Chen Long, Pryce Jennie E, Hayes Ben J, Daetwyler Hans D
Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia.
School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia.
Animals (Basel). 2021 Feb 19;11(2):541. doi: 10.3390/ani11020541.
Structural variations (SVs) are large DNA segments of deletions, duplications, copy number variations, inversions and translocations in a re-sequenced genome compared to a reference genome. They have been found to be associated with several complex traits in dairy cattle and could potentially help to improve genomic prediction accuracy of dairy traits. Imputation of SVs was performed in individuals genotyped with single-nucleotide polymorphism (SNP) panels without the expense of sequencing them. In this study, we generated 24,908 high-quality SVs in a total of 478 whole-genome sequenced Holstein and Jersey cattle. We imputed 4489 SVs with R2 > 0.5 into 35,568 Holstein and Jersey dairy cattle with 578,999 SNPs with two pipelines, FImpute and Eagle2.3-Minimac3. Genome-wide association studies for production, fertility and overall type with these 4489 SVs revealed four significant SVs, of which two were highly linked to significant SNP. We also estimated the variance components for SNP and SV models for these traits using genomic best linear unbiased prediction (GBLUP). Furthermore, we assessed the effect on genomic prediction accuracy of adding SVs to GBLUP models. The estimated percentage of genetic variance captured by SVs for production traits was up to 4.57% for milk yield in bulls and 3.53% for protein yield in cows. Finally, no consistent increase in genomic prediction accuracy was observed when including SVs in GBLUP.
结构变异（SVs）是指与参考基因组相比，重测序基因组中出现的缺失、重复、拷贝数变异、倒位和易位等大片段DNA。已发现它们与奶牛的多种复杂性状相关，并且可能有助于提高奶牛性状的基因组预测准确性。在使用单核苷酸多态性（SNP）芯片进行基因分型的个体中进行了SVs的填充，而无需对其进行测序。在本研究中，我们在总共478头全基因组测序的荷斯坦奶牛和泽西奶牛中生成了24,908个高质量的SVs。我们使用FImpute和Eagle2.3 - Minimac3这两种流程，将4489个R2>0.5的SVs填充到35,568头具有578,999个SNP的荷斯坦奶牛和泽西奶牛中。对这4489个SVs进行的产奶量、繁殖力和整体类型的全基因组关联研究揭示了4个显著的SVs，其中2个与显著的SNP高度连锁。我们还使用基因组最佳线性无偏预测（GBLUP）估计了这些性状的SNP和SV模型的方差成分。此外，我们评估了将SVs添加到GBLUP模型中对基因组预测准确性的影响。公牛产奶量的生产性状中，SVs捕获的遗传方差估计百分比高达4.57%，母牛蛋白质产量的为3.53%。最后，在GBLUP中纳入SVs时，未观察到基因组预测准确性的一致提高。
