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Epigenetic architecture of Dupuytren’s disease
Keywords: Dupuytren's disease (Epi)genetic epidemiology Precision medicine
Researchers: prof.dr. P.M.N. Werker dr. Ilja M. Nolte Sophie A. Riesmeijer
Type of project: MD/PhD programme
Nature of the research: Genetic epidemiology, bioinformatics
Fields of study: epidemiology and statistics plastic surgery genetics

Background / introduction

Dupuytren’s disease (DD) is a common fibrotic disorder affecting the hands that causes permanent flexion contractures of the fingers. Standard treatment is aimed at reducing flexion contractures, but no cure exists. Recurrence rates following treatment are high: 21% to 85% in 5 years, depending on type of treatment.(1) Although the aetiology of DD is not well understood, it is recognized as a complex disorder with a strong genetic basis. Environmental factors associated to DD include body mass index, diabetes mellitus, hyperlipidaemia, and frozen shoulder.(2) Genome-wide association studies have identified numerous genetic variants (single nucleotide polymorphisms [SNPs]) for DD.(3) These genetic variants each have a small contribution to genetic risk for DD. Significant genetic correlations have been found between DD and (metabolic) traits and diseases, including type 2 diabetes mellitus, body mass index, and frozen shoulder.(4) Causal relationships between DD and most of these correlated traits are yet to be inferred.(5) Although known genetic factors for DD already explain a substantial portion of the disease variance (26%, unpublished work), there is also still a large part that cannot be explained. One reason for this may be epigenetics (Figure 1). Epigenetics means ‘outside or around the genetics’ and studies how environment and behaviour (e.g. smoking) can cause changes that affect the way your genes work. While genetics study the effects of heritable factors (genes) on a disease, epigenetics study the mechanism driving activity and expression of genes. Epigenetic factors can be transmitted from generation to generation, so a logical next step would therefore be to investigate the influence of epigenetic factors on DD.

Research question / problem definition

In treatment of DD, timing and type of treatment are important factors for risk of recurrence. Choice of these for the individual patient is difficult and currently based on clinical risk characteristics, often before the full extent of their DD phenotype has become apparent. Even though genetic profiling has already proven to be predictive of DD recurrence, its predictive accuracy was insufficient for clinical implementation (unpublished work). Studying epigenetics could help to uncover more genetic mechanisms driving risk for DD and DD recurrence. These epigenetic risk factors can be summarised in a patient’s epigenetic risk profile and in combination with a genetic risk profile, this profile could be incremental to clinical decision making.

Workplan

During this MD/PhD you will study epigenetic risk for DD and DD recurrence, construct epigenetic profiles, and determine their predictive abilities for DD recurrence, aiming at personalized disease risk prediction and thus precision medicine.(6) Because the epigenetic data are not yet available, you will first evaluate the causal pathway between DD and genetically correlated traits to help identify clinical risk characteristics that can also be integrated with genetic risk profiling for DD recurrence to increase predictive accuracy even further.

Study 1: In order to assess their added value in prediction of DD recurrence, causal relationships between traits and disorders genetically correlated to DD will be inferred using Mendelian Randomization (Figure 2). This statistical method uses naturally occurring genetic variation to interrogate possible causal effect of one trait on another.
Study 2: DNA methylation levels of DD patients with and without recurrence will be compared to identify epigenetic risk factors for DD in an epigenome-wide association study (EWAS).
Study 3: A genome-wide association study (GWAS) of DD patients with and without recurrence will be carried out to identify genetic risk variants contributing to recurrence in order to improve genetic risk profiling of recurrence. GWASs will also be carried out for DD in men versus women, early age of onset of DD, and diabetic DD.
Study 4: Epigenetic risk profiles will be constructed from the identified risk factors in study 2 and their predictive abilities and accuracy for DD recurrence will be assessed in an independent cohort (the Lifelines Cohort). Thereafter, genetic risk scores, epigenetic risk scores, and causal clinical characteristics (from studies 1, 3, and 4) will be integrated into a prediction model for DD recurrence and its performance assessed.

During the MD/PhD the student will learn about the concepts of (epi)genetics and performing bioinformatic analyses using R programming and bioinformatics software.

References

1. Van Rijssen AL, Ter Linden H, Werker PMN. Five-year results of a randomized clinical trial on treatment in Dupuytren’s disease: Percutaneous needle fasciotomy versus limited fasciectomy. Plastic and Reconstructive Surgery. 2012;129(2):469–77.
2. Alser OH, Kuo RYL, Furniss D. Nongenetic factors associated with dupuytren’s disease: A systematic review. Plastic and Reconstructive Surgery. 2020;799–807.
3. Ng M, Thakkar D, Southam L, Werker P, Ophoff R, Becker K, et al. A Genome-wide Association Study of Dupuytren Disease Reveals 17 Additional Variants Implicated in Fibrosis. American Journal of Human Genetics. 2017;101(3):417–27.
4. Major M, Freund MK, Burch KS, Mancuso N, Ng M, Furniss D, et al. Integrative analysis of Dupuytren’s disease identifies novel risk locus and reveals a shared genetic etiology with BMI. Genetic Epidemiology. 2019;43(6):629–45.
5. Majeed M, Wiberg A, Ng M, Holmes M v, Furniss D. The relationship between body mass index and the risk of development of Dupuytren’s disease: a Mendelian randomization study. J Hand Surg Eur Vol. 2021;46(4):406–10.
6. Shah S, Bonder MJ, Marioni RE, Zhu Z, McRae AF, Zhernakova A, et al. Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations. American Journal of Human Genetics. 2015;97(1):75–85.

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