Inferring Genetic Architecture of Chicken Genome Using the Brand-New Omnigenic Model

Background and Aim: Abstract: Inferring genetic architecture of quantitative traits is the most dynamic era in genetics studies. Since Fisher's infinitesimal model cannot support the underlying genetic architecture of all traits in all situations and in the other hand, genome-wide association results based on the finite loci model couldn't sufficiently explain the phenotypic variation of complex traits, the world of genetic research requires a new comprehensive model. Therefore the alternative new Omnigenic model has uncovered a deep correlation gene network in various complex traits, yet the application of this model is not fully explored. The adjacent genes to GWAS signals related to several complex traits in chicken extracted. The extracted genes related to the complex traits uploaded to the GeneMANIA web tool in order to predict the sorts of networks. The GWAS joint analysis of the ۳ distinct trait groups revealed ۱۲۶ distinct genetic loci. The network prediction of these ۱۲۶ hub genes displayed different networks including ۶۴.۰۷ % co-expression, ۱۱.۳۵% physical interaction, ۱۰.۵۵% genetic interaction, ۷.۴۲% pathway-related networks, ۵.۳۸% co-localization, ۰.۷% predicted network and ۰.۵۴% shared protein domains in one composite network. The result indicates that it is not adequate to simply focus on a single phenotype because of complex interactions between genes related to different traits. Additionally, the high level of co-expression and dense interactions resulted in this study justifies the Omnigenic model better than infinitesimal and finite loci models in chicken as an economical domesticated animal. It can be concluded that GWAS findings do not show consistent enrichment of synaptic gene sets in the chicken. Therefore integration of multiple distinct traits will make a clear network of phenotypic relatedness under the curtain of connected genes for better justification of the Omnigenic model.Methods: Material and Method An in-silico network prediction was performed using significant signals of GWASs related to several complex traits that have conducted at Tarbiat Modares University in chicken data. The meat-quality traits, immune phenotypes, blood parameters, Ascites related traits, body weights, and carcass traits measured in the same samples of chickens were included in this analysis. A network prediction was performed for probable adjacent genes to GWAS signals with GenMANIA web software to predict how a list of genes related to seemingly unrelated-trait categories is connected together. The GeneMANIA applies the two-sect algorithm including ۱- a linear regression for calculating a single functional association network from multiple data sources and ۲- a semi-supervised algorithm detecting communities using association network structure for predicting gene function.Results: Results and Discussion The network joint-analyses of significant genes associated with various traits revealed a dense network interaction. This network of the ۱۲۶ hub genes has shown that they are linked by more than one type of network including ۶۴.۰۷ % co-expression, ۱۱.۳۵% physical interaction, ۱۰.۵۵% genetic interactions, ۷.۴۲% pathways, ۵.۳۸% co-localization, ۰.۷% predicted and ۰.۵۴% shared protein domain network (Figure ۱). Figure-۱ GeneMANIA predicted network of ۱۲۶ hub genes related to different traits in chicken illustrating dense gene-gene interaction with various types of network.Conclusion: Since the resulting constructed a well-connected network without any cluster of the disconnected marginal networks we can conclude that the inputted genes are functionally related and the functional associations captured well by the GeneMANIA algorithm (Montojo, Zuberi et al. ۲۰۱۴). Also, the gene list-specific weights calculated by GeneMANIA can be considered as an optimal weighting (Mostafavi, Ray et al. ۲۰۰۸). The output composite network contains the genes that have mostly connected to the uploaded query genes and complex gene-gene interactions linked by ۷ common types of networks.