| Summary: | This study focuses on integrating and applying computational techniques for modelling quantitative traits and complex diseases, such as hypertension and diabetes, using the rat model system and translating the findings to humans. Complex disease traits are heritable, highly polygenic, and influenced by environmental factors. Human studies, like Genome Wide Association Studies (GWAS), have identified many genetic determinants underlying these traits but have provided little information about the functional effects of these variants and mechanisms regulating the disease. This study takes a systems-level approach for looking at the genetic regulation of complex traits in the rat by analysing multiple phenotypes, genomewide genetic variation and gene expression data in multiple tissues. I integrated these multi-modality datasets in the BXH/HXB rat Recombinant Inbred (RI) lines, an established model of the human metabolic syndrome, to identify candidate genes, pathways and networks associated with complex disease phenotypes. I evaluated methods for Expression Quantitative Trait Locus (eQTL) analysis and used sparse Bayesian regression approaches to map eQTLs in the RI lines, delineating a new, large eQTL data resource for the rat genetic community. I have also developed and applied signal processing and time series analysis methods to physiological traits to extract more detailed indices of blood pressure, and integrated these with genetic, expression and eQTL data to inform on the regulation of these traits. Then, using publicly available data, I used comparative genomics approaches to elucidate a set of genes and pathways that can play a role in human diseases. This study has provided a valuable resource for future work in the rat, by means of new eQTLs in multiple tissues, and physiological time series phenotypes and approaches. This has enabled an integrative analysis of these data to give new insights into the regulation of complex traits in rats and humans.
|
|---|
