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2016 | OriginalPaper | Chapter

Novel Algorithm for Multiple Quantitative Trait Loci Mapping by Using Bayesian Variable Selection Regression

Authors : Lin Yuan, Kyungsook Han, De-Shuang Huang

Published in: Intelligent Computing Methodologies

Publisher: Springer International Publishing

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Abstract

Most quantitative trait loci (QTL) mapping experiments typically collect phenotypic data on single traits. However, Research complex correlated traits may provide more available information. We develop a novel algorithm for multiple traits quantitative trait loci mapping by using Bayesian Variable Selection Regression, or BVSR, that allows a new robust genetic models for different and correlated traits. We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performing joint analysis. Taken together, these factors put a premium on having interpretable measures of confidence for individual covariates being included in the model. We conduct extensive simulation studies to assess the performance of the proposed methods and to compare with the conventional single-trait model and existing multiple-trait model. More generally, we demonstrate that, despite the apparent computational challenges, our proposed new algorithm can provide useful inferences in quantitative trait loci mapping.

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previous chapter Identification of HOT Regions in the Human Genome Using Differential Chromatin Modifications
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Metadata
Title
Novel Algorithm for Multiple Quantitative Trait Loci Mapping by Using Bayesian Variable Selection Regression
Authors
Lin Yuan
Kyungsook Han
De-Shuang Huang
Copyright Year
2016
Book
Intelligent Computing Methodologies

Print ISBN: 978-3-319-42296-1

Electronic ISBN: 978-3-319-42297-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-42297-8_80

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