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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Acknowledgements
This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61520106006, 31571364, 61532008, 61572364, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61572447, and 61373098, China Postdoctoral Science Foundation Grant, Nos. 2014M561513 and 2015M580352.
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Yuan, L., Han, K., Huang, DS. (2016). Novel Algorithm for Multiple Quantitative Trait Loci Mapping by Using Bayesian Variable Selection Regression. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_80
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DOI: https://doi.org/10.1007/978-3-319-42297-8_80
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