Genomic-based approaches for identifying risk loci and facilitating precision medicine in human diseases

QC 2026-03-30

Abstract

Genomics represents the first layer of the central dogma, where variations in the genome influence downstream biological processes and finally affect human phenomics. Although next-generation sequencing (NGS) technologies have generated massive amounts of genomic data, gaps remain in translating genomics into clinical and precision medicine applications.

The first part of this thesis (Papers I–II) focuses on developing high-performance computational platforms for clinical genomic and research applications. In Paper I, we developed GenRiskPro, a platform designed to enhance connectivity among key stakeholders in clinical genetics, including hospitals, research facilities, clinicians, and patients. It prioritizes reporting genetic risk variants for rare diseases and includes tools for detecting pharmacogenomic (PGx) and lifestyle-associated variants affecting complex traits. We also revealed population-specific genetic heterogeneity in the enrichment of pathogenic and low-to-rare-frequency risk variants, which frequently exhibit low penetrance in their phenomic presentations.

In Paper II, we developed OncoRisk, a comprehensive web server integrating multiple precision oncology knowledge bases and pan-cancer cohorts. The server comprises four major modules. It enables the query of key oncogenic terms such as mutations, gene fusions, diseases, and therapies, and supports analysis of individual tumor sequencing data to identify potentially significant mutations by mapping with known oncogenic resources. We also built functions for fast visualization and analysis of cancer cohort data and for querying of mutation frequencies for specific genes or variants across large-scale cancer sequencing cohorts.

The second part of this thesis (Papers III–IV) explores the genetic architecture of complex diseases using our in-house cohorts. In Paper III, we explored the survival effects of the expression quantitative trait loci (eQTLs). Firstly, we identified 805 eGenes and 4,558 cis-eQTLs in a Japanese kidney cancer cohort (n=100). Then, we validated these findings cross-ethnically using TCGA data (n=287) through comprehensive survival analyses across different allelic and covariate models, revealing regulatory variants with consistent effects on patient survival. Lastly, in Paper IV, we conducted an integrative genomic-transcriptomic study in a pediatric congenital heart disease (CHD) cohort. We have identified known pathogenic variants and found that rare missense variant burdens of CHD-associated genes were significantly enriched in CHD patients compared to controls. Transcriptomic analysis revealed shifts in oxidative phosphorylation and interferon signaling. Functional analysis of overlapping eGenes and differentially expressed genes (DEGs) highlighted involvement in small GTPase-mediated signaling and cytoskeleton organization, and integration of rare-variant burdens further identified high-confidence candidates.

In summary, this thesis demonstrates the complex impacts of rare and common variants on human health across both rare and complex disease contexts. By developing computational platforms to assist in identifying risk loci and leveraging genomic data to uncover novel potential drivers, this work aims to advance translational genetics and precision medicine.

Link to DiVA