| Title: | Finding associated variants in genome-wide association studies on multiple traits |
| Journal: | Bioinformatics |
| Published: | 27 Jun 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/29949991/ |
| DOI: | https://doi.org/10.1093/bioinformatics/bty249 |
| URL: | https://academic.oup.com/bioinformatics/article-pdf/34/13/i467/25098362/bty249.pdf |
Publication 4856
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
Motivation: Many variants identified by genome-wide association studies (GWAS) have been found to affect multiple traits, either directly or through shared pathways. There is currently a wealth of GWAS data collected in numerous phenotypes, and analyzing multiple traits at once can increase power to detect shared variant effects. However, traditional meta-analysis methods are not suitable for combining studies on different traits. When applied to dissimilar studies, these meta-analysis methods can be underpowered compared to univariate analysis. The degree to which traits share variant effects is often not known, and the vast majority of GWAS meta-analysis only consider one trait at a time.Results: Here, we present a flexible method for finding associated variants from GWAS summary statistics for multiple traits. Our method estimates the degree of shared effects between traits from the data. Using simulations, we show that our method properly controls the false positive rate and increases power when an effect is present in a subset of traits. We then apply our method to the North Finland Birth Cohort and UK Biobank datasets using a variety of metabolic traits and discover novel loci.
Availability and implementation: Our source code is available at https://github.com/lgai/CONFIT.
Supplementary information: Supplementary data are available at Bioinformatics online.
2 Authors
- Lisa Gai
- Eleazar Eskin
Enabling scientific discoveries that improve human health