تجزیه و تحلیل مجموعه های ژنی جهت شناسایی ژن ها و مسیرهای زیستی مرتبط با صفات بیومتری در گوسفند

Abstract
Introduction:
Genomic selection has provided the sheep industry with a powerful tool to increase genetic gains on economically important traits such as meat production (Taylor et al. 2016). In addition identifying genes with large effects on economically important traits, has been one of the important goals in sheep breeding. One way to identify new loci and confirm existing 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. Genome wide association studies typically focus 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 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 genome wide association studies (GWAS) based on Gene-set enrichment analysis for identifying the loci associated with birth weight and biometric traits using the high-density SNPs.
Materials and methods:
Phenotypes records and genotypic data related to birth weight, body length, withers height and chest girth were obtained from 277 Luzhong sheep. The gene set analysis consists basically in 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. Genome wide association study was performed with birth weight and biometric traits using GEMMA software. Using the biomaRt2 R package the SNP were assigned to genes if they were within the genomic sequence of the gene or within a flanking region of 25 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 3 components: biological process, molecular function, and cellular component. The KEGG pathway database contains metabolic and regulatory pathways, representing the actual knowledge on 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, a 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 of genome-wide association analysis (Gambra et al., 2013; Abdalla et al., 2016). 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 (Morota et al., 2015, 2016). We had 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, so it is possible that some of the genes included in these terms are irrelevant for meat production. It is likely that a better understanding of the biology underlying meat production specifically, plus an advance in the annotation of the ovine genome, can provide new opportunities for predicting production using gene set information.
In this research, 14 SNP markers on chromosomes 2, 3, 5, 7, 11, 13, 17, 19. 20 and 25 located in MYL1, MYL3, ACACA (birth weight), PLCB1, BMPR1A, LRPPRC, PTBP1, TMEM117 (body length), ADIPOR2, SYN3, TRAK1 (withers height) and PPARG, HMGA1 (chest girth) genes were identified. Some of the genes that were found are consistent with some previous studies related to birth weight and biometric traits. According to pathway analysis, 21 pathways from gene ontology and biological pathways were associated with the birth weight and biometric traits (P˂0.05). Among these pathways, muscle structure development, carbohydrate derivative metabolic process, anatomical structure formation involved in morphogenesis, skeletal system development, positive regulation of ossification, muscle cell proliferation and GnRH signaling pathway have important functions in development of skeletal muscle, glucose homeostasis, osteogenesis process, regulation of ion calcium and activation of the MAPK signaling pathway. Finally, it is worth noting that our gene-set enrichment analysis was conducted using a panel of SNP obtained from a single marker regression GWAS, which relies on a simplified theory of the genomic background of traits, without considering for instance the joint effect of SNP. Hence, other approaches (e.g., GWAS exploring SNP by SNP interactions) might provide a better basis for biological pathway analysis.
Conclusions
In total, this study supported previous results from GWAS of birth weight and biometric traits, also revealed additional regions in the sheep genome associated with these economically important traits, using these findings could potentially be useful for genetic selection in the breeding programs.
Conclusions
In total, this study supported previous results from GWAS of birth weight and biometric traits, also revealed additional regions in the sheep genome associated with these economically important traits, using these findings could potentially be useful for genetic selection in the breeding programs.
In total, this study supported previous results from GWAS of birth weight and biometric traits, also revealed additional regions in the sheep genome associated with these economically important traits, using these findings could potentially be useful for genetic selection in the breeding programs.