Animal Molecular Breeding 2024, Vol.14, No.1, 95-105 http://animalscipublisher.com/index.php/amb 102 Figure 4 The average reliability (R2PEV) using genomic BLUP model with only bulls (TR1) or bulls and cows (TR2) reference population (Photo credit: Boison et al., 2017) Image caption: Errors bars represent the SD based on the computed reliabilities per animal. PA = parent average EBV computed from pedigree and phenotype data at year 2014; HD = Illumina BovineHD; 50K = Illumina BovineSNP50 (Illumina, San Diego, CA); SGGP-20Ki = GeneSeek SGGP IndicusLD; GGP-75Ki = GeneSeek GGP IndicusHD (Geneseek, Lincoln, NE) (Adopted from Boison et al., 2017) 4 Challenges and Solutions 4.1 Data acquisition and handling challenges The acquisition and handling of genomic data present significant challenges in the field of livestock breeding. High-quality genotype data is crucial for accurate genomic predictions, as demonstrated in a study on beef cattle, where the accuracy of genomic predictions was influenced by the density of genetic markers and the statistical methods used (Lu et al., 2016). The importance of high-quality genotype data cannot be overstated, as it directly impacts the reliability of genomic estimated breeding values (GEBVs), which are essential for the genetic improvement of traits such as feed efficiency (Lu et al., 2016). To address the complexities of analyzing genomic data, the application of big data technologies is essential. These technologies can manage the vast amounts of data generated by high-throughput genotyping platforms, as seen in the utilization of genomic information for livestock improvement (Elsen, 2003). Big data analytics can facilitate the processing and interpretation of genomic data, thereby enhancing the accuracy and efficiency of genomic predictions (Elsen, 2003).
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