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Including non-additive genetic effects in Bayesian methods for the prediction of genetic values based on genome-wide markers

机译:在基于全基因组标记的贝叶斯方法中包括非加性遗传效应以预测遗传值

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摘要

BackgroundMolecular marker information is a common source to draw inferences about the relationship between genetic and phenotypic variation. Genetic effects are often modelled as additively acting marker allele effects. The true mode of biological action can, of course, be different from this plain assumption. One possibility to better understand the genetic architecture of complex traits is to include intra-locus (dominance) and inter-locus (epistasis) interaction of alleles as well as the additive genetic effects when fitting a model to a trait. Several Bayesian MCMC approaches exist for the genome-wide estimation of genetic effects with high accuracy of genetic value prediction. Including pairwise interaction for thousands of loci would probably go beyond the scope of such a sampling algorithm because then millions of effects are to be estimated simultaneously leading to months of computation time. Alternative solving strategies are required when epistasis is studied.
机译:背景分子标记信息是得出关于遗传和表型变异之间关系的推论的常见来源。遗传效应通常被建模为加性标记等位基因效应。当然,生物作用的真实模式可能与这种简单的假设有所不同。更好地了解复杂性状的遗传结构的一种可能性是包括等位基因的基因座内(优势)和基因座间(表位)相互作用以及将模型拟合到性状时的附加遗传效应。存在几种贝叶斯MCMC方法,可以以遗传值预测的高精度实现全基因组范围的遗传效应估计。包括成千上万个基因座的成对相互作用可能会超出此类采样算法的范围,因为随后将同时估计数百万个效应,从而导致数月的计算时间。研究上位性时,需要其他解决方案。

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