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Robust Bayesian mapping of quantitative trait loci using Student-t distribution for residual

文献类型： 外文期刊

作者： Wang, Xin 1 ; Piao, Zhongze 2 ; Wang, Biye 1 ; Yang, Runqing 1 ; Luo, Zhixiang 3 ;

作者机构： 1.Shanghai Jiao Tong Univ, Sch Agr & Biol, Shanghai 200240, Peoples R China

2.Shanghai Acad Agr Sci, Crop Breeding & Cultivat Res Inst, Shanghai 201106, Peoples R China

3.Anhui Acad Agr, Rice Res Inst, Hefei 230036, Peoples R China

期刊名称：THEORETICAL AND APPLIED GENETICS （ 影响因子：5.699； 五年影响因子：5.565 ）

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收录情况： SCI

摘要： In most quantitative trait loci (QTL) mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection, leading to detection of false positive QTL. To improve the robustness of QTL mapping methods, we replace the normal distribution assumption for residuals in a multiple QTL model with a Student-t distribution that is able to accommodate residual outliers. A Robust Bayesian mapping strategy is proposed on the basis of the Bayesian shrinkage analysis for QTL effects. The simulations show that Robust Bayesian mapping approach can substantially increase the power of QTL detection when the normality assumption does not hold and applying it to data already normally distributed does not influence the result. The proposed QTL mapping method is applied to mapping QTL for the traits associated with physics-chemical characters and quality in rice. Similarly to the simulation study in the real data case the robust approach was able to detect additional QTLs when compared to the traditional approach. The program to implement the method is available on request from the first or the corresponding author.

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