Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits
Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits
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DOI：
10.1534/genetics.107.084285
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发表时间：
2008-04-01
期刊：
影响因子：
3.3
通讯作者：
van Kaam, Johannes B. C. H. M.
中科院分区：
文献类型：
--
作者：
Gianola, Daniel;van Kaam, Johannes B. C. H. M.
从理论的角度讨论了同时利用表型和基因组数据预测数量性状总遗传值的再现核希尔伯特空间回归方法。有人认为，可能需要一种非参数处理来捕获全基因组模型中可能出现的多重和复杂的相互作用，即基于数千个单核苷酸多态性（SNP）标记的模型。在回顾了核希尔伯特空间回归的再现后，表明统计规范允许标准的混合效应线性模型表示，平滑参数被视为方差成分。模型捕捉不同形式的相互作用，例如，染色体特异性，提出。实现可以使用基于似然或贝叶斯推理的软件来执行。
Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.