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A predictive assessment of genetic correlations between traits in chickens using markers

机译：利用标记对鸡性状之间遗传相关性的预测评估

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Genomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations. A multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λ G + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation. Estimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0  λ  1, i.e., when both sources of information were used together. Our findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.
机译：基因组选择已在动植物育种计划中成功实施，以缩短世代间隔并加快每单位时间的遗传进程。在实践中，基因组选择可用于通过多性状预测同时改善几个相关性状，从而利用性状之间的相关性。但是，很少有研究探索多特征基因组选择。我们的目的是通过基于血统和基于标记的亲缘关系的线性组合，探索亲属矩阵，从而推断出肉鸡所测三个性状之间的遗传相关性。使用预测评估来评估遗传相关性。设计了多变量基因组最佳线性无偏预测模型，以结合来自谱系和全基因组标记的信息，以便评估鸡的三种复杂性状之间的遗传相关性，即35日龄体重（BW），乳房超声区域（BM）和鸡舍产蛋量（HHP）。使用了一个具有6001 K Affymetrix平台基因分型的1351只鸟类的数据集。亲属核（K）构造为K =λG +（1-λ）A，其中A是分子关系矩阵，用于测量基于谱系的相关性，而G是基因组关系矩阵。分配给每个信息源的权重（λ）在网格λ=（0，0.2，0.4，0.6，0.8，1）上变化。在每个λ处获得遗传和遗传相关性的最大似然估计，并使用交叉验证确定“最佳”λ。遗传相关性的估计值受用于构建K的信息源的权重的影响。例如，当λ从0（仅用于测量相关性的A）变为λ时，BW–HHP和BM–HHP之间的遗传相关性发生了显着变化。 1（仅使用基因组信息）。随着λ的增加，预测相关性（观察到的表型与预测育种值之间的相关性）增加，并且均方预测误差降低。但是，预测能力的提高并不是单调的，最佳的发现是在0 <λ<1处，即当两种信息源一起使用时。我们的发现表明，多特征预测可能会受益于谱系和标记信息的组合。同样，似乎从标准理论计算得出的对选择的预期相关响应可能与实际响应有所不同。预测性评估提供了绩效评估的指标，以及表达多特征选择结果不确定性的手段。

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