Supplemental Digital Content is Available in the Text. We administered a pain sensitivity questionnaire in parallel with an experimental cold pressor test in a large, genotyped cohort of 31,000 research participants, and found a novel genome-wide genetic association in TSSC1.
Keywords: Pain sensitivity, Pain sensitivity questionnaire, Cold pressor test, Genome-wide association study
Abstract
We deployed an online pain sensitivity questionnaire (PSQ) and an at-home version of the cold pressor test (CPT) in a large genotyped cohort. We performed genome-wide association studies on the PSQ score (25,321 participants) and CPT duration (6853). We identified one new genome-wide significant locus associated with the PSQ score, which was located in the TSSC1 (also known as EIPR1) gene (rs58194899, OR = 0.950 [0.933-0.967], P-value = 1.9 × 10−8). Although high pain sensitivity measured by both PSQ and CPT was associated with individual history of chronic and acute pains, genetic correlation analyses surprisingly suggested an opposite direction: PSQ score was inversely genetically correlated with neck and shoulder pain (r g = −0.71), rheumatoid arthritis (−0.68), and osteoarthritis (−0.38), and with known risk factors, such as the length of working week (−0.65), smoking (−0.36), or extreme BMI (−0.23). Gene-based analysis followed by pathway analysis showed that genome-wide association studies results were enriched for genes expressed in the brain and involved in neuronal development and glutamatergic synapse signaling pathways. Finally, we confirmed that females with red hair were more sensitive to pain and found that genetic variation in the MC1R gene was associated with an increase in self-perceived pain sensitivity as assessed by the PSQ.
1. Introduction
It has been established that pain sensitivity is predictive of acute postoperative pain, and of risk for the development of chronic pain conditions. 34 The precise assessment of pain sensitivity requires well-controlled experimental pain and emotional stimuli. 5 In general, such assessments are time-consuming in clinical settings because there is substantial interindividual variability in pain sensitivity and perception, 13,40 and they can only be deployed for modest cohort sizes. As a result, there have been few studies with large sample sizes, impeding progress in understanding the genetic architecture of pain sensitivity. Although hundreds of genes have been proposed to have associations with different types of pain, 53 most pain genetics studies analyzed small sample sizes, often using candidate gene or gene panel approaches. To date, the number of genome-wide association studies (GWAS) on pain phenotypes is still very limited. The largest pain GWAS have been performed in the UK Biobank and 23andMe, Inc cohorts for chronic pain, 49,56 knee pain, 45 neck and shoulder pain, 32 and migraine. 19 These studies have identified dozens of putative causal genes, which are primarily expressed within brain tissues and have been implicated in neurogenesis, neuronal development, neural connectivity, and cell-cycle processes. Pain phenotypes have been correlated with a range of psychiatric, personality, autoimmune, anthropometric, and circadian traits. 31 Only a couple of small GWAS studies have directly explored the genetic architecture of pain sensitivity. 38,52 They have found a small number of associations, but none of them have been replicated.
Several clinical and population-based studies have also reported that individuals who naturally have red hair tend to be more resistant to local anesthetics and more sensitive to thermal and dental pain. 24 Red hair, as well as fair skin and freckles, is associated with genetic variations of the melanocortin-1 receptor (MC1R), and it has been suggested that these mutations could directly modulate pain sensitivity, particularly in women. 9,14,18,55
We recently validated an online version of the Pain Sensitivity Questionnaire (PSQ) and an at-home version of the cold pressor test (CPT), both of which are used in clinical assessments of pain. 29 The PSQ asks participants to imagine 14 painful situations and 3 nonpainful control situations. Subjects are asked to rate their painfulness on a 0 to 10 numeric scale. The CPT measures how long subjects can immerse one hand in ice water. The validation study demonstrated that these 2 pain sensitivity measures can be consistently collected online and allow pain sensitivity analyses in very large cohorts.
In this study, we performed GWAS on PSQ score and CPT duration in a large European ancestry cohort (31K) of genotyped individuals, followed by gene-based tests and enrichment analyses. We also attempted to replicate the association between hair color, MC1R genetic variants, and pain sensitivity.
2. Materials and methods
2.1. Study sample
All participants included in the analyses were drawn from the research participant base of 23andMe, Inc, a personal genetics company. Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services (E&I Review) (http://www.eandireview.com; OHRP/FDA registration number IRB00007807, study number 10044-11). We restricted all analyses to a set of unrelated participants having >97% European ancestry, as determined through an analysis of local ancestry. Participants were labelled as related if they shared more than 700 cM of identity-by-descent. 53
2.2. Pain sensitivity traits
For the assessment of the pain sensitivity, we used a PSQ and an at-home version of CPT on 2 subsets of 23andMe consented research customers who were invited to participate, without restrictions for the PSQ cohort and with some safety restrictions for the CPT cohort. The PSQ is an English-language version of the PSQ, 40,41 supplemented with additional questions about the participant's own memory of self-perceived painful experiences. The PSQ contains 14 questions in which participants should imagine themselves in certain situations. Participants should then grade how painful they would be, from 0 that stands for no pain to 10 that stands for the most severe pain that participants can imagine or consider possible. The total PSQ score is the mean of the 14 responses. We also computed 2 PSQ subscales: PSQ-minor score based on the least painful questions (#14, 3, 6, 12, 11, 10, and 7, ordered from least to most painful) and PSQ-moderate score (#8, 15, 2, 16, 17, 1, and 4). For the CPT, participants were asked to prepare their own bath of ice water at home and to keep their nondominant hand submerged to the wrist for no more than 150 seconds. A separate consent for the CPT was used: participants reporting neurological or temperature-triggered conditions (eg, migraine, history of syncope, or Raynaud phenomenon) or current injuries to their nondominant hands at the time of recruitment were ineligible. Two primary outcomes were assessed: cold pain threshold and cold pain tolerance. Cold pain threshold was the time to the first report of pain, and cold pain tolerance was the time to removal of the hand from the water. The 2 cold pain outcomes were partially correlated (Spearman r s = 0.64). On the day of the test, 6.6% of the CPT participants reported using pain medication (“Did you take a medication to treat pain today?”). These participants logged a similar cold pain threshold (35.2 [32.1-38.3] vs 35.2 [34.3-36.0] seconds for participants who did not reported taking pain medication) and a significant lower cold pain tolerance (73.0 [68.4-77.7] vs 80.6 [79.4-81.9] seconds; general linear model P-value = 9.6 × 10−5, after rank-inverse normalizing and controlling for age, sex, and ancestry principal components). For the GWAS analysis, we used the cold pain tolerance as our main CPT duration, without excluding participants who reported taking pain medication on the day of test. For additional information on the PSQ and CPT, in particular the validity of these approaches for estimating pain sensitivity, see Ref. 29.
For all the participants in both cohorts, we also collected pain diagnosis and pain medication usage with the following 2 online questions: “Have you ever been diagnosed with, or treated for, any of the following conditions related to pain?” (16 pain traits, including chronic, acute, low-back, joint, complex regional pain syndrome, dental pain, …) and “In the past 5 years, have you taken any of these medications to relieve pain after injury or surgery that lasted more than 3 months?” (33 drug classes including celecoxib, codeine, oxycodone, fentanyl, duloxetine, …) (Table 1).
Table 1.
Description of pain sensitivity questionnaire and cold pressor test cohorts.
| PSQ cohort | CPT cohort | |||
|---|---|---|---|---|
| Females | Males | Females | Males | |
| No. of participants | 18,060 | 7261 | 4343 | 2510 |
| Age (y) | 50.2 (0.4) | 50.9 (0.4) | 42.5 (0.3) | 42.1 (0.2) |
| Reported pain | ||||
| Chronic | 28.7% | 24.0% | 19.0% | 18.6% |
| Acute | 56.3% | 52.5% | 42.8% | 48.7% |
| Low back | 39.6% | 35.7% | 28.5% | 32.9% |
| Db neuropathy | 1.0% | 1.7% | 0.9% | 0.4% |
| CRPS | 4.8% | 2.8% | 1.4% | 3.6% |
| Migraine | 35.6% | 16.1% | 8.8% | 12.4% |
| Dental | 59.0% | 55.1% | 54.0% | 52.1% |
| Shingles | 21.9% | 15.7% | 15.6% | 16.6% |
| Medication usage | ||||
| OTC NSAID | 95.3% | 92.0% | 92.6% | 94.2% |
| Opioids | 53.9% | 49.0% | 47.3% | 47.4% |
| Antiepileptics | 17.9% | 10.1% | 8.7% | 6.7% |
| Antidepressants | 13.7% | 5.9% | 6.2% | 7.0% |
| PSQ score and CPT duration | ||||
| All | 3.23 (0.01) | 3.11 (0.01) | 72.39 (0.75) | 93.9 (1.01) |
| Per age classes (y) | ||||
| <40 | 3.16 (0.018) | 3.04 (0.028) | 73.38 (1.00) | 94.67 (1.36) |
| 40-60 | 3.27 (0.017) | 3.12 (0.026) | 68.95 (1.42) | 94.72 (1.81) |
| >60 | 3.25 (0.017) | 3.16 (0.024) | 74.83 (1.91) | 89.46 (2.71) |
| MC1R carriers | ||||
| Noncarrier | 3.22 (0.012) | 3.10 (0.018) | 71.98 (0.91) | 92.70 (1.19) |
| Carrier1 | 3.26 (0.019) | 3.14 (0.029) | 73.51 (1.42) | 96.98 (2.01) |
| Carrier2+ | 3.33 (0.062) | 3.18 (0.093) | 72.36 (4.39) | 100.35 (6.18) |
| Hair color sub-cohort | ||||
| No. of participants with hair color information | 10,928 | 4239 | 3375 | 1868 |
| Proportion of redhead | 5.1% | 3.2% | 5.0% | 3.8% |
| Not redhead | 3.22 (0.013) | 3.13 (0.02) | 72.12 (0.88) | 92.22 (1.20) |
| Redhead | 3.37 (0.060) | 3.01 (0.10) | 71.69 (3.77) | 100.43 (5.77) |
Pain phenotypes were collected with the following question: “Have you ever been diagnosed with, or treated for, any of the following conditions related to pain?” Medication usage was collected with “In the past 5 years, have you taken any of these medications to relieve pain after injury or surgery that lasted more than 3 months.” Chronic and acute are defined as pain after injury or surgery that lasted >3 and <3 months, respectively. CPT duration (mean and SE) is reported in seconds.
