RESUMO
In livestock breeding, single nucleotide polymorphism arrays have become a cornerstone of modern livestock breeding. SNP arrays facilitate the identification of genetic markers linked to economically important traits and provide a powerful tool for predicting breeding values. However, conventional breeding programs often overlook additional genomic features contained in the SNP array data that can provide valuable insights into the genetic diversity, copy number variation, inbreeding levels and potential challenges in breeding lines. Here we present genomeprofile, a tool using SNP array-based genomic data, offering a comprehensive profile of breeding animals including the identification of copy number variants and runs of homozygosity, and screening for aneuploidy. By integrating these features into the breeding landscape, genomeprofile enables a more comprehensive picture of genomic variation, ultimately enhancing precision breeding strategies. To illustrate the practicality and efficacy of genomeprofile, we applied the tool to a dataset of four pig breeding lines. The genomeprofile tool is a user-friendly tool that processes genotype data in finalreport or plink ped format efficiently into useful output. The output contains copy number variations, runs of homozygosity, selection signatures, aneuploidy and inbreeding per individual and across populations. This allows breeding companies and researchers to identify unique individuals or regions in the genome of interest based on routinely collected data.
RESUMO
BACKGROUND: Mastitis is a disease that incurs significant costs in the dairy industry. A promising approach to mitigate its negative effects is to genetically improve the resistance of dairy cattle to mastitis. A meta-analysis of genome-wide association studies (GWAS) across multiple breeds for clinical mastitis (CM) and its indicator trait, somatic cell score (SCS), is a powerful method to identify functional genetic variants that impact mastitis resistance. RESULTS: We conducted meta-analyses of eight and fourteen GWAS on CM and SCS, respectively, using 30,689 and 119,438 animals from six dairy cattle breeds. Methods for the meta-analyses were selected to properly account for the multi-breed structure of the GWAS data. Our study revealed 58 lead markers that were associated with mastitis incidence, including 16 loci that did not overlap with previously identified quantitative trait loci (QTL), as curated at the Animal QTLdb. Post-GWAS analysis techniques such as gene-based analysis and genomic feature enrichment analysis enabled prioritization of 31 candidate genes and 14 credible candidate causal variants that affect mastitis. CONCLUSIONS: Our list of candidate genes can help to elucidate the genetic architecture underlying mastitis resistance and provide better tools for the prevention or treatment of mastitis, ultimately contributing to more sustainable animal production.