Genome-wide association study (GWAS) is a powerful tool to identify candidate genes and genomic regions underlying key biological mechanisms associated with economically important traits. In this context, the aim of this study was to identify genomic regions and metabolic pathways associated with backfat thickness (BFT) and rump fat thickness (RFT) in Nellore cattle, raised in pasture-based systems. Ultrasound-based measurements of BFT and RFT (adjusted to 18 months of age) were collected in 11,750 animals, with 39,903 animals in the pedigree file. Additionally, 1,440 animals were genotyped using the GGP-indicus 35K SNP chip, containing 33,623 SNPs after the quality control. The single-step GWAS analyses were performed using the BLUPF90 family programs. Candidate genes were identified through the Ensembl database incorporated in the BioMart tool, while PANTHER and REVIGO were used to identify the key metabolic pathways and gene networks. A total of 18 genomic regions located on 10 different chromosomes and harbouring 23 candidate genes were identified for BFT. For RFT, 22 genomic regions were found on 14 chromosomes, with a total of 29 candidate genes identified. The results of the pathway analyses showed important genes for BFT, including TBL1XR1, AHCYL2, SLC4A7, AADAT, VPS53, IDH2 and ETS1, which are involved in lipid metabolism, synthesis of cellular amino acids, transport of solutes, transport between Golgi Complex membranes, cell differentiation and cellular development. The main genes identified for RFT were GSK3β, LRP1B, EXT1, GRB2, SORCS1 and SLMAP, which are involved in metabolic pathways such as glycogen synthesis, lipid transport and homeostasis, polysaccharide and carbohydrate metabolism. Polymorphisms located in these candidate genes can be incorporated in commercial genotyping platforms to improve the accuracy of imputation and genomic evaluations for carcass fatness. In addition to uncovering biological mechanisms associated with carcass quality, the key gene pathways identified can also be incorporated in biology-driven genomic prediction methods.

中文翻译：

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基于牧场系统饲养的内洛尔牛脂肪沉积性状的全基因组关联研究和通路分析

全基因组关联研究 (GWAS) 是一种强大的工具，可用于识别与重要经济性状相关的关键生物学机制背后的候选基因和基因组区域。在这种情况下，本研究的目的是确定与基于牧场系统饲养的 Nellore 牛背膘厚度 (BFT) 和臀部脂肪厚度 (RFT) 相关的基因组区域和代谢途径。在 11,750 只动物中收集了基于超声的 BFT 和 RFT 测量值（调整到 18 个月大），其中 39,903 只动物在系谱文件中。此外，使用 GGP-indicus 35K SNP 芯片对 1,440 只动物进行基因分型，在质量控制后包含 33,623 个 SNP。使用 BLUPF90 系列程序进行单步 GWAS 分析。候选基因是通过集成在 BioMart 工具中的 Ensembl 数据库确定的，而 PANTHER 和 REVIGO 用于识别关键的代谢途径和基因网络。总共 18 个基因组区域位于 10 条不同的染色体上，并含有 23 个候选基因用于 BFT。对于 RFT，在 14 条染色体上发现了 22 个基因组区域，总共确定了 29 个候选基因。通路分析结果显示了 BFT 的重要基因，包括 TBL1XR1、AHCYL2、SLC4A7、AADAT、VPS53、IDH2 和 ETS1，它们参与脂质代谢、细胞氨基酸合成、溶质转运、高尔基复合体膜之间的转运、细胞分化和细胞发育。RFT 鉴定的主要基因是 GSK3β、LRP1B、EXT1、GRB2、SORCS1 和 SLMAP，它们参与糖原合成、脂质转运和体内平衡等代谢途径，多糖和碳水化合物代谢。位于这些候选基因中的多态性可以整合到商业基因分型平台中，以提高对胴体脂肪进行插补和基因组评估的准确性。除了揭示与胴体质量相关的生物学机制外，鉴定的关键基因途径也可以纳入生物学驱动的基因组预测方法。