Studying the function of human genetic variation in the light of 3D genome organization

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Abstract The search for causal genetic variants associated with specific diseases among many variants identified by genome-wide association studies (GWAS) has been rejuvenated several times by the increase in throughput, resolution and the number/modality of experimental techniques that are broadly available. Advances in capturing cell-type-specific physical proximity among genomic regions also added a new dimension for this search by revealing the importance of 3D genome organization in interpreting the role of genetic variants in gene regulation. A number of combinations of these different techniques have proven useful in identifying a number of causal variants but many major challenges remain in our goal towards creating complete maps of genotype-phenotype associations for complex diseases. Our recent focus has been to address an important gap in the current knowledge of how genetic variants may impact 3D genome organization with or without a measurable impact on gene expression of the cell state/type that is available for molecular characterization. We have identified a number of genetic variants that associate with read coverage, strengths of specific loops and/or overall connectivity of large genomic regions. Leveraging our expertise in computational analysis and the newly established experimental component of our lab, we will address a number of questions emerging from our recent findings within the next five years. We will first define and characterize the role of genetic variants, which we found to be associated with specific chromatin loops and/or overall connectivity of regions harboring regulatory elements in specific human immune cell types. Next, we will perform long read-based assays and develop accompanying analysis methods to resolve allele-specificity and connection modality of multi-way interactions involving regulatory elements. Throughout the project period, we will continue developing computational methods for integrative, comparative and high-resolution analysis of conformation capture data. As we have done before, our methods development will be in alignment with biological questions we are trying to answer but with flexibility and generalizability in mind for their broad utility by other researchers.