Habier D, Fernando R L, Dekkers J C M
Institute of Animal Breeding and Husbandry, Christian-Albrechts University of Kiel, Kiel, Germany.
Genetics. 2009 May;182(1):343-53. doi: 10.1534/genetics.108.100289. Epub 2009 Mar 18.
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.
利用高密度单核苷酸多态性（SNP）进行基因组选择（GS）有望改善人工选择群体对选择的响应。然而，每一代对所有选择候选个体进行高密度SNP基因分型可能不具有成本效益。可以使用与表型有强关联的SNP组成的较小面板，但这可能需要针对每个性状和每个群体使用单独的SNP。作为一种替代方法，我们建议使用全基因组均匀间隔的低密度SNP面板来估计系谱群体中选择候选个体的基因组辅助育种值。这种方法的原理是利用低密度SNP的共分离信息来追踪家系内高密度SNP等位基因的效应。通过模拟分析了在贝叶斯分析中，与使用所有高密度SNP相比，使用均匀间隔和选定的SNP面板估计育种值时准确性的损失。采用向前逐步选择和贝叶斯方法来选择SNP。对于均匀间隔的SNP，准确性损失几乎与模拟数量性状位点（QTL）的数量无关，但对于选定的SNP面板，准确性损失随QTL数量增加。均匀间隔SNP的准确性损失在几代中稳步增加，但当每代选择用于育种的较少数个体也使用高密度SNP面板进行基因分型时，准确性损失保持不变。在低密度SNP数量相等的情况下，基于贝叶斯方法选择SNP组成的面板对于单个性状的准确性损失最小，但间隔为10厘摩（cM）的均匀间隔SNP面板仅略差，而通过向前逐步选择选择SNP组成的面板则较差。然而，均匀间隔SNP组成的面板可用于不同性状和群体，其性能与影响性状的QTL数量以及用于估计训练数据中效应的方法无关，因此更适合在人工选择的系谱群体中广泛应用
