Department of Pain & Translational Symptom Science, University of Maryland School of Nursing, Baltimore, Maryland, United States of America.
Center to Advance Chronic Pain Research (CACPR), University of Maryland Baltimore, Baltimore, Maryland, United States of America.
PLoS Genet. 2023 Oct 16;19(10):e1010977. doi: 10.1371/journal.pgen.1010977. eCollection 2023 Oct.
Chronic pain is at epidemic proportions in the United States, represents a significant burden on our public health system, and is coincident with a growing opioid crisis. While numerous genome-wide association studies have been reported for specific pain-related traits, many of these studies were underpowered, and the genetic relationship among these traits remains poorly understood. Here, we conducted a joint analysis of genome-wide association study summary statistics from seventeen pain susceptibility traits in the UK Biobank. This analysis revealed 99 genome-wide significant risk loci, 65 of which overlap loci identified in earlier studies. The remaining 34 loci are novel. We applied leave-one-trait-out meta-analyses to evaluate the influence of each trait on the joint analysis, which suggested that loci fall into four categories: loci associated with nearly all pain-related traits; loci primarily associated with a single trait; loci associated with multiple forms of skeletomuscular pain; and loci associated with headache-related pain. Overall, 664 genes were mapped to the 99 loci by genomic proximity, eQTLs, and chromatin interaction and ~15% of these genes showed differential expression in individuals with acute or chronic pain compared to healthy controls. Risk loci were enriched for genes involved in neurological and inflammatory pathways. Genetic correlation and two-sample Mendelian randomization indicated that psychiatric, metabolic, and immunological traits mediate some of these effects.
慢性疼痛在美国已达到流行程度,对我们的公共卫生系统造成了重大负担,且与日益严重的阿片类药物危机同时发生。虽然已经有许多针对特定疼痛相关特征的全基因组关联研究报告,但其中许多研究的效力不足,这些特征之间的遗传关系仍未得到充分理解。在这里,我们对英国生物库中十七种疼痛易感性特征的全基因组关联研究汇总统计数据进行了联合分析。这项分析揭示了 99 个具有全基因组意义的风险位点,其中 65 个与早期研究中确定的位点重叠。其余 34 个位点是新的。我们应用了一种留一特征外的荟萃分析方法来评估每个特征对联合分析的影响,结果表明这些位点可分为四类:与几乎所有与疼痛相关的特征相关的位点;主要与单一特征相关的位点;与多种骨骼肌肉疼痛相关的位点;以及与头痛相关的疼痛相关的位点。总体而言,通过基因组邻近性、eQTL 和染色质相互作用,将 99 个位点映射到了 664 个基因上,其中约 15%的基因在急性或慢性疼痛患者与健康对照者之间表现出差异表达。风险位点富集了涉及神经和炎症途径的基因。遗传相关性和两样本 Mendelian 随机化表明,精神、代谢和免疫特征介导了其中一些影响。