GeneSight: Advancing Insights into Complex Genetic Traits through CFDE-Enhanced Knowledge Graphs

Abstract: Unraveling the molecular mechanisms that link SNPs and genes identified in GWAS studies to the disease is a challenge that must be overcome to translate these genetic discoveries into actionable health insights. We would like to build a machine learning tool on the basis of visible neural networks (vNN) that recently showed success to provide predictive and explanatory

power on cellular responses to gene regulations or disease treatment. Key to the vNN approach is an understandable network such as Knowledge Graph that contains rich annotations of relevant entities and relationships among entities organized by integrative data sources. Our hypothesis is that organizing and integrating data from the Common Fund Data Ecosystem

(CFDE) can enhance the explanatory power of vNN to illuminate GWAS results. We propose combining the ROBOKOP Knowledge Graph with diverse biological data from the CFDE, and vNN as a knowledge-based architecture to provide high interpretability in supervised learning. ROBOKOP Knowledge Graph will serve as an organizational hub for integrating CFDE data

with existing knowledge. This query-able resource for CFDE data will be our first deliverable. We will extract network-based relationships from this data and train vNN using genotypes and phenotypes from T2D-focused GWAS, providing our second deliverable. The trained vNN, our third deliverable, will enable the prediction of T2D phenotypes from genotype data. Lastly, we'll

provide the code base as a platform to expand this KG and vNN approach to other GWAS studies and potentially be generalized for genome wide ‘omic analyses with large data sets.