Pain phenotype Participants were asked to indicate the site of any pain expe- rienced for 1 day or longer in the past month on blank body manikins (front, back and sides) and to indicate if their pain had lasted for more than 3 months. Participants were classiﬁed two ways. (1) A ‘total pain’ score was constructed which ranged from 0 (no pain) to 29 (pain in all sites of the body) using the Manchester coding system18 19 (ﬁgure 1). For each participant the maximum number of pain sites across the three time points was determined. (2) CWP was classiﬁed using American College of Rheumatology criteria. Cases were deﬁned as participants with CWP ≥2 time points and controls were pain free at all three time points. Ascertainment of comorbidities Anxiety, depression, psychological distress and sleep quality were assessed at baseline. Anxiety and depression were mea- sured using the Hospital Anxiety and Depression (HAD) scale.20 The HAD questionnaire contains seven items on anxiety and seven items on depression in the last week. Each question is answered on a 4-point Likert scale (0–3) with total scores ranging from 0 to 21. Higher scores are indicative of a higher probability of having an anxiety or depressive disorder. The General Health Questionnaire 21 (12-item version) was used to assess levels of psychological distress. Scores range from 0 to 12 with higher scores indicating higher levels of psychological distress. Sleep quality was assessed using the Estimation of Sleep Problems Scale.22 A total of 4 items on recent sleep problems (sleep onset, sleep maintenance, early wakening and non-restorative sleep) were assessed on a 5-point scale (0–5) resulting in a total score of between 0 and 20, with higher scores indicating poorer sleep quality. Genetic analysis Participants were asked to provide a buccal swab sample for genetic analysis with informed consent. The sample collection and DNA extraction methods were adapted from Freeman et al.23 Pairwise tagging single nucleotide polymorphisms (SNPs, r2≥0.8) with a minor allele frequency ≥5% were selected for CRH, CRHBP, CRHR1, POMC, MC2R, NR3C1 and SERPINA6 and their 10 kb ﬂanking regions using Tagger.24 SNPs were geno- typed using Sequenom MassARRAY technology following the manufacturer’s instructions (http://www.sequenom.com). Figure 1 Pain sites used to calculate total pain score (Manchester coding). Ann Rheum Dis 2010;69:556–560. doi:10.1136/ard.2009.116137 Extended report Sample and assay quality control thresholds were set to 90%. Allele frequencies were checked for consistency with HapMap data and tested for deviation from Hardy–Weinberg Equilibrium (HWE) and excluded from the analysis if p≤0.01 in the total pop- ulation. Linkage disequilibrium (LD) between SNPs was exam- ined using Haploview version 3.32.25 Statistical analysis Analysis was conducted using STATA V.9.2 (Stata, College Station, Texas, USA). p Values <0.05 were considered statisti- cally signiﬁcant. Total number of pain sites The distribution of the maximum number of pain sites was pos- itively skewed with 17% of participants reporting no pain. To account for this and overdispersion of the data a zero-inﬂated negative binomial regression (ZINB) model was used. ZINB tests whether a SNP is associated with the odds of reporting no pain (logit) as well as testing for association between a SNP and the maximum number of pain sites in participants who have reported pain (negative binomial). Participants report- ing pain can range from having acute pain at a single site to chronic pain at multiple sites, therefore, treating them as a referent group to determine if a SNP is associated with the odds of reporting no pain is a crude analysis and so the results of the logit portion of the model are not reported. Initially a test for trend was conducted. Where there was no evidence of a trend association, recessive (AA and Aa vs aa) and dom- inant (AA vs Aa and aa) models of association were tested. The results of the negative binomial portion of the model are reported as the proportional change in the number of pain sites with 95% CIs. The effects of four variables; depression, anxiety, psycholog- ical distress and sleep quality on the maximum number of pain sites was assessed using ZINB and only depression and sleep were independently associated (data not shown). Therefore, SNPs signiﬁcantly associated with the maximum number of pain sites were tested for association with HAD depression score (using negative binomial regression) and sleep quality score (using ZINB). Where there was evidence of an associa- tion between a SNP and depression or sleep quality the model was adjusted for the associated comorbidity (depression and/or sleep) to determine if the SNP associations with the maximum number of pain sites were occurring independently of these comorbid factors. Interactions between SNPs (associated with the maximum number of pain sites) and depression and sleep were also tested to determine if they were acting as effect modi- ﬁers on the associations observed with pain. CWP SNPs were tested for association with CWP using the Cochran– Armitage trend test. Where there was no evidence of a trend association, recessive and dominant models of association were tested using a χ2 test. Logistic regression was used to estimate odds ratios (OR) and 95% CI. Haplotype structure was determined using the conﬁdence bounds method in Haploview.26 The expectation–maximisation algorithm was used to estimate haplotype frequencies. The overall distribution of haplotypes was compared between patients with CWP and pain-free controls using a χ2 test. The frequency of each individual haplotype was also compared to the combined frequency of the other haplotypes between cases and controls using the χ2 test. Haplotype inference and associa- tion testing was conducted in PLINK.27 557