Open Collections

Chapel, Madison

University of British Columbia

Genetic variation underlies phenotypic diversity and contributes to both evolutionary processes and human disease. Understanding the complex relationship between genotype and phenotype is a long-standing goal in genetics. Results from genome-wide association studies (GWAS) suggest that many complex traits, including disease liability, are highly polygenic. Unlike Mendelian forms of diseases, where most causal mutations are rare and occur in coding sequences, loci identified by GWAS have relatively high allele frequencies, and over 90% occur in non-coding DNA, suggesting that complex disease is largely driven by changes in gene expression. While both rare and common variants contribute to human disease, emerging evidence demonstrates that they combine additively to determine individual disease liability. However, due to the non-linear relationship between disease liability and disease prevalence, risk variants have more severe phenotypic consequences in high-risk polygenic backgrounds. Because variant effects may be masked or revealed in different genomic contexts, selection should be modeled as a distribution that changes across populations, time, environments, and individuals, rather than a single value. In this work, I present key consequences of polygenic background, including considerations for variant effect characterization experiments and clinical genetics. The dependence of variant effects on polygenic background raises questions about what features of a gene regulatory network (GRN) confer robustness or susceptibility to mutations. Using evolution simulations, I explore the relationship between robustness and complexity. Eukaryotic GRNs are highly complex, with non-specific transcription factor (TF) binding and combinatorial regulation of gene expression. In contrast, prokaryotes typically have sparse GRNs, with specific binding between TFs and target sites. While recombination, the reproductive strategy favoured by eukaryotes, has been shown to result in more robust GRNs, the role of GRN complexity has been underexplored. I demonstrate that robustness can evolve independently of complexity. However, in a dynamic environment, complexity emerges more rapidly in recombining populations than non-recombining ones. Overall, the work in this thesis investigates how selective pressures shape GRNs, including features such as complexity and robustness. Understanding how regulatory networks respond to mutational perturbations offers insight into both the evolution of gene regulation and the genetics of complex disease.

Text

2025-04-10

Attribution 4.0 International

http://hdl.handle.net/2429/90632

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by/4.0/