Next generation sequencing in disease-relevant tissues - Clifford, H (2016)
Studies of RNA and the transcriptome are of great importance in providing functional information and unravelling the genetic mechanisms that underlie complex disorders and diseases. With the vast majority of complex disease-associated variants falling outside protein-coding regions of the genome, it is likely that variations in gene expression regulation will be essential to understanding disease aetiology. Information on RNA quantity and splicing isoforms is therefore likely to be crucial for understanding complex pathologies of deleterious genetic variation. The advent of next generation sequencing has allowed the development of an assortment of technologies for interrogating aspects of the genome, one of which is high-throughput RNA sequencing (RNA-Seq). This technology allows rapid, relatively cheap, and accurate quantification of transcripts at a genome-wide scale. By providing a greater number of advantages and fewer caveats than alternative methods of transcriptome quantification, RNA-Seq is a disruptive technology that is likely to supersede most others. Throughout this thesis, I have sought to demonstrate how these advantages assist in revealing significant and novel developmental, noncoding, coding, and alternative isoform information of relevance to disorders and diseases. I take advantage of methods that utilize the truly genome-wide coverage of RNA-Seq, that quantify large numbers of transcripts, and that interrogate novel splicing events. More specifically, I present (i) the identification of novel biomarkers of the various placode-derived vertebrate cranial nerves, (ii) differential gene networks which highlight the genetics of autism intellectual disability co-morbidity, and (iii) differential gene expression underlying a form of severe influenza susceptibility. In addition to these studies, this thesis presents an R package for RNA-Seq time-series experiments, including functionality for efficient model-based clustering, and the integration of gene ontology information for cluster number selection and for subsequent profiling. Overall, this thesis demonstrates how RNA-Seq is a powerful tool for understanding disease aetiology.