Sparse variable and covariance selection for high-dimensional seemingly unrelated Bayesian regression

BioRxiv : the Preprint Server for Biology
Marco BanterleAlex Lewin


High-throughput technology for molecular biomarkers is increasingly producing multivariate phenotype data exhibiting strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate Quantitative Trait Loci analysis generally either ignore correlation structure or make other restrictive assumptions about the associations between phenotypes and genetic loci. We present a Bayesian Variable Selection (BVS) model with sparse variable and covariance selection for high-dimensional seemingly unrelated regressions. The model includes a matrix of binary variable selection indicators for multivariate regression, thus allowing different phenotype responses to be associated with different genetic predictors (a seemingly unrelated regressions framework). A general covariance structure is allowed for the residuals relating to the conditional dependencies between phenotype variables. The covariance structure may be dense (unrestricted) or sparse, with a graphical modelling prior. The graphical structure amongst the multivariate responses can be estimated as part of the model. To achieve feasible computation of the large and complex model space, we exploit a factorisation of the covariance matrix...Continue Reading

Related Concepts

Biological Markers
Anatomical Space Structure
Genetic Loci
Gene Polymorphism
Single Nucleotide Polymorphism

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