SUPPLEMENTARY METHODS

Stage 1 samples.Cases. Within the arcOGEN consortium, five locations (London, Nottingham, Oxford, Sheffield and Southampton) had existing DNA collections and contributed OA DNA samples for stage 1 of the study. All cases were collected in the UK, were unrelated and of European origin. The phenotype was determined by two criteria; radiographic evidence of disease and clinical evidence of disease to a level requiring joint replacement. Each investigator defined radiographic disease as a Kellgren-Lawrence (KL) grade[1] of ≥2. In addition, each centre had some site-specific variations. London contributed 348 cases from the TwinsUK cohort[2] and the Chingford Study[3] with radiographic OA, of which 51 also had clinical disease and had undergone a total joint replacement (20 knee, 29 hip and 2 both hip and knee). Radiographic knee OA was defined as a KL grade ≥2 for the tibio-femoral compartment and radiographic hip OA as a Croft grade ≥2.[4]Nottingham contributed 879 cases from two sources: 321 from the “Nottingham” collection and 558 from the Generalised Osteoarthritis collection. Cases were over age 40 with clinically severe primary hip or knee OA referred to hospital for joint surgery.[5,6] All participants underwent clinical enquiry and examination to exclude other arthropathies or cause of hip disease, and radiographs were examined by the same trained observer to confirm radiographic OA (all had KL grade =>2).The Oxford collection contributed 1540 OA cases. All patients had undergone joint replacement surgery for primary osteoarthritis of the hip or knee. They had a minimum KL grade of 3. Sheffield contributed 240 patients, ascertained through joint replacement surgery for primary osteoarthritis of the hip (KL grade >2). All subjects were free from known inflammatory disorders at the time of recruitment, and were not taking any drug medications known to affect bone metabolism. The characteristics of this study population are published in detail elsewhere.[7] The Southampton collection contributed 170 patients. All cases were subjects with symptomatic, radiographic knee OA defined by a KL score of 2 or more that were recruited from the community into a randomized, placebo controlled trial of vitamin D replacement (VIDEO Study). All participants underwent clinical enquiry and examination to exclude other arthropathies.All stage 1 cases listed in Supplementary Table 1 have primary OA with exclusion of inflammatory arthritis (rheumatoid, polyarthritic or autoimmune disease), post-traumatic arthritis, post-septic arthritis, skeletal dysplasia and developmental dysplasia.

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Controls. The main study used 4,894 population-based UK controls from an early release of the Wellcome Trust Case Control Consortium 2 data.[8] Control samples came from 2 distinct sources (the 1958 Birth Cohort and the UK Blood Donor Service) and were unrelated. For sensitivity analysis purposes, we used data from the TwinsUK cohort, which consists of a group of twins ascertained to study the heritability and genetics of age-related diseases.[9]These unselected twins were recruited from the general population through national media campaigns in the UK and shown to be comparable to age-matched population singletons in terms of disease-related and lifestyle characteristics.[10]

Stage 1 genome-wide genotyping.DNA sample preparation and quality control. Genomic DNA from extant cohorts and blood samples from new cases were shipped by the collection centres to The Centre for Integrated Genomic Medical research (CIGMR), University of Manchester, for processing. DNA was extracted from blood samples using a PSS Magtration XL DNA Extraction System. All DNA samples and associated patient information were logged into a Laboratory Information Management System (LIMS) database. 2D bar coded matrix plates and patient data manifests for each set of 96 DNAs were shipped to The Wellcome Trust Sanger Institute, Hinxton. The quality of the DNA and subject identity were validated using the Sequenom iPLEX assay designed to genotype 4 gender-specific SNPs and 26 SNPs present on the Illumina Human 610-Quad array. DNA concentrations were quantified using a PicoGreen assay (Invitrogen) and an aliquot assayed by agarose gel electrophoresis. A DNA sample was considered to pass quality control if the original DNA concentration was >50ng/ul, the DNA was not degraded, the gender assignment from the iPLEX assay matched that provided in the patient data manifest and genotypes were obtained for over 65% of the SNPs on the iPLEX. Genotyping and allele calling. DNA samples passing QC were rearrayed into 96-well plates for genotyping using Illumina Human 610-Quad BeadChips. Approximately 200ng of genomic DNA was used for genotyping. Briefly, each sample was whole-genome amplified, fragmented, precipitated and resuspended in appropriate hybridization buffer. Denatured samples were hybridised on prepared Illumina Human 610-Quad BeadChips overnight. After hybridisation, the BeadChip oligonucleotides were extended by addition of a single fluorescently labelled base which was detected by fluorescence imaging with an Illumina Bead Array Reader. Normalised bead intensity data obtained for each sample were converted into SNP genotypes using a customised genotype calling algorithm.[11]

Stage 1 genotype quality control.

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Sample QC. Unless otherwise stated, quality control was performed using plink [12] and was carried out separately for OA cases and each control dataset. Samples were excluded if their call rate was <97%, and if they showed gender discrepancies (estimated from genotypic data against external information). Individuals were also excluded on the basis of excess genome-wide heterozygosity or homozygosity. For each sample group, heterozygosity histograms were inspected to determine exclusion thresholds empirically. For the cases, these were set to >35% and <28% for excess heterozygosity and excess homozygosity respectively. These thresholds were set to >36% and <32% for both of the control groups. We identified samples that were accidentally duplicated or closely-related by calculating genome-wide IBD (given IBS information) for pairs of individuals. Multidimensional scaling (MDS) was performed in conjunction with data from the three HapMap phase II populations in order to identify and exclude individuals of non-European descent (Supplementary Figure 5). In addition, we used GoldSurfer2 to carry out principal component analysis (PCA).[13] Of the 3324 OA cases, 2587 UKBS controls and 2482 1958C controls present in the original dataset, these sample quality control filters resulted in the inclusion of 3177 cases, 2474 UKBS and 2420 1958BC controls in downstream analyses. SNP QC. SNPs were excluded from further analysis based on the following criteria: Non autosomal, call rate <95% if minor allele frequency (MAF)≥5% or call rate <99% if MAF<5%, HWE exact p values <0.0001 and MAF <1% (MAF exclusion doesn’t apply for the rare variant analysis). GC/AT SNPs were also removed. Of the 582,585 autosomal SNPs genotyped in the OA cases using the Illumina Human610 chip, 537,772 passed these filters. 517,868 of these overlapped with SNPs genotyped on the Illumina Human1M chip in the two control groups. SNP and sample quality control of the control groups was performed after extracting the 517,868 overlapping clean SNPs. Furthermore, 43 SNPs with multiplicative p value <5.7x10-7 from the association test between the two control groups were removed before pooling them together as one control group. This resulted in 514,964 SNPs to be used for case/control analysis. Subsequently, intensity plots were inspected for SNPs with multiplicative p value <10-4 from the case/control analysis and 66 SNPs were further removed, resulting in a final dataset comprising 514,898 SNPs.

