Wongpom Bodin, Koonawootrittriron Skorn, Elzo Mauricio A, Suwanasopee Thanathip, Jattawa Danai
Department of Animal Science, Kasetsart University, Bangkok 10900, Thailand.
Department of Animal Sciences, University of Florida, Gainesville, FL 32611-0910, USA.
Asian-Australas J Anim Sci. 2019 Feb 14;32(9):1340-1348. doi: 10.5713/ajas.18.0816. Print 2019 Sep.
The objectives were to compare variance components, genetic parameters, prediction accuracies, and genomic-polygenic EBV rankings for milk yield (MY) and fat yield (FY) in the Thai multibreed dairy population computed using five SNP sets from GeneSeek GGP80K chip.
The dataset contained monthly MY and FY of 8,361 first-lactation cows from 810 farms. Variance components, genetic parameters, and EBV for five SNP sets from the GeneSeek GGP80K chip were obtained using a 2-trait single-step average-information REML procedure. The SNP sets were the complete SNP set (all available SNP; SNP100), top 75% set (SNP75), top 50% set (SNP50), top 25% set (SNP25) and top 5% set (SNP5). The 2-trait models included herd-year-season, heterozygosity and age at first calving as fixed effects, and animal additive genetic and residual as random effects.
The estimates of additive genetic variances for MY and FY from SNP subsets were mostly higher than those of the complete set. The SNP25 MY and FY heritability estimates (0.276 and 0.183) were higher than those from SNP75 (0.265 and 0.168), SNP50 (0.275 and 0.179), SNP5 (0.231 and 0.169) and SNP100 (0.251and 0.159). The SNP25 EBV accuracies for MY and FY (39.76% and 33.82%) were higher than for SNP75 (35.01% and 32.60%), SNP50 (39.64% and 33.38%), SNP5 (38.61% and 29.70%) and SNP100 (34.43% and 31.61%). All rank correlations between SNP100 and SNP subsets were above 0.98 for both traits, except for SNP100 and SNP5 (0.93 for MY; 0.92 for FY).
The high SNP25 estimates of genetic variances, heritabilities, EBV accuracies, and rank correlations between SNP100 and SNP25 for MY and FY indicated that genotyping animals with SNP25 dedicated chip would be a suitable alternative to maintain genotyping costs low while speeding up genetic progress for MY and FY in the Thai dairy population.
本研究旨在比较使用GeneSeek GGP80K芯片的五个单核苷酸多态性(SNP)集计算泰国多品种奶牛群体中牛奶产量(MY)和脂肪产量(FY)的方差成分、遗传参数、预测准确性以及基因组-多基因估计育种值(EBV)排名。
数据集包含来自810个农场的8361头头胎泌乳奶牛的月度MY和FY数据。使用双性状单步平均信息约束最大似然法(REML)程序获得GeneSeek GGP80K芯片五个SNP集的方差成分、遗传参数和EBV。这些SNP集分别是完整SNP集(所有可用SNP;SNP100)、前75%集(SNP75)、前50%集(SNP50)、前25%集(SNP25)和前5%集(SNP5)。双性状模型包括畜群-年份-季节、杂合度和头胎产犊年龄作为固定效应,以及动物加性遗传效应和残差作为随机效应。
SNP子集对MY和FY的加性遗传方差估计大多高于完整集。SNP25对MY和FY的遗传力估计值(分别为0.276和0.183)高于SNP75(分别为0.265和0.168)、SNP50(分别为0.275和0.179)、SNP5(分别为0.231和0.169)和SNP100(分别为0.251和0.159)。SNP25对MY和FY的EBV准确性(分别为39.76%和33.82%)高于SNP75(分别为35.01%和32.60%)、SNP50(分别为39.64%和33.38%)、SNP5(分别为38.61%和29.70%)和SNP100(分别为34.43%和31.61%)。除SNP100和SNP5外(MY为0.93;FY为0.92),两个性状的SNP100与SNP子集之间的所有秩相关均高于0.98。
SNP25对MY和FY的遗传方差、遗传力、EBV准确性以及SNP100与SNP25之间的秩相关估计值较高,表明使用SNP25专用芯片对动物进行基因分型是一种合适的选择,既能保持较低的基因分型成本,又能加快泰国奶牛群体中MY和FY的遗传进展。