Bang Nguyen N, Hayes Ben J, Lyons Russell E, Randhawa Imtiaz A S, Gaughan John B, Trach Nguyen X, McNeill David M
School of Veterinary Science, The University of Queensland, Gatton, Queensland, Australia.
Faculty of Animal Science, Vietnam National University of Agriculture, Hanoi, Vietnam.
J Anim Breed Genet. 2025 May;142(3):322-341. doi: 10.1111/jbg.12907. Epub 2024 Oct 27.
Genomic selection (GS) and genome-wide association studies (GWAS) have not been investigated in Vietnamese dairy cattle, even for basic milk production traits, largely due to the scarcity of individual phenotype recording in smallholder dairy farms (SDFs). This study aimed to estimate heritability (h ) and test the applicability of GS and GWAS for milk production, body conformation and novel heat tolerance traits using single test day phenotypic data. Thirty-two SDFs located in either the north (a lowland vs. a highland) or the south (a lowland vs. a highland) of Vietnam were each visited for an afternoon and the next morning to collect phenotype data of all lactating cows (n = 345). Tail hair from each cow was sampled for subsequent genotyping with a 50K SNP chip at that same visit. Milk production traits (single-test day) were milk yield (MILK, kg/cow/day), energy corrected milk yield adjusted for body weight (ECMbw, kg/100 kg BW/day), fat (mFA, %), protein (mPR, %) and dry matter (mDM, %). Conformation traits were body weight (BW, kg) and body condition score (BCS, 1 = thin to 5 = obese). Heat tolerance traits were panting score (PS, 0 = normal to 4.5 = extremely heat-stressed) and infrared temperatures (IRTs, °C) at 11 areas on the external body surface of the cow (inner vulval lip, outer vulval surface, inner tail base surface, ocular area, muzzle, armpit area, paralumbar fossa area, fore udder, rear udder, forehoof and hind hoof), assessed by an Infrared Camera. Univariate linear mixed models and a 10-fold cross-validation approach were applied for GS. Univariate single SNP mixed linear models were applied for the GWAS. Estimated h (using the genotype information to build relationships among animals) were moderate (0.20-0.37) for ECMbw, mFA, mPR, mRE, BW, BCS and IRT at rear udder; low (0.08-0.19) for PS and other IRTs; and very low (≤ 0.07) for MILK, ECM and mDM. Accuracy of genomic estimated breeding values (GEBVs) was low (≤ 0.12) for MILK, ECM, mDM and IRT at hind hoof; and moderate to high (0.32-0.46) for all other traits. The most significant regions on chromosomes (BTA) associated with milk production traits were 0.47-1.18 Mb on BTA14. Moderate to high h and moderate accuracies of GEBVs for mFA, mPR, ECMbw, BCS, BW, PS and IRTs at rear udder and outer vulval surface suggested that GS using single test day phenotypic data could be applied for these traits. However, a greater sample size is required to decrease the bias of GEBVs by GS and increase the power of detecting significant quantitative trait loci (QTLs) by GWAS.
基因组选择（GS）和全基因组关联研究（GWAS）尚未在越南奶牛中进行调查，即使是针对基本的产奶性状，这主要是由于小农户奶牛场（SDFs）中个体表型记录的稀缺。本研究旨在利用单个测试日的表型数据估计遗传力（h²），并测试GS和GWAS在产奶、体型外貌和新的耐热性状方面的适用性。位于越南北部（一个低地和一个高地）或南部（一个低地和一个高地）的32个小农户奶牛场，每个农场都在下午和第二天上午进行了访问，以收集所有泌乳奶牛（n = 345）的表型数据。在同一次访问中，采集了每头奶牛的尾毛，以便随后使用50K SNP芯片进行基因分型。产奶性状（单个测试日）包括产奶量（MILK，kg/头/天）、根据体重校正的能量校正产奶量（ECMbw，kg/100 kg体重/天）、脂肪（mFA，%）、蛋白质（mPR，%）和干物质（mDM，%）。体型外貌性状包括体重（BW，kg）和体况评分（BCS，1 = 瘦到5 = 肥胖）。耐热性状包括喘气评分（PS，0 = 正常到4.5 = 极度热应激）和奶牛体表11个区域（内阴唇、外阴表面、尾基部内表面、眼部区域、口鼻部、腋窝区域、腰旁窝区域、前乳房、后乳房、前蹄和后蹄）的红外温度（IRTs，°C），通过红外热像仪进行评估。GS采用单变量线性混合模型和10折交叉验证方法。GWAS采用单变量单SNP混合线性模型。利用基因型信息建立动物间关系估计的h²，对于ECMbw、mFA、mPR、mRE、BW、BCS和后乳房的IRTs为中等（0.20 - 0.37）；对于PS和其他IRTs为低（0.08 - 0.19）；对于MILK、ECM和mDM为非常低（≤ 0.07）。基因组估计育种值（GEBVs）的准确性，对于MILK、ECM、mDM和后蹄的IRTs为低（≤ 0.12）；对于所有其他性状为中等至高（0.32 - 0.46）。与产奶性状相关的染色体（BTA）上最显著的区域在BTA14上为0.47 - 1.18 Mb。mFA、mPR、ECMbw、BCS、BW、PS以及后乳房和外阴表面的IRTs具有中等至高的h²和中等的GEBVs准确性，这表明利用单个测试日的表型数据进行GS可应用于这些性状。然而，需要更大的样本量来减少GS估计GEBVs的偏差，并提高GWAS检测显著数量性状位点（QTLs）的能力。