Association analysis of stage 1 genotype data. 514,898 autosomal SNPs were analysed under the multiplicative (or log additive) model using plink.[12] The genomic control (GC) value for directly-typed SNPs was estimated to be 1.077; after correcting for population structure, by including the first ancestry informative principal component (PC)[14] as a covariate, the GC value was

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estimated to be 1.078 (Supplementary Results). We also carried out stratified analysis by joint (hip, knee) and gender.

Prioritisation of SNPs for follow-up. We prioritised 102 SNPs (Supplementary Table 2) for follow-up by in silico replication by the deCODE, Rotterdam and Framingham studies based on a set of heuristic criteria we developed. Briefly, we focused on signals with multiplicative model p values<0.0001 in the OA v. controls and joint-stratified cases v. controls analyses. We defined independent SNPs on the basis of r2<0.4. We investigated QC properties (call rate, exact HWE p value in cases and controls, differential missingness between cases and controls) for these independent SNPs with p<0.0001 and examined cluster plots for the cases and two sets of controls separately. We preferentially selected SNPs with frequencies over 0.10 to boost power of replication studies. We additionally examined the genic location of variants and up-weighted signals within or near strong candidate genes for OA. We used GeneSniffer[15] to assign candidacy scores to genes within 0.1centiMorgans either side of each index SNP. We selected a subset of 52 SNPs for follow-up by in silico replication by the TwinsUK study primarily by ranking the selected 102 signals on the basis of statistical significance.After obtaining in silico replication data from the deCODE, Rotterdam and TwinsUK studies, we carried out a meta-analysis across all available data and prioritised 36 of the 102 SNPs for de novo replication mainly on the basis of their overall statistical significance.

Stage 1 genotype imputation and analysis of imputed data. We imputed genotypes for autosomal SNPs that were present in HapMap Phase II but were not present in the genome-wide chip or did not pass direct genotyping QC. In each sample, genotypes were imputed using the directly typed data and phased HapMap II genotype data from the 60 CEU HapMap founders.[16] Genotypes were imputed using the program IMPUTE,[17] which determines the probability distribution of missing genotypes conditional on a set of known haplotypes and an estimated fine-scale recombination map. Imputation was based on 514,865 autosomal SNPs with MAF>0.01 (excluding SNPs that demonstrated poor genotype clustering upon manual inspection). We analysed 2,018,811 imputed SNPs (excluding the directly typed SNPs passing QC) that had MAF>0.01 in cases and controls using SNPTEST, appropriately accounting for the probability distributions of imputed genotypes.[18] We included 1,829,948 imputed autosomal SNPs with MAF>0.01 in cases and controls and an imputation information score >0.8 in downstream association analyses of all OA cases v. controls, 1,830,059 SNPs in hip OA v. controls, 1,829,812 SNPs in knee OA v. controls, 1,829,458 SNPs in female OA v. female controls, 1,829,623 SNPs in female hip OA v. female controls, 1,829,251 SNPs in female

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knee OA v. female controls, 1,829,817 SNPs in male OA v. male controls, 1,829,690 SNPs in male hip OA v. male controls, and 1,829,567 SNPs in male knee OA v. male controls. The genomic control values for imputed SNPs in each of the analyses above were 1.079, 1.056, 1.081, 1.060, 1.037, 1.057, 1.035, 1.021, and 1.036 respectively.

In silico replication samples, genotyping and analysis. Four OA cohorts that had previously been subjected to a GWAS were used for in silico replication of promising signals from the arcOGEN GWAS in the first instance: the Rotterdam study, deCODE, Framingham and TwinsUK (Supplementary Table 1). Summary association statistics (under the log-additive model) were shared for 102 SNPs (Rotterdam, deCODE and Framingham) and 52 SNPs (TwinsUK) respectively. The results of these analyses guided the selection of 36 SNPs for further de novo and in silico (in the Estonian Genome Center, University of Tartu dataset) follow-up.The Rotterdam Study. The study population comprises men and women aged 55 years and older from the Rotterdam Study, which is a prospective population-based study on determinants of chronic disabling diseases. The rationale and study design have been described previously.[19] The medical ethics committee of Erasmus University Medical School approved the study and written informed consent was obtained from each participant. Subjects were scored for the presence of OA using standardized radiographs of the hip and knee with cases having a Kellgren and Lawrence (KL) grade of ≥2 and controls a KL grade of <2. Genotyping of the samples with the HumanHap550v3 Genotyping BeadChip (Illumina, San Diego, USA) was carried out at the Genetic Laboratory of the Department of Internal Medicine of Erasmus Medical Center, Rotterdam, the Netherlands. The BeadStudio GenCall algorithm was used for genotype calling and quality control procedures were as described previously.[20] The following sample exclusion criteria were applied: call rate <97.5%; gender mismatches with typed X-linked markers; autosomal heterozygosity >0.336 ~FDR>0.1%, duplicates and/or 1st or 2nd degree relatives using IBS probabilities >97% from plink; ethnic outliers using IBS distances >3SD from plink.[12] Analysis was restricted to SNPs with a call rate ≥98%, minor allele frequency (MAF) ≥0.01, and HWE p value ≥1x10-6. MACH software was used for imputation and statistical analysis was carried out using MACH2QTL.[21]deCODE. The study population comprises patients with OA of the knee and/or hip obtained on the basis of patients’ records at hospitals and health care centres in Iceland.[22] All OA patients had undergone joint replacement surgery of the knee or the hip. A clinician reviewed the patient records to verify the diagnosis. Control individuals were

