A research paper on Copy Number Variation Landscapes Underlying Breed-Specific Genome Structure in Nepalese buffaloes
Year: 2026
Type: preprint
Abstract: Copy number variations (CNVs) are large structural genomic variants characterized by the presence or absence of different copies of genomic segments relative to a reference genome, contributing substantially to phenotypic variation within populations. The present study aimed to identify genomic hotspots of CNVs in five Nepalese buffalo breeds using 64 whole-genome sequence datasets. A read-depth approach implemented in CNVnator with a bin size of 500 bp was used for CNV detection. Overlapping CNVs were merged into copy number variation regions (CNVRs). High-frequency CNVRs were defined as regions present in at least 80% of individuals within a breed. These regions were considered genomic hotspots of structural variation and were investigated for their potential association with disease resistance, environmental adaptation, and productive traits. Gene annotation was performed using ClusterProfiler and DAVID, followed by functional pathway enrichment analysis and literature mining. A total of 20,594 CNVs and 7,767 CNVRs were identified across the five breeds. Lime buffalo exhibited the highest number of CNVs (5,978), whereas Parkote buffalo showed the lowest number (2,964). Deletions outnumbered duplications in all breeds. Functional annotation revealed genes associated with mastitis resistance (CLDN34) in Lime buffalo, genes associated to metabolic substrates required for lactose synthesis during lactation (PDHA1), and immune-related genes including BTNL2 and neighboring BoLA loci implicated in tuberculosis and paratuberculosis susceptibility in Gaddi buffalo. The results provide important insights into the genomic divergence and adaptive differentiation of Nepalese buffalo breeds. This study demonstrates the potential utility of breed-specific CNVRs as genomic markers for disease resistance, productivity, and environmental adaptation in buffalo breeding programs.
Source: Research Square
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225792808