A Bayesian Framework for Robust Quantitative Trait Locus Mapping and Outlier Detection

Mutshinda Crispin M. (), Irwin Andrew J. () and Sillanpää Mikko J. ()
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Mutshinda Crispin M.: Department of Mathematics and Statistics, Dalhousie University, 6316 Coburg Road, Halifax, Nova Scotia B3H 4R2, Canada
Irwin Andrew J.: Department of Mathematics and Statistics, Dalhousie University, 6316 Coburg Road, Halifax, Nova Scotia B3H 4R2, Canada
Sillanpää Mikko J.: Department of Mathematical Sciences, University of Oulu, Oulu, Finland

The International Journal of Biostatistics, 2020, vol. 16, issue 2, 15

Abstract: We introduce a Bayesian framework for simultaneous feature selection and outlier detection in sparse high-dimensional regression models, with a focus on quantitative trait locus (QTL) mapping in experimental crosses. More specifically, we incorporate the robust mean shift outlier handling mechanism into the multiple QTL mapping regression model and apply LASSO regularization concurrently to the genetic effects and the mean-shift terms through the flexible extended Bayesian LASSO (EBL) prior structure, thereby combining QTL mapping and outlier detection into a single sparse model representation problem. The EBL priors on the mean-shift terms prevent outlying phenotypic values from distorting the genotype-phenotype association and allow their detection as cases with outstanding mean shift values following the LASSO shrinkage. Simulation results demonstrate the effectiveness of our new methodology at mapping QTLs in the presence of outlying phenotypic values and simultaneously identifying the potential outliers, while maintaining a comparable performance to the standard EBL on outlier-free data.

Keywords: extended Bayesian LASSO; Hamiltonian Monte Carlo; mean-shift outlier model; QTL mapping; Stan (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/ijb-2019-0038

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