Decoding Complex Traits Through Functional Genomics, Multi-Omics, and Machine Learning Models

Genome-wide association studies (GWAS) have uncovered tens of thousands of genetic variants associated with complex traits and diseases. However, translating these associations into mechanistic insights and therapeutic opportunities remains a fundamental challenge. In this talk, I will present several recent studies that bridge this gap by integrating GWAS discoveries with large-scale functional and computational genomics. We combine experimental approaches-including Massively Parallel Reporter Assays (MPRA), single-cell and bulk RNA sequencing, long read sequencing and CRISPR-based perturbations-with advanced computational frameworks that leverage statistical modeling, causal inference, and machine learning models. By jointly analyzing multi-omics data at scale, we are able to identify regulatory variants, resolve cell-type-specific effects, and prioritize causal genes underlying complex phenotypes. I will also discuss emerging strategies for building predictive models of disease risk and cellular function, illustrating how close integration of computation and experimentation is reshaping our ability to connect genetic variation with molecular mechanisms and clinical outcomes.