Ryan C A, Berry D P, O'Brien A, Pabiou T, Purfield D C
Teagasc, Co. Cork, Ireland.
Munster Technological University, Cork, Ireland.
Front Genet. 2023 May 15;14:1120312. doi: 10.3389/fgene.2023.1120312. eCollection 2023.
The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition. All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle. When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels <2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction. Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software.
利用基因组信息准确预测品种组成的能力有许多潜在用途，包括提高遗传评估的准确性、优化配种计划以及作为基因型质量控制的一个参数。本研究的目的是使用一个基因分型的纯种和杂交牛数据库，比较使用免费软件Admixture预测品种组成与单核苷酸多态性最佳线性无偏预测（SNP-BLUP）方法预测品种组成的结果；另一个目的是确定低密度基因型面板预测品种组成的准确性和总体稳健性。所有动物都有49,213个常染色体单核苷酸多态性（SNP）的基因型信息。分析中包括13个品种，每个品种使用500头纯种动物来建立品种训练群体。使用一个由3,146头经核实的纯种牛和4,330头二元和三元杂交牛组成的单独验证群体来确定品种组成预测的准确性。当使用所有49,213个常染色体SNP进行品种预测时，Admixture与SNP-BLUP品种预测之间的最小绝对平均差异为0.04。对于杂交牛，使用SNP-BLUP和Admixture生成的品种预测估计值的平均绝对差异为0.068，均方根误差为0.08。使用SNP-BLUP和Admixture从低密度SNP面板生成品种预测，并与使用所有49,213个SNP（代表金标准）的品种预测估计值进行比较。预测杂交牛的品种组成比预测纯种牛的品种组成需要更多的SNP。SNP-BLUP预测杂交牛品种组成需要≥3,000个SNP，但预测纯种牛品种状态仅需2,000个SNP。当使用Admixture和SNP-BLUP分别与金标准预测相比预测所有动物的品种组成时，所有<2,000个SNP的面板的绝对平均（标准差）差异分别为0.091（0.054）和0.315（0.316）。然而，一旦考虑≥3,000个SNP，SNP-BLUP和Admixture在品种预测中的绝对平均（标准差）差异可忽略不计，为0.009（0.123），这表明品种组成的预测可以很容易地整合到用于基因组评估的SNP-BLUP流程中，从而避免了使用独立软件的必要性。
