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Depression and chronic pain are known to be associated with each other. In this study, published in PLoS Medicine, we sought to examine whether risk of chronic pain was, like depression, conferred by the action of many genetic changes, each of small effect. Secondly, we then sought to test whether there were shared environmental contributions to chronic pain that might also explain similarities between affected family members. Thirdly, we tested whether chronic pain and depression shared overlapping genetic risk factors. Our results found that chronic pain risk was due to the action of many genetic risk factors, but that the environments shared by people in partner/spouse relationships was also important. We also found that these genetic risk factors and shared environments were common to both chromic pain and depression, and could partly explain their correlation.
STratifying Resilience and Depression Longitudinally (STRADL)
Progress in understanding the causes of major depressive disorder has been slow. Dividing depression into subtypes, a process called stratification, could ultimately lead to faster progress.
We will stratify or divide individuals with MDD and depressive syndromes into more similar groups of people in UK Biobank.
Our aims are to:
1. Identify and describe specific subtypes of depression
2. Identify the causes underlying different types of depression using GWAS and MRI
3. Test whether resistance to depression (i.e. resilience) to depression can be accurately measured.
4. Identify the mechanisms underlying resilience using genetic and brain imaging data. This research seeks to use the medical, cognitive, imaging and genetic data from UKBiobank to study the mechanisms of common medical conditions and use them as a platform to better diagnosis. These aims are consistent with UK Biobank's. Providing this information will help to identify new drug targets for depression. Stratifying depression into more homogenous categories will provide better 'disease' targets for other research studies because there will be less lumping together of individuals with different causes for their illness within the same broad category of depression. We will test whether these sub-classes of depression and depressive symptom have neurobiological associations in UKbiobank by comparing them with depressed individuals as w whole, as well as controls, using MRI and genetic data.
We will firstly examine the associations of depression with cognition (baseline measures and web-based measures of attention and memory, for example), brain structure, function and connection strength (MRI).
We will examine the association of different depression types with biological intermediates (measurable variables important in the causation of depression) using a technique called polygenic profiling.
We will also compare resilient and non-resilient individuals. We are interested in the full UKbiobank cohort for most analyses - and the subgroup of UKbiobank with genetic and imaging (brain MRI) data for more detailed analysis.
We appreciate the time scale for the availability of genotyping and imaging data.