Predicting the Structural Impact of Genetic Variants on 3D Genome Architecture Chromatin Interaction

Abstract

Mutations and single nucleotide polymorphisms are examples of genetic variations which play a critical role in the development of the structure of the genome, which is critical for its regulation. Unlike having a linear structure, the genome is structured into three-dimensional structures where various regions of the genome, for example, enhancers and promoters can interact with each other. Chromatin interactions are crucial for gene expression patterns, and genetic variations leading to disruption of these interactions can cause chromatin folding and loops to become distorted and gene expression pattern to become irregular, thus causing illnesses such as cancer and genetic disorders. Despite its importance for diagnostics, studying the effects of genetic variations on the structure of the genome poses a challenge in light of the genome’s hierarchical and dynamic nature. The Hi-C approach generates huge volumes of chromatin interaction data, which cannot be easily analyzed using conven-tional computational methods due to high dimensionality. Additionally, conventional computational algorithms lack efficiency in analysis of large data volumes as well as ability to find nonlinear relationships among different genetic elements. To address these challenges, this study proposes a machine learning approach to estimate structural disruptions resulting from genetic mutations based on the information of chromatin interactions. The genomic features used in the model are inter-action frequencies, genomic distances between two binding sites, and relative locations of a mutation in relation to nearby regu-latory regions. Machine learning algorithms like Support Vector Machine and Random Forest capture patterns of structurally disruptive mutations and enable predictions regarding the effect of mutations on structures. This study finds that the proposed approach is capable of iden-tifying structurally disruptive mutations and providing insights into the structure of the genome and biological processes relevant to the disease. This approach enables scientists to prioritize genetic variations based on their potential to disrupt the structure of the genome. Thus, this approach can be viewed as a viable substitute for traditional approaches and advances genomics and precision medicine