Principal Investigator: Professor Kari Stefansson

Institution:

deCODE genetics ehf, CEO, Sturlugata 8,  Reykjavik IS101, Iceland

Summary:

1a: Aims:To replicate, in a different population, findings from a GWAS in Iceland on lumbar disc disease. For these purposes, we are requesting access to UK Biobank genotype data for relevant phenotypes and age and gender-matched population controls.

1b: The stated aim of UK Biobank is to support research that improves prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses. While lumbar disc disease is not life-threatening, it causes serious pain and disability. Indeed, chronic lower back pain is the most common cause of disability in young adults (<45); Its socioeconomic impact is therefore considerable. The pathophysiology of lumbar disc disease or its consequences is not well understood. Genetic findings can improve understanding of the biological underpinnings of this complex and common condition and generate knowledge that could eventually improve prevention and treatment.

1c: A set of common markers associating with lumbar disc disease have been discovered in the Icelandic population. To replicate our findings, we are requesting access to genotype data associated with relevant lumbar disc disease phenotypes and population control data available in the UK Biobank. We intend to conduct a case/control GWAS using the UK Biobank data and compare results with our GWAS findings in the Icelandic population.

1d: As the proposed research involves cases and controls, we would like to receive the full cohort with the requested subset of phenotype data.

PROJECT EXTENSION – APPROVED BY UK BIOBANK 24/07/2017:
As described in our original application, the deCODE project on the genetics of lumbar disc disease is part of an ongoing study on the genetics of chronic and neuropathic pain, with special emphasis on the affective component of the pain experience (anxiety, depression, and pain-catastrophizing known to affect both acute pain sensations and risk of developing chronic pain). In light of the complexity of pain phenotypes studied, larger datasets than we have access to in Iceland will most likely be required to generate significant GWAS findings. We therefore respectfully request an extension of scope of our application to include discovery GWAS using the UK Biobank data and GWAS meta-analytical studies with our Icelandic data of the pain phenotypes that we have defined for the deCODE pain study and have already run GWAS on (sample sizes ranging from 200 to 12,000 depending on phenotypes and over 100,000 ctrls). These include various musculoskeletal sources of chronic pain including Fibromyalgia (Chronic widespread pain), Temporomandibular joint disorder (TMD), and painful conditions of the mouth and face, such as trigeminal neuralgia, post-tooth extraction pain, and painful gum problems. We also study lower back pain with and without spinal disorders or lumbar disc disease, and genetic risk of various neuropathic sources of pain including sciatica, diabetic neuropathy, pain resulting from neuropathies associated with neurotoxic cancer treatments, post-herpetic and post-surgical pain, and last but not least, migraine, which demonstrates considerable comorbidity with both Fibromaylgia and TMD and may share with these pain disorders yet undiscovered genetic etiology.

In all defined pain conditions we study, we aim to define novel pain phenotypes within the context of the cognitive and emotional components of pain (lower education, pain catastrophizing, high neuroticism, anxiety and depression e.g. all known to associate with chronic pain). This, in keeping with the the IASP (International Association for the Study of Pain (http://www.iasp-pain.org/) emphasizing the intertwined sensory, emotional and cognitive aspects of human pain. We believe that to reveal genetic underpinnings of the complex conditions of chronic and neuropathic pain, which heritability studies and animal models of pain strongly suggest exist, pain genetics must study pain phenotypes that adequately reflect this inherent complexity of human pain.

To define deCODE pain phenotypes we have gathered large sets of diagnostic and questionnaire data representing sensory, cognitive and emotional aspects of pain. We have also collected lab results representing inflammatory processes. For subsets of participants, we have collected T1 weighted MRI images that are analyzed using Freesurfer v5.3.0 (www.freesurfer.net) for subcortical segmentation and cortical surface reconstruction. This allows for: 1) vertex-wise analysis, where local differences in cortical surface area, cortical thickness and cortical volume can be compared, 2) Region-of-interest (ROI) analysis, where a summary for previously defined regions (e.g. Hippocampus, fusiform gyrus) can be compared to detect differences between groups. Despite small samples, we have suggestive associations of genetic variants affecting both pain phenotypes and brain volume phenotypes, that require follow-up in larger samples such as available in the UK Biobank NIFTI MRI dataset. We therefore request copies of the NIFTI images themselves rather than the derived data, as some of the phenotypes we are interested in are not included in the UK Biobank brain volume calculations.

Studying the genetics of chronic pain in this wide context is of great public interest in light of the fact that chronic pain is among the most disabling and costly afflictions in Europe, North America, and Australia; its burden likely to be equally important in developing countries, for which data are not as well established. Better understanding of the biological underpinnings of pain will hopefully lead to improved preventive efforts and treatment options.

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Last updated Jul 26, 2017