Antiepileptics, antiepileptic or anticonvulsant drugs; CPT, cold pressor test; CRPS, complex regional pain syndrome; Db neuropathy, diabetic neuropathy; MC1R, melanocortin-1 receptor; OTC NSAID, over-the-counter nonsteroidal anti-inflammatory drugs; PSQ, pain sensitivity questionnaire; Shingles, included pain related to shingles, cold sores, or herpes.
2.3. Genotyping and variant imputation
DNA extraction and genotyping were performed on saliva samples by LabCorp, Inc. Participants were genotyped on 1 of 5 Illumina genotyping platforms, containing between 550,000 and 950,000 variants, for a total of 1.6 million of genotyped variants. Samples that failed to reach 98.5% call rate were reanalyzed. Genotyping quality controls included discarding variants with a Hardy–Weinberg P-value < 10−20, a call rate of <90%, or batch effects. About 57.5 M of variants were then imputed against a single unified imputation reference panel, combining the May 2015 release of the 1000 Genomes Phase 3 haplotypes with the UK10K imputation reference panel. Principal components were computed using ∼65,000 high-quality genotyped variants present in all 5 genotyping platforms. For more details on genotyping, imputation process, and variant quality controls, see Ref. 47.
2.4. Genome-wide association study analysis
Imputed dosages and genotyped data were both tested for association with PSQ score or CPT duration (between 0 and 150 seconds). The PSQ scores were inverse normalized and analyzed using a Gaussian linear model. The association P-values were computed using a likelihood ratio test. The CPT duration was analyzed using a Cox proportional hazards model, 35 a survival model on the CPT time. We included covariates for age, sex, genotyping platform, and the top 5 principal components to account for residual population stratification. The PSQ association model did not include platform covariables because PSQ participants were all genotyped on platform v4. Results for the X chromosome were computed similarly, with males coded as if they were homozygous diploid for the observed allele. A total of 1.3 M genotyped and 25.5 M imputed variants passed the pre- and post-GWAS quality controls. We furthermore filtered out variants with MAF <0.1%, which are extremely sensitive to quantitative trait overdispersion, reducing to 13.7 M variants available for follow-up analyses. A detailed description of the variant quality control and GWAS methods can be found in Ref. 47. The full GWAS summary statistics for the 23andMe discovery data set will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Please visit https://research.23andme.com/dataset-access/ for more information and to apply to access the data.
2.5. Melanocortin-1 receptor and hair color
We defined 3 categories of MC1R variant carriers by combining 3 variants, rs1805007, rs1805008, and rs1805009: Noncarrier (0 MC1R alleles), Carrier1 (1 allele), and Carrier2+ (>1 alleles). 18 A self-reported hair color phenotype was available for 63% of the participants in the cohort. 33 It contained 6 hair color categories: red, light blond, dark blond, light brown, dark brown, and black. We also built a binary red hair variable from this categorical hair color phenotype. Association of MC1R carrier, hair color, and red variables were tested against PSQ score or CPT duration, using Gaussian linear and Cox proportional hazards models, respectively. To facilitate the visualization of the results, we also converted CPT duration to ranks, and tested associations using analysis of variances. The same set of covariables used in GWAS was included in these models.
2.6. Genetic correlation, Mendelian randomization, gene-based and pathway analyses
Genetic correlations between PSQ and a broad list of 832 diseases and traits were estimated with LD Hub v1.9.1, 54 using the default analysis parameters. In addition, we computed genetics correlations with 8 published pain 31 and a derived multisite chronic pain (MCP) GWAS 21 from UK Biobank. The UK Biobank participants were offered a pain-related questionnaire, which included the question: “In the last month have you experienced any of the following that interfered with your usual activities?” The options were: (1) headache (74,761 cases); (2) facial pain (2610); (3) neck or shoulder pain (53,994); (4) back pain (43,991); (5) stomach or abdominal pain (8217); (6) hip pain (10,116); (7) knee pain (22,204); and (8) pain all over the body (5670). From the same question, MCP was defined as the sum of body sites at which chronic pain (at least 3 months' duration) was reported. A 2-sample Mendelian randomization (MR) between PSQ score and the 9 published pain traits were conducted in accordance with published methods. 6 The gene-based analysis was performed on MAGMA (v1.07). 11 After correction with a Benjamini–Hochberg procedure, genes with adjusted P-values < 10−4 were selected and used in pathway analyses performed on FUMA (GENE2FUNC, v1.3.5; https://fuma.ctglab.nl/). Pathway enrichment was tested for different gene sets, including canonical pathways, Reactome, and GO biological processes. Tissue specificity for the set of selected genes was tested using an enrichment analysis of differentially expressed genes.
3. Results
A total of 25,321 and 6853 research participants of European ancestry were included in the PSQ and CPT GWAS analyses, respectively (Table 1). For the PSQ cohort, no specific selection criteria were used; all participants from the 23andMe research database were invited to contribute. However, for the CPT cohort, research participants with a history of severe migraine and a number of other chronic conditions that might be directly exacerbated by the cold stress were not invited to participate to ensure their safety. Consequently, the proportion of participants reporting migraine but also any types of pain was lower in the CPT than the PSQ cohort. Overall, the 2 cohorts included between 18.8% to 27.3% and 44.9% to 55.2% of participants having been diagnosed or treated for chronic and acute pain, respectively. In the PSQ cohort, a higher proportion of females than males reported been diagnosed or treated for pain whereas, in the CPT cohort, the proportion was more balanced or slightly higher in males. The 2 cohorts were largely independent; only 1534 participants were included in both analyses. The sex ratio was unbalanced in both cohorts, with 71% and 63% of females, respectively. A more detailed description of the 2 cohorts has been published elsewhere. 29
On average, females reported a higher pain sensitivity with a mean PSQ score of 3.23 ± 0.01 vs 3.11 ± 0.01 in males, as well as a lower tolerance to the CPT with 72.4 +/- 0.8 vs 94 ± 1.0 seconds of hand immersion in ice water (Table 1; and Supplementary Fig. 1, available at http://links.lww.com/PAIN/B557). A higher PSQ score (higher pain sensitivity) was systematically associated with pain diagnosis, reported multiple pain conditions, and with medication usage (Table 1; Supplementary Table 1, and Supplementary Figs. 2 and 3, available at http://links.lww.com/PAIN/B557). A lower CPT duration (higher pain sensitivity) also tended to be associated with pain diagnosis and with medication usage. However, many of these associations were not significant (Supplementary Fig. 4, available at http://links.lww.com/PAIN/B557).
The GWAS on PSQ score produced one locus that reached genome-wide significance. The lead SNP (rs58194899: OR = 0.950 [95% CI 0.933-0.967], P-value = 1.9 × 10−8, MAF = 0.47; Table 2) was located in the TSSC1 gene (Figs. 1A and 2). The associated haplotype was relatively small (54 variants in the 99% credible set) and entirely located within the TSSC1 gene boundary (Fig. 2A). To date, no studies have reported a phenotypic or eQTL association with this haplotype (https://genetics.opentargets.org/). However, a cognitive decline GWAS reported an independent association in the TSSC1 gene (lead variant rs75365287). 26 TSSC1 is a component of the endosomal retrieval pathway. It plays a critical role as a regulator of both Golgi-associated retrograde protein and endosome-associated recycling protein functions, as well as the transport of internalized proteins to the plasma membrane. 3,17 TSSC1 and the Golgi-associated retrograde protein/endosome-associated recycling protein complexes are not known to be involved in pain traits. However, TSSC1 is overexpressed in the brain, particularly in the hypothalamus, and in the frontal and interior cingulate cortices (GTEx v8).
Table 2.
