Genomic Prediction Ability for Novel Profitability Traits Using Different Models in Nelore Cattle.

IF 1.9 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Letícia Silva Pereira, Cláudio Ulhôa Magnabosco, Guilherme Rosa, Nedenia Bonvino Stafuzza, Tiago Zanett Albertini, Minos Carvalho, Raysildo Barbosa Lobo, Elisa Peripolli, Eduardo da Costa Eifert, Fernando Baldi
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引用次数: 0

Abstract

The aim of this study was to assess the accuracy, bias and dispersion of genomic predictions for accumulated profitability (APF) and profit per kilogram of liveweight gain (PFT) in Nelore cattle using different prediction approaches. The dataset consisted of 3969 phenotypic records for each trait. The pedigree harboured information from 38,930 animals born between 1998 and 2016, including 2691 sires and 19,884 dams. A total of 2449 animals were genotyped using the Clarifide Nelore 3.0 SNP panel. Nine models for genomic prediction were evaluated: a linear animal model was applied to estimate genetic parameters and perform the genomic single-trait best linear unbiased prediction (ST_ss-default). Additionally, a two-trait (ssGBLUP TT_W450 and TT_DMI), three-trait (TTT_CAR) and multi-trait ssGBLUP (MT_ss) were tested. Finally, two models employing the weighted linear (ST_sswl1 and ST_sswl2) and non-linear (ST_sswnl1 and ST_sswnl2) single-step genomic approach (WssGBLUP) were used to predict genomic breeding values (GEBV). The ability to predict future performance was assessed by calculating the correlation between GEBV and adjusted phenotypes. The average prediction accuracy of the GEBV models ranged from 0.345 to 0.665 for PFT and from 0.425 to 0.603 for APF. The predictive capability of the MT_ss model (0.665) was significantly higher than that of the other models for PFT, except for the TTT_CAR model (0.604), which also showed an improvement in predictive performance. For APF, the MT_ss (0.561) and TT_W450 (0.556) models demonstrated improved genomic prediction accuracy compared to the other models. In general, the single trait ssGBLUP (ST_ss-default) models and the non-linear weighting approach did not enhance prediction accuracy for either trait. For the phenotypic prediction ability of PFT, the linear WssGBLUP models ST_sswl1 (0.65) and ST_sswl2 (0.70), TT_W450 (0.64) and ssGBLUP-M (0.66) demonstrated the highest prediction accuracies. Similar results were observed for the phenotypic prediction ability of APF for both models. However, the linear WssGBLUP models ST_sswl1 (0.84) and ST_sswl2 (0.94) provided higher prediction performance compared to the two-, three- and multi-trait models. The results indicate that the multi-trait model achieved better predictive ability for the novel traits PFT and APF. Multi-trait genomic selection may yield greater genetic gains than other models for these forthcoming economically important traits in breeding programmes.

利用不同模型对耐洛尔牛新盈利性状的基因组预测能力。
本研究的目的是评估使用不同预测方法对内洛雷牛累积盈利能力(APF)和每公斤活重增重利润(PFT)基因组预测的准确性、偏倚性和分散性。该数据集由每个性状的3969个表型记录组成。该谱系包含了1998年至2016年间出生的38930只动物的信息,其中包括2691只公鹿和19884只母鹿。使用Clarifide Nelore 3.0 SNP面板共对2449只动物进行基因分型。对9种基因组预测模型进行了评估:采用线性动物模型估计遗传参数,并进行基因组单性状最佳线性无偏预测(ST_ss-default)。此外,还对ssGBLUP (TT_W450和TT_DMI)、TTT_CAR和多性状ssGBLUP (MT_ss)进行了双性状测试。最后,采用加权线性(ST_sswl1和ST_sswl2)和非线性(ST_sswnl1和ST_sswnl2)单步基因组法(WssGBLUP)预测基因组育种值(GEBV)。通过计算GEBV和调整后表型之间的相关性来评估预测未来表现的能力。GEBV模型对PFT的平均预测精度为0.345 ~ 0.665,对APF的平均预测精度为0.425 ~ 0.603。除TTT_CAR模型(0.604)外,MT_ss模型对PFT的预测能力(0.665)显著高于其他模型,TTT_CAR模型的预测能力也有所提高。对于APF,与其他模型相比,MT_ss(0.561)和TT_W450(0.556)模型显示出更高的基因组预测精度。总体而言,单性状ssGBLUP (ST_ss-default)模型和非线性加权方法对两种性状的预测精度都没有提高。对于PFT的表型预测能力,线性WssGBLUP模型ST_sswl1(0.65)和ST_sswl2(0.70)、TT_W450(0.64)和ssGBLUP-M(0.66)的预测精度最高。两种模型的APF表型预测能力结果相似。然而,线性WssGBLUP模型ST_sswl1(0.84)和ST_sswl2(0.94)的预测性能优于两性状、三性状和多性状模型。结果表明,多性状模型对新性状PFT和APF具有较好的预测能力。在育种计划中,多性状基因组选择可能比其他模式产生更大的遗传收益。
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来源期刊
Journal of Animal Breeding and Genetics
Journal of Animal Breeding and Genetics 农林科学-奶制品与动物科学
CiteScore
5.20
自引率
3.80%
发文量
58
审稿时长
12-24 weeks
期刊介绍: The Journal of Animal Breeding and Genetics publishes original articles by international scientists on genomic selection, and any other topic related to breeding programmes, selection, quantitative genetic, genomics, diversity and evolution of domestic animals. Researchers, teachers, and the animal breeding industry will find the reports of interest. Book reviews appear in many issues.
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