Bayesian QTL mapping using skewed Student-t distributions.

Peter von Rohr; Ina Hoeschele

doi:10.1186/1297-9686-34-1-1

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Bayesian QTL mapping using skewed Student-t distributions.

Peter von Rohr·Ina Hoeschele

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Abstract

In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.

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Algorithms Alleles Bayes Theorem Chi-Square Distribution Chromosome Mapping Computer Simulation Genotype Humans Inbreeding Markov Chains Models, Genetic Monte Carlo Method Pedigree Phenotype Quantitative Trait, Heritable Statistical Distributions Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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Peter von Rohr, Ina Hoeschele. Bayesian QTL mapping using skewed Student-t distributions.. https://doi.org/10.1186/1297-9686-34-1-1

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