Department of Animal Breeding, University of Kassel, 37213 Witzenhausen, Germany.
J Anim Sci. 2012 Oct;90(10):3418-26. doi: 10.2527/jas.2011-5005. Epub 2012 Jun 4.
Selection index theory was used to compare different selection strategies aiming at the improvement of meat quality in beef cattle. Alternative strategies were compared with a reference scenario with three basic traits in the selection index: BW at 200 d (W200) and 400 d (W400) and muscling score (MUSC). These traits resemble the combination currently used in the German national beef genetic evaluation system. Traits in the breeding goal were defined as the 3 basic traits plus marbling score (MARB), to depict a situation where an established breeding program currently selecting for growth and carcass yield intends to incorporate meat quality in its selection program. Economic weights were either the same for all 4 traits, or doubled or tripled for MARB. Two additional selection criteria for improving MARB were considered: Live animal intramuscular fat content measured by ultrasound (UIMF) as an indicator trait and a genomic breeding value (GEBV) for the target trait directly (gMARB). Results were used to estimate the required number of genotyped animals in an own calibration set for implementing genomic selection focusing on meat quality. Adding UIMF to the basic index increased the overall genetic gain per generation by 15% when the economic weight on MARB was doubled and by 44% when it was tripled. When a genomic breeding value for marbling could be estimated with an accuracy of 0.5, adding gMARB to the index provided larger genetic gain than adding UIMF. Greatest genetic gain per generation was obtained with the scenario containing GEBV for 4 traits (gW200, gW400, gMUSC, and gMARB) when the accuracies of these GEBV were ≥0.7. Adding UIMF to the index substantially improved response to selection for MARB, which switched from negative to positive when the economic weight on MARB was doubled or tripled. For all scenarios that contained gMARB in the selection index, the response to selection in MARB was positive for all relative economic weights on MARB, when the accuracy of GEBV was >0.7. Results indicated that setting up a calibration set of ∼500 genotyped animals with carcass phenotypes for MARB could suffice to obtain a larger response to selection than measuring UIMF. If the size of the calibration set is ∼2,500, adding the ultrasound trait to an index containing already the GEBV would bring little benefit, unless the relative economic weight for marbling is much larger than for the other traits.
选择指数理论被用于比较不同的选择策略,旨在提高牛肉的肉质。替代策略与选择指数中的三个基本特征的参考方案进行了比较:200 日龄体重(W200)和 400 日龄体重(W400)和肌肉评分(MUSC)。这些特征类似于德国国家牛肉遗传评估系统中目前使用的组合。在育种目标中的特征被定义为 3 个基本特征加上大理石花纹评分(MARB),以描绘一个目前正在选择生长和胴体产量的既定育种计划,打算将肉质纳入其选择计划的情况。经济权重要么对所有 4 个特征相同,要么 MARB 的权重加倍或三倍。还考虑了提高 MARB 的另外两个选择标准:通过超声波测量的活体动物肌内脂肪含量(UIMF)作为指示特征和直接针对目标特征的基因组育种值(GEBV)(gMARB)。结果用于估计在专注于肉质的基因组选择中实施所需的基因分型动物数量。当 MARB 的经济权重加倍时,将 UIMF 添加到基本指数中会使每代的总体遗传增益增加 15%,当 MARB 的经济权重增加三倍时,每代的总体遗传增益增加 44%。当可以以 0.5 的精度估计大理石花纹的基因组育种值时,将 gMARB 添加到指数中比添加 UIMF 提供更大的遗传增益。当包含 4 个特征的 GEBV(gW200、gW400、gMUSC 和 gMARB)的 GEBV 精度≥0.7 时,通过包含 4 个 GEBV(gW200、gW400、gMUSC 和 gMARB)的情景获得了最大的每代遗传增益。当 MARB 的经济权重加倍或三倍时,当 MARB 的经济权重加倍或三倍时,将 UIMF 添加到指数中会大大改善 MARB 的选择响应,使其从负变为正。对于包含选择指数中的 gMARB 的所有情景,当 GEBV 的精度>0.7 时,MARB 的选择响应为正,MARB 的相对经济权重。结果表明,建立一个包含 MARB 胴体表型的约 500 只基因分型动物的校准集足以获得比测量 UIMF 更大的选择响应。如果校准集的大小约为 2500,则在已经包含 GEBV 的指数中添加超声特征几乎没有益处,除非大理石花纹的相对经济权重比其他特征大得多。