Top genome-wide association study variants and melanocortin-1 receptor association results for pain sensitivity questionnaire and cold pressor test traits.
| CHR | POS | ID | Alleles | MAF | Gene context | PSQ GWAS | CPT GWAS | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Effect | SE | P | Effect | SE | P | ||||||
| Top PSQ GWAS variants | |||||||||||
| 2 | 3280983 | rs58194899 | G/A | 0.47 | TSSC1 | −0.051 | 0.009 | 1.90E-08 | −0.01 | 0.018 | 5.80E-01 |
| 18 | 10008669 | rs142738119 | T/C | 0.001 | VAPA, APCDD1 | 0.696 | 0.138 | 4.70E-07 | −0.035 | 0.275 | 9.00E-01 |
| 13 | 1.02E+08 | rs12583902 | G/A | 0.166 | NALCN | 0.059 | 0.012 | 7.60E-07 | −0.025 | 0.023 | 2.90E-01 |
| Top CPT GWAS variants | |||||||||||
| 17 | 65520139 | rs141828201 | G/A | 0.027 | PITPNC1 | 0.036 | 0.031 | 2.60E-01 | 0.356 | 0.066 | 2.40E-07 |
| MC1R variants | |||||||||||
| 16 | 89986144 | rs1805008 | C/T | 0.071 | MC1R | 0.031 | 0.017 | 6.80E-02 | −0.032 | 0.034 | 3.50E-01 |
| 16 | 89986117 | rs1805007 | C/T | 0.075 | MC1R | 0.047 | 0.017 | 5.10E-03 | −0.039 | 0.033 | 2.40E-01 |
| 16 | 89986546 | rs1805009 | G/C | 0.019 | MC1R | 0.041 | 0.032 | 2.00E-01 | −0.089 | 0.063 | 1.50E-01 |
PSQ score was analyzed using a linear model. CPT duration (in seconds) was analyzed using a survival model. Bold-italic indicates genome-wide significant association.
CHR, chromosome; CPT, cold pressor test; GWAS, genome-wide association studies; MAF, minor allele frequency; MC1R, melanocortin-1 receptor; POS, genomic position; PSQ, pain sensitivity questionnaire.
Figure 1.
Manhattan plots for PSQ and CPT GWAS (panel A and B, respectively). CPT, cold pressor test; GWAS, genome-wide association studies; PSQ, pain sensitivity questionnaire.
Figure 2.
Results of MC1R and hair color association with PSQ score. MC1R, melanocortin-1 receptor; PSQ, pain sensitivity questionnaire.
Using the PSQ GWAS summary statistics, we performed a gene-based association analysis in MAGMA (v1.07), followed by a gene-set enrichment analysis in FUMA (GENE2FUNC, v1.3.5). A total of 58 genes were identified with an adjusted P-value < 1.0 × 10−4 (Table 3). These genes were overexpressed in the brain, especially in the frontal cortex, basal ganglia, amygdala, and hypothalamus (Supplementary Fig. 5, available at http://links.lww.com/PAIN/B557). They were significantly enriched for genes involved in brain development and synaptic signaling pathways (Table 3). Like TSSC1, many of these genes are involved in Golgi apparatus function (RBFOX1, PARK2, WWOX, PRKG1, LARGE, GPC6, PCSK6, HS3ST4, WWOX, SLC39A11, PRKCE, TENM2, and CNTNAP2; Supplementary Table 2 and Supplementary Fig. 6, available at http://links.lww.com/PAIN/B557). Several genes identified by MAGMA are active in glutamatergic synapses, which are involved in pain sensation and transmission (PTPRD, NRG1, NRG3, DLG2, GPC6, and GRID2). Finally, among the genes that were most strongly associated with PSQ score, CSMD1, LRP1B, and DMD were not known to be directly involved in pain sensitivity but were linked to neurological diseases like bipolar disorder. The traits with the strongest genetic correlations with PSQ, as computed on LD Hub (bivariate linkage disequilibrium score regression [LDSC] on 832 tested traits), are listed in Table 4. Pain sensitivity questionnaire score was negatively genetically correlated with chronic pain related phenotypes, such as neck-and-shoulder pain (r g = −0.71), rheumatoid arthritis (0.68), or mononeuropathies (0.53), but positively correlated with acute pain phenotypes, such as pneumothorax (0.82) or fracture (0.71). It was also negatively correlated with health risk factors and behaviors, such as the length of working week (−0.65), working in a noisy environment (−0.42), shift work (−0.41), smoking (−0.36), and extreme BMI (−0.23). We also observed a negative genetic correlation between the PSQ score and ADHD (−0.67), but positive correlations with schizophrenia (0.21), bipolar disorder (0.25), and neuroticism (0.22). Many of these genetic correlations were marginally significant and none of them passed the multiple testing discovery threshold (0.05/832 = 6 × 10−5). The genetic correlations with the 8 additional published pain susceptibility traits 31 and the MCP 21 also suggested a negative genetic correlation with PSQ score but none of them were significant. The causality between pain sensitivity, measured by PSQ score, and pain susceptibility traits was assessed by MR analyses (Supplementary Fig. 7, available at http://links.lww.com/PAIN/B557). Although we observed few significant MR results, mainly from the inverse-variance weighted model, the directions of effect were inconsistent. The comparison between PSQ score and the strongest published pain susceptibility trait (MCP) showed no evidence of directionality.
Table 3.
Gene-based (MAGMA) and pathway analysis results for pain sensitivity questionnaire and cold pressor test.
| Gene | Position | PSQ | CPT | Pathways | ||
|---|---|---|---|---|---|---|
| No. of variants | P | No. of variants | P | |||
| ADARB2 | 10:1223253-1779670 | 296 | 8.86E-03 | 219 | 9.37E-05 | |
| ASIC2 | 17:31340105-32483825 | 486 | 5.61E-07 | 431 | 3.29E-09 | GO synaptic signalling |
| C10orf11 | 10:77191217-78317133 | 187 | 1.04E-01 | 241 | 6.35E-07 | |
| CACNA2D3 | 3:54156620-55108584 | 250 | 1.39E-03 | 358 | 6.66E-06 | |
| CADPS | 3:62384021-62861064 | 79 | 1.04E-01 | 85 | 1.53E-05 | GO neurogenesis |
| CAMTA1 | 1:6845384-7829766 | 379 | 6.46E-05 | 349 | 1.49E-02 | |
| CDH13 | 16:82660399-83830215 | 321 | 2.39E-07 | 797 | 8.53E-12 | |
| CDH23 | 10:73156691-73575704 | 161 | 3.10E-03 | 205 | 6.32E-05 | GO synaptic signalling |
| CDH4 | 20:59827482-60515673 | 330 | 1.20E-06 | 121 | 3.90E-07 | GO neurogenesis |
| CNTN4 | 3:2140550-3099645 | 469 | 7.03E-07 | 482 | 1.20E-06 | GO neurogenesis; GO synaptic signalling |
| CNTN5 | 11:98891706-100229616 | 679 | 9.18E-03 | 600 | 1.14E-06 | |
| CNTNAP2 | 7:145813453-148118090 | 706 | 5.37E-06 | 1294 | 2.47E-08 | GO neurogenesis; GO Golgi apparatus |
| COL23A1 | 5:177664617-178017573 | 156 | 4.91E-05 | 133 | 6.87E-03 | |
| CSMD1 | 8:2792875-4852328 | 1918 | 1.54E-17 | 1968 | 3.45E-30 | |
| CTNNA3 | 10:67672276-69455949 | 712 | 3.08E-05 | 612 | 9.32E-02 | |
| DAB1 | 1:57460453-58716211 | 424 | 3.73E-05 | 226 | 1.07E-02 | GO neurogenesis |
