Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction

Hao Cheng; Dorian J.Garrick; Rohan L.Fernando

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Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction

机译：用于基因组最佳线性无偏预测的留一法交叉验证的有效策略

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Background:A random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction,using whole-genome data.Leave-one-out cross validation can be used to quantify the predictive ability of a statistical model.Methods:Naive application of Leave-one-out cross validation is computationally intensive because the training and validation analyses need to be repeated n times,once for each observation.Efficient Leave-one-out cross validation strategies are presented here,requiring little more effort than a single analysis.Results:Efficient Leave-one-out cross validation strategies is 786 times faster than the naive application for a simulated dataset with 1,000 observations and 10,000 markers and 99 times faster with 1,000 observations and 1 00 markers.These efficiencies relative to the naive approach using the same model will increase with increases in the number of observations.Conclusions:Efficient Leave-one-out cross validation strategies are presented here,requiring little more effort than a single analysis.

机译：背景：利用全基因组数据，提出了一种随机多重回归模型，该模型同时拟合所有等位基因替代效应对加性标记或单倍型的不相关随机效应，以实现最佳线性无偏预测。可使用留一法交叉验证进行量化方法：留一法交叉验证的天真应用需要大量计算，因为训练和验证分析需要重复n次，每次观察一次。有效的留一法交叉验证策略结果：对于具有1,000个观察值和10,000个标记的模拟数据集，有效的留一法交叉验证策略比朴素应用程序快786倍，而对于1,000个观察值和1000个观察值则要快99倍。 1 00个标记。与使用相同模型的简单方法相比，这些效率将随着数字的增加而增加结论：结论：这里提出了高效的留一法交叉验证策略，与单次分析相比，所需的工作量很少。

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《畜牧与生物技术杂志(英文版)》 |2017年第3期|733-737|共5页
作者
Hao Cheng; Dorian J.Garrick; Rohan L.Fernando;
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Department of Animal Science,Iowa State University,50011,Ames,Iowa,USA;

Department of Statistics,Iowa State University,50011,Ames,Iowa,USA;

Department of Animal Science,Iowa State University,50011,Ames,Iowa,USA;

Institute of Veterinary,Animal & Biomedical Science,Massey University,Palmerston North,New Zealand;

Department of Animal Science,Iowa State University,50011,Ames,Iowa,USA;

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Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction

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