Hyperactivated serine recombinases are promising genome engineering tools because they mediate precise DNA recombination without inducing double-strand breaks. However, the genomic distribution of endogenous hyperactivated Beta recombinase recognition sites with six base spacers in goat (Capra hircus) genome has not been systematically investigated. A genome-wide computational search was performed using the Capra hircus ARS1.2 reference genome and a degenerate recognition motif for the hyperactivated Beta recombinase with a 6-bp spacer. Target sites identified on both DNA strands were merged, duplicate loci were removed, and unique sites were annotated using the reference GFF3 file. Functional enrichment of associated genes was assessed using Gene Ontology (GO) and KEGG pathway analyses. A total of 509 unique Beta recombinase recognition sites were identified across the goat genome. Most sites were located in intergenic (52.7%) and intronic (40.7%) regions, while a smaller proportion occurred within CDS (3.1%), promoters (2.2%), and exons (1.4%). Functional enrichment analysis revealed significant GO terms related to cellular regulation, signaling, and small GTPase-mediated signal transduction. GO Molecular Function analysis highlighted protein binding and GTPase regulator activity, whereas GO Cellular Component analysis showed enrichment of cytosolic and cytoplasmic components. KEGG analysis identified axon guidance as the significantly enriched pathway. This study presents the first genome-wide catalogue of naturally occurring hyperactivated Beta recombinase recognition sites with six base spacer in the goat genome. These findings provide a valuable resource for recombinase-mediated genome engineering and future precision genetic improvement in goats.
S. Pathak, Subodh Kumar, A. Sonwane· Genetics and Molecular Resea...· 0 citations
Genomic selection (GS) has transformed modern animal breeding by enabling the prediction of genetic merit using dense genome-wide molecular markers rather than relying solely on pedigree and phenotypic information. Since its conceptual introduction in 2001, GS has become a cornerstone of genetic improvement programs in livestock species, particularly dairy cattle, and has subsequently expanded to beef cattle, sheep, goats, swine, poultry, and aquaculture. The integration of high-density single nucleotide polymorphism (SNP) genotyping with advanced statistical prediction models has substantially increased the accuracy of breeding value estimation, shortened generation intervals, and accelerated rates of genetic gain. Compared with conventional best linear unbiased prediction (BLUP) and marker-assisted selection (MAS), genomic selection captures the combined effects of thousands of loci distributed throughout the genome, making it highly effective for complex quantitative traits governed by many genes of small effect. Recent advances, including single-step genomic BLUP, Bayesian prediction methods, whole-genome sequence analysis, functional genomics, multi-omics integration, artificial intelligence, and precision livestock farming technologies, have further enhanced the scope and efficiency of genomic prediction. These innovations are facilitating simultaneous improvement in productivity, fertility, feed efficiency, disease resistance, animal welfare, and environmental sustainability. Moreover, genomic information is increasingly being integrated with genome editing technologies such as CRISPR to support precision breeding strategies. This review summarizes the historical evolution, fundamental principles, methodological developments, and practical applications of genomic selection in livestock breeding while highlighting emerging innovations and future research directions that are expected to shape next-generation animal improvement programs.
S. Pathak, Amit Kumar, Vaishali Sah· International Journal of Env...· 0 citations
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