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Gene- and pathway-based association tests for multiple traits with GWAS summary statistics

View ORCID ProfileIl-Youp Kwak, Wei Pan
doi: https://doi.org/10.1101/052068
Il-Youp Kwak
1Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
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Wei Pan
1Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
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Abstract

To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene-or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP-and trait-levels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or p-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods.

The methods are implemented in R package aSPU, freely and publicly available on CRAN at: https://cran.r-project.org/web/packages/aSPU/.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 07, 2016.
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Gene- and pathway-based association tests for multiple traits with GWAS summary statistics
Il-Youp Kwak, Wei Pan
bioRxiv 052068; doi: https://doi.org/10.1101/052068
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Gene- and pathway-based association tests for multiple traits with GWAS summary statistics
Il-Youp Kwak, Wei Pan
bioRxiv 052068; doi: https://doi.org/10.1101/052068

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