Additional file 1: of Interaction of the GCKR and A1CF loci with alcohol consumption to influence the risk of gout

Tables S1 and S2 and Figures S1 and S2. (DOCX 3399 kb

Additional file 1: of Population-specific association between ABCG2 variants and tophaceous disease in people with gout

Full SLC2A9 and ABCG2 genotype distributions according to tophus status. (PDF 42 kb

Additional file 2: of Mediation analysis to understand genetic relationships between habitual coffee intake and gout

Table S1. Coding of food-frequency intakes for analysis. (DOC 40 kb

Additional file 4: of Mediation analysis to understand genetic relationships between habitual coffee intake and gout

Table S2. Complete dietary information for the study population. (DOCX 16 kb

Additional file 5: of Mediation analysis to understand genetic relationships between habitual coffee intake and gout

Table S3. Association analysis of genotype with gout risk. (DOC 57 kb

Additional file 1: of Mediation analysis to understand genetic relationships between habitual coffee intake and gout

Figure S1. Regional association plots of genome-wide significant urate and habitual coffee loci. In each panel, SNPs identified as associated with both urate [2] and coffee intake [22] are plotted with their –log10 (P values) as a function of genomic position using HG build 19 and 1000 genomes European reference for LD (November 2014). Each SNP is coloured according to its correlation with the index SNP (demonstrating the lowest P value within the region, labelled in purple) according to a scale from r2 = 0 to r2 = 1. Urate-raising alleles are displayed on the left and coffee-associated alleles are on the right. LocusZoom plots were drawn from publicly available data ex [2] and taken from [22]. GCKR rs2911711 is in complete linkage disequilibrium with rs1260326. (PDF 826 kb

Additional file 6: of Mediation analysis to understand genetic relationships between habitual coffee intake and gout

Table S4. Association analysis of genotype with habitual coffee intake. (DOC 54 kb

Table_1_Giant cell arteritis: A population-based retrospective cohort study exploring incidence and clinical presentation in Canterbury, Aotearoa New Zealand.DOCX

Background/aimTo determine the epidemiology and clinical features of giant cell arteritis (GCA) in Canterbury, Aotearoa New Zealand, with a particular focus on extra-cranial large vessel disease.MethodsPatients with GCA were identified from radiology and pathology reports, outpatient letters and inpatient hospital admissions in the Canterbury New Zealand from 1 June 2011 to 31 May 2016. Data was collected retrospectively based on review of electronic medical records.ResultsThere were 142 cases of GCA identified. 65.5% of cases were female with a mean age of 74.2 years. The estimated population incidence for biopsy-proven GCA was 10.5 per 100,000 people over the age of 50 and incidence peaked between 80 and 84 years of age. 10/142 (7%) people were diagnosed with large vessel GCA, often presenting with non-specific symptoms and evidence of vascular insufficiency including limb claudication, vascular bruits, blood pressure and pulse discrepancy, or cerebrovascular accident. Those with limited cranial GCA were more likely to present with the cardinal clinical features of headache and jaw claudication. Patients across the two groups were treated similarly, but those with large vessel disease had greater long-term steroid burden. Rates of aortic complication were low across both groups, although available follow-up data was limited.ConclusionThis study is the first of its kind to describe the clinical characteristics of large vessel GCA in a New Zealand cohort. Despite small case numbers, two distinct subsets of disease were recognized, differentiating patients with cranial and large vessel disease. Our results suggest that utilization of an alternative diagnostic and therapeutic approach may be needed to manage patients with large vessel disease.</p

Additional file 3: Figures S2a-c. of Lack of direct evidence for natural selection at the candidate thrifty gene locus, PPARGC1A

Fay and Wu’s H calculated across chromosome 4 using a 30 kbp sliding window. Chromosome 4:22.7–24.9 Mbp is shown with chromosome mean (blue) and 2.5%, 97.5% quantiles (purple) from Table 4 marked. Location of PPARGC1A is marked in red. Rs8192678 is marked by a red dashed line. Figure S2b Fay and Wu’s H calculated across chromosome 4 using a 5 kbp sliding window. Chromosome 4:22.7–24.9 Mbp is shown with chromosome mean (blue) and 2.5%, 97.5% quantiles (purple) from Table 4 marked. Location of PPARGC1A is marked in red. Rs8192678 is marked by a red dashed line. Figure S2c Fay and Wu’s H calculated across chromosome 4 using a 1 kbp sliding window. Chromosome 4:22.7–24.9 Mbp is shown with chromosome mean (blue) and 2.5%, 97.5% quantiles (purple) from Table 4 marked. Location of PPARGC1A is marked in red. Rs8192678 is marked by a red dashed line. (ZIP 841 kb

Additional file 1: Table S1. of Lack of direct evidence for natural selection at the candidate thrifty gene locus, PPARGC1A

Fourteen previously detected combinations of populations and genes from Voight et al. (2006) were analysed using the selscan software package (Szpiech and Hernandez 2014) as positive controls to provide insight into possible power of detection of signatures of selection. Seven of the previously detected 14 associations were repeated, where we defined evidence of selection as >1 SNP exceeding the 5% threshold of iHS values. For two genes (LCT in both CEU and GBR populations and SLC44A5 in CHS and CHB populations) showed evidence of selection in populations with similar ancestry (Caucasian and Asian, respectively). Evidence of selection for one other gene (SNTG1) was observed in only one of two populations with the same ethnicity. (DOCX 25 kb