Selection for desirable phenotypes has been practiced in cattle since their domestication approximately 10,000 years ago. This process, alongside breed development and genetic improvement programs, has led to the vast diversity of cattle breeds known today. Traditional breeding methods, grounded in quantitative theory, have facilitated continuous genetic gains in most economically important traits. However, until recently, selection has been conducted without knowledge of the specific genes influencing these traits. In recent years, research has focused on integrating information from molecular markers associated with genetic variation into the selection process. This has led to the development of marker-assisted selection (MAS), aimed at reducing generation intervals and progeny testing costs by enhancing animal selection reliability. Incorporating markers into selection processes can potentially double genetic gains and decrease progeny testing costs by up to 92%. Various molecular markers have been identified for use in MAS over the past two decades, with single nucleotide polymorphisms (SNPs) emerging as the most densely distributed and reliable markers, commonly utilized in DNA chips. In addition to DNA chips, genome sequencing using Next Generation Sequencing (NGS) technologies offers an efficient approach for conducting genome-wide association studies. These studies enable the identification of genomic regions subjected to natural or artificial selection in production animals. Identification of these regions can provide insights into genes involved in economically important traits in dairy cattle. Additionally, analysis of selection signatures can lead to the identification of causal mutations conferring adaptive or productive advantages to populations, breeds, or species. Identifying these regions is a key focus of geneticists, as it enhances understanding of genome evolution processes and gene function. This proposal aimed to establish an institutional network to conduct population genomics studies efficiently, combined with NGS, to generate sufficient data for identifying polymorphisms in the bovine genome. These polymorphisms served as input data for selection signature analysis to identify regions/genes important in cattle farming. Some significant findings included the discovery of variants with high impact on protein sequences containing relevant genes in Caracu, Crioulo Lageano, and Pantaneiro breeds. Functional annotation revealed genes associated with immunity and resilience to adverse environments, as well as QTLs related to body conformation and milk traits. The project also yielded diverse results, including the development and/or improvement of methodologies for integrating SNP information into progeny tests. Using genome sequencing data from Holstein, Gir, and Girolando breeds, numerous non-synonymous SNVs and InDels affecting phenotypic variation were identified. Functional enrichment analysis revealed overrepresented pathways associated with cattle breeds, providing insights into molecular mechanisms impacting economically important traits in breeding programs. The integration of these results facilitated the pre-selection of young bulls, leading to increased genetic gains, reduced costs, and more reliable cow selection. Notably, this proposal solidified the country's position as a leader in genomic research in cattle, fostering international cooperation. The information presented in this document contributes to achieving the Sustainable Development Goals (SDGs) number 1 - Eradication of poverty: End poverty in all its forms, everywhere; 2 - Eradication of hunger: End hunger, achieve food security and improved nutrition and promote sustainable agriculture; and 8 - Dignified employment and economic growth: Promoting growth sustained, inclusive and sustainable economic development, full and productive employment, and decent work for all).