The effect of high-density genotypic data and different methods on joint genomic prediction: A case study in large white pigs

发表于 2025-3-17 08:20:18

In this study, the effects of high-density genotypic data and different methods on joint genomic prediction were observed in Large White Pigs. The application of genomics in animal breeding has enormous potential for improving genetic gains. This study aimed to evaluate the advantages and disadvantages of using different genomic prediction methods with high-density genotypic data in Large White pigs.

The different genomic prediction methods that were used include GBLUP (Genomic Best Linear Unbiased Prediction), Bayes C, and gBLUP (generalized Best Linear Unbiased Prediction). These were analyzed in comparison to each other and also regarding the influence of high-density genotypic data.

Results from the investigation indicated that high-density genotypic data did positively affect the accuracy of the genomic prediction, with all models benefiting from this. However, differences between methods were also found. The study suggests that choosing the appropriate genomic prediction method should consider the characteristic of the trait for which prediction is being made and the structure of the dataset.

This study contributes significantly to understanding the effect of high-density genotypic data and various genomic prediction methods. It provided essential insights that can help select the suitable methods for genomic prediction depending on the specific traits of interest and characteristics of the dataset.

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