Accuracies of Genomic Prediction for Growth Traits at Weaning and Yearling Ages in Yak

Ge, Fei; Chen, Jia; Bao, Pengjia; Wu, Xiaoyun; Liang, Chunnian; Yan, Ping

doi:10.3390/ani10101793

Cited by 7 publications

(7 citation statements)

References 38 publications

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“…Hayes et al [ 53 ] also found that the GBLUP and the BayesB methods resulted in similar accuracies in a multibreed dairy population. Additionally, Ostersen et al [ 54 ] reported no difference among the GBLUP, the Bayesian LASSO, and the Bayesian mixture methods based on 60,000 SNP data, and Ge et al [ 55 ] reported the similar predictive accuracy for the GBLUP and the Bayesian methods for growth traits at weaning and yearling ages in Yaks. Although the predictive abilities of the GBLUP and the Bayesian methods were quite similar for growth and carcass traits and k-means and random clusters ( Figure 5 and Figure 6 ) in the current study, the realized accuracies of the GEBV of the GBLUP and the Bayesian methods were not similar ( Figure 7 and Figure 8 ) because of the heritability estimates from the GBLUP and the Bayesian (BayesA, BayesB, BayesC and Lasso) methods.…”

Section: Resultsmentioning

confidence: 99%

Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population

Peters

Kızılkaya

Sinecen

et al. 2023

Animals

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The predictive abilities and accuracies of genomic best linear unbiased prediction (GBLUP) and the Bayesian (BayesA, BayesB, BayesC and Lasso) genomic selection (GS) methods for economically important growth (birth, weaning, and yearling weights) and carcass (depth of rib fat, apercent intramuscular fat and longissimus muscle area) traits were characterized by estimating the linkage disequilibrium (LD) structure in Brangus heifers using single nucleotide polymorphisms (SNP) markers. Sharp declines in LD were observed as distance among SNP markers increased. The application of the GBLUP and the Bayesian methods to obtain the GEBV for growth and carcass traits within k-means and random clusters showed that k-means and random clustering had quite similar heritability estimates, but the Bayesian methods resulted in the lower estimates of heritability between 0.06 and 0.21 for growth and carcass traits compared with those between 0.21 and 0.35 from the GBLUP methodologies. Although the prediction ability of the GBLUP and the Bayesian methods were quite similar for growth and carcass traits, the Bayesian methods overestimated the accuracies of GEBV because of the lower estimates of heritability of growth and carcass traits. However, GBLUP resulted in accuracy of GEBV for growth and carcass traits that parallels previous reports.

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Section: Resultsmentioning

confidence: 99%

Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population

Peters

Kızılkaya

Sinecen

et al. 2023

Animals

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show abstract

“…The statistical model, marker density, LD, and sample size influenced on selection accuracy 43 . The accuracy of GEBVs for growth traits was reported 0.391 (GBLUP) and 0.379 (Bayes Lasso) in Yak 50 . Because of low samples size in this research, it was suggested to predict unbiased GEBVs using the associated SNPs from GWAS.…”

Section: Discussionmentioning

confidence: 99%

Genomic prediction for growth using a low-density SNP panel in dromedary camels

Sani

Harofte

Banabazi

et al. 2021

Sci Rep

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For thousands of years, camels have produced meat, milk, and fiber in harsh desert conditions. For a sustainable development to provide protein resources from desert areas, it is necessary to pay attention to genetic improvement in camel breeding. By using genotyping-by-sequencing (GBS) method we produced over 14,500 genome wide markers to conduct a genome- wide association study (GWAS) for investigating the birth weight, daily gain, and body weight of 96 dromedaries in the Iranian central desert. A total of 99 SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.002). Genomic breeding values (GEBVs) were estimated with the BGLR package using (i) all 14,522 SNPs and (ii) the 99 SNPs by GWAS. Twenty-eight SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.001). Annotation of the genomic region (s) within ± 100 kb of the associated SNPs facilitated prediction of 36 candidate genes. The accuracy of GEBVs was more than 0.65 based on all 14,522 SNPs, but the regression coefficients for birth weight, daily gain, and body weight were 0.39, 0.20, and 0.23, respectively. Because of low sample size, the GEBVs were predicted using the associated SNPs from GWAS. The accuracy of GEBVs based on the 99 associated SNPs was 0.62, 0.82, and 0.57 for birth weight, daily gain, and body weight. This report is the first GWAS using GBS on dromedary camels and identifies markers associated with growth traits that could help to plan breeding program to genetic improvement. Further researches using larger sample size and collaboration of the camel farmers and more profound understanding will permit verification of the associated SNPs identified in this project. The preliminary results of study show that genomic selection could be the appropriate way to genetic improvement of body weight in dromedary camels, which is challenging due to a long generation interval, seasonal reproduction, and lack of records and pedigrees.

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“…The genetic mechanism of yaks also shows differences between cattle and buffalo. Based on the GWAS results, the genomic predicted accuracy of BW, body size, and other traits is far lower than that found in cattle [ 29 ]. Many anticipated candidate genes were not detected in the GWAS results, indicating that alternative analyses, such as pathway analysis, are needed to identify whether genes associated with these candidates are detected.…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Association Study of Body Weight Trait in Yaks

Wang

Li²,

Wei

et al. 2022

Animals

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The yak is the largest meat-producing mammal around the Tibetan Plateau, and it plays an important role in the economic development and maintenance of the ecological environment throughout much of the Asian highlands. Understanding the genetic components of body weight is key for future improvement in yak breeding; therefore, genome-wide association studies (GWAS) were performed, and the results were used to mine plant and animal genetic resources. We conducted whole genome sequencing on 406 Maiwa yaks at 10 × coverage. Using a multiple loci mixed linear model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK), we found that a total of 25,000 single-nucleotide polymorphisms (SNPs) were distributed across chromosomes, and seven markers were identified as significantly (p-values < 3.91 × 10−7) associated with the body weight trait,. Several candidate genes, including MFSD4, LRRC37B, and NCAM2, were identified. This research will help us achieve a better understanding of the genotype–phenotype relationship for body weight.

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Accuracies of Genomic Prediction for Growth Traits at Weaning and Yearling Ages in Yak

Cited by 7 publications

References 38 publications

Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population

Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population

Genomic prediction for growth using a low-density SNP panel in dromedary camels

Genome-Wide Association Study of Body Weight Trait in Yaks

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