Genetics of comorbidity trajectories

in cardiovascular disease and mental disorders

Research Questions

How different genes and environmental factors are involved in the comorbid appearance of cardiovascular disease and mental ill-health?

If an individual is first diagnosed with mental ill-health – what are their chances of developing cardiovascular disease? Could their medications/genes/stressful life-experiences increase this risk? 
Are different genes involved when cardiovascular disease occurs first?

Principal Investigator: Professor Thomas Werge,

OrganisationRegion H

Background

Multiple Conditions [Comorbidity]

Most clinical studies focus on one disease at a time, ignoring the fact that several conditions may occur simultaneously within the same person (co-morbidity). 

In the CoMorment project, we are investigating what happens when cardiovascular disease (CVD) and mental ill-health occur together.

Would these patients benefit from a different set of treatments?
Are they likely to have different long term outcomes?
Do genes play a role in determining these things?

Multiple Genes [polygenic]

So far, research into comorbidity [medical conditions that occur together] has only been done for rare conditions, because they are the most likely to be linked to a single gene. However, most genes have only a tiny effect on the body, so studying them individually is not very informative.  

In this project we will study many hundreds of genes and lifestyle factors simultaneously, to look for patterns. With further research, these patterns could help doctors answer their patient’s questions about future prognosis and outcomes, and could be useful to the health service for predicting future demand for services.

Our Research

Large Database

Work package 2 will make use of the tools and infrastructure created by work-package 1, which include records from hospitals, outpatient clinics and prescription registries.

From this database of around 1.8 million people, we will identify those who have both cardiovascular disease and mental ill-health (schizophrenia, bipolar disorder, major depressive disorder) and compare them to those who have neither condition or just one of them.

We are interested not only in their genetics but also in their lifestyle, disease history, and what happened to them after diagnosis (disease trajectory).

Stressful Life events

In addition to the data sets above, we will also make use of data from the SAGA cohort (The Stress And Gene Analysis cohort), in Iceland. This group of 50,000 women (~1/3rd of the female population of Iceland) were all recruited after experiencing a stressful life event or trauma. 

As well as initial assessments and gene sequencing, this group of women (aged 18-69) has given permission for researchers to track their long-term health through national records.

Using their data, we will look to answer questions such as:

  • How many of them have gone on to develop cardiovascular disease?

  • What is the timescale between the stress and the onset of symptoms?

  • Which genes or lifestyle factors have increased their risk of becoming ill – or indeed appear to have been protective?

  • Are there any genes associated with better recovery?

  • Did it matter whether or not they were diagnosed with a stress-related mental disorder (e.g. post-traumatic stress disorder?

Analytical Tools

We will use a range of both standard and newly developed statistical tools. These include:

  • Newly developed Bayesian statistics tools

  • Standard analytical tools (LDSR,[1] GWAS, polygenic risk score[2])

  • Novel MiXeR tool [3] with recent model extensions including rare sequence variants

  • Sequence Analysis (SA) [4] – diagnosis is not always a straightforward process. Timescales and other factors can vary from person-to-person. Sequence Analysis attempts to decipher how similar two patients actually are, by measuring the number of types of substitutions it would take to make their outcomes identical.

In this way we hope to identify the relevant genes, medications or lifestyle choices that increase a patient’s chance of comorbidity and/or poor health outcomes in the future. This is called a risk profile.

Validation of results

Validation is an important step. It is where we check that our results hold true, within a different sample of people.

For CoMorMent, we will use Nordica and Estonian samples to make our initial discoveries, and then check them against:

  • samples from the UK,
  • updated registry data, which becomes available every single year in the Nordic and Estonian countries (2020-2023),
  • large heterogenous non-European cohorts – to check that our results hold true for non-Caucasians too. This will increase the utility of our results across the world.

Interactions with other work packages

Work Package 2 will use their tools to investigate any rare genetic variants discovered by Work Package 3.

Work Package 4 will take the genetic patterns that WP2 identify, and try to discover what is actually happening in the cells of the body or brain as a result.

References

[1] Bulik-Sullivan, et al. LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies. Nature Genetics, 2015.

[2] Fullerton JM, Nurnberger JI. Polygenic risk scores in psychiatry: Will they be useful for clinicians? F1000Res. 2019;8:F1000 Faculty Rev-1293. Published 2019 Jul 31

[3] Frei, O., Holland, D., Smeland, O.B. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat Commun 10, 2417 (2019).

[4] ABBOTT, A. & TSAY, A. Sequence Analysis and Optimal Matching Methods in Sociology: Review and Prospect.  Sociological Methods & Re- search 29, 3{33. issn: 0049-1241 (2000).

Staff involved

Thomas Werge

Alfonso Buil

Joeri Meijsen

Frants Lüttichau

Published May 11, 2020 1:30 PM - Last modified June 23, 2020 4:37 PM