Seleção genômica e estudo de associação em um rebanho experimental da raça Nelore - Silva, Rafael Medeiros Oliveira [UNESP] (2015)
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. Animal feeding is the most important economic component of beef production systems. Due the conventional processing of cattle carcasses refrigeration after slaughter, an adequate quality carcass must have enough fat covering to guarantee its preservation and desirable quality for consume. Selection for feed efficiency has not been effective mainly due to difficult and high costs to obtain the phenotypes. The application of genomic selection using single nucleotide polymorphisms (SNPs) can decrease the cost of animal evaluation as well as the generation interval. However, there is no consensus among researches about the best methodology to obtain genomic prediction for each trait. The objective of this study was to compare methods of genomic evaluation using high-density SNP panel (BovineHD BeadChip - Illumina) for feed efficiency traits and to identify genomic regions associated to carcass traits in a small beef cattle population. After quality control, a total of 437,197 SNP genotypes were available for 761 Nelore animals from Institute of Animal Science, Sertãozinho, SP, Brazil. The data set contained 896 records for efficiency traits, such as residual feed intake (RFI), feed conversion ratio (FCR), average daily gain (ADG), and dry matter intake (DMI), 2,306 ultrasound records for longissimus muscle area (LMA), 1,832 for backfat thickness (BF), and 1,830 for rump fat thickness (RF). Methods of analysis were traditional BLUP, single step genomic BLUP (ssGBLUP), genomic BLUP (GBLUP), a Bayesian regression method (BayesCπ) and single-step genome-wide association (ssGWAS). Average accuracies ranged from 0.10 to 0.58 using BLUP, from 0.09 to 0.48 using GBLUP, from 0.06 to 0.49 using BayesCπ and from 0.22 to 0.49 using ...