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Genome wide association joint analysis reveals 99 risk loci for pain susceptibility and pleiotropic relationships with psychiatric, metabolic, and immunological traits [1]

['Evelina Mocci', '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']

Date: 2023-11

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.

Genome-wide association studies (GWASs) of pain-related traits have begun to elucidate genetic risk factors for susceptibility to chronic pain. Here we introduce a meta-analysis of 17 pain-related GWASs from the UK Biobank, selected for their significant heritability and genetic correlation. This approach aims to increase the statistical power to identify and interpret pleiotropic loci. Overall, we detected 99 genomic-wide significant risk loci, of which 65 validate previous findings from pain-related GWASs, while 34 are novel. Leave-one-trait-out meta-analyses, together with MTAG and replication analyses, suggest that the 99 loci range from those that primarily influence a specific pain-related trait to those linked to headache or muscular pain. Fewer loci had shared effects across most or all forms of pain. The pain risk loci identified in this study showed high pleiotropy and significant genetic correlation with psychiatric, metabolic, cardiovascular, and immune/inflammatory traits allowing us to generate hypotheses on the biological mechanisms triggered by pain.

Funding: This work was supported by a grant from the National Institute of Nursing Research to S.G.D. (P30 NR016579). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: Complete GWAS summary statistics from the PLEIO 17-trait joint analyses have been uploaded to the GWAS Catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ), including results for both sexes (GCST90104572), as well as for females only (GCST90104573) and males sonly (GCST90104574). All other data and results are contained in the manuscript and supplementary files .

Copyright: © 2023 Mocci et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Here, we describe a joint GWAS analysis of 17 pain-related traits from the UK Biobank. The goals of our joint analysis were two-fold. First, joint analysis can maximize statistical power to detect risk loci across genetically correlated traits. Second, joint analysis can reveal genetic pleiotropy among pain-related traits. The traits selected, other than having significant heritability, covered a broad spectrum of the pain phenotypes, from the body site to the duration and intensity, as well as pain secondary to other pathologies and commonly used pain relief medications as proxies. Our study identified 99 genomic regions associated with pain, of which 65 validate previous findings and 34 are novel. We performed statistical fine-mapping to identify potential risk genes at these loci and their enriched functional categories, as well as analyses of genetic correlations and Mendelian randomization to gain insight into pleiotropic relationships among pain susceptibility and other phenotypes.

Genome-wide association studies (GWASs) have identified risk loci for several pain-related phenotypes, including migraine [ 12 – 16 ], headache [ 17 ], osteoarthritis [ 18 , 19 ], low back pain [ 20 , 21 ], neck or shoulder pain [ 22 ], knee pain [ 23 ], and multisite chronic pain [ 24 – 26 ]. Risk loci for the same (or similar) pain phenotypes are reproducible across cohorts, as best established for migraine and related headache phenotypes [ 12 – 17 ]. Other loci have been detected consistently across several forms of musculoskeletal pain such as osteoarthritis, back, neck or shoulder pain GWASs [ 18 – 22 ]. However, the extent of pleiotropy for many risk loci remains unclear. Correlating genetic risk factors with pain phenotype could assist with identification of the biological mechanisms activated in the complex pain process, as well as differentiate between those that act at a systemic level and those that are activated in a specific tissue.

Chronic pain is a public health epidemic, costing more than $600 billion in healthcare and lost work wages [ 1 ]. There are substantial individual differences in pain susceptibility, with patients who have seemingly identical injuries often reporting different pain intensities [ 2 – 4 ], likely explained by genetic factors [ 5 , 6 ]. Broad-sense heritability estimates ascribe up to ~50% of pain susceptibility to genetic factors [ 7 – 11 ]. Characterizing the genomic mechanisms that contribute to these individual differences in pain susceptibility may lead to genetic biomarkers to predict outcomes in patients, as well as to the discovery of targets for the development of novel therapeutics.

