Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.
Research & Development, Cobb-Vantress Inc., Siloam Springs, AR 72761, USA.
J Anim Sci. 2023 Jan 3;101. doi: 10.1093/jas/skad177.
In broiler breeding, superior individuals for growth become parents and are later evaluated for reproduction in an independent evaluation; however, ignoring broiler data can produce inaccurate and biased predictions. This research aimed to determine the most accurate, unbiased, and time-efficient approach for jointly evaluating reproductive and broiler traits. The data comprised a pedigree with 577K birds, 146K genotypes, phenotypes for three reproductive (egg production [EP], fertility [FE], hatch of fertile eggs [HF]; 9K each), and four broiler traits (body weight [BW], breast meat percent [BP], fat percent [FP], residual feed intake [RF]; up to 467K). Broiler data were added sequentially to assess the impact on the quality of predictions for reproductive traits. The baseline scenario (RE) included pedigrees, genotypes, and phenotypes for reproductive traits of selected animals; in RE2, we added their broiler phenotypes; in RE_BR, broiler phenotypes of nonselected animals, and in RE_BR_GE, their genotypes. We computed accuracy, bias, and dispersion of predictions for hens from the last two breeding cycles and their sires. We tested three core definitions for the algorithm of proven and young to find the most time-efficient approach: two random cores with 7K and 12K animals and one with 19K animals, containing parents and young animals. From RE to RE_BR_GE, changes in accuracy were null or minimal for EP (0.51 in hens, 0.59 in roosters) and HF (0.47 in hens, 0.49 in roosters); for FE in hens (roosters), it changed from 0.4 (0.49) to 0.47 (0.53). In hens (roosters), bias (additive SD units) decreased from 0.69 (0.7) to 0.04 (0.05) for EP, 1.48 (1.44) to 0.11 (0.03) for FE, and 1.06 (0.96) to 0.09 (0.02) for HF. Dispersion remained stable in hens (roosters) at 0.93 (1.03) for EP, and it improved from 0.57 (0.72) to 0.87 (1.0) for FE and from 0.8 (0.79) to 0.88 (0.87) for HF. Ignoring broiler data deteriorated the predictions' quality. The impact was significant for the low heritability trait (0.02; FE); bias (up to 1.5) and dispersion (as low as 0.57) were farther from the ideal value, and accuracy losses were up to 17.5%. Accuracy was maintained in traits with moderate heritability (~0.3; EP and HF), and bias and dispersion were less substantial. Adding information from the broiler phase maximized accuracy and unbiased predictions. The most time-efficient approach is a random core with 7K animals in the algorithm for proven and young.
在肉鸡育种中，生长表现优异的个体成为亲代，并在后续的独立评估中对其繁殖性能进行评估；然而，忽略肉鸡数据可能会导致预测结果不准确和有偏差。本研究旨在确定最准确、无偏和最节省时间的方法，以联合评估繁殖性能和肉鸡性状。该研究的数据来自一个包含 577K 只鸟类、146K 个基因型、3 个繁殖性状（产蛋量[EP]、受精率[FE]、种蛋孵化率[HF]；每个性状 9K 个数据）和 4 个肉鸡性状（体重[BW]、胸肉百分比[BP]、脂肪百分比[FP]、剩余饲料摄入量[RF]；多达 467K 个数据）的 pedigree。肉鸡数据是逐步添加的，以评估其对繁殖性能预测质量的影响。基线情景（RE）包括所选动物的繁殖性状的 pedigree、基因型和表型；在 RE2 中，我们添加了它们的肉鸡表型；在 RE_BR 中，添加了非选择动物的肉鸡表型，在 RE_BR_GE 中，添加了它们的基因型。我们计算了最后两个繁殖周期母鸡及其父本的预测准确性、偏差和离散度。我们测试了三种核心算法的定义，以找到最节省时间的方法：两种包含 7K 和 12K 只动物的随机核心算法和一种包含 19K 只动物的随机核心算法，其中包含亲代和年轻动物。从 RE 到 RE_BR_GE，EP（母鸡为 0.51，公鸡为 0.59）和 HF（母鸡为 0.47，公鸡为 0.49）的母鸡和公鸡的准确性变化为零或最小；FE 的母鸡（公鸡）的准确性从 0.4（0.49）变为 0.47（0.53）。在母鸡（公鸡）中，偏差（加性标准差单位）从 EP 的 0.69（0.7）降至 0.04（0.05），FE 的 1.48（1.44）降至 0.11（0.03），HF 的 1.06（0.96）降至 0.09（0.02）。母鸡（公鸡）的离散度保持稳定，EP 约为 0.93（1.03），FE 从 0.57（0.72）提高到 0.87（1.0），HF 从 0.8（0.79）提高到 0.88（0.87）。忽略肉鸡数据会降低预测质量。对于低遗传力性状（0.02；FE），这种影响是显著的；偏差（高达 1.5）和离散度（低至 0.57）离理想值更远，准确性损失高达 17.5%。对于具有中等遗传力（~0.3；EP 和 HF）的性状，准确性保持不变，偏差和离散度较小。从肉鸡阶段添加信息可以最大程度地提高准确性和无偏预测。最节省时间的方法是在 Proven 和 Young 算法中使用包含 7K 只动物的随机核心。
