Finding associated variants in genome-wide association studies on multiple traits

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...

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Bibliographic Details
Published in:Bioinformatics
Main Authors: Gai, Lisa, Eskin, Eleazar
Other Authors: National Science Foundation, National Institutes of Health
Format: Article in Journal/Newspaper
Language:English
Published: Oxford University Press (OUP) 2018
Subjects:
Computational Mathematics
Computational Theory and Mathematics
Computer Science Applications
Molecular Biology
Biochemistry
Statistics and Probability
North Finland
Online Access:http://dx.doi.org/10.1093/bioinformatics/bty249
https://academic.oup.com/bioinformatics/article-pdf/34/13/i467/50316183/bioinformatics_34_13_i467.pdf
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Summary: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.

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