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recruited as part of various genetic programs at deCODE. All individuals that had chip genotype data that passed quality control were included in the control group with the exception of those on any OA list ever, who were excluded. The study was approved by the Data Protection Authority of Iceland and the National Bioethics Committee of Iceland. Informed consent was obtained from all participants. The samples were assayed with the Infinium HumanHap300 or humanCNV370 SNP chips (Illumina) and called with BeadStudio. All of the genotyped SNPs tested in this report passed quality filtering (call rate >96%, MAF>0.01, HWE p>10-6 on any of the three chip types used [HumanHap300, HumanHap300-duo and HumanCNV370]). SNPs were imputed using the IMPUTE software[17] and phased haplotypes for the HapMap CEU sample set v22. The difference in frequency of each SNP was tested, assuming an additive model, using the SNPTEST program[17] with the –proper option which uses a likelihood ratio test to incorporate the uncertainty in imputed genotypes. Any samples with a yield <98% were excluded from the analysis.The Framingham osteoarthritis study. This is a longitudinal population-based cohort study established in 1948 in Framingham, Massachusetts to examine risk factors for heart disease.[23] In addition to the original cohort, a study of the offspring and their spouses of this cohort was initiated in 1971. The Framingham OA study, which includes participants of both cohorts, was developed to study the inheritance of OA.[24] Knee OA cases and controls were available for association analyses, with cases having a KL grade of ≥2 in at least one knee and controls having KL grades of 0 or 1 in both knees. The Boston University Medical Center IRB approved the Osteoarthritis Protocol. Written informed consent was obtained from all subjects. Samples were genotyped using the Affymetrix GeneChip® Human Mapping 500K array set and the 50K supplemental array set focused on coding SNPs and SNPs tagging protein-coding genes (Santa Clara, California) as part of the SHARe initiative. Sample exclusion criteria included call rate <97% and a per subject heterozygosity >±5 standard deviations from the mean, while any samples that demonstrated excessive Mendelian errors were excluded. Imputation was performed using MACH (version 1.0.15)[21] to impute all autosomal SNPs using the publicly available phased haplotypes from HapMap (release 22, build 26, CEU population) as a reference population. A sample of 200 known unrelated participants with high call rates and low Mendelian errors were used to determine parameter estimates that were subsequently applied in a model for all subjects. Dosage estimates outputted by MACH were used in analysis.TwinsUK. The study participants were white monozygotic and dizygotic twin pairs from the TwinsUK adult twin registry, a group used to study the heritability and genetics of age-related diseases.[25] These unselected twins were recruited from the general population

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through national media campaigns in the UK. In this study, cases had a KL grade of ≥2 at the hip or the knee, whereas controls had a KL grade of <2 at the hip or the knee. Ethics approval was obtained from the Guy’s and St. Thomas’ Hospital Ethics Committee. Written informed consent was obtained from every participant. Samples were genotyped with the Infinium HumanHap300 assay (Illumina) at the Duke University Genotyping Center (NC USA), Helsinki University (Finland) and the Wellcome Trust Sanger Institute. The Illuminus calling algorithm was used for genotype calling. Analysis was restricted to SNPs with a call rate >90%, MAF ≥0.01, and HWE p value ≥1x10-4 [20] whilst imputation was performed using IMPUTE[17]. At imputed loci, all genotypes with posterior probabilities <0.9 were discarded and the imputed loci were filtered out using usual QC filters.Estonian Genome Center, University of Tartu. The Estonian cohort is from the population-based biobank of the Estonian Genome Project of University of Tartu (EGCUT). The whole project is conducted according to the Estonian Gene Research Act and all participants have signed the broad informed consent.[26-27] The current cohort size is over 47,000, from 18 years of age and up, which reflects closely the age distribution in the adult Estonian population. Subjects are recruited by the general practitioners (GP) and physicians in the hospitals were randomly selected from individuals visiting GP offices or hospitals. Each participant filled out a Computer Assisted Personal interview during 1-2 hours at a doctor’s office, including personal data (place of birth, place(s) of living, nationality etc.), genealogical data (family history, three generations), educational and occupational history and lifestyle data (physical activity, dietary habits, smoking, alcohol consumption, women’s health, quality of life). Anthropometric and physiological measurements were also taken. Osteoarthritis was diagnosed by a specialist as a clinical finding and was usually confirmed by a radiograph (KL score>2). The OA cases for the current study had an ICD10 M16 and/or M17 diagnosis. All diseases are defined according to the ICD10 coding.[28] All the samples are genotyped with Illumina HumanCNV370 or HumanOmniExpress according to the Illumina protocol[29] in the Estonian Biocenter Genotyping Core Facility. Data quality control was performed with plink[12] (SNP call rate>98%; sample call rate>95%; MAF >0.01; HWE p>10-6; cryptic relatedness). Imputation was performed with IMPUTE v1.0 (CEU HapMap rel22 build 36) and association analyses were carried out with SNPTEST.[17] Inflation factors for directly genotyped and imputed data were 1.02 and 1.01 respectively.

De novo replication samples. Nine datasets were used for de novo replication: arcOGEN extant cases, arcOGEN new cases, GARP, Santiago, Santander, Greece, SOF, MrOS, Oxford controls (Supplementary Table 1).

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arcOGEN extant cases. These cases, contributed by the 5 arcOGEN stage 1 centres (Nottingham, London, Oxford, Sheffield and Southampton), were intended for inclusion in the GWAS but failed subsequent sample QC due to low concentration or minor degradation (DNA sample preparation and quality control section). These cases were ascertained using the same criteria as cases that were included in the GWAS from these sites (Stage 1 samples section).arcOGEN new cases. These cases were specifically collected as part of the arcOGEN study at the 5 sites that contributed extant cases to the GWAS and at two other UK sites: Newcastle and Edinburgh. The ascertainment criteria for the majority of cases was primary OA that was severe enough for the individual to require joint replacement of the hip or of the knee, with a small proportion (those collected as part of the VIDEO study, Southampton) not having undergone joint replacement but having radiographic disease of the knee, with a KL grade of ≥2. Exclusion criteria included the need for joint replacement due to joint fracture and injury or any non-OA clinical conditions. Ethical approval for the study was obtained from appropriate ethics committees and informed consent was obtained from each individual.GARP. The Genetics OsteoArthritis and Progression (GARP) study from Leiden, the Netherlands, consisted of sibling pairs concordant for clinical and radiographically (KL grade of ≥2) confirmed OA at two or more joint sites among hand, spine (cervical or lumbar), knee or hip.[30] Random controls were partners of the offspring of the Leiden longevity study.[31] Written informed consent was obtained from each subject as approved by the ethical committees of the Leiden University Medical Center.Santiago. This study included cases undergoing hip or knee joint replacement.[32] Patients were included if a rheumatologist considered them to suffer from severe primary OA. Exclusion criteria were inflammatory, infectious, traumatic or congenital joint pathology and lesions due to crystal deposition or osteonecrosis. Controls were recruited among subjects older than 55 years of age undergoing preoperative work-up for elective surgeries other than joint surgery and who did not show clinical manifestations of OA. All participants were of Spanish ancestry. This study was approved by the Ethical Committee for Clinical Research of Galicia and all cases and controls gave their written informed consent to participate.Santander. Cases were patients undergoing hip or knee joint replacement for primary osteoarthritis.[33] Controls were elderly individuals who did not show any clinical signs of OA. All participants were of Spanish ancestry. This study was approved by an appropriate ethical Committee and all cases and controls gave their written informed consent to participate.Greece. Individuals were of Greek origin living in the district of Thessalia in central Greece.[34] The cases had undergone hip or knee

