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

Abstract

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.

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

“…Even though RRM are routinely used in genetic evaluations of longitudinal traits in several countries (Interbull, 2018), GEBV are usually generated for the accumulated yield (e.g., Winkelman et al, 2015;Jenko et al, 2017) or for phenotypes taken at specific times (e.g., Martínez et al, 2017;Campos et al, 2018), which does not allow selecting animals for the complete curve pattern. Among the reasons why RRM are not combined with genomic information in official evaluations is the fact that selection decisions made on components of RRM may be less accurate than those based on accumulated production, which may reduce the possible gains due to the inclusion of genomics compared with genomic analyses of accumulated production (Oliveira et al, 2019b).…”

Ideas in Genomic Selection with the Potential to Transform Plant Molecular Breeding: A Review

“…Since then, ssBLUP has dominated the application of genomic selection in animal breeding (Campos et al 2018;Zhang et al 2016). Figure 2.…”

Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

“…Since then, ssBLUP has dominated the application of genomic selection in animal breeding (Campos et al 2018;Zhang et al 2016).…”

“…Machine learning methods (especially deep learning) have the ability to deal with complex phenotypes. Common pitfalls of applying ML methods in genomic prediction include crucial but non-trivial hyperparameter selection; determination of training, validation, and testing datasets to be used and ML methods to be applied; feature selection and dimension reduction; and dealing with overfitting and hard-to-interpret results(Crossa et al 2019; de losCampos et al 2018;Sperschneider 2019). However, a few studies have demonstrated that combining multiple ML methods(González-camacho et al 2018;Grinberg et al 2019), applying domain knowledgeLi et al 2019), and taking population structure into account(Grinberg et al 2019) can improve genomic prediction accuracy of complex phenotypes.…”