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首页> 外文期刊>Journal of Animal Science >Prediction of genetic values for feed intake from individual body weight gain and total feed intake of the pen
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Prediction of genetic values for feed intake from individual body weight gain and total feed intake of the pen

机译:从个体体重增加和围栏总采食量预测采食量的遗传值

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Records of individual feed intake (FI) and BW gain (GN) were obtained from the Germ Plasm Evaluation (GPE) program at US Meat Animal Research Center (USMARC). Animals were randomly assigned to pens. Only pens with 6 to 9 steers (n = 289) were used for this study (data set 1). Variance components and genetic parameters were estimated using data set 1. Estimated genetic values (EGV) for FI were calculated by 5 methods using single and 2-trait analyses: 1) individual FI and individual GN, 2) individual FI alone, 3) 2-trait with individual GN but with FI missing, 4) individual GN and pen total FI, and 5) pen total FI alone. Analyses were repeated but with some of the same records assigned artificially to 36 pens of 5 and 4 paternal half sibs per pen (data sets 2 and 3). Models included year as a fixed factor and birth and weaning weights, age on test, and days fed as covariates. Estimates of heritability were 0.42 +/- 0.16 and 0.34 +/- 0.17 for FI and GN. The estimate of the genetic correlation was 0.57 +/- 0.23. Empirical responses to selection were calculated as the average EGV for the top and bottom 10% based on rank for each method but with EGV from method 1 substituted for the EGV on which ranking was based. With data set 1, rank correlations between EGV from method 1 and EGV from methods 2, 3, 4, and 5 were 0.99, 0.53, 0.32, and 0.15, respectively. Empirical responses relative to method 1 agreed with the rank correlations. Accuracy of EGV for method 4 (0.44) was greater than for method 3 (0.35) and for method 5 (0.29). Accuracies for methods 4 and 5 were greater than indicated by empirical responses and correlations with EGV from method 1. Comparisons of the 5 methods were similar for data sets 2 and 3. With data set 2, rank correlations between EGV from method 1 and EGV from methods 3, 4, and 5 were 0.47, 0.64, and 0.62. Average accuracies of 56, 75, and 75% relative to method 1 (0.67) generally agreed with the empirical responses to selection. As expected, accuracy using pen total FI and GN to obtain EGV for FI was greater than using GN alone. With data set 1, empirical response to selection with method 4 was one-third of that for method 1, although average accuracy was 65% of that for method 1. With assignment of 5 paternal half sibs to artificial pens, using pen total FI and individual GN was about 81% as effective for selection as using individual FI and GN to obtain EGV for FI and was substantially more effective than use of GN alone.
机译:个体饲料摄入量(FI)和体重增加(GN)的记录是从美国肉类动物研究中心(USMARC)的胚芽血浆评估(GPE)程序获得的。将动物随机分配至围栏。本研究仅使用具有6到9个转向(n = 289)的钢笔(数据集1)。使用数据集1估计方差成分和遗传参数。FI的估计遗传值(EGV)通过5种方法进行单特征和2性状分析:1)个体FI和个体GN,2)个体FI,3)2个体GN但缺少FI的性状; 4)个体GN和笔的总FI,以及5)笔的总FI。重复进行分析,但将一些相同的记录人为地分配给每支笔5只和4只父本同胞的36只笔(数据集2和3)。模型包括以年为固定因素,出生和断奶体重,试验年龄和喂养天数作为协变量。 FI和GN的遗传力估计为0.42 +/- 0.16和0.34 +/- 0.17。遗传相关性的估计为0.57 +/- 0.23。对选择的经验响应计算为每种方法基于排名的前10%和后10%的平均EGV,但用方法1的EGV代替排名所依据的EGV。在数据集1中,方法1的EGV与方法2、3、4和5的EGV之间的等级相关性分别为0.99、0.53、0.32和0.15。相对于方法1的经验响应与等级相关性一致。方法4(0.44)的EGV准确性高于方法3(0.35)和方法5(0.29)的EGV准确性。方法4和5的准确度大于经验响应和方法1的EGV的相关指示。数据集2和3的5种方法的比较相似。对于数据集2,方法1的EGV与方法1的EGV之间的等级相关性。方法3、4和5分别为0.47、0.64和0.62。相对于方法1(0.67)的平均准确度为56、75和75%,总体上与选择的经验响应一致。不出所料,使用笔的总FI和GN来获得FI的EGV的准确性要比单独使用GN的准确性高。对于数据集1,使用方法4进行选择的经验响应是方法1的三分之一,尽管平均准确度是方法1的65%。使用笔总FI和5父半同胞分配给人工笔。与使用单独的FI和GN获得FI的EGV相比,单个GN的选择效果约为81%,并且比单独使用GN的效果明显更好。

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