Radial basis function regression methods for predicting quantitative traits using SNP markers
Radial basis function regression methods for predicting quantitative traits using SNP markers
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DOI：
10.1017/s0016672310000157
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发表时间：
2010-06-01
影响因子：
1.5
通讯作者：
Gonzalez-Recio, Oscar
中科院分区：
文献类型：
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作者：
Long, Nanye;Gianola, Daniel;Gonzalez-Recio, Oscar
在预测复杂数量性状的总遗传值时的一个挑战是，未知数量的数量性状基因座可能通过隐性相互作用影响表型。如果标记是可用的，假设它们对表型的影响是加性的，可能会导致预测能力差。通过对肉鸡体重和饲料转化率数据的模拟和分析，研究了非参数径向基函数（RBF）回归方法，该方法不假设基因型/表型关系的特定形式。模拟包括一个玩具的例子，其中一个任意的非线性基因型表型关系被假定，并创建了五个不同的情况下，代表不同的广义遗传力水平（0.1，0.25，0.5，0.75和0.9）。此外，还进行了全基因组模拟，其中考虑了三种不同的基因作用模式（纯加性、加性+显性和纯上位性）。在所有分析中，训练集用于拟合模型，测试集用于评价预测性能。后者通过测试数据的相关性和预测均方误差（PMSE）来测量。为了进行比较，使用称为贝叶斯A的线性加性模型作为基准。检查了具有单核苷酸多态性（SNP）特异性（RBF I）和共同（RBF II）权重的两个RBF模型。结果表明，在复杂的基因型表型关系（即非线性和非加性）的存在下，RBF优于贝叶斯A在预测总的遗传值使用SNP标记。将贝叶斯A推广到包括所有的加性、显性和上位性效应，可以提高其预测精度。RBF I通常优于RBF II，并且能够在玩具示例中识别相关SNP。
A challenge when predicting total genetic values for complex quantitative traits is that an unknown number of quantitative trait loci may affect phenotypes via cryptic interactions. If markers are available, assuming that their effects on phenotypes are additive may lead to poor predictive ability. Non-parametric radial basis function (RBF) regression, which does not assume a particular form of the genotype phenotype relationship, was investigated here by simulation and analysis of body weight and food conversion rate data in broilers. The simulation included a toy example in which an arbitrary non-linear genotype phenotype relationship was assumed, and five different scenarios representing different broad sense heritability levels (0.1, 0.25, 0.5, 0.75 and 0.9) were created. In addition, a whole genome simulation was carried out, in which three different gene action modes (pure additive, additive +dominance and pure epistasis) were considered. In all analyses, a training set was used to fit the model and a testing set was used to evaluate predictive performance. The latter was measured by correlation and predictive mean-squared error (PMSE) on the testing data. For comparison, a linear additive model known as Bayes A was used as benchmark. Two RBF models with single nucleotide polymorphism (SNP)-specific (RBF I) and common (RBF II) weights were examined. Results indicated that, in the presence of complex genotype phenotype relationships (i.e. non-linearity and non-additivity), RBF outperformed Bayes A in predicting total genetic values using SNP markers. Extension of Bayes A to include all additive, dominance and epistatic effects could improve its prediction accuracy. RBF I was generally better than RBF II, and was able to identify relevant SNPs in the toy example.