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joint replacement due to severe knee or hip OA. None of the patients had evidence of arthritis due to another disease, with the exclusion criteria including rheumatoid arthritis and other autoimmune diseases as well as chondrodysplasias, infection-induced OA and posttraumatic OA. All controls had a KL grade of 0 and had undergone treatment for injuries or fractures. This study was approved by the ethics committee of the Larissa University Hospital and all individuals gave their informed consent.MrOS. Osteoporotic Fractures in Men Study (MrOS) is a multi-centre prospective, longitudinal, observational study of risk factors for vertebral and all non-vertebral fractures in older men, and of the sequelae of fractures in men.[35] The study population consists of community dwelling, ambulatory men aged 65 years or older, recruited from different clinical centres in the US: Birmingham, AL; Minneapolis, MN; Palo Alto, CA; Pittsburgh, PA; Portland, OR; and San Diego, CA. Inclusion criteria were designed to provide a study cohort that is representative of the broad population of older men. The inclusion criteria were: (1) ability to walk without the assistance of another, (2) absence of bilateral hip replacements, (3) ability to provide self-reported data, (4) residence near a clinical site for the duration of the study, (5) absence of a medical condition that (in the judgment of the investigator) would result in imminent death, and (6) ability to understand and sign an informed consent. OA cases were defined as those individuals without an adjudicated hip fracture in both hips and with a Croft grade of ≥2 on the worst hip or presence of radiographically determined total hip replacement (THR) that resulted from OA. Controls were defined as those individuals with a Croft grade of 0 in both hips, no THR in either hip and no adjudicated hip fracture in either hip. A summary grade of 0-4, modified from the Croft definition, was assigned to each hip based on individual radiographic features. Grade 2 hips required the presence of either definite (severity grade ≥2) joint space narrowing (JSN) or osteophytes plus at least one other feature (cysts or subchondral sclerosis). Grade 3 hips required the presence of either definite JSN or osteophytes plus at least two other features. Grade 4 hips met the criteria for grade 3, and had femoral head deformity present. To qualify as an enrolee, the participant had to provide written informed consent. SOF. Study of Osteoporotic Fractures (SOF) is a multicenter cohort study initiated in 1986 to determine risk factors for osteoporotic fractures in elderly women.[36] Participants were all aged >65 years at baseline and were recruited from population-based listings at 4 clinical centres in the US: Baltimore, MD; Minneapolis, MN; Monongahela Valley, PA (near Pittsburgh); and Portland, OR. Exclusion criteria for the parent SOF study included bilateral hip replacement and an inability to walk unassisted. For our study the radiographic hip OA case criteria for the worst hip was defined as the presence of 1 out of the 3 following

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criteria: JSN ≥3, a Croft summary grade ≥3 (see above), or definite osteophytes and JSN (lateral or medial) ≥2. Individuals without the above criteria, but with a THR adjudicated for OA were also included as cases. Controls did not satisfy the above criteria and had no THR in either hip. The study was approved by the institutional review boards at each of the institutions involved. All subjects provided written informed consent at enrolment and at each clinical examination. All SOF participants are of European origin.Oxford controls. These were UK citizens of European ethnicity with no signs or symptoms of arthritis or joint disease (pain, swelling, tenderness or restriction of movement), although the hip and knee joints were not subjected to radiographic analysis.[37] Ethical approval for the study was obtained from appropriate ethics committees and informed consent was obtained from each individual.In silico controls. To carry out the association analysis on the arcOGEN extant cases and the arcOGEN new cases we used de novo genotype data from the Oxford controls and in silico data from the 1958 British Birth Cohort controls, genotyped as part of the Type 1 Diabetes Genetics Consortium (T1DGC) on the HumanHap550 BeadChip (Illumina) and made accessible to us through the Wellcome Trust Case Control Consortium. Genotypes from the Oxford and T1DGC replication datasets for 35 SNPs (rs143383 was not genotyped in the T1DGC controls) were merged. Prior to pooling the control groups, we compared frequencies between them and ensured that the differences observed were consistent with the null distribution. 

De novo replication genotyping and analysis.The 36 prioritised SNPs were genotyped in the arcOGEN extant cases and in the arcOGEN new cases at the Wellcome Trust Sanger Institute. Genotyping was performed using the Sequenom MassArray iPLEX Gold assay for 34 of the SNPs and KASPar assays (KBioscience) for rs11583036 and rs2280465. For the iPLEX Gold, assays for all SNPs were designed using the eXTEND suite and MassARRAY Assay Design software version 3.1 (Sequenom). Samples were amplified in multiplexed PCR reactions before allele-specific extension. Allelic discrimination was obtained by analysis with a MassARRAY Analyzer Compact mass spectrometer. Genotypes were automatically assigned and manually confirmed using MassArray TyperAnalyzer software version 4.0 (Sequenom). Gender markers were included in iPLEX assays as a quality control metric for confirmation of plate/sample identity. Mean assay call rates were 99.4%. For the KASPar assays primers were designed using an online tool provided by KBioscience. Genotypes were obtained by endpoint fluorescence readings on a LightCycler 480 Real-Time PCR instrument (Roche). Assay call rates were 98.3% and 98.0% for rs11583036 and rs2280465 respectively.

