Multi-tool copy number detection highlights common body size-associated variants in miniature pig breeds from different geographical regions

Abstract

Background

Copy number variations (CNVs) represent a common and highly specific type of variation in the genome, potentially influencing genetic diversity and mammalian phenotypic development. Structural variants, such as deletions, duplications, and insertions, have frequently been highlighted as key factors influencing traits in high-production pigs. However, comprehensive CNV analyses in miniature pig breeds are limited despite their value in biomedical research.

Results

This study performed whole-genome sequencing in 36 miniature pigs from nine breeds from America, Asia and Oceania, and Europe. By employing a multi-tool approach (CNVpytor, Delly, GATK gCNV, Smoove), the accuracy of CNV identification was improved. In total, 34 homozygous CNVs overlapped with exonic regions in all samples, suggesting a role in expressing specific phenotypes such as uniform growth patterns, fertility, or metabolic function. In addition, 386 copy number variation regions (CNVRs) shared by all breeds were detected, covering 33.6 Mb (1.48% of the autosomal genome). Further, 132 exclusive CNVRs were identified for American breeds, 47 for Asian and Oceanian breeds, and 114 for European breeds. Functional enrichment analysis revealed genes within the common CNVRs involved in body height determination and other growth-related parameters. Exclusive CNVRs were located in the region of genes enriched for lipid metabolism in American minipigs, reproductive traits in Asian and Oceanian breeds, and cardiovascular features and body height in European breeds. In the selected groups, quantitative trait loci associated with body size, meat quality, reproduction, and disease susceptibility were highlighted.

Conclusion

This investigation of the CNV landscape of minipigs underlines the impact of selective breeding on structural variants and its role in the development of specific breed phenotypes across geographical areas. The multi-tool approach provides a valuable resource for future studies on the effects of artificial selection on livestock genomes.