使用稳健不对称分布的全基因组分析。

Luis Varona, Wagdy Mekkawy, Daniel Gianola, Agustín Blasco
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引用次数: 8

摘要

本研究旨在改进用于鉴定数量性状中标记相关效应的数据分析,特别是考虑性状数据可能偏离高斯分布的情况,并考虑亲本系之间表型差异导致的标记效应的不对称性。一个贝叶斯程序分析标记效应在全基因组水平提出。该方法采用偏态t分布作为标记效应的先验分布。具有偏态t过程的模型包括高斯先验分布、偏态高斯先验分布和对称t分布作为特例。给出了一种求未知数的边际后验分布的马尔可夫链蒙特卡罗算法。该方法应用于伊比利亚和长白猪F2杂交猪的三个性状(活重、胴体长度和背膘深度)数据集。标记效应在胴体长度和背膘深度上呈明显不对称分布,而在活重上呈对称分布。t分布似乎更适合于描述标记效应对背膘深度的分布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A whole-genome analysis using robust asymmetric distributions.

This study is aimed at improving the analysis of data used in identifying marker-associated effects on quantitative traits, specifically to account for possible departures from a Gaussian distribution of the trait data and to allow for asymmetry of marker effects attributable to phenotypic divergence between parental lines. A Bayesian procedure for analysing marker effects at the whole-genome level is presented. The procedure adopts a skewed t-distribution as a prior distribution of marker effects. The model with the skewed t-process includes Gaussian prior distributions, skewed Gaussian prior distributions and symmetric t-distributions as special cases. A Markov Chain Monte Carlo algorithm for obtaining marginal posterior distributions of the unknowns is also presented. The method was applied to a dataset on three traits (live weight, carcass length and backfat depth) measured in an F2 cross between Iberian and Landrace pigs. The distribution of marker effects was clearly asymmetric for carcass length and backfat depth, whereas it was symmetric for live weight. The t-distribution seems more appropriate for describing the distribution of marker effects on backfat depth.

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