Trevor Doherty

At the end of each chromosome in a human cell is cap-like structure called telomeres. Just like the plastic tip on the end of a shoelace, the telomere keeps the DNA from fraying. As cells divide telomeres gets shorter, overtime the DNA unravels like the shoelace unravelling and the cell dies. As telomeres shorten, our tissues show signs of ageing, thus telomere length (TL) is a marker of aging. Previously, scientists identified 7 genetic determinants of TL, providing novel biological insights into TL and its relationship with disease. However, identifying genetic determinants of TL was only the first step in our journey to understand the role of TL in disease. Recently, scientists have identified a second layer of information (the epigenome) that sits on top of our DNA, acting like a molecular switch by fine-tuning how genes are regulated. The primary aim of this study is to use machine learning methods to train a predictor of telomere shortening using epigenomic profiling data. It will provide a framework for identifying biological predictors of aging, uncovering biological insights into telomere biology and may lead to the identification of potential epigenomic biomarkers and/or therapeutic targets of aging and stress-related phenotypes like depression. The primary Objectives of this study are: Use machine learning methods (e.g. LASSO penalised regression models) to train a predictor of TL based on DNA methylation (a type of epigenetic modification) in a large epidemiology sample (n= 819). Develop a methylation risk score (MRS) for telomere shortening, based on the CpG sites identified in the training set. This MRS will be validated in two independent replication blood cohorts (n=192; n=178, respectively) collated in-house that have DNA methylation and TL measured. Test whether the identified MRS for TL shortening are associated with age (e.g. Alzheimer’s Disease) and stress-related diseases (e.g. Depression) results from previously published DNA methylation-wide association studies. 4Identify the causal relationship between DNA methylation changes and TL in humans using mediation analysis. By the end of the project we will have a robust methodology utilising machine learning algorithms, which could be applied to other biological markers, such as pro-inflammatory cytokines, to examine their relationship with DNA methylation.