A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data.

ABSTRACT:

Motivation

Recent advances in high dimensional phenotyping bring time as an extra dimension into the phenotypes. This promotes the quantitative trait locus (QTL) studies of function-valued traits such as those related to growth and development. Existing approaches for analyzing functional traits utilize either parametric methods or semi-parametric approaches based on splines and wavelets. However, very limited choices of software tools are currently available for practical implementation of functional QTL mapping and variable selection.

Results

We propose a Bayesian Gaussian process (GP) approach for functional QTL mapping. We use GPs to model the continuously varying coefficients which describe how the effects of molecular markers on the quantitative trait are changing over

SUBMITTER: Vanhatalo J 

PROVIDER: S-EPMC6761969 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature