Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population1

Silva, R. M. O.; Fragomeni, B. O.; Lourenço, Daniela; Magalhães, Ana Fabrícia Braga; Irano, Natália; Carvalheiro, Roberto; Canesin, Roberta Carrilho; Mercadante, Maria Eugênia Zerlotti; Boligon, Arione Augusti; Baldi, Fernando; Misztal, I.; Albuquerque, Lúcia Galvão de

doi:10.2527/jas.2016-0401

Cited by 54 publications

(38 citation statements)

References 33 publications

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“…Compared to pedigree-BLUP, using genomic data increased prediction accuracy and decreased prediction bias for ADFI and FAT but not for ADG and LMD. Similar results were reported in cattle [ 51 ] and sheep [ 23 ], where the use of genomic data for genomic prediction was not beneficial for all traits. Use of genomic information is generally expected to increase prediction accuracy, such as the reports in chicken [ 18 , 52 ] and pigs [ 53 ], since genomic data can consider the Mendelian sampling terms better compared with pedigree information, and can produce more accurate genetic relationships among animals.…”

Section: Discussionsupporting

confidence: 84%

Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

Zhang

Kemp²,

Stothard

et al. 2018

Genet Sel Evol

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BackgroundIncreasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs.ResultsPhenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG.ConclusionsUse of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.Electronic supplementary materialThe online version of this article (10.1186/s12711-018-0387-9) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 84%

Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

Zhang

Kemp²,

Stothard

et al. 2018

Genet Sel Evol

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“…The differences regarding the pseudophenotype are in agreement with the literature (Boddhireddy et al, 2014;Morota, Boddhireddy, Vukasinovic, Gianola, & Denise, 2014). Genomic selection studies for novel traits in beef cattle (Silva et al, 2016;Fernandes Júnior et al, 2016), such as feed efficiency and beef quality traits, reported higher genomic prediction ability for adjusted phenotypic used as pseudo-phenotypes over EBV obtained from traditional BLUP. In beef cattle, the EBV obtained from BLUP model in general is less appropriated due to poor pedigree structure and small training population.…”

Section: Inflation/deflation Of Genomic Predictionsupporting

confidence: 83%

Genomic prediction for beef fatty acid profile in Nellore cattle

et al. 2017

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The objective of this study was to compare SNP-BLUP, BayesCπ, BayesC and Bayesian Lasso methodologies to predict the direct genomic value for saturated, monounsaturated, and polyunsaturated fatty acid profile, omega 3 and 6 in the Longissimus thoracis muscle of Nellore cattle finished in feedlot. A total of 963 Nellore bulls with phenotype for fatty acid profiles, were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. The predictive ability was evaluated using cross validation. To compare the methodologies, the correlation between DGV and pseudo-phenotypes was calculated. The accuracy varied from -0.40 to 0.62. Our results indicate that none of the methods excelled in terms of accuracy, however, the SNP-BLUP method allows obtaining less biased genomic evaluations, thereby; this method is more feasible when taking into account the analyses' operating cost. Despite the lowest bias observed for EBV, the adjusted phenotype is the preferred pseudophenotype considering the genomic prediction accuracies regarding the context of the present study.

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“…Thus, traditional BLUP yielded higher overestimation of EBV than the ssGBLUP method, which is not desirable as changes in the magnitude of the predictions may result in misinterpretation of the breeding values. These results are similar to those reported by Silva et al (), who concluded that ssGBLUP improves the accuracy and reduces the prediction bias of genomic breeding values in comparison with pedigree‐based traditional BLUP. Therefore, our results imply in a solid methodological support for genomic evaluation of stayability in beef cattle.…”

Section: Resultssupporting

confidence: 90%

Genomic evaluation for novel stayability traits in Nellore cattle

Ramos

Silva

et al. 2020

Reprod Domestic Animals

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Cow stayability plays a major role on the overall profitability of the beef cattle industry, as it is directly related to reproductive efficiency and cow's longevity. Stayability (STAY63) is usually defined as the ability of the cow to calve at least three times until 76 months of age. This is a late‐measured and lowly heritable trait, which consequently constrains genetic progress per time unit. Thus, the use of genomic information associated with novel stayability traits measured earlier in life will likely result in higher prediction accuracy and faster genetic progress for cow longevity. In this study, we aimed to compare pedigree‐based and single‐step GBLUP (ssGBLUP) methods as well as to estimate genetic correlations between the proposed stayability traits: STAY42, STAY53 and STAY64, which are measured at 52, 64 and 76 months of cow's age, considering at least 2, 3 and 4 calving, respectively. ssGBLUP yielded the highest prediction accuracy for all traits. The heritability estimates for STAY42, STAY53, STAY63 and STAY64 were 0.090, 0.151, 0.152 and 0.143, respectively. The genetic correlations between traits ranged from 0.899 (STAY42 and STAY53) to 0.985 (STAY53 and STAY63). The high genetic correlation between STAY42 and STAY53 suggests that besides being related to cow longevity, STAY53 is also associated with the early‐stage reproductive efficiency. Thus, STAY53 is recommended as a suitable selection criterion for reproductive efficiency due to its higher heritability, favourable genetic correlation with other traits, and measured earlier in life, compared with the conventional stayability trait, that is STAY63.

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Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population1

Cited by 54 publications

References 33 publications

Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

Genomic prediction for beef fatty acid profile in Nellore cattle

Genomic evaluation for novel stayability traits in Nellore cattle

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