عنوان مقاله [English]
Introduction: Zebu cattle are highly adapted to tropical regions. However, females reach puberty after taurine heifers, which affects the economic efficiency of beef cattle breeding in tropical regions. A method to identify new loci and confirm existing quantitative trait loci (QTL) is through genome-wide association studies (GWAS). QTL-assisted selection and genomic regions affecting the production and reproduction traits have been considered to increase the efficiency of selection and improve production performance. The GWAS typically focuses on genetic markers with the strongest evidence of association. However, single markers often explain only a small component of the genetic variance and hence offer a limited understanding of the trait under study. A solution to tackle the aforementioned problems, and deepen the understanding of the genetic background of complex traits, is to move up the analysis from the single nucleotide polymorphism (SNP) to the gene and gene-set levels. In a gene-set analysis, a group of related genes that harbor significant SNP previously identified in GWAS is tested for over-representation in a specific pathway. The present study aimed to conduct a GWAS based on a gene-set enrichment analysis for identifying the loci associated with age at first calving trait using the high-density SNPs.
Materials and methods: A total of 2273 Nelore cattle (995 males and 1278 females) genotyped using the Illumina BovineHD BeadChip were used in the current study. The association analysis included females with valid first calving records as well as open heifers. The association analyses were carried out by considering deregressed estimated breeding values (dEBV) for age at first calving as response variables. Before deregression, the estimated breeding values (EBV) were obtained for the dataset by considering both the calved and non-calved heifers. Variance components and EBV were obtained using the DMU software. In the analysis of AFC, a single-trait animal model was run. The gene-set analysis consists of three different steps: the assignment of SNPs to genes, the assignment of genes to functional categories, and finally the association analysis between each functional category and the phenotype of interest. The GWAS was evaluated using the GHap package in the R program. Using the biomaRt2 R package, the SNPs were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 15 kb up- and downstream of the gene. For the assignment of the genes to functional categories, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were used. The GO database designates biological descriptors to genes based on attributes of their encoded products and it is further partitioned into three components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge of molecular interactions and reaction networks. Finally, a Fisher’s exact test was performed to test for overrepresentation of the significant genes for each gene-set.
Results and discussion: The block sizes of five SNPs were chosen to perform association studies. Gene-set enrichment analysis has proven to be a great complement to GWAS. Among available gene-set databases, GO is probably the most popular, whereas KEGG is a relatively new tool that is gaining ground in livestock genomics. We hypothesized that the use of gene set information could improve prediction. However, neither of the gene set SNP classes outperformed the standard whole-genome approach. Gene-sets have been primarily developed using data from model organisms, such as mice and flies; therefore, some of the genes included in these terms may be irrelevant for reproduction. Gene-set enrichment analysis identified candidate genes related to estrogen metabolic process (HSD17B12), synapse organization (PPFIA2 and PPFIA2), sensory perception of mechanical stimulus (MYO3A and KCNMA1), protein tyrosine kinase activity (IGF1R), the cell-cell junction (FRMD4A), GnRH signaling pathway (ADCY5), and focal adhesion (PPP1R12A). Some of the genes which were found are consistent with some previous studies and are involved in biological pathways related to fertilization, age at first calving, estrogen biosynthesis, heifer conception rate, early development of the fetus, puberty, and glucose homeostasis in the ovary.
Conclusions: This study supported previous results from GWAS of reproductive traits, and also revealed additional regions in the cattle genome associated with these economically important traits. These findings could potentially be useful for genetic selection in cows.