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Investigation and identification of potential biomarkers for gastrointestinal parasite resistance indicator traits in Santa Inês breed sheep

Grant number: 18/25247-2
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): April 15, 2019
Effective date (End): April 14, 2020
Field of knowledge:Agronomical Sciences - Animal Husbandry
Principal Investigator:Fernando Sebastián Baldi Rey
Grantee:Mariana Piatto Berton
Supervisor abroad: Flavio Schramm Schenkel
Home Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
Local de pesquisa : University of Guelph, Canada  
Associated to the scholarship:17/10493-5 - Genomic-wide association study for gastrointestinal parasites resistance in Santa Inês sheep breed using SNPs and CNVs, BP.PD

Abstract

The large-scale omics data provide great opportunity for biological understanding, however, the challenge in the omics studies consists in combining GWAS findings with molecular data to characterize the associations. This study aimed evaluate the different approaches effects for identification of associated with resistance parasites through meta-analyses study, as well identify integrated candidate genes and metabolic pathways related with the studied traits, by network approaches. The phenotypic and genotypic data were collected in a previously study (FAPESP 2014/07566-2). The genotypes and phenotypic records belongs to the Sergipe State Association of Goat and Sheep Breeders (ASCCO), and is used by the ASCCO/USP, Program for Animal Genetic Evaluation, which is led by the Animal Breeding and Biotechnology Group of FZEA-USP in Pirassununga. The haplotypes will be constructed using FIMPUTE software. A descriptive identification of runs of homozygosity will be identified in every individual. The data set of genomic regions identified from different methods for growth, reproduction and parasites resistance traits will be constructed from independent publications/papers related with the traits in study. The gene data will be collected from the identified QTL regions based on the flanking and peak marker locations. The markers genomic location will be determined by searching the NCBI UniSTS database. To compare between the QTL and gene expression data, the pathways will be identified through the analysis of each data type and then will be compared to determine the common set of pathways through the Bayesian network analysis. (AU)