BACKGROUND Whole-genome sequence (WGS) data could contain information on genetic variants at or in high linkage disequilibrium with causative mutations that underlie the genetic variation of polygenic traits. Thus far, genomic prediction accuracy has shown limited increase when using such information in dairy cattle studies, in which one or few breeds with limited diversity predominate. The objective of our study was to evaluate the accuracy of genomic prediction in a multi-breed Australian sheep population of relatively less related target individuals, when using information on imputed WGS genotypes. METHODS Between 9626 and 26,657 animals with phenotypes were available for nine economically important sheep production traits and all had WGS imputed genotypes. About 30% of the data were used to discover predictive single nucleotide polymorphism (SNPs) based on a genome-wide association study (GWAS) and the remaining data were used for training and validation of genomic prediction. Prediction accuracy using selected variants from imputed sequence data was compared to that using a standard array of 50k SNP genotypes, thereby comparing genomic best linear prediction (GBLUP) and Bayesian methods (BayesR/BayesRC). Accuracy of genomic prediction was evaluated in two independent populations that were each lowly related to the training set, one being purebred Merino and the other crossbred Border Leicester x Merino sheep. RESULTS A substantial improvement in prediction accuracy was observed when selected sequence variants were fitted alongside 50k genotypes as a separate variance component in GBLUP (2GBLUP) or in Bayesian analysis as a separate category of SNPs (BayesRC). From an average accuracy of 0.27 in both validation sets for the 50k array, the average absolute increase in accuracy across traits with 2GBLUP was 0.083 and 0.073 for purebred and crossbred animals, respectively, whereas with BayesRC it was 0.102 and 0.087. The average gain in accuracy was smaller when selected sequence variants were treated in the same category as 50k SNPs. Very little improvement over 50k prediction was observed when using all WGS variants. CONCLUSIONS Accuracy of genomic prediction in diverse sheep populations increased substantially by using variants selected from whole-genome sequence data based on an independent multi-breed GWAS, when compared to genomic prediction using standard 50K genotypes.

中文翻译：

---

基于从澳大利亚绵羊种群中估算的全基因组序列数据中选择的变异体进行的基因组预测。

背景技术全基因组序列（WGS）数据可以包含关于在高连锁不平衡处或处于高连锁不平衡的遗传变异的信息，所述遗传变异具有引起多基因性状遗传变异的原因突变。迄今为止，在奶牛研究中使用此类信息时，基因组预测准确性显示出有限的提高，在该研究中，一个或几个多样性有限的品种占主导地位。我们研究的目的是，在使用推算的WGS基因型信息时，评估相关目标个体相​​对较少的多品种澳大利亚绵羊群体中基因组预测的准确性。方法在9626至26657种具有表型的动物中，有9种具有重要经济意义的绵羊生产性状均可用，且均具有WGS估算的基因型。大约30％的数据用于基于全基因组关联研究（GWAS）来发现预测性单核苷酸多态性（SNP），其余数据用于训练和验证基因组预测。使用从推算序列数据中选择的变异体与使用50k SNP基因型标准阵列的预测准确性进行了比较，从而比较了基因组最佳线性预测（GBLUP）和贝叶斯方法（BayesR / BayesRC）。在两个独立的种群中评估了基因组预测的准确性，每个种群与训练集之间的相关性都很低，一个是纯种美利奴羊，另一个是边境莱斯特x美利奴绵羊杂交。结果当选择的序列变体，装有沿着50K基因型如GBLUP（2GBLUP）单独的方差分量中观察到的预测精度的显着改善或贝叶斯分析的SNP（BayesRC）的单独的一类。从50k阵列的两个验证集中的平均准确度为0.27，对于纯种和杂交动物，使用2GBLUP的性状的平均准确度平均绝对增加分别为0.083和0.073，而使用BayesRC时，其为0.102和0.087。当将选定的序列变体与50k SNPs归类在同一类别时，准确度的平均增益较小。使用所有WGS变体时，观察到的超过50k的预测几乎没有改善。