Penalized multivariate linear mixed model for longitudinal genome-wide association studies
Liu, J Huang, J Ma, S
Huang, J
Ma, S
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Abstract
We consider analysis of Genetic Analysis Workshop 18 data, which involves multiple longitudinal traits and dense genome-wide single-nucleotide polymorphism (SNP) markers. We use a multivariate linear mixed model to account for the covariance of random effects and multivariate residuals. We divide the SNPs into groups according to the genes they belong to and score them using weighted sum statistics. We propose a penalized approach for genetic variant selection at the gene level. The overall modeling and penalized selection method is referred to as the penalized multivariate linear mixed model. Cross-validation is used for tuning parameter selection. A resampling approach is adopted to evaluate the relative stability of the identified genes. Application to the Genetic Analysis Workshop 18 data shows that the proposed approach can effectively select markers associated with phenotypes at gene level. © 2014 Liu et al.; licensee BioMed Central Ltd.
Keywords
Conference Paper, diastolic blood pressure, gene identification, genetic analysis, genetic variation, genome-wide association study, human, longitudinal study, mathematical computing, phenotype, single nucleotide polymorphism, statistical model, systolic blood pressure
Source Title
BMC Proceedings
Publisher
Series/Report No.
Collections
Rights
Attribution 4.0 International
Date
2014
DOI
10.1186/1753-6561-8-S1-S73
Type
Conference Paper