| DLG2 | 11:83166055-85338314 | 781 | 1.21E-06 | 368 | 1.35E-05 | GO synaptic signalling; GO glutamatergic synapse |
| DLGAP1 | 18:3496030-4455310 | 363 | 6.23E-04 | 506 | 1.01E-10 | GO neurogenesis |
| DLGAP2 | 8:877021-1656642 | 272 | 9.90E-02 | 618 | 1.83E-09 | GO neurogenesis |
| DMD | X:31137345-33357726 | 653 | 1.18E-11 | 532 | 1.64E-06 | |
| DSCAM | 21:41384343-42219039 | 279 | 2.97E-05 | 282 | 2.04E-04 | GO neurogenesis |
| EGFR | 7:55086678-55279262 | 34 | 1.04E-01 | 59 | 1.21E-05 | GO neurogenesis; GO synaptic signalling |
| FAM155A | 13:107820879-108519460 | 311 | 1.98E-08 | 250 | 5.93E-03 | |
| FHIT | 3:59735036-61237133 | 693 | 5.08E-07 | 757 | 9.88E-11 | |
| FRMD4A | 10:13685706-14372866 | 194 | 1.36E-04 | 372 | 1.01E-07 | |
| FSTL4 | 5:132532152-132948223 | 122 | 3.04E-05 | 75 | 6.75E-03 | GO neurogenesis |
| GALNT18 | 11:11292421-11643561 | 82 | 1.04E-01 | 163 | 2.78E-05 | |
| GLIS3 | 9:3824128-4300036 | 151 | 7.30E-06 | 128 | 2.81E-04 | |
| GPC6 | 13:93879078-95060274 | 323 | 1.91E-05 | 96 | 3.87E-02 | GO Golgi apparatus; GO glutamatergic synapse |
| GRID2 | 4:93225453-94695707 | 736 | 7.90E-06 | 402 | 8.30E-03 | GO neurogenesis; GO synaptic signalling; GO glutamatergic synapse |
| GSE1 | 16:85203152-85709812 | 134 | 3.40E-06 | 131 | 6.68E-02 | |
| HHAT | 1:210501596-210849638 | 123 | 5.90E-05 | 109 | 1.05E-01 | |
| HS3ST4 | 16:25703347-26149009 | 137 | 2.41E-05 | 127 | 4.77E-04 | GO Golgi apparatus |
| KAZN | 1:14219646-15444544 | 314 | 2.28E-08 | 512 | 7.58E-10 | |
| KCNIP4 | 4:20730234-21950424 | 950 | 7.40E-06 | 617 | 1.05E-01 | |
| KCNQ3 | 8:133133105-133493004 | 169 | 9.40E-06 | 162 | 4.99E-03 | GO synaptic signalling |
| KIRREL3 | 11:126293388-126870766 | 169 | 4.91E-03 | 272 | 2.31E-06 | GO synaptic signalling |
| LARGE | 22:33668509-34316464 | 259 | 1.58E-05 | 258 | 3.86E-05 | GO Golgi apparatus |
| LRP1B | 2:140988996-142889270 | 1070 | 1.14E-12 | 1374 | 7.01E-08 | |
| MACROD2 | 20:13976146-16033842 | 621 | 2.93E-06 | 623 | 7.14E-12 | |
| MAGI1 | 3:65339200-66024511 | 445 | 2.72E-05 | 252 | 4.21E-03 | |
| MAGI2 | 7:77646374-79083121 | 423 | 1.10E-04 | 551 | 1.64E-05 | GO synaptic signalling |
| MAML2 | 11:95711440-96076344 | 267 | 1.50E-08 | 66 | 1.05E-01 | |
| MCTP1 | 5:94038280-94620279 | 265 | 6.98E-08 | 252 | 4.93E-03 | GO synaptic signalling |
| NAV2 | 11:19372271-20143147 | 205 | 6.74E-04 | 122 | 4.61E-05 | GO synaptic signalling |
| NCKAP5 | 2:133429361-134399118 | 125 | 1.79E-05 | 274 | 1.33E-03 | |
| NELL1 | 11:20691117-21597232 | 294 | 7.47E-03 | 380 | 7.64E-06 | |
| NKAIN2 | 6:124124991-125146786 | 409 | 8.77E-05 | 87 | 1.87E-02 | |
| NPAS3 | 14:33404115-34273382 | 260 | 7.74E-11 | 469 | 1.25E-07 | |
| NRG1 | 8:31496820-32622558 | 604 | 5.38E-05 | 340 | 1.05E-01 | GO neurogenesis; GO synaptic signalling; GO glutamatergic synapse; ERBB2/4 pathway |
| NRG3 | 10:83635070-84746935 | 309 | 2.33E-05 | 405 | 2.95E-05 | GO neurogenesis; GO synaptic signalling; GO glutamatergic synapse; ERBB2/4 pathway |
| NRXN3 | 14:78636716-80334633 | 381 | 3.99E-07 | 426 | 3.56E-04 | GO neurogenesis |
| NTM | 11:131240371-132206716 | 395 | 3.29E-08 | 302 | 1.47E-03 | GO neurogenesis |
| OPCML | 11:132284875-133402403 | 326 | 1.52E-06 | 322 | 2.30E-04 | GO neurogenesis |
| PALM2-AKAP2 | 9:112542577-112934792 | 146 | 2.72E-05 | 334 | 1.35E-03 | |
| PARK2 | 6:161768590-163148834 | 1097 | 5.06E-10 | 648 | 7.70E-03 | GO neurogenesis; GO synaptic signalling; GO Golgi apparatus |
| PCSK5 | 9:78505560-78977255 | 182 | 3.45E-04 | 201 | 4.34E-07 | |
| PCSK6 | 15:101844133-102030187 | 36 | 2.07E-05 | 82 | 2.25E-02 | GO Golgi apparatus |
| PDZD2 | 5:31639345-32111038 | 178 | 9.69E-05 | 253 | 1.58E-04 | |
| PLCB1 | 20:8112912-8865547 | 217 | 1.03E-04 | 296 | 4.06E-05 | GO neurogenesis |
| PPP2R2C | 4:6322305-6565327 | 82 | 1.04E-01 | 69 | 1.31E-05 | |
| PRKCE | 2:45878454-46415129 | 278 | 2.19E-07 | 253 | 3.58E-04 | GO synaptic signalling; GO Golgi apparatus; ERBB2/4 pathway |
| PRKG1 | 10:52750911-54058110 | 493 | 2.27E-06 | 822 | 3.93E-07 | GO neurogenesis; GO Golgi apparatus |
| PTPRD | 9:8314246-10612723 | 943 | 1.24E-11 | 1232 | 6.80E-15 | GO neurogenesis; GO synaptic signalling; GO glutamatergic synapse |
| PTPRG | 3:61547243-62280573 | 123 | 3.71E-02 | 137 | 4.35E-06 | GO synaptic signalling |
| PTPRN2 | 7:157331750-158380482 | 611 | 1.08E-04 | 427 | 1.35E-05 | GO neurogenesis |
| PTPRT | 20:40701392-41818557 | 227 | 5.55E-05 | 268 | 2.39E-02 | |
| RBFOX1 | 16:5289469-7763342 | 1457 | 6.38E-24 | 1632 | 2.09E-25 | GO Golgi apparatus |
| RBFOX3 | 17:77085427-77512230 | 233 | 4.28E-05 | 100 | 1.27E-02 | |
| ROBO2 | 3:75955845-77699115 | 846 | 3.05E-05 | 407 | 5.49E-05 | GO neurogenesis |
| SDK1 | 7:3341080-4308632 | 170 | 3.34E-05 | 231 | 1.36E-03 | GO neurogenesis |
| SGCZ | 8:13947373-15095792 | 564 | 1.04E-01 | 348 | 1.08E-05 | |
| SLC39A11 | 17:70642085-71088853 | 172 | 5.62E-05 | 191 | 1.05E-01 | GO Golgi apparatus |
| SNX29 | 16:12070602-12668146 | 316 | 1.04E-01 | 328 | 4.75E-05 | |
| SORCS2 | 4:7194374-7744564 | 452 | 2.36E-10 | 290 | 4.41E-05 | |
| SOX5 | 12:23682438-24715383 | 373 | 7.65E-08 | 207 | 6.39E-03 | GO neurogenesis |
| TENM2 | 5:166406083-167691162 | 124 | 1.45E-06 | 487 | 7.62E-04 | GO neurogenesis; GO Golgi apparatus |
| TENM3 | 4:183065112-183724177 | 331 | 1.24E-03 | 268 | 1.03E-07 | GO synaptic signalling |
| TENM4 | 11:78364328-79152014 | 178 | 6.73E-02 | 283 | 3.06E-05 | GO synaptic signalling |
| TMEM132D | 12:129556271-130388212 | 190 | 2.52E-03 | 325 | 7.91E-06 | |
| TRA | 14:22090057-23021075 | 409 | 1.04E-01 | 332 | 1.81E-05 | |
| TSHZ2 | 20:51588946-52111869 | 200 | 1.04E-01 | 222 | 3.38E-05 | |
| TSSC1 | 2:3192741-3381653 | 631 | 6.35E-05 | 120 | 1.05E-01 | GO Golgi apparatus |
| USH2A | 1:215796236-216596738 | 197 | 4.32E-05 | 341 | 1.86E-03 | GO neurogenesis |
| WWOX | 16:78133310-79246567 | 687 | 1.19E-09 | 667 | 4.57E-07 | GO Golgi apparatus; ERBB2/4 pathway |
| ZNF385B | 2:180306709-180726232 | 197 | 1.04E-01 | 327 | 7.94E-05 | |
| ZNF385D | 3:21459911-22414132 | 497 | 3.33E-04 | 369 | 2.87E-06 | |
A total of 58 and 50 significantly associated genes (P-value < 10−4, highlighted in bold-italic) for PSQ and CPT, respectively, including 21 genes identified in both pain sensitivity measures.
CPT, cold pressor test; PSQ, pain sensitivity questionnaire.
Table 4.
Genetic correlations between pain sensitivity questionnaire and published genome-wide association study traits.
