Journal article
Penalized multivariate linear mixed model for longitudinal genome-wide association studies
BMC proceedings, Vol.8(Suppl 1), pp.S73-S73
06/17/2014
DOI: 10.1186/1753-6561-8-S1-S73
PMCID: PMC4143695
PMID: 25519343
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.
Details
- Title: Subtitle
- Penalized multivariate linear mixed model for longitudinal genome-wide association studies
- Creators
- Jin Liu - University of Illinois ChicagoJian Huang - University of IowaShuangge Ma - Yale University
- Resource Type
- Journal article
- Publication Details
- BMC proceedings, Vol.8(Suppl 1), pp.S73-S73
- DOI
- 10.1186/1753-6561-8-S1-S73
- PMID
- 25519343
- PMCID
- PMC4143695
- NLM abbreviation
- BMC Proc
- ISSN
- 1753-6561
- eISSN
- 1753-6561
- Publisher
- BioMed Central
- Language
- English
- Date published
- 06/17/2014
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257628302771
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