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The 36 SNPs were genotyped in samples from the GARP, Santiago, Santander, Oxford controls and Greek studies at the Leiden University Medical Centre, using the Sequenom MassArray iPLEX Gold for 34 SNPs and TaqMan allelic discrimination assays for rs2433051 and rs11583036. Samples with a valid genotype ≥70% were entered into the Leiden database. For each 384-well plate, 4 CEPH samples were included and checked for consistent genotyping. The error rate observed was 0.05%. To adjust for the family relationship among sibling pairs of the GARP study, standard errors were estimated from the variance between the sibling pairs (robust standard errors) and used in all the comparisons between the GARP subjects and controls including the meta-analyses.[38] Robust standard error analyses were performed using Stata SE8 software (Stata Corporation, USA). For the SOF study, whole genome amplified DNA was used for the genotyping. 10ng of genomic DNA was amplified using Repli-g mini kit (Qiagen) and quantified by picogreen. The average yield of DNA was 7g. Of this, 500ng was used to genotype 31 of the 36 SNPs at The Feinstein Institute for Medical Research, Manhasset, using the Illumina Golden Gate genotyping assay on the Veracode bead platform. Of the SNPs genotyped, 8 deviated from HWE with p<0.05. The mean call rate for the successfully genotyped SNPs was 96.02% (range, 53.8% to 99.9%). The 3 SNPs that did not pass assay design (rs11583036, rs143383 and rs7180823) were genotyped in the SOF samples by TaqMan allelic discrimination assays at the Erasmus Medical Centre, Rotterdam. Primer and probe sequences were optimized using the SNP assay-by-design service of Applied Biosystems and reactions were performed on the TaqMan Prism 7900HT 384-well format. These 3 SNPs had call rates >95%. For MrOS, all 36 SNPs were attempted for genotyping using the Sequenom MassArray iPLEX Gold assay. Laboratory personnel were blinded to case-control status and samples from cases and controls were mixed on each 384-well plate. Two SNPs could not be successfully genotyped on the Sequenom platform (rs7683009 and rs2744718). These SNPs were not attempted on another platform. The mean call rate for the 34 successfully genotyped SNPs was 98% (range, 94.1% to 99.9%). The mean reproducibility for 142-160 blind replicate samples per SNP was 99.3% (range, 97.4% to 100.0%).All de novo genotype data, with the exception of the GARP study which included family members and was analysed in Leiden, underwent the following QC and analysis processes. Samples with call rate less than 70% across the SNPs of interest (33-36 SNPs depending on the study) were excluded from further analysis. SNPs with HWE p<0.0001 and call rate<95% were additionally excluded. Association analysis was carried out as for the main GWAS study under the multiplicative model.

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Meta-analysis. We used a meta-analysis framework to combine results across replication studies and across all data. Combined estimates of ORs for reference alleles were obtained by weighting the logORs of each study by the inverse of their variance using a fixed effects model. We investigated evidence of heterogeneity of ORs using the Cochran’s Q and I2 statistics.[39] We additionally assessed the combined results using a random effects meta-analysis model. We created regional plots of association using SNAP.[40]

Quantitative trait analysis. We tested the 102 SNPs taken forward to replication for association with BMI in OA cases (n=2068), using linear regression and an additive genetic model. BMI values were log-transformed to normality.

Adjustment for covariates. OA and joint-specific association analyses were also carried out using logistic regression with gender as a covariate (as implemented in plink).[12] We additionally repeated the association analysis genome-wide after adjusting for the first few principal components in a logistic regression framework.

Sensitivity analysis. To evaluate signal robustness, we carried out genome-wide association analyses between the arcOGEN OA cases and different control datasets. We compared the genotypes of 2019 knee and/or hip female OA cases against 2029 unrelated female “supercontrols” from a subset of the UK-based TwinsUK cohort genotyped on the same platform as our cases for 535,136 overlapping directly typed SNPs that passed our QC criteria (described above). Analyses were also performed stratified by joint (1057 females with hip OA v. “supercontrols”; 1087 females with knee OA v. “supercontrols”). Additionally, we carried out a test of association between all 3177 OA cases against 1383 population controls from the 1958 Birth Cohort genotyped on the Illumina HumanHap550 platform for 490,702 overlapping directly typed SNPs that passed our QC criteria. Joint-stratified analyses were also performed (1728 cases with hip OA v. controls; 1643 cases with knee OA v. controls). Details of these additional control samples, their genotyping methods and QC are described below.TwinsUK “supercontrols” genotyped on Illumina Human610. A total of 3820 samples from the TwinsUK cohort were typed with the Infinium 610k assay (Illumina, San Diego, USA) at two different centres: the Center for Inherited Diseases Research (USA) and the Wellcome Trust Sanger Institute (WTSI, UK). The intensity data were pooled and genotype calling was carried out at WTSI. Samples were excluded following the same QC criteria as for the main GWAS apart from excess genome-wide heterozygosity and excess homozygosity thresholds which were determined empirically after inspection of

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heterozygosity histograms and were set at >32.67% and <30.42% respectively. Unrelated samples that were not affected by either hip or knee OA (defined either by radiographs or medically diagnosed) were selected to serve as “supercontrols” for the sensitivity analysis. Male subjects were also excluded because of the small number in the resulting set. 2029 female controls and 542,037 autosomal SNPs passed sample and SNP QC criteria from this dataset. 535,136 autosomal directly typed SNPs that were common between the 2019 female OA cases and 2029 “supercontrols” were analysed under the multiplicative model using plink.[12] The genomic control (GC) value was estimated to be 1.030. We also carried out stratified analyses by hip and knee (1057 and 1087 cases respectively) (GC estimated at 1.031 and 1.023 respectively). All 102 SNPs prioritized for in silico replication had been directly typed in the TwinsUK dataset.1958 Birth Cohort samples genotyped on Illumina HumanHap550. These samples overlapped with the main analysis control samples, but had been genotyped on a different platform. Quality control was performed as for the main study except genome-wide excess heterozygosity and homozygosity thresholds which were set (after inspection of heterozygosity histograms) at >33% and <30% respectively. Of the 555,174 genotyped markers and 1438 samples originally present in the dataset, 1383 samples and 547,980 SNPs passed our QC criteria (described above). Of the 102 SNPs prioritized for in silico replication, 100 had been directly typed in this dataset.