| Trait | r g | r g SE | z | P | h2 | h2 SE |
|---|---|---|---|---|---|---|
| CPT | −0.7336 | 0.3808 | −1.9264 | 0.0541 | 0.129 | 0.0686 |
| Neck/shoulder pain for 3+ mo | −0.7118 | 0.3416 | −2.0835 | 0.0372 | 0.0214 | 0.0064 |
| Rheumatoid arthritis | −0.6804 | 0.3244 | −2.0974 | 0.036 | 0.0044 | 0.0014 |
| ADHD | −0.6738 | 0.3204 | −2.1031 | 0.0355 | 0.2428 | 0.0988 |
| Length of working week for main job | −0.6473 | 0.2681 | −2.4141 | 0.0158 | 0.0191 | 0.0029 |
| Complications of internal orthopaedic prosthetic devices | −0.614 | 0.3208 | −1.9138 | 0.0557 | 0.0052 | 0.0017 |
| Cholelithiasis/gall stones | −0.5583 | 0.2568 | −2.1743 | 0.0297 | 0.0083 | 0.0015 |
| Mononeuropathies of upper limb | −0.5271 | 0.2086 | −2.5264 | 0.0115 | 0.0131 | 0.0018 |
| Internal derangement of knee | −0.4772 | 0.231 | −2.0662 | 0.0388 | 0.0081 | 0.002 |
| Hip pain | −0.4486 | 0.3536 | −1.2687 | 0.2046 | 0.0071 | 0.0029 |
| Duration of walks | −0.4281 | 0.1406 | −3.0447 | 0.0023 | 0.0463 | 0.0029 |
| Heavy physical activity (eg, weeding, carpentry) | −0.4168 | 0.1579 | −2.6392 | 0.0083 | 0.0343 | 0.002 |
| Noisy workplace | −0.4153 | 0.1782 | −2.3303 | 0.0198 | 0.062 | 0.006 |
| Job involves shift work | −0.4098 | 0.1847 | −2.2185 | 0.0265 | 0.03 | 0.0029 |
| Osteoarthritis | −0.3844 | 0.1659 | −2.3169 | 0.0205 | 0.0186 | 0.0018 |
| Disability diagnosed by doctor | −0.3595 | 0.1612 | −2.23 | 0.0257 | 0.025 | 0.0019 |
| Number of cigarettes previously smoked daily | −0.3557 | 0.165 | −2.1554 | 0.0311 | 0.0996 | 0.0143 |
| Falls in the last year | −0.3544 | 0.1513 | −2.3416 | 0.0192 | 0.0321 | 0.002 |
| Getting up in morning | −0.3128 | 0.1151 | −2.7181 | 0.0066 | 0.0686 | 0.0031 |
| Mouth/teeth dental problems | −0.3069 | 0.1363 | −2.2521 | 0.0243 | 0.048 | 0.0028 |
| Pain all over body | −0.2673 | 0.1397 | −1.9136 | 0.0557 | 0.0309 | 0.0034 |
| Pack years of smoking | −0.2538 | 0.1376 | −1.8445 | 0.0651 | 0.1086 | 0.0104 |
| Risk taking | −0.247 | 0.1176 | −2.1013 | 0.0356 | 0.0561 | 0.0028 |
| Extreme BMI | −0.2348 | 0.1271 | −1.8468 | 0.0648 | 0.6852 | 0.0576 |
| Disability or infirmity | −0.2339 | 0.1252 | −1.8685 | 0.0617 | 0.0496 | 0.0025 |
| Overweight | −0.221 | 0.1114 | −1.9843 | 0.0472 | 0.1093 | 0.0068 |
| MCP | −0.1479 | 0.092 | −1.6075 | 0.1079 | 0.0783 | 0.0029 |
| Neck or shoulder pain | −0.0909 | 0.119 | −0.7634 | 0.4452 | 0.0491 | 0.0032 |
| Headache | −0.0584 | 0.0959 | −0.6088 | 0.5427 | 0.0867 | 0.0043 |
| Back pain | −0.0476 | 0.1474 | −0.3228 | 0.7468 | 0.0326 | 0.0032 |
| Knee pain | 0.0283 | 0.18 | 0.1575 | 0.8748 | 0.0187 | 0.0039 |
| Stomach or abdominal pain | 0.0496 | 0.286 | 0.1733 | 0.8624 | 0.0109 | 0.0029 |
| Facial pain | 0.0966 | 0.2035 | 0.475 | 0.6348 | 0.0165 | 0.0034 |
| Morning/evening person | 0.1871 | 0.1022 | 1.83 | 0.0672 | 0.1157 | 0.0046 |
| Schizophrenia | 0.2109 | 0.0886 | 2.3805 | 0.0173 | 0.462 | 0.0192 |
| Neuroticism score | 0.2165 | 0.1132 | 1.9126 | 0.0558 | 0.1191 | 0.0064 |
| Guilty feelings | 0.2183 | 0.122 | 1.7895 | 0.0735 | 0.052 | 0.0028 |
| Coronary artery disease | 0.2341 | 0.1325 | 1.7672 | 0.0772 | 0.0792 | 0.0058 |
| Bipolar disorder | 0.2473 | 0.1442 | 1.7147 | 0.0864 | 0.4282 | 0.0362 |
| Suffer from nerves | 0.2499 | 0.1295 | 1.9297 | 0.0536 | 0.046 | 0.0031 |
| Narcolepsy | 0.2509 | 0.1315 | 1.9081 | 0.0564 | 0.049 | 0.0027 |
| Time spent watching television | 0.2701 | 0.1257 | 2.148 | 0.0317 | 0.0991 | 0.004 |
| Nervous feelings | 0.2843 | 0.1283 | 2.2158 | 0.0267 | 0.0669 | 0.0042 |
| Psoriasis | 0.4522 | 0.2555 | 1.7702 | 0.0767 | 0.0075 | 0.0017 |
| Triglycerides in medium VLDL | 0.5529 | 0.2981 | 1.8549 | 0.0636 | 0.0946 | 0.0301 |
| Celiac disease | 0.5604 | 0.2962 | 1.8918 | 0.0585 | 0.2959 | 0.0502 |
| Acetate | 0.5861 | 0.3195 | 1.8344 | 0.0666 | 0.0556 | 0.0192 |
| Nasal polyps | 0.6148 | 0.3046 | 2.0187 | 0.0435 | 0.0053 | 0.0016 |
| Fracture resulting from simple fall | 0.7123 | 0.3442 | 2.0692 | 0.0385 | 0.0518 | 0.0154 |
| Pneumothorax | 0.8217 | 0.4166 | 1.9722 | 0.0486 | 0.0031 | 0.0014 |
Genetic correlation (r g) and heritability estimates (h2), as well as the standard errors (SEs) and test statistics were computed with the linkage disequilibrium score regression (LDSC) method. PSQ genetic correlations with 832 traits were computed on LD Hub. We also computed PSQ r g with CPT, and an additional 9 pain phenotypes (in italic) from Refs. 21, 31 The table includes 49 entries (top 40 traits from LD hub, based on P-values and after excluding related phenotypes, + CPT + 8 additional pain phenotypes). None of the r g P-values passed the discovery threshold P-values (0.05/832 = 6 × 10−5).
ADHD, attention deficit hyperactivity disorder; BMI, body mass index; CPT, cold pressor test; GWAS, genome-wide association studies; MCP, multisite chronic pain; PSQ, pain sensitivity questionnaire.
The GWAS on CPT duration was underpowered and did not produce any significant genome-wide associations (Fig. 1B; Supplementary Fig. 8 and Supplementary Table 1, available at http://links.lww.com/PAIN/B557). The genetic correlation between PSQ score and CPT duration was r g = −0.73 ± 0.38 (P-value = 0.054), and the results from the CPT gene-based analysis and pathway analysis were consistent with the PSQ results. Among the 50 CPT genes identified by MAGMA, 21 were also identified in the PSQ analysis (Table 3). Similarly, these 50 genes were overexpressed in brain (Supplementary Fig. 9, available at http://links.lww.com/PAIN/B557) and enriched for brain development and synaptic signaling pathways (Supplementary Table 3, available at http://links.lww.com/PAIN/B557). Despite the shared genetic architecture between PSQ and CPT, the genome-wide significant association in TSSC1 (lead variant rs75365287) identified in the PSQ GWAS was not replicated in the CPT GWAS (P-value = 5.8 × 10−1; Table 2).
We also specifically focused on the association results for genes reported to be associated with nociception. We obtained a list of 21 nociception genes from the Human Pain Genes Database (HPGDB, https://humanpaingenetics.org/hpgdb/). 30 None of the 21 nociception genes showed evidence of association with PSQ and CPT (Supplementary Table 4, MAGMA analysis, available at http://links.lww.com/PAIN/B557).
Finally, we assessed the relationship between both pain sensitivity measures, PSQ and CPT, and hair color on a subset of the PSQ and CPT cohorts with available hair color data (Table 1). A total of 15,167 and 5243 participants were included in the analyses for PSQ score and CPT duration, respectively. Using a Gaussian linear model including age, sex, and the first 5 genetic principal components as covariables, we showed that participants with red hair reported significantly higher PSQ scores than participants with light blond, dark blond, light brown, dark brown, or black hair (Fig. 3). Furthermore, females with red hair reported on average higher PSQ scores than nonred hair females and red hair or nonred hair males (sex-by-red hair interaction P-value = 0.046; Supplementary Table 5, available at http://links.lww.com/PAIN/B557). We did not observe significant PSQ differences between red hair and nonred hair males. For CPT, we did not observe any significant associations using a Cox proportional hazards model (Supplementary Table 6 and Supplementary Fig. 10, available at http://links.lww.com/PAIN/B557) or an analysis of variance on CPT duration converted in ranks (Supplementary Table 7 and Supplementary Fig. 11, available at http://links.lww.com/PAIN/B557). Because red hair color is partially determined by recessive genetic polymorphism in the MC1R gene, we explored the association of the 3 main variants rs1805009 (D294H), rs1805008 (R160W) and rs1805007 (R151C) in the PSQ and CPT GWAS (Table 2; and Supplementary Table 5, available at http://links.lww.com/PAIN/B557). None of these individual variants passed the genome-wide significant threshold in the GWAS analyses (P-values > 5.1 × 10−3). We combined these 3 variants and defined 3 categories of MC1R variant carriers, Noncarrier (0 MC1R recessive allele), Carrier1 (1 allele), and Carrier2+ (>1 alleles), and tested their association with PSQ score and CPT duration. MC1R carriers reported significantly higher PSQ score that noncarriers (P-value = 6.8 × 10−3 and 1.5 × 10−2, respectively). However, we did not observe any significant associations between MC1R carriers and CPT duration (Supplementary Tables 6 and 7, and Supplementary Figs. 10 and 11, available at http://links.lww.com/PAIN/B557).
Figure 3.
Regional association plot for TSSC1 locus and QQ plot from the PSQ GWAS. GWAS, genome-wide association studies; PSQ, pain sensitivity questionnaire.
