Genomic Selection Using Low-Density Marker Panels
Genomic Selection Using Low-Density Marker Panels
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DOI：
10.1534/genetics.108.100289
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发表时间：
2009-05-01
期刊：
影响因子：
3.3
通讯作者：
Dekkers, J. C. M.
中科院分区：
文献类型：
--
作者：
Habier, D.;Fernando, R. L.;Dekkers, J. C. M.
利用高密度单核苷酸多态性（SNPs）进行基因组选择（GS）有望改善人工选择下种群对选择的反应。然而，每一代所有选择候选物的高密度SNP基因分型可能不具有成本效益。可以使用具有显示与表型强关联的SNP的较小的组，但是这可能需要针对每个性状和每个群体的单独的SNP。作为一种替代方案，我们建议使用一组均匀分布的低密度SNPs在整个基因组中估计基因组辅助育种值的选择候选人在家系人口。该方法的原理是利用来自低密度SNP的共分离信息来跟踪高密度SNP等位基因在家族内的效应。使用模拟来分析与在贝叶斯分析中使用所有高密度SNP相比，使用均匀间隔和选择的SNP组估计育种值的准确性损失。使用前向逐步选择和贝叶斯方法来选择SNP。准确性的损失几乎与模拟的具有均匀间隔的SNP的数量性状基因座（QTL）的数量无关，但随着所选SNP组的QTL数量的增加而增加。随着世代的推移，均匀间隔的SNP的准确性损失稳步增加，但当每一代选择用于育种的较小数量的个体也使用高密度SNP面板进行基因分型时，准确性损失是恒定的。在相同数量的低密度SNP的情况下，基于贝叶斯方法选择SNP的小组对单个性状的准确性损失最小，但在10 cM处具有均匀间隔的SNP的小组仅略差，而通过向前逐步选择选择SNP的小组较差。然而，具有均匀间隔的SNP的面板可以跨性状和群体使用，并且它们的表现与影响性状的QTL的数量无关。和用于估计训练数据中的效应的方法，因此，在人工选择下的家系群体中的广泛应用是优选的。
Genomic selection (GS) using high-density single-nucleotide polymorphisms (SNPs) is promising to improve response to selection in populations that are under artificial selection. High-density SNP genotyping of all selection candidates each generation, however, may not be cost effective. Smaller panels with SNPs that show strong associations with phenotype can be used, but this may require separate SNPs for each trait and each population. As an alternative, we propose to use a panel of evenly spaced low-density SNPs across the genome to estimate genome-assisted breeding values of selection candidates in pedigreed populations. The principle of this approach is to utilize cosegregation information from low-density SNPs to track effects of high-density SNP alleles within families. Simulations were used to analyze the loss of accuracy of estimated breeding values from using evenly spaced and selected SNP panels compared to using all high-density SNPs in a Bayesian analysis. Forward stepwise selection and a Bayesian approach were used to select SNPs. Loss of accuracy was nearly independent of the number of simulated quantitative trait loci (QTL) with evenly spaced SNPs, but increased with number of QTL For the selected SNP panels. Loss of accuracy with evenly spaced SNPs increased steadily over generations but was constant when the smaller number individuals that are selected for breeding each generation were also genotyped using the high-density SNP panel. With equal numbers of low-density SNPs, panels with SNPs selected on the basis of the Bayesian approach had the smallest loss in accuracy for a single trait, but a panel with evenly spaced SNPs at 10 cM was only slightly worse, whereas a panel with SNPs selected by forward stepwise selection was inferior. Panels with evenly spaced SNPs can, however, be used across traits and populations and their performance is independent of the number of QTL affecting the trait. and of the methods used to estimate effects in the training data and are, therefore, preferred for broad applications in pedigreed Populations under artificial selection.