Chromosome X analysis. We carried out association analysis for chromosome X SNPs. Only samples that passed all QC steps for the autosomal analysis were included. For the SNP-level QC, the call rate inclusion threshold was >99% for SNPs with MAF <5% and >95% for SNPs with a MAF ≥5%. All SNPs with a MAF <0.01 were excluded. Any SNPs with a HWE p<0.0001 in females were excluded. All GC/AT SNPs were excluded. The WTCCC2 1958C and UKBS control sets were compared to each other using 4,678 SNPs (the chromosome X SNPs that passed all QC steps), and 2 further SNPs were subsequently excluded as they were significantly different between the two groups (p<5.7x10-7). Overall, 15 SNPs were included for the pseudoautosomal region and 4,663 for the non-pseudoautosomal region. The pseudoautosomal region was analysed as for the autosomes. The non-pseudoautosomal region was analysed in females only.

Analysis of overlap between linkage and association signals. Eight genome-wide linkage scans have been published for OA to date: one for knee/hip OA,[41-46] six for hand OA,[22,24,47-50] one for spine OA,[51] and one for generalised OA.[52] To investigate the overlap between linkage and association scan signals, we used information on six linkage peaks (with LOD scores >1.5) from the knee

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and/or hip OA linkage scan[41-46] and association statistics from our GWAS. We used the microsatellite marker-denoted intervals reported by the authors in subsequent fine-mapping studies to define the start and end points of each linkage peak region. We took the 514,898 GWAS SNPs passing QC and grouped them into those found inside or outside the linkage peaks. We sub-grouped these into signals with p values <0.001 (n=841), <0.0001 (n=89) and <0.00001 (n=7). We investigated the over-representation of association signals under linkage peaks by means of Fisher’s exact tests (one-sided p values) and carried out three comparisons, one for each of the three different association signal p value thresholds. We performed this analysis for common SNP associations, and for rare variant gene-based analysis results (Rare variant analysis section below).

Interaction analysis. We carried out pairwise interaction analysis for the 102 prioritised SNPs on the basis of stage 1 data, by testing for deviation of SNP pair association from the log additive model.

Rare variant analysis. We investigated association between OA and low frequency/rare variation (MAF≤0.05) genome-wide. To increase relative power, compared to single-point approaches, we applied a super-locus approach to detect over-representation of cumulative rare variant minor alleles in cases compared to controls. We defined gene regions as 50kb either side of the transcriptional start and end sites of genes across the genome and collapsed rare variant minor allele counts into 2x2 tables within each gene region.[53] The method is described and evaluated in detail in Morris & Zeggini, 2009.[54]

Genetic model analysis. In addition to the multiplicative (log additive) model, we evaluated the fit of the dominant and recessive inheritance models for reference alleles at the 102 prioritised SNPs on the basis of stage 1 data.

Polygene analysis. SNPs selected for the polygene analysis represented the maximum number of independent signals as defined by LD (pairwise r2<0.05). arcOGEN stage 1 GWAS case and control data were randomly split into 2 subsets of 90% and 10%. Association with OA was then calculated from the set containing 90% of the data. Based on the results of this association analysis, SNPs were sorted depending on their p value into the following 20 discreet sets (0>p>0.05, 0.05>p>0.1, 0.1>p>0.15….0.9>p>0.95, 0.95>p>1) and 20 cumulative sets (0>p>0.05, 0>p>0.1, 0>p>0.15….0.9>p>0, 0>p>1). For SNPs in each of the sets the “score” alleles (OA risk alleles) were counted (weighted by the ln(OR) for each SNP) in the cases and controls in the remaining 10% of the data (the test set). For each individual in the test set this produced a “score” allele count

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which was compared between the case group and the control group using logistic regression. Nagelkerke’s pseudo r2 [55] was used to evaluate the proportion of case-control status accounted for.[56] To measure the significance of the results case-control status was scrambled in the test set, logistic regression was rerun and Nagelkerke’s r2 recalculated. Permutations of this nature were repeated until either >1000 permutation results exceeded the significance of the unpermuted data, or >1,000,000 permutations had completed and >10 permutation results exceeded the significance of the unpermuted data. This procedure was repeated for each of the p value-defined SNP sets. This entire process was performed 10 times (starting with the random splitting of case and control data into 90% and 10%). The number of independent SNPs derived from each of the 10 sets ranged between 62,112 and 62,503. For each of the p value-defined sets, the results from each of the 10 different random data splits were pooled and the mean and standard deviation of the Nagelkerke's r2 values were calculated. The same summary statistics were also calculated for the pooled permutation results corresponding to each p value set.

SUPPLEMENTARY RESULTS

Stratified analysis results. Supplementary Figures 6-10 summarise the results from our genome-wide analyses stratified by joint and gender: hip OA v. controls (GC λ=1.051), knee OA v. controls (GC λ=1.069), female OA v. female controls (GC λ=1.055), female hip OA v. female controls (GC λ=1.033), female knee OA v. female controls (GC λ=1.055), male OA v. male controls (GC λ=1.037), male hip OA v. male controls (GC λ=1.024), and male knee OA v. male controls (GC λ=1.037). We investigated overlap between results from the knee and/or hip OA (i.e. all OA) and the hip OA- and knee OA-specific analyses. Out of the 65 independent signals (composed of 89 SNPs) with p<10-4 in the all OA analysis, 17 are present with p<10-4 in the hip OA analysis and 9 are present with p<10-4 in the knee OA analysis, but none reach p<10-4

in both.

Chromosome X association results. There were no chromosome X pseudoautosomal signals with multiplicative model p values<0.0001 in the OA v. controls and joint- or gender-stratified cases v. controls analyses. Analysis of the non-pseudoautosomal region identified a single SNP (rs2093134) with p<0.0001 (allele T OR [95%CIs] 0.77 [0.68-0.87], p=1.67x10-5) in the comparison of female cases v. female

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controls. This variant was also associated within the female knee stratum (allele T OR [95%CIs] 0.68 [0.59-0.80], p=1.20x10-6), as was rs240170, representing an independent signal (allele T OR [95%CIs] 0.75 [0.66-0.86], p=1.95x10-5). There were no signals with multiplicative model p<0.0001 in the female hip subgroup.

Imputed data association results. To examine whether imputation gave rise to novel signals that were worth following up, we examined imputed SNPs with p<0.00001 that were not in LD (r2<0.2) with directly typed SNPs which also exhibited low p values (p<0.0001) in the OA v. controls, hip OA v. controls and knee OA v. controls association analyses. No such SNPs were identified in the OA v. controls and knee OA v. controls analyses. Of the two perfectly correlated imputed SNPs identified in the hip OA v. controls analysis with p<0.00001 neither passed our quality control filters. Supplementary Figures 11-12.

