عنوان مقاله [English]
Introduction: Identifying genes with large effects on economically important traits, has been one of the important goals in sheep breeding. 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 traits have been considered to increase the efficiency of selection and improve production performance. 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 gene-set enrichment analysis for identifying the loci associated with economic traits using the high-density SNPs.
Materials and methods: In this research, to identify genes and biological pathways associated with some economic traits, GWAS based on gene-set enrichment analysis was conducted in a F2 population derived from a reciprocal cross by using Illumina iSelect 4K Japanese quail SNP Bead chip. For each bird, traits including body weight gain, feed intake, feed conversion ratio, tibia ash, and foot ash were measured. The SNPs that were associated with traits were identified based on mixed linear models using GCTA software and no correction was made to adjust the error rate. The gene-set analysis consisted of three different steps: (1) the assignment of SNPs to genes, (2) the assignment of genes to functional categories, and (3) the association analysis between each functional category and the phenotype of interest. In brief, for each trait, nominal P<0.005 from the GWAS analyses were used to identify significant SNPs. Using the biomaRt R package, the SNPs were assigned to genes if they were within the genomic sequence of the gene or a flanking region of 15 kb up- and downstream of the gene, to include SNP located in regulatory regions. 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. The gene enrichment analysis was performed with the goseq R package. In the next step, bioinformatics analysis was implemented to identify the biological pathways performed in BioMart, Panther, DAVID, and GeneCards databases.
Results and discussion: 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. It is likely that a better understanding of the biology underlying meat production specifically, plus an advance in the annotation of the quail genome, can provide new opportunities for predicting production using gene-set information. 11 SNPs on chromosomes 2, 3, 4, 5, 10, 18, 20, 24, and 27 located in NPY, DRD2, PTPRN2, BMPR1B, MYF5, IGF2BP1, MYO1E, FGF2, LDB2, BMP4, ACOX1, PCK1, PLCB4, PLCB, and PLCG1 genes were identified. According to gene-set enrichment analysis, 23 categories from gene ontology and the KEGG pathway were associated with the traits (P˂0.05). Among those categories, Protein glycosylation, Myoblast differentiation, Positive regulation of muscle cell differentiation and Biological MAPK signaling pathway, and Calcium signaling pathway have a significant association with skeletal muscle fiber, feed intake, and availability utilization.
Conclusions: This study supported previous results from GWAS and revealed additional regions associated with these economically important traits. Using the findings of this study could potentially be useful for genetic selection to improve production in Japanese quail.