Genome wide association study based on gene-set enrichment analysis of growth traits in a Chicken advanced intercross line

Introduction: Understanding the genetic control of growth traits is one of the most important breeding goals in poultry industry. Genomic selection has provided the poultry industry with a powerful tool to increase genetic gains on economically important traits such as meat production. One way to identify new loci and confirm existing QTL is through genome-wide association analysis (GWAA). In addition identifying of genes loci with large effects on economically important traits, has been one of the important goal to poultry breeding. 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.
Material and methods: The aim of the present study genome wide association studies (GWAS) based on Gene set enrichment analysis for identifying the loci associated with related to body weight and shank length and diameter traits in advanced intercross line (AIL) using the high-confidence SNPs that enable us to study 161376 SNP markers simultaneously. For this purpose, the 599 advanced intercross line and 161376 markers were performed with mixed linear model (MLM) approach was used for the GWAS of the F9 generation, as implemented in the GCTA package (v1.92) (Yang et al., 2011) and no any correction to adjust the error rate. The gene set analysis consists basically in three different steps: (1) the assignment of SNPs to genes, (2) the assignment of genes to functional categories, and finally (3) the association analysis between each functional category and the phenotype of interest.
In brief, for each trait, nominal P-values < 0.005 from the GWAS analyses were used to identify significant SNP. Using the biomaRt R package the SNP 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, 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 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 (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 chicken genome, can provide new opportunities for predicting production using gene set information.11 SNP markers on chromosomes 1, 2, 4, 5, 7, 8, 10, 11, and 27 located in MSTN, CAPN3, PNPLA3, ANXA2, IGF1, LDB2, LEPR, FN1, ‌TMEM135, MC4R, EDN1, and ADAMTS18 genes were identified. Some of the genes were found are consistent with some previous studies and to be involved biological pathways related to muscle skeletal growth, energy metabolism and bone growth and development. According to pathway analysis, 19 pathways from gene ontology and KEGG pathway were associated with the body weight and shank length and diameter trait (P˂0.05). Among those pathways, the regulation of muscle organ development, regulation of cell growth and anatomical structure homeostasis biological pathway has an important role in the growth and skeletal muscle development. Also, the anatomical structure formation involved in morphogenesis, positive regulation of ossification and calcium signaling pathway significant association with body weight and shank length and diameter traits.
Some of these regulatory regions, such as enhancers, are located far from the genes. Therefore, although the gene might be part of the analysis, the relevant variant would probably not be included in the gene set SNP class. Finally, linkage disequilibrium interferes with the use of biological information in prediction because irrelevant regions (regions without any biological role) capture part of the information encoded in relevant regions, causing both regions to exhibit similar predictive abilities. The use of very high density SNP data or even whole genome sequence data could alleviate some of these issues.
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.
Conclusion: Considering, this study supported previous results from GWAS of body weight and shank length and diameter traits, also revealed additional regions in the chicken genome associated with these economically important traits, presented here should be contribute to a better understanding of the genetic control of growth traits in broiler chicken and using these findings can accelerate the genetic progress in poultry breeding programs.