Deep characterization of trans acting context specific expression quantitative trait loci

Supervisor: Dr Benjamin Fairfax

The majority of genetic loci associated with both normal phenotypic variation and susceptibility to common diseases are non-coding. Rather than qualitatively altering the sequence of transcribed RNA product, these loci typically reside within regulatory genomic regions and may modulate gene expression.

We are interested in characterizing the genetics of human immunity to further understand mechanisms of disease and the effects of inflammation and ageing.  Genetic loci associated with differential gene expression are called expression quantitative trait loci (eQTL). Along with others, we have demonstrated that many locally active cis eQTL only manifest in specific cell types and under certain immunological contexts.  We have similarly observed a number of cis eQTL that impact the expression of many genes in trans to the identified locus.  These eQTL are of particular interest as they provide unbiased insights into regulatory pathways in man.  They modulate key nodal genes including cytokines and transcription factors and examples include eQTL observed specific to B-cells at KLF4 as well in activated monocytes at IFNB1, IRF2 and NFE2L3.

Although we are currently refining many of these observations using RNA-sequencing, our knowledge of how other factors such as individual age and cell maturity interact with these eQTL is limited. Moreover, the immunological consequences of these trans acting variants remain uninvestigated and for many context specific eQTL the definitive inducing cytokines and pathways are unknown.

We are a dynamic and friendly group and we use a variety of immunological, genetic and computational approaches to explore interesting questions of high relevance to human health with an emphasis on chronic inflammation. 

Training opportunities: This project will involve training in standard molecular and cell biology techniques, single cell sequencing and its analysis, polychromatic flow cytometry and bioinformatics. Students will play a key role in exploring hypotheses and generating data which they will be encouraged to present at national and international meetings and publish in peer-reviewed journals.

References of work relevant to project:

For further information please contact Dr Ben Fairfax