Identifying novel genes and pathways regulating human telomere length

About the Project

Telomeres are protein bound stretches of repetitive DNA at the end of linear chromosomes that prevent the end of the DNA being incorrectly recognised as a DNA break. The telomere cannot be fully replicated during cell division leading to a loss in length with each cycle. When a critically short length is reached the cell enters replicative senescence. This is thought to be a tumour suppressor mechanism, limiting the accrual of mutations within a cell by limiting its lifespan1.

Telomere length (TL) varies both between chromosomes, between cell types and between individuals, even at birth. Both short and long TL has been linked to increased risk of common disease in humans, with longer TL increasing risk of many cancers and shorter TL increasing risk of age-related diseases such as coronary artery disease2. Whilst variable, TL is highly heritable (estimated at ~70%) and whilst certain lifestyle factors can influence it, such effects are relatively modest, suggest that variation is likely to be largely genetically determined.

We have led research into discovering the genetic determinants of human TL, recently completing genome-wide association analyses in 500,000 individuals from UK Biobank2. We identified 197 variants in 137 genomic loci that associate with TL, many of which contain genes with known roles in telomere biology. We have also previously identified novel pathways contributing to TK regulation3. However, many identified regions do not contain obvious candidate genes and investigating these regions has the potential to identify new genes and pathways. We will use a combination of statistical and bioinformatic approaches to identify causal variants (fine mapping) within TL loci alongside integrating large scale gene and protein expression data (co-localisation of eQTL / pQTL, TWAS) to identify the genes most likely regulating TL. These genes will then be experimentally validated in cellular models.

Training opportunities:

The student will have the opportunity to learn and apply a number of statistical and bioinformatics approaches to large scale “omics” datasets, whilst also being trained in fundamental molecular and cellular biology techniques.