4. Discussion
The purpose of this study was to identify genetic factors contributing to the individual perception of pain. 51 We used 2 self-administered pain sensitivity measurements, the PSQ and CPT, and performed, for the first time, a comprehensive genetic association analysis on these pain sensitivity metrics. Pain sensitivity questionnaire score and CPT duration have been previously shown to be only moderately phenotypically correlated (r = −0.22 [−0.27, −0.17]), 29 and we showed that they are also genetically correlated (r g = −0.73 [−1.49, −0.02]). The PSQ is a self-perceived pain intensity rating of imagined painful situations occurring in daily life, whereas the CPT directly measures pain tolerance. Although the PSQ was not designed to cover all dimensions of pain experience, it explicitly incorporates some emotional and cognitive components of the pain sensitivity. 23
Most of the genes identified by the association analyses are overexpressed in the brain. This is especially true for the amygdala, the emotional pain processing center, but other components of the pain matrix (eg, frontal cortex, basal ganglia, and hypothalamus) also showed significant enrichment. 46,50 We found that no brain area seemed to be selectively and exclusively associated with pain sensitivity. These genetic results were in line with recent brain imaging studies that suggested that the individual variability in pain sensitivity is most probably produced by the connectivity of multiple brain areas. 28,43 It is interesting to notice the absence of association enrichments in nonbrain tissues. According to the current multifaceted experience concept, pain results from the integration of nociception and the cognitive–emotional state of the individual. Nociception occurs with the activation of nociceptors, found in skin and mucosa, as well as in a variety of organs, such as the digestive tract, the bladder, the gut, and muscles, followed by the propagation and modulation of the nociceptive signal through the peripheral nervous system. However, our analysis of 21 genes previously reported involved in nociception showed no evidence that genetic polymorphism near these genes were associated with PSQ score or CPT duration. The CPT was designed to quantify the evoked pain and signal sensory responses of cold pain sensitivity. Although underpowered, analyses showed very similar enrichment patterns than the PSQ. Among the combined 87 genes identified by the PSQ and CPT gene-based analyses, 10 of them were present in HPGDB (ADARB2, CACNA2D3, CTNNA3, DMD, KCNQ3, PARK2, PCSK6, PRKG1, PTPRD, and TRA). The vast majority were identified in various migraine GWAS. 19 Pathway enrichment analyses showed that many of these 87 genes are involved in neuron and brain development, and neuron signaling. In particular, they highlighted genes active in glutamatergic synapses. Glutamate receptors have a leading role in pain signal transmission and are often considered promising targets for the treatment of chronic pain. 37 Among the 6 genes identified in this pathway by PSQ associations, NGR1 and NGR3 were already known to be linked to pain sensitivity. Both are involved in the ErbB2 and ErbB4 signaling pathways, which have repeatedly been demonstrated to directly contribute to the development of neuropathic pain. 7 To our knowledge, however, our study is the first where genetic polymorphism in these 2 genes has been associated with pain sensitivity measurements. However, the 4 other genes in the pathway, PTPRD, DLG2, GPC6, and GRID2, have been all associated with diverse neurological disorders. 1,4,20,48 This observation supports earlier findings that neurological disorders and pain sensitivity are intimately linked. 8 The genome-wide significant locus, TSSC1, has not previously been associated with pain traits. Some suggestive associations (P-value = 1.1 × 10−7) were recently identified in small fiber neuropathy or joint disorders (http://r4.finngen.fi/gene/TSSC1). It has been also associated with psychiatric disorders, 25 cognitive decline, 26 and was recently identified in a study that examined alterations in the postsynaptic protein profile as a consequence of prolonged exposure to morphine. 44 Finally, we confirmed that women with red hair are more sensitive to pain, but we did not observe this relationship in men. However, the increased sensitivity in red hair women was only detected by the PSQ, and the lack of association with CPT duration was surprising because it is generally accepted that red hair women are more sensitive to cold and hot stimuli. 15,27 However, more recent publications did not confirm this association, 2 and the CPT was never used as cold stimuli in these published studies. None of the 3 main variants in MC1R that control red hair color showed genome-wide significant associations with PSQ score or CPT duration. Nevertheless, in combination, individuals carrying one or more copies of these 3 variants reported a significant higher self-perceived pain sensitivity.
The opposite direction of the genetic and phenotypic correlations between pain sensitivity and susceptibility traits was unexpected. Although higher pain sensitivity, measured by the PSQ and CPT, was consistently associated with higher pain susceptibility and medication usage, the bivariate LDSC analyses suggested that the genetic architectures of pain sensitivity and pain susceptibility were inversely correlated. It is an unusual result because empirical evidence across plant and animal species, including human, had supported the Cheverud Conjecture, which states that genetic correlations usually mirror phenotypic correlations. 42 The opposite direction suggests a strong and positive environmental correlation between pain sensitivity and susceptibility traits. It is generally recognized that chronic exposure to pain increases pain sensitivity through central sensitization, an excessive responsiveness to pain in the nociceptive pathway. 22,34 Our results seemed to support this relationship with a higher measured sensitivity pain in participants reporting pain and pain medication usage, but also in older participants and participants reporting multiple pains. However, it has also been reported that pain tolerance in patients with acute pain could increase via self-induced positive expectancies or cognitive–behavioral therapy. 34,36 It may explain the observed positive genetic correlations between the PSQ score and acute pain traits, such as pneumothorax and fracture resulting from a simple fall, and the apparent decrease of pain sensitivity for participants reporting only one pain condition (Supplementary Fig. 3, available at http://links.lww.com/PAIN/B557). We explored the causal relationship between pain sensitivity and susceptibility with an MR approach, but the results were inconclusive, probably because of the lack of statistical power in the PSQ GWAS. It was reassuring to also observe negative genetic correlations between pain sensitivity and disability phenotypes, including complications associated with prosthetic devices, and with unhealthy behaviors such as smoking, extreme BMI, length of working week, shift work, or working in a noisy environment. 10 Finally, we also observed significant genetic correlations between pain sensitivity and neurological disorders or personality traits. Notably, pain sensitivity showed a strong negative genetic correlation with ADHD. 12 However, we observed positive genetic correlations between pain sensitivity and schizophrenia, neuroticism, and bipolar disorder. It is also interesting to note the absence of genetic correlation with migraine and depression despite the fact that migraine, in particular, is associated with intense pain.
Even with the largest PSQ score and CPT duration datasets collected to date in a general population, the results of this study should be interpreted with caution. Uncovering the genetic architecture of complex traits requires generally large datasets, and our results confirmed that pain sensitivity will not be an exception. Because of the limited statistical power of the PSQ and CPT GWAS, many of the highlighted genes, pathways, and genetic correlations were marginally significant and will require validation in larger datasets or with functional experiments. The study focused on only 2 measures of pain sensitivity, but because the interindividual and temporal variation of pain sensitivity depends on the integration of the sensory pathways and the emotional–cognitive states of individuals, it is unlikely that the PSQ and CPT captured the total variability of pain sensitivity within the 23andMe cohort. The PSQ hinges on the participant's own memory of painful experiences, whereas the CPT measures sensitivity to cold temperatures. More advanced or combination of measures covering the full pain sensitivity spectrum, such as quantitative sensory tests but also self-perceived pain sensitivity instruments, will be required to identify and disentangle the genetic architecture of pain sensitivity. 16,39 The lack of statistical power also limited our ability to fully characterize the home-based CPT used in this study. Larger datasets and CPT measured in more traditional clinical setups will certainly be required. Nevertheless, despite these limitations, we demonstrated that it is now possible to study the genetics of pain sensitivity at a population scale, by deploying and collecting relevant pain sensitivity data for fairly sophisticated instruments such as the CPT. We showed that the genetic architecture of pain sensitivity is related to the genetic architecture of pain susceptibility, but the relationship is probably more complex than it was initially perceived. Our results also provided some support to previous reports, suggesting that people with red hair are more sensitive to certain types of pain and that the higher pain sensitivity could be modulated by genetic polymorphism in MC1R. However, the bulk of the genetic architecture identified in this study implicated the brain, and not the peripheral nociception system, as the main modulator of pain sensitivity.
Conflict of interest statement
The authors have no conflicts of interest to declare.
Acknowledgments
This research was conducted by 23andMe and Grünenthal GmbH. All authors are employees of these companies. The funders provided support in the form of salaries for all authors but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors like to thank the contributions from the 23andMe Research Team (Michelle Agee, Adam Auton, Robert K. Bell, Katarzyna Bryc, Sarah L. Elson, Pierre Fontanillas, Nicholas A. Furlotte, David A. Hinds, Naomi Iwata, Jennifer C. McCreight, Karen E. Huber, Aaron Kleinman, Nadia K. Litterman, Joanna L. Mountain, Elizabeth S. Noblin, Carrie A.M. Northover, Steven J. Pitts, J. Fah Sathirapongsasuti, Olga V. Sazonova, Anjali J. Shastri, Janie F. Shelton, Suyash Shringarpure, Chao Tian, Vladimir Vacic, Catherine Weldon, Keng-Han Lin, Yunxuan Jiang, Kimberly McManus, David Poznik, Ethan Jewett, Xin Wang, and Barry Hicks).
The authors thank 23andMe research participants and employees for making this work possible.
P. Fontanillas and J. Y. Tung are employed by and hold stock or stock options in 23andMe, Inc.
Data availability: The complete summary statistics for the PSQ and CPT GWAS can be requested at https://research.23andme.com/dataset-access/.
Footnotes
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.painjournalonline.com).
Appendix A. Supplemental digital content
Supplemental digital content associated with this article can be found online at http://links.lww.com/PAIN/B557.
