Publications by authors named "Ana Guillenea"
Background: Recently, crossbred animals have begun to be used as parents in the next generations of dairy and beef cattle systems, which has increased the interest in predicting the genetic merit of those animals. The primary objective of this study was to investigate three available methods for genomic prediction of crossbred animals. In the first two methods, SNP effects from within-breed evaluations are used by weighting them by the average breed proportions across the genome (BPM method) or by their breed-of-origin (BOM method).
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- This study evaluated the effectiveness of a genomic prediction method that factors in breed origin of alleles (BOA) for predicting milk traits in a genetically diverse population of Nordic Red cattle.
- The research involved a large dataset of 39,550 animals for reference and 11,786 for validation, using traits like milk, fat, and protein as key performance indicators.
- Results showed that different modeling approaches (BOA vs. joint model) impacted the reliability of genomic predictions, with the joint model generally providing better results compared to the BOA_uncor model, although incorporating whole-genome sequencing data improved prediction reliability.