Enhancing Genetic Insights in Animal Breeding: Optimizing Low-Density SNP Panels for Distinguishing Common SNPs in Diverse Sheep Breeds

Abstract

Advancements in animal genetics, propelled by high-throughput genotyping methods like SNP arrays, have significantly expanded our  understanding of genetic diversity, evolution, and livestock breeding. Low-density SNP chips offer a cost-effective means of genotyping  large populations simultaneously. While Venn diagrams are a valuable tool for data exploration, they typically provide static views of  up to datasets. Venn diagrams illustrated the proportions of common SNPs, distinguishing unique and shared SNPs across datasets. In  this study, we aimed to develop low-density SNP panels of varying densities using Ovine 50K SNP Bead Chip data from Indian, Asian,  and exotic sheep breeds, (a) Select unique and breed specific common SNP via Quality pruning and (b) select 20k panel Using Venn  diagram through four methods. Genotyping data were sourced from publicly available databases, consortiums, and datasets referenced  in scientific literature. To facilitate our analysis, we merged three sets of sheep breeds into four combinations using appropriate merger  commands within the PLINK software. These four datasets underwent quality pruning based on various parameters and thresholds.  We generated informative SNP panels for each dataset using the TRES approach, employing the delta, FST, info and combine method  to rank markers that distinguish between the underlying breeds. Our findings, obtained through the all four methods, indicated that  the 20K SNP panel outperformed the 50K panel in distinguishing common SNPs between Asian, Indian, and exotic sheep breeds. The  incorporation of these practices elevates the validity and applicability of genetic insights, fostering informed decision-making and  propelling advancements in animal genetics and breeding.