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Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization
Journal article   Open access   Peer reviewed

Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization

Jin Liu, Jian Huang and Shuangge Ma
PloS one, Vol.7(12), pp.e51198-e51198
12/14/2012
DOI: 10.1371/journal.pone.0051198
PMCID: PMC3522680
PMID: 23272092
url
https://doi.org/10.1371/journal.pone.0051198View
Published (Version of record) Open Access

Abstract

Genome-wide association studies have been extensively conducted, searching for markers for biologically meaningful outcomes and phenotypes. Penalization methods have been adopted in the analysis of the joint effects of a large number of SNPs (single nucleotide polymorphisms) and marker identification. This study is partly motivated by the analysis of heterogeneous stock mice dataset, in which multiple correlated phenotypes and a large number of SNPs are available. Existing penalization methods designed to analyze a single response variable cannot accommodate the correlation among multiple response variables. With multiple response variables sharing the same set of markers, joint modeling is first employed to accommodate the correlation. The group Lasso approach is adopted to select markers associated with all the outcome variables. An efficient computational algorithm is developed. Simulation study and analysis of the heterogeneous stock mice dataset show that the proposed method can outperform existing penalization methods. Citation: Liu J, Huang J, Ma S (2012) Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization. PLoS ONE 7(12): e51198. doi: 10.1371/journal.pone.0051198
 Multidisciplinary Sciences Science & Technology Science & Technology - Other Topics

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