New challenges have arisen with the development of large marker panels for livestock species. Models easily become overparameterized when all available markers are included. Solutions have led to the development of shrinkage or regularization techniques. The objective of this study was the application and comparison of Bayesian LASSO (B-L), thick-tailed (Student-t), and semiparametric multiple shrinkage methods. The B-L and Student-t methods were also each analyzed within a single shrinkage and a multiple shrinkage framework. Simulated and real data were used to evaluate each method's performance. Real data consisted of SNP genotypes of 4,069 Holstein sires. Traits included in analysis of real data were milk, fat, protein yield, and somatic cell score. The performance of each model was compared based on correlations between true and predicted genomic predicted transmitting abilities. Model performance was also compared with the performance of routinely used methods such as Bayes-A and GBLUP through cross-validation techniques. When using simulated data regardless of shrinkage framework, shrinkage models outperformed genomic BLUP (GBLUP). The average advantage of shrinkage models ranged from 1% to approximately 8% depending on the prior specification. When analyzing real data, shrinkage models slightly outperformed GBLUP for most traits. Shrinkage models were better able to model traits for which 1 or more SNP of large effect have been identified. Overall, results suggested a relatively small advantage in multiple shrinkage models. Multiple shrinkage methods could represent a useful alternative to current methods of prediction; however, their performance in a variety of scenarios needs to be investigated further.
This review presents the evolution of dairy genetic methods to estimate breeding values. For centuries, human action has shaped animal populations by choosing progenitors of the next generation. Since the twentieth century, applied concepts were integrated into a new discipline, quantitative genetics. The past quarter-century in genetic evaluation of dairy cattle has been marked by evolution in methodology and computer capacity, expansion in the array of evaluated traits, and globalization. Selection index was replaced by mixed model procedures and animal models replaced sire and sire-maternal grandsire models. Recently, application of Bayesian theory to breeding values prediction and variance components estimation has become standard. Individual test-day observations have been used more effectively in the estimation of lactation yield as direct input to evaluation models. Computer speed and storage are less limiting in choosing procedures. National evaluations combined internationally provide evaluations for bulls from all participating countries on each of the national scales, facilitating choices from among many more bulls. Selection within countries has increased inbreeding and the use of similar genetics across countries reduces the previously available genetic diversity. Finally, considerable progress in genomics has created a new tool, genomic selection. The collection and analysis of several types of phenotypic data to evaluate genetic merit will continue to be the most important tool for genetic progress in the foreseeable future. Information will increasingly be obtained from smaller reference populations and the extrapolation from these data will require careful validation.
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