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

Crispin M Mutshinda1, Andrew J Irwin1, Mikko J Sillanpää2

  • 1Department of Mathematics and Statistics, Dalhousie University, 6316 Coburg Road, Halifax, Nova Scotia B3H 4R2, Canada.

The International Journal of Biostatistics
|February 16, 2020
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Summary
This summary is machine-generated.

This study presents a Bayesian method for simultaneously selecting genetic features and detecting outliers in high-dimensional regression, improving quantitative trait locus (QTL) mapping accuracy in experimental crosses.

Keywords:
Hamiltonian Monte CarloQTL mappingStanextended Bayesian LASSOmean-shift outlier model

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-dimensional regression models are crucial for genetic analysis.
  • Outlying phenotypic values can distort genotype-phenotype associations.
  • Simultaneous feature selection and outlier detection remain challenging.

Purpose of the Study:

  • To develop a Bayesian framework for integrated quantitative trait locus (QTL) mapping and outlier detection.
  • To address the challenge of outlying phenotypic data in sparse high-dimensional regression.
  • To enhance the accuracy of QTL mapping in experimental crosses.

Main Methods:

  • Incorporation of a robust mean shift outlier handling mechanism.
  • Application of LASSO regularization to genetic effects and mean-shift terms.
  • Utilizing the extended Bayesian LASSO (EBL) prior structure for sparse modeling.

Main Results:

  • The proposed method effectively maps QTLs in the presence of outlying phenotypic values.
  • Simultaneous identification of potential outliers alongside QTL mapping.
  • Comparable performance to standard EBL on outlier-free data.

Conclusions:

  • The developed Bayesian framework offers a robust solution for QTL mapping with outlier detection.
  • This integrated approach prevents outlying data from compromising genetic association studies.
  • The methodology enhances the reliability of genetic analyses in experimental populations.