BMI association results. None of the 102 SNPs taken forward to replication were associated with BMI in the OA cases (p>0.01) (Supplementary Table 3).

Gender adjustment association results. Adjustment for gender did not attenuate unadjusted analysis association results for any of the 102 SNPs selected for follow-up on the basis of stage 1 data (Supplementary Table 4). All fold-changes in multiplicative model p values after adjusting for gender led to differences that were smaller than an order of magnitude.

Deviation from additivity. None of the 102 SNPs taken forward to replication demonstrated stronger association (>two orders of magnitude) under the dominant or recessive models compare to the additive or multiplicative model for the OA, hip OA or knee OA analyses. A single variant, rs7843140, was more strongly associated (>one order of magnitude) under the recessive model (p=6.2x10-5) compared to the multiplicative model (p=3.8x10-3) (Supplementary Table 5).

Association results for established OA loci. Two OA susceptibility loci have been robustly replicated to date: rs143383 in GDF5 and rs3815148 on chromosome 7q22.Association between rs143383 and hip and knee OA was first reported in a study of Japanese and Chinese individuals.[57] The most significant association was observed for hip osteoarthritis in two independent Japanese populations (combined p=2x10–13). Weaker evidence for association was obtained for knee OA in datasets from China (p=3x10–4) and Japan (p=0.002). A large-scale meta-analysis

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employing 4,791 hip OA cases and 6,006 controls; and 4,367 knee OA cases and 6,291 controls[58] showed that in samples of European descent there was less compelling evidence for association with hip OA (1.07 [1.01-1.14], p=0.034) and knee OA (1.13 [1.06-1.20], p=9x10-5) compared to the East Asian discovery set (both in terms of significance and effect size) (Supplementary Table 6). These differences can be ascribed to allele frequency disparities between ethnic groups. rs143383 was not directly typed as part of the arcOGEN GWAS and, when imputed on the basis of HapMap, was not associated with hip and/or knee OA (p>0.50, Supplementary Table 6). Similarly, there was no association with the highly correlated (r2=0.93) directly typed rs6088813 variant. In the female-only comparison of arcOGEN cases against TwinsUK “supercontrols” we observed some evidence for association with knee (p=0.017) but not with hip (p=0.311) OA (Supplementary Table 6). The chr7q22 OA signal at rs3815148 was first detected in the population-based prospective Rotterdam study GWAS using disease-free controls (C allele OR 1.32 for knee and/or hand OA in females, p=7x10-5) and validated by the same group in a meta-analysis involving up to 14,938 OA cases and 39,000 controls (OR 1.14 for knee and/or hand OA, p=8x10-8)[59] (Supplementary Table 6). rs3815148 shows a weak trend for association with knee OA (p=0.082) in the arcOGEN stage 1 analysis. In the female-only comparison of arcOGEN cases against TwinsUK “supercontrols” we observed some evidence for association with knee (p=0.009) and hip (p=0.079) OA (Supplementary Table 6). For both signals, risk allele frequencies in the WTCCC2 controls were similar to the HapMap phase II (rel24) allele frequencies and consistently slightly higher than the TwinsUK “supercontrols”.

Interaction analysis results. There was no evidence for significant interaction between any pair of the 102 SNPs prioritised for follow-up (Supplementary Table 7). The lowest interaction p value (p=0.0002) was generated for the rs1229403- rs559901 SNP pair (on chromosomes 1 and 6 respectively) and did not deviate from the expectation based on the combination of the 2 SNP individual p values. Knee- and hip-specific analyses also failed to identify any interactions with p<10-4 (data not shown).

Sensitivity analysis results comparison. Results from each of the two sensitivity analyses were compared with results from the main study for the overlapping set of SNPs that were selected for follow-up (Supplementary Table 8).In the TwinsUK “supercontrol” association analysis, 95 of the 102 overlapping prioritised SNPs had ORs in the same direction as in the main analysis results. The correlation (r) between effect estimates

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(ORs) was 0.88. The correlation between p values was much lower (0.37), which is not unexpected given the differences in power between the two datasets. When this analysis was performed for hip OA, the direction of ORs was found to be the same for 93 SNPs (correlation for ORs 0.90, correlation of p values 0.71). For the knee OA cases, the direction of ORs was the same for 88 SNPs (correlation for ORs 0.90, correlation of p values 0.59).In the 1958 Birth Cohort - HumanHap550 sensitivity analysis, all 100 overlapping prioritised SNPs had ORs in the same direction as the main analysis. The correlation of ORs was 0.94 and for p values 0.34. When this analysis was performed for the hip OA, the direction of ORs was found to be the same for 95 SNPs (correlation for ORs 0.95, correlation of p values 0.70). For knee OA, the direction of ORs was the same for 96 SNPs (correlation for ORs 0.95, correlation of p values 0.75). The correlation between p values was somewhat higher for this sensitivity analysis compared to the TwinsUK “supercontrol” comparison because the control set here was a subset of the main control sample set used (albeit typed using a different genotyping assay).For the two established OA loci to date there was stronger evidence of association in the analysis employing “supercontrols” than in the main analysis. The 7q22 SNP, rs3815148, shows a weak trend for association with knee OA (p=0.082) in the arcOGEN female stage 1 analysis but in the female-only comparison of arcOGEN cases against TwinsUK “supercontrols” we observed some evidence for association with knee (p=0.009) and hip (p=0.079) OA (Supplementary Results; Supplementary Table 6). Some evidence for association with knee OA was observed for the best proxy GDF5 SNP rs6088813 (r2=0.93 with rs143383) in the female-only comparison of arcOGEN cases against TwinsUK “supercontrols” (p=0.017) but no association was found in the arcOGEN female stage 1 analysis.

Rare variant analysis results. Following genome-wide rare variant association analysis, we inspected the genotype clustering qualities of all SNPs contributing to association signals with p<10-4. Thirty-two SNPs with poor clustering performance were detected and excluded from the dataset. The analysis was subsequently rerun for any gene regions in which poorly-clustering SNPs had been identified. After performing this cleaning step, only two gene regions (on chromosomes 2 and 3) remained associated at the p<10-4 level. The signal on chromosome 2 (in the alanine-glyoxylate aminotransferase [AGXT] gene, Fisher’s exact p=5.9x10-5) is due to a single rare variant (MAF 0.002) with good clustering properties. The signal on chromosome 3 is in the neuroligin 1 gene (NLGN1) (p=1.8x10-5) and contains 14 rare/ low frequency variants (MAF 0.0009-0.047). Supplementary Figure 13 summarises the genome-wide gene-based results.

