USDA-ARS U.S. Meat Animal Research Center, Clay Center, NE 68933, USA.
J Anim Sci. 2013 Feb;91(2):537-52. doi: 10.2527/jas.2012-5784. Epub 2012 Oct 24.
Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by functionally informed genomic predictions being more accurate than naive whole-genome predictions of beef tenderness, coupling evidence from livestock genotypes, phenotypes, gene expression, and genomic variants with existing knowledge of gene functions and interactions may provide greater insight into the genes and genomic mechanisms affecting polygenic traits and facilitate functional genomic selection for economically important traits.
许多影响肉、奶和纤维生产盈利能力和可持续性的特性是多基因的，没有单个基因对观察到的变异有压倒性的影响。在没有了解控制这些特性的特定基因的情况下，通过选择已经取得了实质性的改进。通过表型选择取得了重大进展，这种选择通过系谱关系得到增强，并且统计方法不断得到改进。基因组选择最近通过对整个基因组中密集 SNP 的检测得以实现，有望通过强调与表型最密切相关的 SNP 来提高选择准确性并加速遗传改良，尽管影响表型的基因和序列变异仍然很大程度上未知。这些基因组预测理论上依赖于 SNP 基因型与未知功能变异之间的连锁不平衡（LD），但当预测与训练数据中个体相关的个体时，家族连锁可能会提高预测的有效性。具有功能 SNP 基因型的基因组选择应该较少依赖于训练和目标群体共享的 LD 模式，这可能允许在不相关的群体中进行稳健的预测。尽管引起多基因变异的特定变体可能永远无法确定，但有许多工具和资源可用于识别最有可能影响表型的变体。密集 SNP 基因型与表型的关联为识别影响特定性状的基因提供了一种一维方法；相反，与多个性状的关联允许定义相互作用以影响相关性状的基因网络。当这些网络得到现有功能注释和已建立的分子途径的证实时，它们就特别引人注目。可以根据对基因产物的预期影响，根据 SNP 基因型在网络基因内的位置，对其进行分类。如通过功能信息丰富的基因组预测比牛肉嫩度的简单全基因组预测更准确地说明，将来自家畜基因型、表型、基因表达和基因组变异的证据与基因功能和相互作用的现有知识相结合，可能会深入了解影响多基因性状的基因和基因组机制，并促进对经济上重要性状的功能基因组选择。
