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首页> 外文期刊>BMC proceedings. >Linear models for breeding values prediction in haplotype-assisted selection - an analysis of QTL-MAS Workshop 2011 Data
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Linear models for breeding values prediction in haplotype-assisted selection - an analysis of QTL-MAS Workshop 2011 Data

机译：单倍型选择中育种值预测的线性模型-QTL-MAS Workshop 2011数据分析

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Background The aim of this study was to estimate haplotype effects and then to predict breeding values using linear models. The haplotype based analysis enables avoidance of loosing information due to linkage disequilibrium between single markers. There are also less explanatory variables in the linear model which makes the estimation more reliable. Methods Different methods and criteria for marker and haplotype selection were considered. First, markers with MAF lower than 5% where excluded from the data set. Then, SNPs in complete linkage disequilibrium where selected. Next step was to construct haplotypes and to estimate their frequencies basing on selected SNPs . The haplotypes with a frequency lower than 1% were not considered in further analysis. Chosen haplotypes were used as the explanatory variables in the linear models for breeding values prediction. Linear models with fixed and random haplotype effects as well as animal model were tested. Results The number of markers was limited to 1206, 1189, 1249, 1288 and 1167 for chromosome 1, 2, 3, 4 and 5, respectively due to MAF criterion. In total 409 subsets of SNPs with r2=1 were found. 1476 haplotypes with different lengths were inferred. The frequencies of 817 haplotypes were higher than 1% - 184 for the first chromosome, 172 for the second, 131 for the third, 146 for the forth and 184 haplotypes for the fifth chromosome. The haplotype effects estimated using random models were comparable and more precise in prediction for individuals with unknown phenotypes. A few haplotypes with large effects were found when their effects were defined as fixed in the linear model . The correlations of the predicted breeding values with true breeding values were not that high. This could be brought about by selection criteria imposed on the genotype data which led to substantial reduction of number of markers. Conclusions Although not many markers were considered in the study, the results obtained show that the implemented approach can be considered as quite promising. The haplotype approach let to avoid high dimensional models as compared with single SNPs models.
机译：背景技术这项研究的目的是估计单倍型效应，然后使用线性模型预测育种值。基于单倍型的分析能够避免由于单个标记之间的连锁不平衡而导致信息丢失。线性模型中的解释变量也较少，这使估算更加可靠。方法考虑标记和单倍型选择的不同方法和标准。首先，从数据集中排除MAF低于5％的标记。然后，选择完全连锁不平衡的SNP。下一步是构建单倍型并根据选定的SNP估计其频率。在进一步分析中未考虑频率低于1％的单倍型。选择的单倍型被用作线性模型中用于繁殖值预测的解释变量。测试具有固定和随机单倍型效应的线性模型以及动物模型。结果根据MAF标准，染色体1、2、3、4和5的标记数量分别限制为1206、1189、1249、1288和1167。在总共409个子集中找到了r 2 = 1的SNP。推断出1476个不同长度的单倍型。 817个单倍型的频率高于1％-第一个染色体184个，第二个172个，第三个131个，第四个146个，第五个染色体184个。使用随机模型估计的单倍型效应具有可比性，并且在未知表型个体的预测中更精确。当线性模型中将它们的作用定义为固定时，会发现一些具有较大作用的单体型。预测育种值与真实育种值的相关性不是很高。这可以通过强加于基因型数据的选择标准来实现，该选择标准导致标记数量的大幅减少。结论尽管研究中考虑的标志物不多，但获得的结果表明，实施的方法可以被认为是很有前途的。与单个SNPs模型相比，单倍型方法可以避免高维模型。

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