Genetic determinants of human phenotypes: understanding human infectious diseases by computational biology methods

This thesis aims to understand the genetic basis of complex human traits using systems biology (SB).SB exploits quantitative measurements, computational models, and high-throughput screening to decipher biological systems.

Next-generation sequencing technologies allow profile human molecular traits cost-effectively at large scales, which brought biology research into the multi-omics era.

Omics studies initiated with genomics.Combining genomics with transcriptomics and epigenomics enables us to study regulatory networks underlying phenotypes.This led me to study genetic regulatory relationships in health and diseases by identifying allele-specific expression and allele-specific open chromatin.

Advances in omics methods also offer opportunities to identify and study phenotype- or disease-associated genetic variants using methods like genome-wide association studies (GWAS).GWAS generated numerous genetic associations with phenotypes and diseases.However, it is not yet possible to interpret most of these findings due to limited biological knowledge.Therefore, dissecting molecular functions of GWAS variants is important in the post-GWAS era.

Importantly, integrating multi-omics data can answer: How does information flow in biological systems?The integration is biologically informative as it represents the biological signals flowing underlying phenotypes of interest or disease conditions.Moreover, single-cell methods deepen our biological knowledge by capturing characteristics and functions per cell.

I explored genetic determinants of human molecular traits - allelic imbalance and host responses to pathogenic viruses in this thesis.

Dissertation: http://hdl.handle.net/11370/a3332bad-2b7e-455d-978e-4a071b904e37