References
- [1]. Allen JD, Bishop JR. A systematic review of genome-wide association studies of antipsychotic response. Pharmacogenomics 2019;20:291–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2]. Andresen T, Lunden D, Drewes AM, Arendt-Nielsen L. Pain sensitivity and experimentally induced sensitisation in red haired females. Scand J Pain 2011;2:3–6. [DOI] [PubMed] [Google Scholar]
- [3]. Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, Haudenschild CD, Beckman KB, Shi J, Mei R, Urban AE, Montgomery SB, Levinson DF, Koller D. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res 2014;24:14–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4]. BIOS Consortium, Social Science Genetic Association Consortium, Baselmans BML, Jansen R, Ip HF, van Dongen J, Abdellaoui A, van de Weijer MP, Bao Y, Smart M, Kumari M, Willemsen G, Hottenga JJ, Boomsma DI, de Geus EJC, Boomsma DI, de Geus EJC, Nivard MG, Bartels M. Multivariate genome-wide analyses of the well-being spectrum. Nat Genet 2019;51:445–51. [DOI] [PubMed] [Google Scholar]
- [5]. Borsook D, Youssef AM, Simons L, Elman I, Eccleston C. When pain gets stuck: the evolution of pain chronification and treatment resistance. PAIN 2018;159:2421–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6]. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015;44:512–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7]. Calvo M, Zhu N, Tsantoulas C, Ma Z, Grist J, Loeb JA, Bennett DL. Neuregulin-ErbB signaling promotes microglial proliferation and chemotaxis contributing to microgliosis and pain after peripheral nerve injury. J Neurosci 2010;30:5437–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8]. Cao S, Fisher DW, Yu T, Dong H. The link between chronic pain and Alzheimer's disease. J Neuroinflammation 2019;16:204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9]. Chen X, Chen H, Cai W, Maguire M, Ya B, Zuo F, Logan R, Li H, Robinson K, Vanderburg CR, Yu Y, Wang Y, Fisher DE, Schwarzschild MA. The melanoma-linked “redhead” MC1R influences dopaminergic neuron survival. Ann Neurol 2017;81:395–406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10]. Dean E, Söderlund A. What is the role of lifestyle behaviour change associated with non-communicable disease risk in managing musculoskeletal health conditions with special reference to chronic pain? BMC Musculoskelet Disord 2015;1316:87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11]. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 2015;11:e1004219. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12]. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, Baldursson G, Belliveau R, Bybjerg-Grauholm J, Bækvad-Hansen M, Cerrato F, Chambert K, Churchhouse C, Dumont A, Eriksson N, Gandal M, Goldstein JI, Grasby KL, Grove J, Gudmundsson OO, Hansen CS, Hauberg ME, Hollegaard MV, Howrigan DP, Huang H, Maller JB, Martin AR, Martin NG, Moran J, Pallesen J, Palmer DS, Pedersen CB, Pedersen MG, Poterba T, Poulsen JB, Ripke S, Robinson EB, Satterstrom FK, Stefansson H, Stevens C, Turley P, Walters GB, Won H, Wright MJ, ADHD Working Group of the Psychiatric Genomics Consortium (PGC); Early Lifecourse & Genetic Epidemiology (EAGLE) Consortium; 23andMe Research Team, Andreassen OA, Asherson P, Burton CL, Andreassen OA, Asherson P, Burton CL, Boomsma DI, Cormand B, Dalsgaard S, Franke B, Gelernter J, Geschwind D, Hakonarson H, Haavik J, Kranzler HR, Kuntsi J, Langley K, Lesch KP, Middeldorp C, Reif A, Rohde LA, Roussos P, Schachar R, Sklar P, Sonuga-Barke EJS, Sullivan PF, Thapar A, Tung JY, Waldman ID, Medland SE, Stefansson K, Nordentoft M, Hougaard DM, Werge T, Mors O, Mortensen PB, Daly MJ, Faraone SV, Børglum AD, Neale BM. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet 2019;51:63–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13]. Fillingim RB, Loeser JD, Baron R, Edwards RR. Assessment of chronic pain: domains, methods, and mechanisms. J Pain 2016;17:T10–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14]. Flegr J, Sýkorová K. Skin fairness is a better predictor for impaired physical and mental health than hair redness. Sci Rep 2019;9:18138. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15]. Frost P, Kleisner K, Flegr J. Health status by gender, hair color, and eye color: red-haired women are the most divergent. PLoS One 20172017;12:e0190238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16]. Garland EL. Pain processing in the human nervous system: a selective review of nociceptive and biobehavioral pathways. Prim Care 2012;39:561–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17]. Gershlick DC, Schindler C, Chen Y, Bonifacino JS. TSSC1 is novel component of the endosomal retrieval machinery. Mol Biol Cell 2016;27:2867–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18]. Healy E, Jordan SA, Budd PS, Suffolk R, Rees JL, Jackson IJ. Functional variation of MC1R alleles from red-haired individuals. Hum Mol Genet 2001;10:2397–402. [DOI] [PubMed] [Google Scholar]
- [19]. Gormley P, Anttila V, Winsvold BS, Palta P, Esko T, Pers TH, Farh KH, Cuenca-Leon E, Muona M, Furlotte NA, Kurth T, Ingason A, McMahon G, Ligthart L, Terwindt GM, Kallela M, Freilinger TM, Ran C, Gordon SG, Stam AH, Steinberg S, Borck G, Koiranen M, Quaye L, Adams HH, Lehtimäki T, Sarin AP, Wedenoja J, Hinds DA, Buring JE, Schürks M, Ridker PM, Hrafnsdottir MG, Stefansson H, Ring SM, Hottenga JJ, Penninx BW, Färkkilä M, Artto V, Kaunisto M, Vepsäläinen S, Malik R, Heath AC, Madden PA, Martin NG, Montgomery GW, Kurki MI, Kals M, Mägi R, Pärn K, Hämäläinen E, Huang H, Byrnes AE, Franke L, Huang J, Stergiakouli E, Lee PH, Sandor C, Webber C, Cader Z, Muller-Myhsok B, Schreiber S, Meitinger T, Eriksson JG, Salomaa V, Heikkilä K, Loehrer E, Uitterlinden AG, Hofman A, van Duijn CM, Cherkas L, Pedersen LM, Stubhaug A, Nielsen CS, Männikkö M, Mihailov E, Milani L, Göbel H, Esserlind AL, Christensen AF, Hansen TF, Werge T. International Headache Genetics Consortium, International Headache Genetics Consortium, Gormley P, Anttila V, Winsvold BS, Palta P, Esko T, Pers TH, Farh KH, Cuenca-Leon E, Muona M, Furlotte NA, Kurth T, Ingason A, McMahon G, Ligthart L, Terwindt GM, Kallela M, Freilinger TM, Ran C, Gordon SG, Stam AH, Steinberg S, Borck G, Koiranen M, Quaye L, Adams HH, Lehtimäki T, Sarin AP, Wedenoja J, Hinds DA, Buring JE, Schürks M, Ridker PM, Hrafnsdottir MG, Stefansson H, Ring SM, Hottenga JJ, Penninx BW, Färkkilä M, Artto V, Kaunisto M, Vepsäläinen S, Malik R, Heath AC, Madden PA, Martin NG, Montgomery GW, Kurki MI, Kals M, Mägi R, Pärn K, Hämäläinen E, Huang H, Byrnes AE, Franke L, Huang J, Stergiakouli E, Lee PH, Sandor C, Webber C, Cader Z, Muller-Myhsok B, Schreiber S, Meitinger T, Eriksson JG, Salomaa V, Heikkilä K, Loehrer E, Uitterlinden AG, Hofman A, van Duijn CM, Cherkas L, Pedersen LM, Stubhaug A, Nielsen CS, Männikkö M, Mihailov E, Milani L, Göbel H, Esserlind AL, Christensen AF, Hansen TF, Werge T, Kaprio J, Kaprio J, Aromaa AJ, Raitakari O, Ikram MA, Spector T, Järvelin MR, Metspalu A, Kubisch C, Strachan DP, Ferrari MD, Belin AC, Dichgans M, Wessman M, van den Maagdenberg AM, Zwart JA, Boomsma DI, Smith GD, Stefansson K, Eriksson N, Daly MJ, Neale BM, Olesen J, Chasman DI, Nyholt DR, Palotie A. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat Genet 2016;48:856–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20]. Chang D, Nalls MA, Hallgrímsdóttir IB, Hunkapiller J, van der Brug M, Cai F. International Parkinson’s Disease Genomics Consortium, 23andMe Research Team, International Parkinson's Disease Genomics Consortium; 23andMe Research Team, Chang D, Nalls MA, Hallgrímsdóttir IB, Hunkapiller J, van der Brug M, Cai F, Kerchner GA, Ayalon G, Bingol B, Sheng M, Hinds D, Behrens TW, Singleton AB, Bhangale TR, Graham RR. A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci. Nat Genet 2017;49:1511–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21]. Johnston KJA, Adams MJ, Nicholl BI, Ward J, Strawbridge RJ, Ferguson A, McIntosh AM, Bailey MES, Smith DJ. Genome-wide association study of multisite chronic pain in UK Biobank. PLoS Genet 2019;15:e1008164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22]. Kim D, Chae Y, Park HJ, Lee IS. Effects of chronic pain treatment on altered functional and metabolic activities in the brain: a systematic review and meta-analysis of functional neuroimaging studies. Front Neurosci 2021;15:684926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23]. Kuperman P, Granovsky Y, Bahouth H, Fadel S, Ben Lulu H, Bosak N, Buxbaum C, Sprecher E, Crystal S, Granot M. Explaining very early acute mild traumatic brain injury after motor vehicle collision pain variability: additive value of pain sensitivity questionnaire. Pain Rep 2020;5:e821. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [24]. Li WQ, Gao X, Tworoger SS, Qureshi AA, Han J. Natural hair color and questionnaire-reported pain among women in the United States. Pigment Cell Melanoma Res 2016;29:239–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25]. Li Q, Wang Z, Zong L, Ye L, Ye J, Ou H, Jiang T, Guo B, Yang Q, Liang W, Zhang J, Long Y, Zheng X, Hou Y, Wu F, Zhou L, Li S, Huang X, Zhao C. Allele-specific DNA methylation maps in monozygotic twins discordant for psychiatric disorders reveal that disease-associated switching at the EIPR1 regulatory loci modulates neural function. Mol Psychiatry 2021. doi: 10.1038/s41380-021-01126-w [Epub ahead of print]. [DOI] [PubMed] [Google Scholar]
