Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle

Cheng, Hao; Garrick, Dorian J.; Golden, Bruce L.; Dekkers, Jack C. M.; Park, Kyung-Do; Lee, Deukhwan; Fernando, Rohan L.

doi:10.1186/s12711-016-0279-9

Cited by 35 publications

(52 citation statements)

References 20 publications

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“…To reduce the bias of genomic predictions, future research should be done using, for example, the single-step Bayesian regression method (ssBR; Fernando et al, 2014Fernando et al, , 2016, which considers different assumptions for the effects of markers, unlike ssGBLUP. Lee et al (2017) comparing different single-step methodologies concluded that in general the results were similar between ssGBLUP and ssBR; however, the ssBR was superior when the trait is associated with QTL with large effects.…”

Section: Blending Of Traditional Information and Genomic Predictionsmentioning

confidence: 98%

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle

Campos

Reimann

Cardoso

et al. 2018

Journal of Animal Science

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The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling, and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variance components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into 4 or 5 groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyses using the historical pedigree and phenotypes contributed additional information to calculate the GEBV, and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.

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Section: Blending Of Traditional Information and Genomic Predictionsmentioning

confidence: 98%

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle

Campos

Reimann

Cardoso

et al. 2018

Journal of Animal Science

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show abstract

“…The direct genomic values obtained from these models can then be blended with BLUP breeding values. Alternatively, genomically enhanced breeding values could be obtained directly with a Bayesian single-step method (Lee et al, 2017).…”

Section: Achieving Genetic Gainmentioning

confidence: 99%

“…As this model is able to use geneological information and genotype information simultaneously, the accuracies of ssGBLUP and improvements thereof are usually at least as high as for any other method (Legarra et al, 2014). Exceptions are traits that are predominantly affected by few QTL with large effect (Lee et al, 2017), in which case direct genomic values are more appropriately estimated by a Bayesian SNP method or a Bayesian single-step method.…”

Section: Achieving Genetic Gainmentioning

confidence: 99%

Key Genetic Parameters for Population Management

Wellmann

Bennewitz

2019

Front. Genet.

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Population management has the primary task of maximizing the long-term competitiveness of a breed. Breeds compete with each other for being able to supply consumer demands at low costs and also for funds from conservation programs. The competition for consumer preference is won by breeds with high genetic gain for total merit who maintained a sufficiently high genetic diversity, whereas the competition for funds is won by breeds with high conservation value. The conservation value of a breed could be improved by increasing its contribution to the gene pool of the species. This may include the recovery of its original genetic background and the maintenance of a high genetic diversity at native haplotype segments. The primary objective of a breeding program depends on the genetic state of the population and its intended usage. In this paper, we review the key genetic parameters that are relevant for population management, compare the methods for estimating them, derive the formulas for predicting their value at a future time, and clarify their usage in various types of breeding programs that differ in their main objectives. These key parameters are kinships, native kinships, breeding values, Mendelian sampling variances, native contributions, and mutational effects. Population management currently experiences a transition from using pedigree-based estimates to marker-based estimates, which improves the accuracies of these estimates and thereby increases response to selection. In addition, improved measures of the factors that determine the competitiveness of a breed and utilize auxiliary parameters, such as Mendelian sampling variances, mutational effects, and native kinships, enable to improve further upon historic recommendations for genetic population management.

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“…However, it is still unclear how different components such as LD between markers and QTL, in addition to population structure, contribute to GEBV accuracy in the single-step analysis. Furthermore, very few studies to date have investigated the relative performance of various single-step Bayesian models (Lee et al 2017). Therefore, by using a simulation study, we investigated the contributions of GEBV accuracy in the single-step analysis in this study.…”

Section: Introductionmentioning

confidence: 99%

Factors affecting GEBV accuracy with single-step Bayesian models

et al. 2017

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A single-step approach to obtain genomic prediction was first proposed in 2009. Many studies have investigated the components of GEBV accuracy in genomic selection. However, it is still unclear how the population structure and the relationships between training and validation populations influence GEBV accuracy in terms of single-step analysis. Here, we explored the components of GEBV accuracy in single-step Bayesian analysis with a simulation study. Three scenarios with various numbers of QTL (5, 50, and 500) were simulated. Three models were implemented to analyze the simulated data: single-step genomic best linear unbiased prediction (GBLUP; SSGBLUP), single-step BayesA (SS-BayesA), and single-step BayesB (SS-BayesB). According to our results, GEBV accuracy was influenced by the relationships between the training and validation populations more significantly for ungenotyped animals than for genotyped animals. SS-BayesA/BayesB showed an obvious advantage over SSGBLUP with the scenarios of 5 and 50 QTL. SS-BayesB model obtained the lowest accuracy with the 500 QTL in the simulation. SS-BayesA model was the most efficient and robust considering all QTL scenarios. Generally, both the relationships between training and validation populations and LD between markers and QTL contributed to GEBV accuracy in the single-step analysis, and the advantages of single-step Bayesian models were more apparent when the trait is controlled by fewer QTL.

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Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle

Cited by 35 publications

References 20 publications

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle

Key Genetic Parameters for Population Management

Factors affecting GEBV accuracy with single-step Bayesian models

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