Genomic-assisted prediction of genetic value with semiparametric procedures
Genomic-assisted prediction of genetic value with semiparametric procedures
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DOI：
10.1534/genetics.105.049510
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发表时间：
2006-07-01
期刊：
影响因子：
3.3
通讯作者：
Stella, Alessandra
中科院分区：
文献类型：
--
作者：
Gianola, Daniel;Fernando, Rohan L.;Stella, Alessandra
提出了同时利用表型和基因组数据预测数量性状总遗传值的半参数方法。这些方法侧重于处理由单核苷酸多态性等提供的大量信息。有人认为，用于定量遗传分析的标准参数方法无法处理具有数十万个标记的模型中产生的潜在相互作用的多样性，并且在人工和自然群体中，方差正交分解所需的大多数假设都被违反了。这使得非参数过程具有吸引力。核回归和重现核希尔伯特空间回归过程嵌入到标准混合效应线性模型中，出于操作原因，保留了多元正态性下的可加性遗传效应。给出了推理程序，并提出了一些扩展建议。给出了一个例子，说明了该方法的潜力。实施可以在修改由动物育种者开发的基于似然或贝叶斯分析的标准软件后进行。
Semiparametric procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are presented. The methods focus on the treatment of massive information provided by, e.g., single-nucleotide polymorphisms. It is argued that standard parametric methods for quantitative genetic analysis cannot handle the multiplicity of potential interactions arising in models with, e.g., hundreds of thousands of markers, and that most of the assumptions required for an orthogonal decomposition of variance are violated in artificial and natural populations. This makes nonparametric procedures attractive. Kernel regression and reproducing kernel Hilbert spaces regression procedures are embedded into standard mixed-effects linear models, retaining additive genetic effects under multivariate normality for operational reasons. Inferential procedures are presented, and some extensions are suggested. An example is presented, illustrating the potential of the methodology. Implementations can be carried out after modification of standard software developed by animal breeders for likelihood-based or Bayesian analysis.