Genomic prediction and GWAS based on copy number variation (CNV) for milk related traits in buffalo

• Grant number: 19/25642-1
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Effective date (Start): August 01, 2020
• Effective date (End): April 30, 2023
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Humberto Tonhati
• Grantee: Alessandra Alves Silva
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil

Abstract

Copy Number Variation (CNVs) is a structural variant involving changes in copy number of specific regions of DNA (insertion or deletion). Compared to SNP, CNV may show more drastic effects on gene expression and function. Currently, SNP panels have been commonly used for the detection of CNVs in humans, which is also an interesting option for animals of production, since SNP panels have been used for genomic prediction and GWAS analysis. Given the advantages of CNV, predicting genomic breeding values (GEBV) and identifying associated genes for milk-related traits in buffalo through these markers could provide a better understanding of the genetic architecture for these traits. Thus, the objectives of this project would be to detect Copy Number Variation (CNV) in the buffalo population using a SNP panel (90K); Implement and compare genomic prediction and GWAS approaches based on CNV and SNP markers for buffalo milk related traits; and investigate the biological processes and functions for the important genes. A total of 4,588 Murrah buffaloes, of which 978 animals were genotyped with the 90K panel through the development of FAPESP-supported projects, will be used. CNVs shared between different buffaloes will be obtained using SNPs. Genetic evaluation for multiple lactations will be performed by a multivariate random regression model considering the ssGBLUP approach, in which phenotypes, pedigree, and genotypes will be combined to generate GEBV directly through the H matrix, which integrates A and the genomic relationship (G) matrices, which will be constructed using SNPs or CNVs markers. The approaches will be compared by predictive ability. GWAS analyses also will be based on both markers (SNP or CNV) for milk-related traits in each lactation. The approaches will be compared through genes found and biological processes using gene networks. (AU)