Departamento de Zootecnia, Universidade Federal de Pelotas, Pelotas, Brazil.
Embrapa Pecuária Sul, Bagé, Brazil.
J Anim Breed Genet. 2020 Sep;137(5):449-467. doi: 10.1111/jbg.12458. Epub 2019 Nov 27.
The aim of this study was to perform a Bayesian genomewide association study (GWAS) to identify genomic regions associated with growth traits in Hereford and Braford cattle, and to select Tag-SNPs to represent these regions in low-density panels useful for genomic predictions. In addition, we propose candidate genes through functional enrichment analysis associated with growth traits using Medical Subject Headings (MeSH). Phenotypic data from 126,290 animals and genotypes for 131 sires and 3,545 animals were used. The Tag-SNPs were selected with BayesB (π = 0.995) method to compose low-density panels. The number of Tag-single nucleotide polymorphism (SNP) ranged between 79 and 103 SNP for the growth traits at weaning and between 78 and 100 SNP for the yearling growth traits. The average proportion of variance explained by Tag-SNP with BayesA was 0.29, 0.23, 0.32 and 0.19 for birthweight (BW), weaning weight (WW205), yearling weight (YW550) and postweaning gain (PWG345), respectively. For Tag-SNP with BayesA method accuracy values ranged from 0.13 to 0.30 for k-means and from 0.30 to 0.65 for random clustering of animals to compose reference and validation groups. Although genomic prediction accuracies were higher with the full marker panel, predictions with low-density panels retained on average 76% of the accuracy obtained with BayesB with full markers for growth traits. The MeSH analysis was able to translate genomic information providing biological meanings of more specific gene products related to the growth traits. The proposed Tag-SNP panels may be useful for future fine mapping studies and for lower-cost commercial genomic prediction applications.
本研究旨在进行贝叶斯全基因组关联研究（GWAS），以鉴定与赫里福德牛和布拉福德牛生长性状相关的基因组区域，并选择标记单核苷酸多态性（Tag-SNP）来代表这些区域在用于基因组预测的低密度面板中。此外，我们还通过使用医学主题词（MeSH）进行功能富集分析，提出与生长性状相关的候选基因。使用了 126290 头动物的表型数据和 131 头公牛和 3545 头动物的基因型数据。使用 BayesB（π=0.995）方法选择 Tag-SNP 来组成低密度面板。标记单核苷酸多态性（SNP）的数量在断奶时的生长性状之间在 79 到 103 SNP 之间，在育肥期的生长性状之间在 78 到 100 SNP 之间。使用 BayesA 解释 Tag-SNP 的平均方差比例分别为出生体重（BW）的 0.29、0.23、0.32 和 0.19，断奶体重（WW205）的 0.29、0.23、0.32 和 0.19，育肥期体重（YW550）的 0.32 和 0.19，断奶后增重（PWG345）的 0.19。使用 BayesA 方法，Tag-SNP 的准确性值在 k-均值聚类中从 0.13 到 0.30 不等，在动物随机聚类中从 0.30 到 0.65 不等，用于组成参考和验证组。尽管全标记面板的基因组预测准确性更高，但使用低密度面板的预测平均保留了使用全标记的 BayesB 获得的生长性状准确性的 76%。MeSH 分析能够将基因组信息转化为与生长性状相关的更具体基因产物的生物学意义。所提出的 Tag-SNP 面板可能对未来的精细图谱研究和成本较低的商业基因组预测应用有用。
