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

战略优先领域：数学，统计学和计算关键词：慢性疼痛，抑郁症，基因组学重度抑郁症（MDD）和慢性疼痛经常共病（Nicholl et al 2014），遗传相关（McIntosh et al 2016），具有共同的环境风险因素（McIntosh et al 2016），它们构成了全球因残疾而损失的天数的最大贡献者（GBD合作者2013）。他们的遗传相关性在多大程度上是由于多效性（影响多个性状的单一遗传变异）或临床异质性（MDD患者的遗传不同亚组与慢性疼痛患者更相似，反之亦然），疼痛，慢性疼痛相关疾病和MDD之间的因果方向关系也是未知的。可以利用大型一般人群队列中的全基因组基因分型信息，以便使用BUHMBOX（基于交叉（X）-位点相关性的打破异质混合物）（Han et al 2016）、孟德尔随机化（MR）和结构方程模型（SEM）等分析更好地研究慢性疼痛与MDD之间的关系。背景：MDD和慢性疼痛非常普遍，并且通常是共病，是全球残疾的两个最常见原因。MDD是一种严重的情绪障碍，其特征是心理和身体症状，包括持续的情绪低落，快感缺乏，疼痛和睡眠变化等症状。慢性疼痛被定义为持续时间超过12周的疼痛，是包括MDD在内的许多疾病的症状。这两种情况都是异质的，具有复杂的多基因结构。在调查公共卫生中的重要情况时，强调了“皮肤以上”和先发制人/预防性干预的重要性（Gillman &哈蒙德，2016年）。从临床异质性中区分多效性，绘制疼痛-MDD关系中的因果关系沿着方向性，并将社会，生活方式，心理和经济因素纳入这些条件的分析是非常重要的。这有可能改善MDD和疼痛患者的分类和治疗，并突出新的因果遗传途径和干预目标，可以使用BUHMBOX，MR和SEM等分析，使用大型，良好的表型数据集，如UK Biobank和Generation Scotland及其全基因组基因分型数据来实现。目的：本项目旨在量化英国大型队列（即GS和UKB）中抑郁症和慢性疼痛之间在责任量表上的遗传相关性，以挑选MDD和慢性疼痛病例中的多效性和异质性，使用孟德尔随机化方法评估疼痛和MDD之间关系的方向和因果关系，使用SEM方法评估疼痛和抑郁症与"皮肤以上“社会，心理，医学和生活方式因素之间关系的方向性，并分析上述在翻译应用方面的影响。参考文献：Nicholl et al（2014）”Chronic multisite pain in major depression and bipolar disorder：英国生物银行的149，611名参与者的横断面研究BMC心理学14（1）：350全球疾病负担研究合作者（2015）“1990-2013年188个国家301种急性和慢性疾病和损伤的全球区域和国家发病率，患病率和残疾生活年数：2013年全球疾病负担研究的系统分析“柳叶刀386：743 McIntosh et al（2016）“慢性疼痛的遗传和环境风险以及重度抑郁症风险变体的贡献：基于家庭的混合模型分析”PLoS Medicine 13（8）：e1002090 Gillman &哈蒙德（2016）“精准治疗和精准预防：整合”皮下和皮下“《美国医学会儿科杂志》170（1）：9 Han et al（2016）“A method to decrypt pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune &神经精神疾病

Strategic priority area:Mathematics, statistics & computationKeywords:Chronic pain, depression, genomicsMajor 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): 350Global 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: 743McIntosh 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): 9Han et al (2016) 'A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune & neuropsychiatric diseases