Data from: Integrating nonadditive genomic relationship matrices into the study of genetic architecture of complex traits

The study of genetic architecture of complex traits has been dramatically influenced by implementing genome-wide analytical approaches during recent years. Of particular interest are genomic prediction strategies which make use of genomic information for predicting phenotypic responses instead of detecting trait-associated loci. In this work, we present the results of a simulation study to improve our understanding of the statistical properties of estimation of genetic variance components of complex traits, and of additive, dominance, and genetic effects through best linear unbiased prediction methodology. Simulated dense marker information was used to construct genomic additive and dominance matrices, and multiple alternative pedigree- and marker-based models were compared to determine if including a dominance term into the analysis may improve the genetic analysis of complex traits. Our results showed that a model containing a pedigree- or marker-based additive relationship matrix along with a pedigree-based dominance matrix provided the best partitioning of genetic variance into its components, especially when some degree of true dominance effects was expected to exist. Also, we noted that the use of a marker-based additive relationship matrix along with a pedigree-based dominance matrix had the best performance in terms of accuracy of correlations between true and estimated additive, dominance, and genetic effects.

近年来，全基因组分析方法的应用极大地推动了复杂性状遗传结构的研究。其中尤为受关注的是基因组预测策略——这类方法借助基因组信息预测表型响应，而非仅检测性状关联位点。本研究通过模拟研究，旨在深化对复杂性状遗传方差组分估计的统计特性的理解，并明晰借助最佳线性无偏预测(Best Linear Unbiased Prediction, BLUP)方法解析加性、显性及遗传效应的相关原理。研究采用模拟高密度标记信息构建基因组加性与显性矩阵，对比了多种基于系谱和标记的备选模型，以探究在分析中纳入显性项是否可优化复杂性状的遗传分析。研究结果显示，同时纳入基于系谱或标记的加性关系矩阵与基于系谱的显性关系矩阵的模型，能够最佳地将遗传方差分解为各组分，尤其在预期存在一定程度真实显性效应的场景中表现更优。此外，我们发现结合使用基于标记的加性关系矩阵与基于系谱的显性关系矩阵的模型，在真实与估计的加性、显性及遗传效应间的相关性精度方面性能最佳。