Genomic selection and genome-wide association study with carcass composition indicator traits in Nellore cattle - Silva, Rosiane Pereira da (2021)
The growing global demand for safe and sustainable food production has motived a restructuring in the beef production sector aiming the production of better quality products without increasing the productive cost. Thus, animal breeding aims to improve economic productivity of future generations of domestic species through selection. Most of the economic important traits in livestock has a complex and quantitative expression, that is, they are influenced by a large number of genes and affected by environmental factors. However, there is no consensus among researches about the best methodology to obtain genomic prediction for each trait. There are different methods and pseudophenotypes used in genomic predictions, being necessary to determine the ideal for each trait of interest. In Chapter 2, the aim of this study was to estimate genetic parameters and identify genomic regions associated with carcass traits obtained by ultrasound and visual scores in Nellore cattle. Data from ~66,000 animals from the National Association of Breeders and Researchers (ANCP) were used. The variance components for back fat thickness (BF), rump fat thickness (RF) and Longissimus muscle area (LMA) were estimated considering a linear model whereas a threshold model were fitted for body structure (BS), finishing precocity (FP) and musculature (MS) traits were used. The SNP solutions were estimated using the ssGBLUP approach by considering windows of 10 consecutive SNPs. Regions that explained for more than 1.0% of the additive genetic variance were used. Gene enrichment analysis revealed GO biological processes that might be directly influenced the organism growth and development. In Chapter 3, the aim of this study was to compare the genomic prediction ability for carcass composition indicator traits in Nellore cattle using the Best Linear Unbiased Prediction (BLUP), Genomic BLUP (GBLUP), singlestep GBLUP (ssGBLUP), Bayesian methods (BayesA, BayesB, BayesC and BayesianLASSO) and an approach combining the pedigree matrix of genotyped animals and the genomic matrix using a Bayesian analysis. Phenotypic and genotypic information on about 66, and 21,000 animals, respectively, evaluated by ANCP were available for BF, RF, LMA, BS, FP and MS. To obtain the prediction ability, the dataset was split into training (genotyped sires and dams with progenies) and validation (genotyped young animals without progeny records and without phenotypes) subsets. In terms of prediction ability and bias, Bayesian approaches were superior for visual scores traits and the ssGBLUP for carcass traits obtained by ultrasonography, however, more biased results were obtained for BF and RF using the ssGBLUP. The ssGBLUP model showed less biased prediction for low heritability traits, such as LMA, and also it has lower computational demand and it is a straightforward method for implementing genomic selection in beef cattle. In Chapter 4, the aim of this study was to estimate genetic parameters and to identify genomic regions associated with the calving ease (CE) in precocious Nellore heifers. A total of 1,277 CE phenotypes were collected and scored into two categories: i- non assisted calving, categorized as success (1) and ii- assisted calving where heifers required any form of assistance or intervention to give birth, categorized as failure (2). The direct and maternal heritability estimates for CE were low (0.18) and moderate (0.39) respectively, indicating that genetic progress for this trait is feasible, and so, it would respond favorably to direct selection. Gene enrichment analysis revealed processes that might directly influence fetal processes involved in female pregnancy and stress response.