Computational protocol: An omics investigation into chronic widespread musculoskeletal pain reveals epiandrosterone sulfate as a potential biomarker

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Protocol publication

[…] TwinsUK subjects had been genotyped for association markers using a combination of Illumina arrays (Human Hap300 and the Human Hap610Q) as previously reported. For this analysis, single nucleotide polymorphisms (SNPs) were excluded if call rate <97% (SNPs with minor allele frequency, MAF ≥5%) or <99% (for 1% ≤ MAF <5%), Hardy–Weinberg p values <10−6, and MAF <1%. Subjects were removed if genotyping failed in >2% SNP. The overall genotyping efficiency was 98.7%. Imputation of genotypes was carried out using the software IMPUTE version 2 using HapMap II as the reference panel. Population substructure was examined using the STRUCTURE program and correspondingly controlled for spurious associations. In KORA, Affymetrix Axiom chip had been used for genotyping. HapMap build 37 served as population reference, and the criteria of call rate >98% and P (Hardy–Weinberg) >5 × 10−6 were applied as filters for SNP quality. Genotyped SNPs were imputed with IMPUTE v2.3.0 using the 1000G set as reference panel. [...] The analysis was conducted in several steps. First, potential risk factors for CWP including age and anthropometric measurements (weight, height, BMI, and body composition variables) were examined in univariate analysis (Student t test). Second, we sought metabolites significantly associated with CWP, preserving power by including only those metabolites available in ≥2000 subjects in TwinsUK. To this aim, all 324 metabolites available were tested separately in a series of t tests comparing each metabolite among the affected vs nonaffected individuals. Metabolites showing metabolome-wide significant association with CWP (false discovery rate <5%) were tested for association with BMI and body composition variables and then underwent GWAS using GenABEL software, adjusting for familial relationship.To determine the relative contributions of factors influencing CWP, we conducted binary logistic regression, taking into account twin relatedness. Variables that were introduced stepwise into the regression model included age, BMI/body composition, metabolites, the most highly associated genotyped SNP, and co-twin status. This analysis included 4 metabolites that had been identified at the previous stage as statistically significant and independently associated with CWP. We also note that GWAS revealed only a few highly significant “candidate” SNPs, which were included in the binary logistic regression as potential covariates.To assess the causal influence of the identified metabolites on CWP, we performed Mendelian randomisation analysis using the instrumental variable method. Multivariate regression analysis of the selected metabolite(s) with simultaneous adjustment for the top SNP and other significant covariates was used. In KORA, where the sample comprised unrelated individuals and pain phenotype was scored as a semiquantitative variable (CWPq), we used multivariable linear regression analyses to determine the association of CWP with all potential covariates, including age, body composition variables, and circulating levels of metabolites. In addition, the CWP was further classified as a dichotomous variable—controls (CWPq < 3) vs cases (CWPq ≥ 3). […]

Pipeline specifications

Software tools IMPUTE, GenABEL
Databases KORA
Application GWAS
Organisms Dipturus trachyderma
Diseases Cardiovascular Abnormalities
Chemicals Steroids