Prediction of breed composition in an admixed cattle population
Prediction of breed composition in an admixed cattle population
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DOI：
10.1111/j.1365-2052.2012.02345.x
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发表时间：
2012-12-01
期刊：
影响因子：
2.4
通讯作者：
Soelkner, J.
中科院分区：
文献类型：
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作者：
Frkonja, A.;Gredler, B.;Soelkner, J.
Swiss Fleckvieh成立于1970年，是西门塔尔牛（SI）和红荷斯坦黑白花牛（RHF）的混合牛。品种组成目前报告的基础上系谱信息。关于大量分子标记的信息可能提供更准确的信息。为了进行分析，我们使用Illumina BovineSNP 50基因分型珠芯片数据，用于90头纯SI、100头纯RHF和305头混合公牛。该研究的范围是比较隐马尔可夫模型的性能，如结构软件中实现的，与基因组选择中常用的方法[贝叶斯B，偏最小二乘回归（PLSR），最小绝对收缩和选择算子（LASSO）变量选择）]预测品种组成。我们检查了一组40 492个单核苷酸多态性（SNP），均匀分布的SNP的子集和纯人群中具有不同等位基因频率的子集的算法的性能，使用FST作为指标。关键结果是混合物水平的相关性估计与各种算法与混合物的基础上系谱信息。对于全集合，PLSR、贝叶斯B和结构以非常相似的方式进行（相关性为0.97），而LASSO和谱系混合物的相关性较低（0.93）。随着SNP数量的减少，相关性仅在所有SNP中的5%或1%显著降低。对于根据FST选择的SNP，结果与用全集获得的结果相似。只有当使用具有最高FST的96和48个SNP时，相关性分别下降到0.92和0.90。将训练集中的纯动物数量减少到50、20和10只，并没有导致与谱系混合物的相关性下降。
Swiss Fleckvieh was established in 1970 as a composite of Simmental (SI) and Red Holstein Friesian (RHF) cattle. Breed composition is currently reported based on pedigree information. Information on a large number of molecular markers potentially provides more accurate information. For the analysis, we used Illumina BovineSNP50 Genotyping Beadchip data for 90 pure SI, 100 pure RHF and 305 admixed bulls. The scope of the study was to compare the performance of hidden Markov models, as implemented in structure software, with methods conventionally used in genomic selection [BayesB, partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO) variable selection)] for predicting breed composition. We checked the performance of algorithms for a set of 40 492 single nucleotide polymorphisms (SNPs), subsets of evenly distributed SNPs and subsets with different allele frequencies in the pure populations, using FST as an indicator. Key results are correlations of admixture levels estimated with the various algorithms with admixture based on pedigree information. For the full set, PLSR, BayesB and structure performed in a very similar manner (correlations of 0.97), whereas the correlation of LASSO and pedigree admixture was lower (0.93). With decreasing number of SNPs, correlations decreased substantially only for 5% or 1% of all SNPs. With SNPs chosen according to FST, results were similar to results obtained with the full set. Only when using 96 and 48 SNPs with the highest FST, correlations dropped to 0.92 and 0.90 respectively. Reducing the number of pure animals in training sets to 50, 20 and 10 each did not cause a drop in the correlation with pedigree admixture.