Results

Pleiotropic associations of risk loci among pain-related traits The genetic correlations among pain-related traits, as well as the success of our joint GWAS analysis in boosting statistical power, suggested that many risk loci influence multiple pain-related traits. To further explore these relationships, we conducted “leave-one-trait-out” joint analyses. That is, we used PLEIO to conduct 16-trait joint analyses, leaving out each of the 17 traits in turn, and we examined the changes in p-values to gain insight into the influence of each trait on our results. Thirty-seven of the 99 loci reached genome-wide significance in all leave-one-trait-out analyses (Table L in S1 Data), including many of those with the strongest joint p-values, e.g., 12q13.3 (LRP1, STAT6), 1q22 (MEF2D), and 17q21.31 (CRHR1). 22 risk loci were mostly driven by a single trait, including 12p12.1 (SOX5) specific for back pain, 20q11.22 (GDF5) and 7p14.1 (SUGCT) (Table M in S1 Data). The remaining loci had intermediate levels of sensitivity, suggesting they influence some but not all of the pain-related traits. We further quantified the importance of each trait to the associations at each locus by calculating the change in the -log10(p-value) in each leave-one-out analysis compared to the primary GWAS. Headache was the most influential trait for many loci, followed by chronic headache, knee pain, and back pain (S9 Fig). We noticed that many of the loci were associated with similar combination of traits, providing evidence for two genetically distinct categories: Headache/Migraine versus Muscular pain (Table M in S1 Data). To build further evidence for these patterns, we compiled the locus-specific results from all of the validation and replication analyses into a single figure (Fig 4). Four categories of loci emerge from this analysis: seven “all trait” loci with a very high rate of pleiotropy across the 17 traits; 16 “single-trait” loci that are primarily associated with a single pain-related trait; 41 “muscular pain” loci that are associated with several forms of skeletomuscular pain (e.g., pain in multiple sites across the body); and 35 “headache” loci that are more strongly associated with headache than other forms of pain. The genetic distinction between headache and muscular pain at specific loci is consistent with the patterns of genome-wide genetic correlation (Fig 2C). Moreover, we predominantly replicated “headache” loci in the headache replication cohort, whereas we predominantly replicated “muscular pain” loci in the Finnish body pain replication cohort (Table L in S1 Data and Fig 4). Thus, the potential distinction between headache and muscular pain may explain why we replicated non-overlapping sets of loci in the two replication cohorts. The potential distinctions between headache and other types of pain merit further investigation. PPT PowerPoint slide

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TIFF original image Download: Fig 4. Summary of results from sensitivity, validation, and replication analyses. We grouped the 99 loci into four categories corresponding to the extent of pleiotropy across pain-related traits. “All” indicates loci with very broad pleiotropy across pain-related phenotypes; “Headache/Migraine” and “Muscular” loci had pleiotropic effects on multiple traits within one of those broad categories; “Single trait” loci were primarily associated with a single trait. These annotations are based on the results of four analyses: 1. Leave one out sensitivity analysis with PLEIO, 2. technical validation with MTAG, 3. Replication in an independent migraine GWAS, 4. Replication in an independent musculoskeletal pain GWAS. Symbols indicate which of the four analyses support the category for each locus, including six possible combinations of significance in the four analyses (1,1+2,1+3,1+2+3,1+2+4,1+2+3+4). https://doi.org/10.1371/journal.pgen.1010977.g004

