这项研究估计了遗传力(h2)以及生殖性状之间的遗传和表型相关性,包括产卵间隔(CI),第一次产牛年龄(AFC),妊娠长度(GL),每个受孕的人工授精次数(NAIPC),和car体特征,包括屠体重量(CWT),眼肌面积(EMA),背脂肪厚度(BF),韩国Hanwoo奶牛的大理石花纹得分(MS)。此外,通过应用基因组最佳线性无偏预测(GBLUP)和加权GBLUP(WGBLUP)方法评估了基因组预测育种值的准确性.从1,544头Hanwoo奶牛中收集了生殖和car体性状的表型数据,使用Illumina牛50K单核苷酸多态性(SNP)芯片对所有动物进行基因分型。使用MTG2程序使用多性状动物模型估计遗传参数。估计的h2forCI,AFC,GL,NAIPC,CWT,EMA,BF,MS分别为0.10、0.13、0.17、0.11、0.37、0.35、0.27和0.45,根据GBLUP模型。GBLUP的准确性估计范围为0.51至0.74,而WGBLUP对所研究性状的准确性估计范围为0.51至0.79。在GL和NAIPC之间观察到了强而有利的遗传相关性(0.61),CWT和EMA(0.60),NAIPC和CWT(0.49),AFC和CWT(0.48),CI和GL(0.36),BF和MS(0.35),NAIPC和EMA(0.35),CI和BF(0.30),EMA和MS(0.28),CI和AFC(0.26),AFC和EMA(0.24),以及AFC和BF(0.21)。本研究确定了生殖和CWT性状之间的低至中等正遗传相关性,这表明体重越重可能导致CI越长,AFC,GL,NAIPC。CWT与AFC的中度正相关,NAIPC,表型相关性几乎为零,表明基因型-环境相互作用更可能是这些性状的表型表现的原因。因此,育种者将这些特征作为选择标准可能为制定选择指数以增加对选择和识别候选动物的反应提供了一个很好的机会,这可能会导致生产系统的盈利能力显着提高。
This study estimated the heritabilities (h2) and genetic and phenotypic correlations between reproductive traits, including calving interval (CI), age at first calving (AFC), gestation length (GL), number of artificial inseminations per conception (NAIPC), and carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) in Korean Hanwoo cows. In addition, the accuracy of genomic predictions of breeding values was evaluated by applying the genomic best linear unbiased prediction (GBLUP) and the weighted GBLUP (WGBLUP) method. The phenotypic data for reproductive and carcass traits were collected from 1,544 Hanwoo cows, and all animals were genotyped using Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The genetic parameters were estimated using a multi-trait animal model using the MTG2 program. The estimated h2 for CI, AFC, GL, NAIPC, CWT, EMA, BF, and MS were 0.10, 0.13, 0.17, 0.11, 0.37, 0.35, 0.27, and 0.45, respectively, according to the GBLUP model. The GBLUP accuracy estimates ranged from 0.51 to 0.74, while the WGBLUP accuracy estimates for the traits under study ranged from 0.51 to 0.79. Strong and favorable genetic correlations were observed between GL and NAIPC (0.61), CWT and EMA (0.60), NAIPC and CWT (0.49), AFC and CWT (0.48), CI and GL (0.36), BF and MS (0.35), NAIPC and EMA (0.35), CI and BF (0.30), EMA and MS (0.28), CI and AFC (0.26), AFC and EMA (0.24), and AFC and BF (0.21). The present study identified low to moderate positive genetic correlations between reproductive and CWT traits, suggesting that a heavier body weight may lead to a longer CI, AFC, GL, and NAIPC. The moderately positive genetic correlation between CWT and AFC, and NAIPC, with a phenotypic correlation of nearly zero, suggesting that the genotype-environment interactions are more likely to be responsible for the phenotypic manifestation of these traits. As a result, the inclusion of these traits by breeders as selection criteria may present a good opportunity for developing a selection index to increase the response to the selection and identification of candidate animals, which can result in significantly increased profitability of production systems.