A multi-omic approach for the identification of upstream drivers of shared phenotypes

Many conditions exist for which we still do not know the cause, often due to their complexity and rarity. Without knowledge of their underlying mechanisms, development of effective drugs and personalised approaches to treatment of these diseases will never be possible. Consequently, we have a responsibility to develop novel, systematic, and objective analytical approaches which can produce reliable predictions of causative mechanisms as rapidly as possible. The recent trend for such analyses has been to generate extraordinarily large datasets, however these datasets are time consuming to produce, costly, and only accessible by a small percentage of the scientific community. There instead exists a vast quantity of publicly available, modestly sized datasets; alone, these datasets are unreliable - too underpowered to sufficiently support the hypotheses they are associated with - yet the quantity of these small datasets suggests untapped potential for analysis when used in combination.

In this thesis we present a novel approach built to harness the potential of small datasets. We have taken advantage of the inherent variability of tissue types, platforms, and genetic backgrounds to produce gene expression signatures shared by groups of patients exhibiting a common phenotype. We demonstrate application of the pipeline to three diseases phenotypes and show how each signature consistently enriches with genes and pathways associated with the shared phenotype as well as containing known upstream drivers. We also show significant enrichment of snp-associated GWAS genes in our autoimmune arthritis signature, as well as demonstrating preliminary in silico validation of one known and 2 novel upstream drivers of TDP-43 pathology and neurodegeneration through perturbation in an iPSC model. We believe the value of our approach is not only to reveal previously unknown upstream drivers of rare and complex conditions, but do so with a fraction of the resources conventionally devoted to this process.