, a conservative strategy. Within the weighted hypothesis testing framework, we developed a 'quasi-adaptive' method that uses the pairwise correlation (r2) and physical distance (d) from the index association to construct priority functions G =G(r2, d), which assign an SNP-specific α-threshold to each SNP. Family-wise error rate (FWER) and power of the approach were evaluated via simulations based on real GWAS data. We compared a series of different G-functions.</p>
RESULTS: Simulations under the null hypothesis on 1,100 primary SNPs confirmed appropriate empirical FWER for all G-functions. A G-function with optimal r2 = 0.3 between index and secondary SNP which down-weighted SNPs at higher distance step-wise-strong and gave more emphasis on d than on r2 had overall best power. It also gave the best results in application to the real datasets. As a proof of concept, 'quasi-adaptive' method was applied to GWAS on free thyroxine (FT4), inflammatory bowel disease (IBD) and human height. Application of the algorithm revealed 5 secondary signals in our example GWAS on FT4, 5 secondary signals in case of the IBD and 19 secondary signals on human height, that would have gone undetected with the established genome-wide threshold (α=5×10-8).</p>
AVAILABILITY AND IMPLEMENTATION: https://github.com/sghasemi64/Secondary-Signal.</p>
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</p>
4 Authors
- Sahar Ghasemi
- Alexander Teumer
- Matthias Wuttke
- Tim Becker
1 Application
| Application ID | Title |
|---|---|
| 20272 | GWAS of kidney function related traits and kidney disease |
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