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Title: Quantitative trait analysis in a panel of recombinant inbred rat strains
Author: Grieve, Ian C.
ISNI:       0000 0004 2683 9103
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2010
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Expression quantitative trait loci (eQTLs) are generated by the combined use of microarray technology to measure gene expression and genetic linkage analysis to map the expression traits to the genome. This thesis describes co-expression and quantitative trait transcript (QTT) analysis carried out on a dataset consisting of thousands of cis- and trans-eQTLs. These were mapped in 29 rat Recombinant Inbred strains derived from a cross between the Spontaneously Hypertensive Rat (SHR), a widely used model of the human metabolic syndrome, and the normotensive Brown Norway (BN). Gene expression data from four tissues relevant to the metabolic syndrome and cardiovascular disease were analysed: fat, kidney, adrenal gland and left ventricle. By systematically applying a rigorous statistical methodology to the eQTL dataset, a consistent, distinct correlation structure was observed. Co-expression of groups of transcripts linked to a common region of the genome, referred to as trans-eQTL clusters, was investigated. Some of these cluster-forming groups were found to remain significantly correlated after the effect of genotype was accounted for, and functionally enriched. An example of successful application of QTT analysis to the dataset is described. This contributed to the identification of Ogn as a regulator of left ventricular mass in rodents and subsequent implication of the homologue of Ogn in a related role in humans. Correlation of a further 103 physiological traits with cis-eQTLs in each of the four tissues was also carried out; analysis which potentially informs a wide range of hypotheses concerning relevant phenotypes. Together, the findings described here demonstrate the utility of a systematic computational approach using correlation-based methodologies in combination with appropriate statistical techniques to inform the genetic analysis of complex traits. These findings indicate the importance of understanding potential confounding factors in eQTL analysis, as well as the potential of the eQTL approach to stimulate gene discovery.
Supervisor: Aitman, Tim ; Petretto, Enrico ; Stumpf, Michael Sponsor: Wellcome Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral