Genomic studies associated with resistance to endoparasites traits in Santa Inês sheep

Abstract

The Brazilian sheep flock has been increasing but is not self-sufficient to attend the demand of lamb market. Some reasons that contribute to that is the old-fashioned management adopted by most producers in which animals are raised extensively with little or no technology, and the susceptibility to the of the gastrointestinal endoparasites, specially the Haemonchus contortus. Some traits used to measure the resistance of gastrointestinal endoparasites are Color of Ocular Conjunctiva (COC), fecal Eggs Per Gram (EPG), Total Plasmatic Protein (TPP), Packed Cell Volume (PCV) and Stool Test (ST%) which measures the percentage of endoparasite Haemonchus contortus. Taking into account the important to study the endoparasitism resistance of the sheep flock, the objectives of this project will be 1) to genotype about 1,150 Santa Inês sheep with Ovine SNP50 Genotyping BeadChip (Illumina); 2) to estimate genetic parameters for traits related to gastrointestinal endoparasitism resistance using mixed models by the method of restricted maximum likelihood and Bayesian inference; 3) to test Bayesian Lasso and Regularized Neural Networks to predict genomic breeding values using the SNPs genotypes; 4) to conduct genomic association studies (GWAS) for the traits related to endoparasitism resistance; 5) to calculate the linkage disequilibrium of the population and inbreeding levels using traditional pedigree and genomic information by the runs of homozygosity approach (ROH); 6) to identify regions with copy number variation in the DNA segments (CNVs) to detect candidate genes related to the studied traits. Functional enrichment analyzes will be performed from the regions identified by GWAS and CNV using the DAVID software (v.6.7). It is expected with this project to access the estimates of genetic parameters related to endoparasitism resistance to include them in the selection objectives in the breeding program of sheep; to evaluate the gain in prediction accuracy of genomic breeding values using the single nucleotide polymorphism in the genomic models over traditional mixed model; to use the genomic information to access the inbreeding of individuals to evaluate how to control the inbreeding; and to characterize the genomic regions associated with the resistance traits in order to identify candidate genes. (AU)