Investigating causal relationships between chronic pain and major depression using UK general population datasets with whole-genome genotyping

Strategic priority area:Mathematics, statistics & computation
Keywords:Chronic pain, depression, genomics

Major depressive disorder (MDD) & chronic pain are frequently comorbid (Nicholl et al 2014), genetically correlated (McIntosh et al 2016), have shared environmental risk factors (McIntosh et al 2016) & they make up the biggest contributor to days lost due to disability worldwide (GBD Collaborators 2013). The extent to which their genetic correlation is due to pleiotropy (single genetic variants affecting multiple traits) or clinical heterogeneity (genetically distinct subgroups of MDD patients who are more similar to those with chronic pain, and vice versa) is unknown, as are causal directional relationships between pain, chronic-pain-involving disorders and MDD. Whole-genome genotyping information in large general-population cohorts can be exploited in order to better investigate relationships between chronic pain and MDD using analyses such as BUHMBOX (Breaking Up Heterogeneous Mixture Based on cross(X)-locus correlations) (Han et al 2016), Mendelian Randomisation (MR) and Structural Equation Models (SEMs).

Background:MDD & chronic pain are highly prevalent and often co-morbid, & are the two most-common causes of disability globally. MDD is a serious mood disorder characterised by psychological & physical symptoms including persistent low mood, anhedonia, pain & sleep changes amongst other symptoms. Chronic pain is defined as pain lasting longer than 12 weeks, & is a symptom of many disorders including MDD. Both conditions are heterogeneous & have a complex polygenic architecture. The importance of 'above the skin' and pre-emptive/ preventative interventions when investigating important conditions in public health has been emphasised (Gillman & Hammond 2016). Distinguishing pleiotropy from clinical heterogeneity, mapping causality along with directionality in pain-MDD relationships, & bringing in social, lifestyle, psychological & economic factors into the analysis of these conditions is of great importance. This has the potential to improve classification and treatment of both MDD & pain patients and highlight new causal genetic pathways and targets for intervention, & can be achieved using analyses such as BUHMBOX, MR and SEM using large, well-phenotyped datasets such as UK Biobank & Generation Scotland & their whole-genome genotyping data.

Aims:This project aims to quantify the genetic correlation between depression and chronic pain on the liability scale in large UK-based cohorts, namely GS and UKB, to pick apart pleiotropy and heterogeneity in MDD and chronic-pain cases, to assess the direction & causality of relationships between pain and MDD using a Mendelian Randomization approach, to assess directionality of relationships between pain and depression and 'above the skin' social, psychological, medical and lifestyle factors using SEM approaches, and to analyze the implications of the above in terms of translational applications.

References:Nicholl et al (2014) ' Chronic multisite pain in major depression and bipolar disorder: cross-sectional study of 149, 611 participants in UK Biobank' BMC Psychology 14(1): 350
Global Burden of Disease Study Collaborators (2015) ' Global regional and national incidence, prevalence, & years lived with disability for 301 acute & chronic diseases & injuries in 188 countries 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013' Lancet 386: 743
McIntosh et al (2016) ' Genetic and environmental risk for chronic pain and the contribution of risk variants for major depressive disorder: a family-based mixed-model analysis' PLoS Medicine 13(8): e1002090
Gillman & Hammond (2016) 'Precision treatment & precision prevention: integrating 'below & above the skin'' JAMA Pediatrics 170(1): 9
Han et al (2016) 'A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune & neuropsychiatric diseases