The role of enhancer mutations in human complex traits

QC 20250519

Abstract

Understanding the genetic basis of diseases and individual responses to external stimuli holds immense potential for improving quality of life. Since the discovery of DNA as the carrier of genetic information, science has progressively unraveled the mechanisms of information transfer from DNA to RNA and proteins, bringing us closer to comprehending the principles of human biology. The advent of whole-genome sequencing, genome-wide association studies, and expression quantitative trait loci analysis has made the clinical and, in particular, personalized application of genomic data increasingly feasible. However, it soon became clear that highly penetrant protein-coding mutations, which are relatively straightforward to annotate, account for only ~5% of known cases, typically associated with Mendelian disorders. Most diseases are complex traits involving multiple, often regulatory, non-coding variants.

A significant challenge in annotating non-coding variants lies in two interrelated tasks: (a) predicting the regulatory activity of genomic regions, where variants are located, and (b) linking these regulatory regions to their target genes. This thesis employs sequence capture Hi-C as its primary methodological approach to address both points in a cell-type-specific manner.

In Article A, we investigated whether regulatory variants could predict chemotherapy-induced myelosuppression levels in non-small cell lung cancer patients. To this end, we analyzed interactome and bulk transcriptomic changes following carboplatin or gemcitabine treatments using three relevant hematopoietic cell lines (CMK, MOLM-1, and K-562). As a result, we demonstrated that non-coding variants, previously prioritized in 96 patients with varying degrees of myelosuppression, are enriched in interactions withgenes that exhibit differential interaction profiles upon treatment. This proof- of-concept study laid the foundation for follow-up analyses in patient-derived bone marrow samples.

In Article B, we examined the contribution of rare non-coding variants to the missing heritability of a congenital cardiovascular disorder – bicuspid aortic valve. We combined the endothelial interactome of ascending aorta samples from sixteen adult patients, focusing on all promoter-interacting regions, with individuals’ whole-genome sequencing data. Moreover, we integrated embryonic single-cell and spatial transcriptomic datasets to contextualize these findings developmentally. By leveraging innovative analytical approaches, including allele-specific expression, advanced non-redundant transcription factor motif sets, and single-patient network models, we showed that rare regulatory variants complement protein-coding mutations in shaping the fetal heart mesenchyme and fibroblast transcriptome profiles in disease patients. This work is the foundation for an expanded and more comprehensive ongoingstudy using aortic valve cells.

In Article C, we sought to elucidate the regulatory mechanisms underlying the 9p21 locus, the most significant genetic risk locus for coronary artery disease. We integrated the second part of the endothelial cell interactome dataset, focusing on the coronary artery disease-associated SNPs, with smooth muscle cell interactomes derived from the ascending aortas of six additional patients. We proved that the risk variant rs1333042 interacts with the previously unrelated MIR31HG gene, specifically in endothelial cells. Multiple layers of experimental validation supported this finding.

In conclusion, this thesis advances the field of clinical interactomics by illustrating novel cases of enhanceropathies and proposing new frameworks for integrating interactome data with bulk, single-cell, spatial transcriptomics, and whole-genome sequencing across different developmental stages. These studies offer conceptual insights and practical methodologies for understanding the non-coding genome in disease.

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