Non-coding single nucleotide variants (SNVs) are increasingly being identified as genetic contributors to complex disease often exerting their effects through the regulation of gene expression. Classical approaches to characterise the ability of a SNV to affect gene expression at the molecular level include measuring allelic differences in transcription factor binding and transcriptional activity. Neither of these approaches, however, measures a SNV in vivo, in its natural chromatin context, or in multiple cell types. This thesis presents a standardised way of assessing the effects SNVs have on gene expression using the classical approaches and describes two novel methods to assess a SNV in vivo. Our approach can be applied to any non-coding variant in the genome in a robust, scalable way. We applied this method to SNVs in VNN1, a novel cardiovascular disease gene that has been identified as being both cis-regulated and associated with high-density lipoprotein-cholesterol levels. SNVs within VNN1 are first statistically prioritised for likelihood of being functional (i.e. regulating gene expression) and then are subject to our molecular prioritisation strategies. By delineating the effects SNVs on gene expression, the regulation of a gene can be better understood with the ultimate aim of elucidating it’s involvement in disease.
|Qualification||Doctor of Philosophy|
|Publication status||Unpublished - 2013|