Developing and evaluating tools to improve the quality of DNA methylation association studies
Lead Research Organisation:
UNIVERSITY OF EXETER
Department Name: University of Exeter Medical School
Abstract
The overall goal of this project will be to perform a systematic analysis of spatial and temporal changes in genomic regulation occurring across development and aging. The student will use cutting-edge systems biology approaches in conjunction with comprehensive genomics datasets to explore changes in the epigenome and transcriptome across the lifespan, relating these patterns to age-associated phenotypes. They will develop genomic biomarkers of aging using molecular data from human tissue, population cohorts, and animal models of aging.
While the DNA sequence remains developmentally stable across all tissues and cells, both the epigenome and transcriptome are highly dynamic. Robust relationships have been established between gene expression and DNA methylation and age -the "Epigenetic Clock", for example, can accurately predict an individual's chronological age, regardless of tissue, from measurements of DNA methylation at 183 sites across the genome. Deviations in this clock - for example reflecting accelerated aging - have been shown to be associated with adverse outcomes including elevated all-cause mortality and cognitive decline, demonstrating its potential as a molecular biomarker of aging that may be informative for predicting health outcomes.
The student will have access to a number of large human and animal genomic datasets for this project. These include:
- extensive data from ~1,500 post-mortem brain samples with genotype, DNA methylation, DNA hydroxymethylation, ChIP-Seq and gene expression data. These samples are from donors ranging from fetal (25 days post-conception) to elderly (>100 years old).
- human population studies (including studies of twins and longitudinal epidemiological cohorts) with detailed phenotype information, genotype, and DNA methylation data.
- genomic data from animal models of aging including mice and zebrafish
The aims of this project are:
- Development of epigenetic/transcriptomic markers for biological aging. These will tested in combination with genetic risk scores for aging traits (e.g. cognitive decline, cardiovascular factors, muscle strength) to predict health outcomes.
- Assessing the translation of animal models (including zebrafish, rodents) for studying aging of the human brain in terms of gene expression and epigenetic variation.
- Investigating how genetic variation influences the trajectory of epigenetic marks or gene expression across the life course.
While the DNA sequence remains developmentally stable across all tissues and cells, both the epigenome and transcriptome are highly dynamic. Robust relationships have been established between gene expression and DNA methylation and age -the "Epigenetic Clock", for example, can accurately predict an individual's chronological age, regardless of tissue, from measurements of DNA methylation at 183 sites across the genome. Deviations in this clock - for example reflecting accelerated aging - have been shown to be associated with adverse outcomes including elevated all-cause mortality and cognitive decline, demonstrating its potential as a molecular biomarker of aging that may be informative for predicting health outcomes.
The student will have access to a number of large human and animal genomic datasets for this project. These include:
- extensive data from ~1,500 post-mortem brain samples with genotype, DNA methylation, DNA hydroxymethylation, ChIP-Seq and gene expression data. These samples are from donors ranging from fetal (25 days post-conception) to elderly (>100 years old).
- human population studies (including studies of twins and longitudinal epidemiological cohorts) with detailed phenotype information, genotype, and DNA methylation data.
- genomic data from animal models of aging including mice and zebrafish
The aims of this project are:
- Development of epigenetic/transcriptomic markers for biological aging. These will tested in combination with genetic risk scores for aging traits (e.g. cognitive decline, cardiovascular factors, muscle strength) to predict health outcomes.
- Assessing the translation of animal models (including zebrafish, rodents) for studying aging of the human brain in terms of gene expression and epigenetic variation.
- Investigating how genetic variation influences the trajectory of epigenetic marks or gene expression across the life course.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/R505110/1 | 01/10/2017 | 30/09/2021 | |||
| 1929542 | Studentship | BB/R505110/1 | 01/10/2017 | 30/09/2021 |
| Description | Two key findings have been achieved to date as part of my PhD - 1) A method for deconvoluting cell-type proportions from genomic data generated on bulk tissue. Most tissues comprise of many different types of cells, each of which is characterised by a specific genomic profile (i.e. what genes are switched on or off). I have developed a method that uses profiles from purified cell-types to work out the proportion of individual cell-types in genomic data generated on bulk tissue. This will have considerable applicability to the filed of molecular epidemiology and studies of health and disease. 2) A method for pre-processing and filtering DNA methylation sequencing data. The gold-standard method for profiling DNA methylation is bisulfite sequencing, but there are many caveats attached to this approach - e.g. coverage of specific sites and read-depth of genomic sequencing data. I have developed a method that can be used to assess the quality of DNA methylation datasets that will have wide utility for the field. |
| Exploitation Route | I envisage that these outcomes will be adopted by other research groups processing epigenetic data in the context of health and disease. For example it will enable researchers to correct for cellular heterogeneity in their data that might otherwise confound their results. |
| Sectors | Pharmaceuticals and Medical Biotechnology |
| Description | I have taken a great interest in research reproducibility and open science. As part of this, I have arranged workshops at the University of Exeter and am striving to instil a culture of research integrity in my department. |
| First Year Of Impact | 2018 |
| Sector | Pharmaceuticals and Medical Biotechnology |
| Impact Types | Policy & public services |
| Description | Reproducibility workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | I was one of two people who coordinated an event in which early career researchers could learn about the importance of reproducible science. It was a 2 day workshop with talks in day 1 and lessons in reproducible methods in day 2. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://osf.io/qfs2u/ |
| Description | Soapbox science |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Exeter's Soapbox Science is a public speaking event which aims to break down the gender stereotypes associated with careers in STEMM. Soapbox Science showcases the research of women who are making significant contributions to the scientific community. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.exeter.ac.uk/research/events/soapbox/ |