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Precision Medicine DTP – Using Omics to Understand the Genes and Pathways Underlying Alcohol Use Disorder


Project Description

Background

Alcohol use disorder (AUD) is a chronic relapsing brain disease characterised by compulsive alcohol use, loss of control over alcohol intake, and a negative emotional state when not using alcohol. It is a complex trait, influenced by multiple genetic and environmental factors1. We have shown that variation in DNA methylation in human blood explains 13% of the variance in alcohol consumption2 and our recent epigenome-wide association study in 8161 individuals has identified 1712 differentially methylated CpG sites associated with alcohol use (in preparation). Many of these CpGs are located in genes involved in neuron function and KEGG pathway analysis of the differentially methylated genes showed significant enrichment of axon guidance genes. It is not clear however, how DNA methylation signatures in peripheral blood relate to methylation in tissues more relevant for AUD. Here we propose, therefore, to generate a neuronal dataset for comparison with the blood-derived data: genome-wide DNA methylation (approximately 850,000 sites) will be characterised in a human neuronal cell line with and without alcohol exposure.

We have also shown that knockout of the Sorcs2 gene in mice leads to a decrease in alcohol preference (as well as decreased symptoms of withdrawal)3. Furthermore, exposure to alcohol increases SORCS2 expression in a human neuron-like cell line4. The mechanism(s) underlying these phenomena are not currently understood, but have clear relevance for our understanding of alcohol addiction. We therefore propose to generate whole genome methylation profiles in appropriate (dopaminergic) human neuronal cells to complement those described above: genome-wide DNA methylation will be characterised in human neurons lacking and over-expressing SORCS2 (with and without alcohol exposure). Our over-arching hypothesis is that analysis of the cell line and whole-blood derived datasets, singly and in combination, will lead to a greater understanding of the pathways impacted by alcohol.

Aims

The overall aim is to generate the datasets described above and perform analyses leading to the identification of the pathways implicated in alcohol addiction. Proliferating cells will be differentiated into neurons following established protocols and DNA samples will be collected from cells with and without exposure to alcohol. DNA samples will be profiled using the Illumina Methylation EPIC beadchip, which assesses methylation at approximately 850,000 loci. Epigenome-wide association studies (EWASs) will be performed by linear regression modelling in test and replication datasets. The CpGs associated with alcohol exposure in the dopaminergic neurons (wild-type and SORCS2 knockout/overexpression) will be compared to the methylation patterns associated with alcohol consumption in human blood. The statistical techniques will include correlation analyses of the individual CpG data and hypergeometric tests of overlap at the locus, gene and pathway/ontology level. The student will also annotate their data by interrogating existing/publicly available datasets, for example to determine whether direct genetic effects (methylation QTL [meQTL]) influence the patterns of methylation observed in the two datasets. Statistical colocalisation analyses will be used to compare the loci identified to SNPs associated with alcohol-related phenotypes by genome-wide association studies. meQTL will be used to perform Mendelian randomisation experiments in the UK Biobank dataset to assess the casual nature of identified associations.

Training outcomes

The student will gain skills in laboratory and statistical data analysis techniques. Laboratory skills include neuronal differentiation, culture and treatment. They will become adept at handling and managing large genomic datasets and will learn to evaluate the strengths and weaknesses of their data. They will acquire skills in programming (using R and Python), statistical and bioinformatics analyses. They will develop their communication skills and their ability to evaluate both the literature and their own data critically.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2020

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

References

1. Clarke, T.K., Adams, M.J., Davies, G., Howard, D.M., Hall, L.S., Padmanabhan, S., Murray, A.D., Smith, B.H., Campbell, A., Hayward, C., et al. (2017). Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117). Mol Psychiatry 22, 1376–1384.

2. McCartney, D.L., Hillary, R.F., Stevenson, A.J., Ritchie, S.J., Walker, R.M., Zhang, Q., Morris, S.W., Bermingham, M.L., Campbell, A., Murray, A.D., et al. (2018). Epigenetic prediction of complex traits and death. Genome Biol 19, 136.

3. Olsen, D., Kaas, M., Lundhede, J., Molgaard, S., Nykjær, A., Kjolby, M., Østergaard, S.D., and Glerup, S. (2019). Reduced alcohol seeking and withdrawal symptoms in mice lacking the BDNF receptor sorcs2. Front. Pharmacol. 10, 499.

4. Smith, A.H., Ovesen, P.L., Skeldal, S., Yeo, S., Jensen, K.P., Olsen, D., Diazgranados, N., Zhao, H., Farrer, L.A., Goldman, D., et al. (2018). Risk locus identification ties alcohol withdrawal symptoms to SORCS2. Alcohol Clin Exp Res 42, 2337–2348.

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