Detection and Visualization of Heterozygosity-Rich Regions and Runs of Homozygosity in Worldwide Sheep Populations

Autor(es)

Selli, Alana ^[1] ; Ventura, Ricardo V. ^[1] ; Fonseca, Pablo A. S. ^[2] ; Buzanskas, Marcos E. ^[3] ; Andrietta, Lucas T. ^[1] ; Balieiro, Julio C. C. ^[1] ; Brito, Luiz F. ^[4]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sch Vet Med & Anim Sci FMVZ, Dept Nutr & Anim Prod, Pirassununga 13635-900, Sao Paulo - Brazil

^[2] Univ Guelph, Ctr Genet Improvement Livestock, Dept Anim Biosci, Guelph, ON N1G 2W1 - Canada

^[3] Univ Fed Paraiba, Dept Anim Sci, BR-58051900 Joao Pessoa, Paraiba - Brazil

^[4] Purdue Univ, Dept Anim Sci, W Lafayette, IN 47907 - USA

• Tipo de documento: Artigo Científico
• Fonte: ANIMALS; v. 11, n. 9 SEP 2021.
• Citações Web of Science: 0

Resumo

Simple Summary Heterozygosity-rich regions (HRRs) are regions of high heterozygosity, which can harbor important genes associated with key functional traits such as immune response and disease resilience. Runs of homozygosity (ROH) are contiguous homozygous segments of the genome, which can be informative of the population's history, structure, demography events, and overall genetic diversity. We first detected factors impacting the identification of ROH and HRR in worldwide sheep populations, which were artificially selected for specific purposes or under natural conditions. We also identified common regions of high homozygosity or heterozygosity among these populations, where a diversity of candidate genes with distinct functions might indicate differential selection pressure on these regions in breeds with different trait expression. Moreover, we evaluated a tool commonly used in the corporate environment, making use of the business intelligence (BI) concept to support managers in the decision-making process, which allowed us to combine results from multiple analyses and create visualization schemes integrating different information. Our findings and proposed tools contribute to the development of more efficient breeding strategies and conservation of genetic resources in sheep and other livestock species. In this study, we chose 17 worldwide sheep populations of eight breeds, which were intensively selected for different purposes (meat, milk, or wool), or locally-adapted breeds, in order to identify and characterize factors impacting the detection of runs of homozygosity (ROH) and heterozygosity-rich regions (HRRs) in sheep. We also applied a business intelligence (BI) tool to integrate and visualize outputs from complementary analyses. We observed a prevalence of short ROH, and a clear distinction between the ROH profiles across populations. The visualizations showed a fragmentation of medium and long ROH segments. Furthermore, we tested different scenarios for the detection of HRR and evaluated the impact of the detection parameters used. Our findings suggest that HRRs are small and frequent in the sheep genome; however, further studies with higher density SNP chips and different detection methods are suggested for future research. We also defined ROH and HRR islands and identified common regions across the populations, where genes related to a variety of traits were reported, such as body size, muscle development, and brain functions. These results indicate that such regions are associated with many traits, and thus were under selective pressure in sheep breeds raised for different purposes. Interestingly, many candidate genes detected within the HRR islands were associated with brain integrity. We also observed a strong association of high linkage disequilibrium pattern with ROH compared with HRR, despite the fact that many regions in linkage disequilibrium were not located in ROH regions. (AU)

• Processo FAPESP: 16/19514-2 - Desenvolvimento de um banco de dados genômicos para a raça Nelore e criação de ferramentas computacionais objetivando a implementação de estudos em larga escala
• Beneficiário: Ricardo Vieira Ventura
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores
• Processo FAPESP: 20/04461-6 - Uso de machine learning e dados genômicos para melhoria de características econômicas em bovinos de leite
• Beneficiário: Lucas Tassoni Andrietta
• Modalidade de apoio: Bolsas no Brasil - Mestrado