Principal Investigator: Professor Xin Li
Department: Shanghai Institutes for Biological SciencesTags: 54622, Complex Traits, disease etiology, gene regulation, genetic variation, Machine Learning, transcriptome
GWAS studies have discovered numerous genetic loci associated with human complex traits, but the impact of rare genetic variant remains unknown. To complement our knowledge of the genetic architecture of complex phenotypes and disease etiology, we seek to conduct a comprehensive investigation of rare genetic variants and their functional impact. The study will utilize modern machine learning techniques to train a model of both genetic and environmental factors. Utilizing this large cohort, we seek to unveil a clear landscape of the gene regulatory circuitry underlying complex diseases. The method can be divided into two steps. First we will derive a deep learning model incorporating both sequence and epigenetic features. This will help interpret the regulatory contribution of rare genetic variants. Second we will use fine-mapping to further narrow down the causal variants underlying various human diseases and complex traits. Following these steps, we can build a deep regulatory circuitry from genetic variation to disease etiology. A successful outcome of the study can significantly enhance our understanding of pathogenesis and provide important insights on finding new therapeutics.