- [26]. Li QS, Parrado AR, Samtani MN, Narayan VA; Alzheimer’s Disease Neuroimaging Initiative. Variations in the FRA10AC1 fragile site and 15q21 are associated with cerebrospinal fluid Aβ1-42 level. PLoS One 2015;10:e0134000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27]. Liem EB, Joiner TV, Tsueda K, Sessler DI. Increased sensitivity to thermal pain and reduced subcutaneous lidocaine efficacy in redheads. Anesthesiology 2005;102:509–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28]. Mano H, Seymour B. Pain: a distributed brain information network? PLoS Biol 2015;13:e1002037. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29]. McIntyre MH, Team 23andMe Research, Kless A, Hein P, Field M, Tung JY. Validity of the cold pressor test and pain sensitivity questionnaire via online self-administration. PLoS One 2020;15:e0231697. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30]. Meloto CB, Benavides R, Lichtenwalter RN, Wen X, Tugarinov N, Zorina-Lichtenwalter K, Chabot-Doré AJ, Piltonen MH, Cattaneo S, Verma V, Klares R, III, Khoury S, Parisien M, Diatchenko L. Human pain genetics database: a resource dedicated to human pain genetics research. PAIN 2018;159:749–63. [DOI] [PubMed] [Google Scholar]
- [31]. Meng W, Adams MJ, Reel P, Rajendrakumar A, Huang Y, Deary IJ, Palmer CNA, McIntosh AM, Smith BH. Genetic correlations between pain phenotypes and depression and neuroticism. Eur J Hum Genet 2020;28:358–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32]. Meng W, Chan BW, Harris C, Freidin MB, Hebert HL, Adams MJ, Campbell A, Hayward C, Zheng H, Zhang X, Colvin LA, Hales TG, Palmer CNA, Williams FMK, McIntosh A, Smith BH. A genome-wide association study finds genetic variants associated with neck or shoulder pain in UK Biobank. Hum Mol Genet 2020;29:1396–404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33]. Morgan MD, Pairo-Castineira E, Rawlik K, Canela-Xandri O, Rees J, Sims D, Tenesa A, Jackson IJ. Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability. Nat Commun 2018;9:5271. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34]. Nijs J, George SZ, Clauw DJ, Fernández-de-las-Peñas C, Kosek E, Ickmans K, Fernández-Carnero J, Polli A, Kapreli E, Huysmans E, Cuesta-Vargas AI, Mani R, Lundberg M, Leysen L, Rice D, Sterling M, Curatolo M. Central sensitisation in chronic pain conditions: latest discoveries and their potential for precision medicine. Lancet Rheumatol 2021;3:e383–92. [DOI] [PubMed] [Google Scholar]
- [35]. Patanwala AE, Norwood C, Steiner H, Morrison D, Li M, Walsh K, Martinez M, Baker SE, Snyder EM, Karnes JH. Psychological and genetic predictors of pain tolerance. Clin Transl Sci 2019;12:189–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36]. Peerdeman KJ, van Laarhoven AI, Peters ML, Evers AW. An integrative review of the influence of expectancies on pain. Front Psychol 2016;7:1270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [37]. Pereira V, Goudet C. Emerging trends in pain modulation by metabotropic glutamate receptors. Front Mol Neurosci 2019;11:464. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38]. Randall CL, Wright CD, Chernus JM, McNeil DW, Feingold E, Crout RJ, Neiswanger K, Weyant RJ, Shaffer JR, Marazita ML. A preliminary genome-wide association study of pain-related fear: implications for orofacial pain. Pain Res Manag 2017;2017:7375468. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [39]. Rolke R, Baron R, Maier C, Tölle TR, Treede DR, Beyer A, Binder A, Birbaumer N, Birklein F, Bötefür IC, Braune S, Flor H, Huge V, Klug R, Landwehrmeyer GB, Magerl W, Maihöfner C, Rolko C, Schaub C, Scherens A, Sprenger T, Valet M, Wasserka B. Quantitative sensory testing in the German research network on neuropathic pain (DFNS): standardized protocol and reference values. PAIN 2006;123:231–43. [DOI] [PubMed] [Google Scholar]
- [40]. Ruscheweyh R, Marziniak M, Stumpenhorst F, Reinholz J, Knecht S. Pain sensitivity can be assessed by self-rating: development and validation of the Pain Sensitivity Questionnaire. PAIN 2009;146:65–74. [DOI] [PubMed] [Google Scholar]
- [41]. Ruscheweyh R, Verneuer B, Dany K, Marziniak M, Wolowski A, Colak-Ekici R, Schulte TL, Bullmann V, Grewe S, Gralow I, Evers S, Knecht S. Validation of the pain sensitivity questionnaire in chronic pain patients. PAIN 2012;153:1210–8. [DOI] [PubMed] [Google Scholar]
- [42]. Sodini SM, Kemper KE, Wray NR, Trzaskowski M. Comparison of genotypic and phenotypic correlations: Cheverud's conjecture in humans. Genetics 2018;209:941–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [43]. Spisak T, Kincses B, Schlitt F, Zunhammer M, Schmidt-Wilcke T, Kincses ZT, Bingel U. Pain-free resting-state functional brain connectivity predicts individual pain sensitivity. Nat Commun 2020;11:187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [44]. Stockton SD, Jr, Gomes I, Liu T, Moraje C, Hipólito L, Jones MR, Ma'ayan A, Morón JA, Li H, Devi LA. Morphine regulated synaptic networks revealed by integrated proteomics and network analysis. Mol Cell Proteomics 2015;14:2564–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [45]. The 23andMe Research Team, Meng W, Adams MJ, Palmer CNA, Shi J, Shi J, Auton A, Ryan KA, Jordan JM, Mitchell BD, Jackson RD, Yau MS, McIntosh AM, Smith BH. Genome-wide association study of knee pain identifies associations with GDF5 and COL27A1 in UK Biobank. Commun Biol 2019;2:321. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [46]. Thompson JM, Neugebauer V. Amygdala plasticity and pain. Pain Res Manag 2017;2017:8296501. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [47]. Tian C, Hromatka BS, Kiefer AK, Eriksson N, Noble SM, Tung JY, Hinds DA. Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections. Nat Commun 2017;8:599. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [48]. Tomita H, Cornejo F, Aranda-Pino B, Woodard CL, Rioseco CC, Neel BG, Alvarez AR, Kaplan DR, Miller FD, Cancino GI. The protein tyrosine phosphatase receptor delta regulates developmental neurogenesis. Cell Rep 2020;30:215–28. e5. [DOI] [PubMed] [Google Scholar]
- [49]. Tsepilov YA, Freidin MB, Shadrina AS, Sharapov SZ, Elgaeva EE, Zundert JV, Karssen LС, Suri P, Williams FMK, Aulchenko YS. Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions. Commun Biol 2020;3:329. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [50]. Veinante P, Yalcin I, Barrot M. The amygdala between sensation and affect: a role in pain. J Mol Psychiatry 2013;1:9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [51]. Wandner LD, Scipio CD, Hirsh AT, Torres CA, Robinson ME. The perception of pain in others: how gender, race, and age influence pain expectations. J Pain 2012;13:220–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [52]. Williams FM, Scollen S, Cao D, Memari Y, Hyde CL, Zhang B, Sidders B, Ziemek D, Shi Y, Harris J, Harrow I, Dougherty B, Malarstig A, McEwen R, Stephens JC, Patel K, Menni C, Shin SY, Hodgkiss D, Surdulescu G, He W, Jin X, McMahon SB, Soranzo N, John S, Wang J, Spector TD. Genes contributing to pain sensitivity in the normal population: an exome sequencing study. PLoS Genet 2012;8:e1003095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [53]. Young EE, Lariviere WR, Belfer I. Genetic basis of pain variability: recent advances. J Med Genet 2012;49:1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [54]. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, Hemani G, Tansey K, Laurin C, Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium, Pourcain BS, Pourcain BS, Warrington NM, Finucane HK, Price AL, Bulik-Sullivan BK, Anttila V, Paternoster L, Gaunt TR, Evans DM, Neale BM. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 2017;33:272–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [55]. Zorina-Lichtenwalter K, Maixner W, Diatchenko L. Detangling red hair from pain: phenotype-specific contributions from different genetic variants in melanocortin-1 receptor. PAIN 2020;161:938–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [56]. Zorina-Lichtenwalter K, Meloto CB, Khoury S, Diatchenko L. Genetic predictors of human chronic pain conditions. Neuroscience 2016;338:36–62. [DOI] [PubMed] [Google Scholar]
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