Pain susceptibility risk loci are enriched for genes with neurological and immunological functions We functionally annotated the risk-associated variants, genes, and gene sets from our joint analysis to gain biological insights into pain susceptibility. First, we predicted the genes impacted by the SNPs at each risk locus based on proximity to risk-associated SNPs, as well as by integration with expression quantitative trait loci (eQTLs) and chromatin interaction data from the human cortex [30]. This analysis identified a total of 664 genes at the 99 risk loci (Table N in S1 Data and S10 Fig). In parallel, we performed gene-based analyses with MAGMA [31], which aggregates the associations of multiple SNPs in the vicinity of each gene. MAGMA identified 300 pain-associated genes at a genome-wide significance threshold, P < 2.8x10-6, adjusting for 17,620 protein-coding genes (Fig 2F and Table O in S1 Data). Of these, 46 were the closest gene to an LD-independent lead SNP at one of the 99 genome-wide significant risk loci (Table P in S1 Data), while the remaining genes were distal to these SNPs or located outside the genome-wide significant risk loci. To facilitate the identification of causal genes within the risk loci, we checked whether the 664 genes showed differential expression (DE) in individuals with acute or chronic pain compared to healthy participants (HP). We found that 99 genes–significantly more than expected by chance (Fisher’s exact test: P = 1.2e-7)–overlapped the bounds of 41 risk loci were up or downregulated in subjects with acute or chronic pain compared to HP (Table Q in S1 Data). 50% of these genes were predicted as potentially causal by one or more gene mapping methods (Table N in S1 Data). We tested for functional enrichments of the genes at pain susceptibility loci using MAGMA. First, we tested for overlap with genes that have been previously shown to influence pain susceptibility. We found that established pain-related genes both from clinical studies in humans [32] (P = 1.65e-4) and functional experiments in mice [33] (P = 2.0e-2) were enriched at pain susceptibility loci (Table R in S1 Data). Genes with prior evidence from functional studies had diverse molecular functions. For instance, TRPM8 (P = 6.4e-23) encodes a cold-sensing cation channel. LRP1 (P = 3.1e-21) encodes low-density lipoprotein-related protein 1. NCAM1 (P = 2.0e-9) encodes neural cell adhesion molecule 1. Amongst genes at previously unreported risk loci, SGIP1 (P = 6.3e-9) encodes Src homology 3-domain growth factor receptor-bound 2-like endophilin interacting protein 1, which interacts with cannabinoid CB1 receptors and has been shown to modulate nociception in mice [34]. ROBO2 (P = 8.9e-11) encodes roundabout guidance receptor 2, a cell adhesion molecule that has been implicated in pain via its roles in the development of sensory ganglia, required for the sensation of touch, taste and pain [35]. NLGN1 (P = 7.8e-4) encodes neuroligin 1, yet another cell adhesion molecule, which has been implicated in inflammatory pain via its activity-dependent synaptic recruitment within neurons of the spinal dorsal horn [36]. There is a need for additional studies in animal models to elucidate such mechanisms, especially for genes at novel risk loci. Next, we conducted unbiased enrichment analyses for genes with tissue-specific gene expression and in functional categories. Tissue-specificity analysis identified enrichments (False Discovery Rate [FDR] < 0.05) for genes expressed in the cerebellum (Table S in S1 Data and S11 Fig). Analyses of functional categories identified significant (FDR < 0.05) enrichments for gene sets related to neuronal development and function (e.g., neurogenesis, un-adjusted P = 3.2e-08, neuron differentiation, P = 1.4e-06, post synapse, P = 4.6e-07; FDR-adjusted q-values shown in Table T in S1 Data). Top pain-associated neurogenesis genes included LRP1 (P = 3.1e-21), UFL1 (P = 3.7e-14), WNT3 (P = 1.7e-13), SLC44A4 (P = 3.5e-13), DCC (P = 6.4e-11) and GDF5 (P = 1.2e-10) (Table U in S1 Data). Additionally, we found significant enrichment (FDR < 0.05) in categories related to immunological functions, e.g., chemokine secretion (P = 1.41e-4), T-helper T-cell differentiation (P = 1.3e-04) and Reactome T-cell receptor (TCR) signaling, P = 1.1e-04). Top pain-associated genes with immunological functions included STAT6 (P = 1.7e-41) and CRHR1 (P = 8.4e-20) (Table O in S1 Data). These results support dual neurological and immunological etiology for pain susceptibility.

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[1] Url: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010977

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