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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croxfordunivpr:10.1093/bioinformatics/bty249 2024-10-13T14:09:36+00:00 Finding associated variants in genome-wide association studies on multiple traits Gai, Lisa Eskin, Eleazar National Science Foundation National Institutes of Health 2018 http://dx.doi.org/10.1093/bioinformatics/bty249 https://academic.oup.com/bioinformatics/article-pdf/34/13/i467/50316183/bioinformatics_34_13_i467.pdf en eng Oxford University Press (OUP) http://creativecommons.org/licenses/by-nc/4.0/ Bioinformatics volume 34, issue 13, page i467-i474 ISSN 1367-4803 1367-4811 journal-article 2018 croxfordunivpr https://doi.org/10.1093/bioinformatics/bty249 2024-09-17T04:32:13Z 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. Article in Journal/Newspaper North Finland Oxford University Press Bioinformatics 34 13 i467 i474 |
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Oxford University Press |
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croxfordunivpr |
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English |
description |
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. |
author2 |
National Science Foundation National Institutes of Health |
format |
Article in Journal/Newspaper |
author |
Gai, Lisa Eskin, Eleazar |
spellingShingle |
Gai, Lisa Eskin, Eleazar Finding associated variants in genome-wide association studies on multiple traits |
author_facet |
Gai, Lisa Eskin, Eleazar |
author_sort |
Gai, Lisa |
title |
Finding associated variants in genome-wide association studies on multiple traits |
title_short |
Finding associated variants in genome-wide association studies on multiple traits |
title_full |
Finding associated variants in genome-wide association studies on multiple traits |
title_fullStr |
Finding associated variants in genome-wide association studies on multiple traits |
title_full_unstemmed |
Finding associated variants in genome-wide association studies on multiple traits |
title_sort |
finding associated variants in genome-wide association studies on multiple traits |
publisher |
Oxford University Press (OUP) |
publishDate |
2018 |
url |
http://dx.doi.org/10.1093/bioinformatics/bty249 https://academic.oup.com/bioinformatics/article-pdf/34/13/i467/50316183/bioinformatics_34_13_i467.pdf |
genre |
North Finland |
genre_facet |
North Finland |
op_source |
Bioinformatics volume 34, issue 13, page i467-i474 ISSN 1367-4803 1367-4811 |
op_rights |
http://creativecommons.org/licenses/by-nc/4.0/ |
op_doi |
https://doi.org/10.1093/bioinformatics/bty249 |
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Bioinformatics |
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34 |
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13 |
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i474 |
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