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Genomic tools can help in the selection of animals genetically resistant to heat stress, especially the genome-wide association studies (GWAS). The objective of this study was to compare the variance explained by SNPs and direct genomic breeding values (DGVs) at two levels of a temperature and humidity index (THI). Records of milk yield (MY), somatic cell score (SCS), and percentages of casein (CAS), saturated fatty acids (SFA), and unsaturated fatty acids (UFA) in milk from 1,157 Holstein cows were used. Traditional breeding values (EBV) were determined in a previous study and used as pseudo-phenotypes. Two levels of THI (heat comfort zone and heat stress zone) were used as environments and were treated as “traits” in a bi-trait model. The GWAS was performed using the genomic best linear unbiased prediction (GBLUP) method. Considering the top 50 SNPs, a total of 36 SNPs were not common between environments, eight of which were located in gene regions related to the evaluated traits. Even for those SNPs that had differences in their explained variances between the two environments, the differences were very small. The animals showed virtually no rank order, with rank correlation values of 0.90, 0.88, 1.00, 0.88, and 0.97 for MY, CAS, SCS, SFA, and UFA, respectively. The small difference between the environments studied can be attributed to the small difference in the pseudo-phenotypes used between the environments, on-farm acclimation, the polygenic nature of the traits, and the THI values studied near the threshold between comfort and heat stress. It is recommended that future studies be conducted with a larger number of animals and at more extreme THI levels.

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AGRADECIMENTOS À Deus, que esteve comigo em todos os momentos e me iluminou sempre. À Escola Superior de Agricultura "Luiz de Queiroz", principalmente ao Departamento de Zootecnia pela oportunidade. À Capes, CNPq e a FAPESP pelo consentimento de bolsas e pelo apoio à pesquisa. Ao Professor Dr. Gerson Barreto Mourão pela orientação, pelos conselhos e por todo aprendizado acadêmico, profissional e pessoal durante o período de mestrado, pois, um sonho fora do comum exige um orientador fora do comum, valeu Chefe! Aos professores do Departamento de Zootecnia (LZT/ESALQ/USP), em especial aos professores, Drª Carla Maris Machado Bittar, Dr. José Eurico Possebon Cyrino e Dr. Luiz Lehmann Coutinho pelos ensinamentos, pelos aconselhamentos, vocês não só transmitem conhecimentos, mais inspiram novas atitudes. A Drª Carla Cachoni Pizzolante e ao Dr. José Evandro de Moraes do Instituto de Zootecnia (IZ-Nova Odessa) pelo apoio, pela amizade e pelo aprendizado durante o mestrado. Aos meus professores desde a minha infância até aqui, especialmente meus antigos orientadores da Universidade Federal de Lavras (UFLA), Profª Drª Ana Paula Peconick, Prof. Dr. Alessandro Torres Campos e Profª DrªSarah Laguna Conceição Meirelles, que foram verdadeiros mestres, transmitindo conhecimentos e experiências que não só me ajudaram no período de mestrado, mas que guardarei pelo resto da vida.

Background: Traditionally, breeding values are estimated based on phenotypic and pedigree information using the numerator relationship (A) matrix. With the availability of genomic information, genome-wide markers can be included in the estimation of breeding values through genomic kinship. However, the density of genomic information used can impact the cost of implementation. The aim of this study was to compare the rank, accuracy, and bias of estimated breeding values (EBV) for organs [heart (HRT), liver (LIV), gizzard (GIZ), lungs (LUN)] and carcass [breast (BRST), drumstick (DRM) and thigh (THG)] weight traits in a broiler population using pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) methods using various densities of SNP and variants imputed from whole-genome sequence (WGS). Results: For both PBLUP and ssGBLUP, heritability estimates varied from low (LUN) to high (HRT, LIV, GIZ, BRST, DRM and THG). Regression coefficients values of EBV on genomic estimated breeding values (GEBV) were similar for both the high density (HD) and WGS sets of SNPs, ranging from 0.87 to 0.99 across scenarios. Conclusion: Results show no benefit of using WGS data compared to HD array data using an unweighted ssGBLUP. Our results suggest that 10% of the content of the HD array can yield unbiased and accurate EBV.