This research was conducted using the UK Biobank Resource (approval number 12611) [4 (link)]. Data from the first tranche of UK Biobank genotyping and imputation data were used for this analysis (made publicly available in May 2015). Inclusion criteria were European ethnicity, age 40–69 years and genome-wide genotypes available. Exclusion criteria were self-reported sex mismatch with genetic sex, genotyping quality control failure, related individuals, either a primary or secondary hospital diagnosis of kidney disease (International Classification of Diseases, Tenth Revision (ICD-10), codes I12, I13, N00-N05, N07, N11, N14, N17–N19, Q61, N25.0, Z49, Z94.0, Z99.2), participants aged 70 years and over, and those with kidney disease, because these are risk factors for secondary gout.
Gout definitions and combinations of these definitions were identified from previous epidemiological studies [1 (link), 3 (link), 5 (link)]. Self-report of gout was defined by reporting of gout by the participant at the time of the study interview. Hospital diagnosis of gout was defined by either primary or secondary hospital discharge coding for gout (ICD-10 code M10, including sub-codes). Use of ULT required self-report of being on any of allopurinol, febuxostat or sulphinpyrazone and not having a hospital diagnosis of leukaemia or lymphoma (ICD-10 codes C81–C96). Winnard-defined gout was hospital diagnosis of gout or gout-specific medication (ULT or colchicine) as reported by Winnard et al. [5 (link)]. For participants who did not meet any gout definitions, further exclusion criteria were corticosteroid use, non-steroidal anti-inflammatory drug use or probenecid use.
UK Biobank samples had been genotyped using an Axiom array (820,967 markers; Affymetrix, Santa Clara, CA, USA) and imputed to approximately 73.3 million single-nucleotide polymorphisms (SNPs) using SHAPEIT3 and IMPUTE2 with a combined UK10K and 1000 Genomes reference panel. Logistic regression of SNPs against gout as the outcome was performed, adjusting for age, sex, waist circumference, and ratio of waist circumference to height. We analysed 30 urate-associated SNPs reported by Köttgen et al. in the large (>140,000 European participants) Global Urate Genetics Consortium GWAS [1 (link)]. Data were reported on the basis of number of SNPs detected at both genome-wide significance (P < 5 × 10−8) and experiment-wide significance (P < 0.0017). CIs for proportions were calculated using the Wilson score method andwww.openepi.com [6 ]. Heritability estimates were compared using the formula h1-h2 (se = sqrt(se1^2 + se2 ^2)).
Heritability estimates under an additive model were generated using GCTA version 1.26.0 [7 (link)] and PLINK version 1.90b3.32 [8 (link)] by partitioning the genome. To reduce computational time, a smaller control cohort of 10,000 individuals was randomly generated from the UK Biobank and used for each set of cases. SNPs were filtered for deviation from Hardy-Weinberg equilibrium (P > 1 × 10−6) and minor allele frequency >0.01. A genetic relationship matrix was created for each chromosome, which was then used to calculate heritability assuming a prevalence of gout of 2% in the general population.
Gout definitions and combinations of these definitions were identified from previous epidemiological studies [1 (link), 3 (link), 5 (link)]. Self-report of gout was defined by reporting of gout by the participant at the time of the study interview. Hospital diagnosis of gout was defined by either primary or secondary hospital discharge coding for gout (ICD-10 code M10, including sub-codes). Use of ULT required self-report of being on any of allopurinol, febuxostat or sulphinpyrazone and not having a hospital diagnosis of leukaemia or lymphoma (ICD-10 codes C81–C96). Winnard-defined gout was hospital diagnosis of gout or gout-specific medication (ULT or colchicine) as reported by Winnard et al. [5 (link)]. For participants who did not meet any gout definitions, further exclusion criteria were corticosteroid use, non-steroidal anti-inflammatory drug use or probenecid use.
UK Biobank samples had been genotyped using an Axiom array (820,967 markers; Affymetrix, Santa Clara, CA, USA) and imputed to approximately 73.3 million single-nucleotide polymorphisms (SNPs) using SHAPEIT3 and IMPUTE2 with a combined UK10K and 1000 Genomes reference panel. Logistic regression of SNPs against gout as the outcome was performed, adjusting for age, sex, waist circumference, and ratio of waist circumference to height. We analysed 30 urate-associated SNPs reported by Köttgen et al. in the large (>140,000 European participants) Global Urate Genetics Consortium GWAS [1 (link)]. Data were reported on the basis of number of SNPs detected at both genome-wide significance (P < 5 × 10−8) and experiment-wide significance (P < 0.0017). CIs for proportions were calculated using the Wilson score method and
Heritability estimates under an additive model were generated using GCTA version 1.26.0 [7 (link)] and PLINK version 1.90b3.32 [8 (link)] by partitioning the genome. To reduce computational time, a smaller control cohort of 10,000 individuals was randomly generated from the UK Biobank and used for each set of cases. SNPs were filtered for deviation from Hardy-Weinberg equilibrium (P > 1 × 10−6) and minor allele frequency >0.01. A genetic relationship matrix was created for each chromosome, which was then used to calculate heritability assuming a prevalence of gout of 2% in the general population.
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