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多组学数据在预测奶牛繁殖力性状中的应用。

Utility of multi-omics data to inform genomic prediction of heifer fertility traits.

机构信息

School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia Campus, Brisbane 4072, QLD, Australia.

Commonwealth Scientific and Industrial Research Organization, St. Lucia, Brisbane 4072, QLD, Australia.

出版信息

J Anim Sci. 2022 Dec 1;100(12). doi: 10.1093/jas/skac340.

Abstract

Biologically informed single nucleotide polymorphisms (SNPs) impact genomic prediction accuracy of the target traits. Our previous genomics, proteomics, and transcriptomics work identified candidate genes related to puberty and fertility in Brahman heifers. We aimed to test this biological information for capturing heritability and predicting heifer fertility traits in another breed i.e., Tropical Composite. The SNP from the identified genes including 10 kilobases (kb) region on either side were selected as biologically informed SNP set. The SNP from the rest of the Bos taurus genes including 10-kb region on either side were selected as biologically uninformed SNP set. Bovine high-density (HD) complete SNP set (628,323 SNP) was used as a control. Two populations-Tropical Composites (N = 1331) and Brahman (N = 2310)-had records for three traits: pregnancy after first mating season (PREG1, binary), first conception score (FCS, score 1 to 3), and rebreeding score (REB, score 1 to 3.5). Using the best linear unbiased prediction method, effectiveness of each SNP set to predict the traits was tested in two scenarios: a 5-fold cross-validation within Tropical Composites using biological information from Brahman studies, and application of prediction equations from one breed to the other. The accuracy of prediction was calculated as the correlation between genomic estimated breeding values and adjusted phenotypes. Results show that biologically informed SNP set estimated heritabilities not significantly better than the control HD complete SNP set in Tropical Composites; however, it captured all the observed genetic variance in PREG1 and FCS when modeled together with the biologically uninformed SNP set. In 5-fold cross-validation within Tropical Composites, the biologically informed SNP set performed marginally better (statistically insignificant) in terms of prediction accuracies (PREG1: 0.20, FCS: 0.13, and REB: 0.12) as compared to HD complete SNP set (PREG1: 0.17, FCS: 0.10, and REB: 0.11), and biologically uninformed SNP set (PREG1: 0.16, FCS: 0.10, and REB: 0.11). Across-breed use of prediction equations still remained a challenge: accuracies by all SNP sets dropped to around zero for all traits. The performance of biologically informed SNP was not significantly better than other sets in Tropical Composites. However, results indicate that biological information obtained from Brahman was successful to predict the fertility traits in Tropical Composite population.

摘要

基于生物学的单核苷酸多态性 (SNP) 会影响目标性状的基因组预测准确性。我们之前的基因组学、蛋白质组学和转录组学工作确定了与婆罗门小母牛青春期和生育能力相关的候选基因。我们的目的是在另一个品种即热带复合品种中检验这些生物学信息对捕获遗传力和预测小母牛生育力性状的作用。从鉴定的基因中选择 SNP,包括两侧各 10 千碱基 (kb) 区域作为生物学信息 SNP 集。从其余的 Bos taurus 基因中选择 SNP,包括两侧各 10-kb 区域作为生物学非信息 SNP 集。使用牛高密度 (HD) 全 SNP 集 (628,323 SNP) 作为对照。两个群体-热带复合品种 (N = 1331) 和婆罗门品种 (N = 2310)-有三个性状的记录:第一次配种季节后的怀孕 (PREG1,二进制)、第一次受孕评分 (FCS,评分 1 到 3) 和再配种评分 (REB,评分 1 到 3.5)。使用最佳线性无偏预测方法,在两个场景中测试每个 SNP 集对性状的预测效果:在热带复合品种中使用婆罗门研究的生物学信息进行 5 倍交叉验证,以及将一个品种的预测方程应用于另一个品种。预测准确性通过基因组估计育种值与调整表型之间的相关性来计算。结果表明,在热带复合品种中,基于生物学的 SNP 集估计的遗传力并不显著优于对照 HD 全 SNP 集;然而,当与生物学非信息 SNP 集一起建模时,它捕获了 PREG1 和 FCS 所有观察到的遗传方差。在热带复合品种的 5 倍交叉验证中,基于生物学的 SNP 集在预测准确性方面表现稍好 (统计学上无显著差异) (PREG1:0.20,FCS:0.13,REB:0.12),与 HD 全 SNP 集 (PREG1:0.17,FCS:0.10,REB:0.11) 和生物学非信息 SNP 集 (PREG1:0.16,FCS:0.10,REB:0.11) 相比。跨品种使用预测方程仍然是一个挑战:所有 SNP 集的准确性都下降到所有性状的接近零。在热带复合品种中,基于生物学的 SNP 的性能并不明显优于其他 SNP 集。然而,结果表明,从婆罗门获得的生物学信息成功地预测了热带复合品种的生育力性状。

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