Understanding the genetic basis of human diseases by computationally modeling the large-scale gene regulatory networks

Many severe diseases are known to be caused by the genetic disorder of the human genome, including breast cancer and Alzheimer's disease. Understanding the genetic basis of human diseases plays a vital role in personalized medicine and precision therapy. However, the pervasive spatial correlations between the disease-associated SNPs have hindered the ability of traditional GWAS studies to discover causal SNPs and obscured the underlying mechanisms of disease-associated SNPs. Recently, diverse biological datasets generated by large data consortia provide a unique opportunity to fill the gap between genotypes and phenotypes using biological networks, representing the complex interplay between genes, enhancers, and transcription factors (TF) in the 3D space. The comprehensive delineation of the regulatory landscape calls for highly scalable computational algorithms to reconstruct the 3D chromosome structures and mechanistically predict the enhancer-gene links. In this dissertation, I first developed two algorithms, FLAMINGO and tFLAMINGO, to reconstruct the high-resolution 3D chromosome structures. The algorithmic advancements of FLAMINGO and tFLAMINGO lead to the reconstruction of the 3D chromosome structures in an unprecedented resolution from the highly sparse chromatin contact maps. I further developed two integrative algorithms, ComMUTE and ProTECT, to mechanistically predict the long-range enhancer-gene links by modeling the TF profiles. Based on the extensive evaluations, these two algorithms demonstrate superior performance in predicting enhancer-gene links and decoding TF regulatory grammars over existing algorithms. The successful application of ComMUTE and ProTECT in 127 cell types not only provide a rich resource of gene regulatory networks but also shed light on the mechanistic understanding of QTLs, disease-associated genetic variants, and high-order chromatin interactions.