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Overlap between linkage and association signals. There was no significant overlap between linkage peaks (Supplementary Table 9) and common variant association scan signals (Supplementary Table 10). This is perhaps not surprising, as (a) genome-wide linkage scans are now well-recognised for their irreproducibility, possibly due to false positive findings, and (b) linkage signals are more likely to have arisen due to the presence of rare, more penetrant variants. We therefore additionally investigated the overlap between linkage peaks and rare variant association signals (Rare variant analysis results), even though the rare variant content of the Illumina platform is low. We found no overlap between rare variant gene-based association signals and linkage peak localisation (Supplementary Table 11).

Effect of population structure. The correlation between association statistics (multiplicative p value) before and after adjustment for the first principal component was very high (r=0.996) (Supplementary Figure 14). Similarly, association results for the 102 SNPs prioritised for follow-up did not change qualitatively when adjustment for up to 10 PCs took place (Supplementary Table 12), with a few exceptions. The biggest fold differences between unadjusted analysis and analysis adjusted for the first ten PCs were for rs2945230, rs17662123 and rs6601327, with p values changing from 0.00004 to 0.02, from 0.000009 to 0.003, and from 0.0006 to 0.08 respectively. All other unadjusted and adjusted p values differed by less than 2 orders of magnitude. The GC inflation factors for genome-wide analyses adjusting for up to the first 10 PCs were: λPC1=1.078, λPC1-2=1.077, λPC1-3=1.071, λPC1-4=1.071, λPC1-5=1.051, λPC1-6=1.042, λPC1-7=1.042, λPC1-8=1.041, λPC1-9=1.041 and λPC1-10=1.040.

SUPPLEMENTARY FIGURESSF1. Regional plots of OA signals for each of the (a) rs4512391 (all OA), (b) rs4512391 (knee OA), (c) rs4977469 (hip OA), (d) rs2277831 (all OA), (e) rs11280 (knee OA) and (f) rs2615977 (hip OA) regions.

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Genotyped SNPs passing quality control in the stage 1 arcOGEN GWAS are plotted with their p values (as –log10 values) as a function of genomic position (NCBI Build 36). In each panel, the SNP taken forward to replication is represented by a blue diamond (meta-analysis p value across the discovery and replication sets), and its initial p value in stage 1 data is denoted by a red diamond. Estimated recombination rates (taken from HapMap) are plotted to reflect the local LD structure around the associated SNPs and their correlated proxies (colour-coded according to a white to red scale from r2 0 to r2 1; based on pairwise r2

values from HapMap CEU).SF2. qqplot of 514,898 autosomal SNPs in 3,177 OA cases v. 4,894 controls. SF3. qqplot of 514,898 autosomal SNPs in (a) 1,728 hip OA cases v. 4,894 controls and (b) 1,643 knee OA cases v. 4,894 controls. SF4. Forest plots of OA signals for each of the (a) rs4512391 (all OA), (b) rs4512391 (knee OA), (c) rs4977469 (hip OA), (d) rs2277831 (all OA), (e) rs11280 (knee OA) and (f) rs2615977 (hip OA) SNPs.SF5. Principal component analysis plot for stage 1 cases, WTCCC2 controls, and the HapMap phase II populations. SF6. Manhattan plot of 514,898 autosomal SNPs in 3,177 OA cases v. 4,894 controls. SF7. Manhattan plot of 514,898 autosomal SNPs in 1,728 hip OA cases v. 4,894 controls. SF8. Manhattan plot of 514,898 autosomal SNPs in 1,643 knee OA cases v. 4,894 controls. SF9. qq plots of 514,898 autosomal SNPs for the (a) female, (b) female hip, (c) female knee, (d) male, (e) male hip, and (f) male knee analyses. SF10. Manhattan plots of 514,898 autosomal SNPs for the (a) female, (b) female hip, (c) female knee, (d) male, (e) male hip, and (f) male knee analyses. SF11. qq plots for directly typed and imputed data for (a) all OA, (b) hip, (c) knee, (d) female, (e) female hip, (f) female knee, (g) male, (h) male hip, and (i) male knee analyses. SF12. Manhattan plots for directly typed and imputed data for (a) all OA, (b) hip, (c) knee, (d) female, (e) female hip, (f) female knee, (g) male, (h) male hip, and (i) male knee analyses. SF13. Manhattan plot of genome-wide rare variant analysis results for OA. Each point represents a gene within which OA association with multiple rare variants alleles has been investigated. Genes with p<0.0001 are highlighted in red. SF14. Correlation between association statistics (multiplicative model p values) before and after adjustment for the first ancestry-informative principal component.

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SUPPLEMENTARY TABLESST1. Stage 1, in silico replication, and de novo replication sample characteristics.ST2. Individual study and fixed effects meta-analysis results for the 102 prioritised SNPs across stage 1, in silico and de novo (where available) replication datasets. Results on the GDF5 rs143383 SNP have also been included.ST3. BMI association results for the 102 prioritised SNPs within arcOGEN stage 1 cases. ST4. Gender-adjusted and unadjusted analysis results comparison for the 102 prioritised SNPs. ST5. Multiplicative, dominant and recessive model association results for the prioritised 102 SNPs. ST6. Allele frequencies and association summary statistics for previously established OA loci. ST7. Interaction analysis results for the 102 prioritised SNPs (interaction p values <0.05 are shown).ST8. Association summary statistics across sensitivity analyses.ST9. Coordinates and characteristics of the linkage regions used for examining overlap between linkage and association scan signals. ST10. Statistical evaluation of overlap between linkage regions and common SNP association signals. ST11. Statistical evaluation of overlap between linkage regions and rare variant association signals.ST12. Osteoarthritis association p values before and after adjustment for the first 10 ancestry-informative principal components (PC) for the 102 prioritised SNPs.

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SUPPLEMENTARY REFERENCES1 Kellgren JH, Lawrence JS, Bier F. Genetic Factors in Generalized Osteo-Arthrosis. Ann Rheum Dis 1963;22:237-55.

2 Spector TD, MacGregor AJ. The St. Thomas' UK Adult Twin Registry. Twin Res 2002;5:440-3.

3 Hart DJ, Spector TD. The relationship of obesity, fat distribution and osteoarthritis in women in the general population: the Chingford Study. J Rheumatol 1993;20:331-5.

4 Croft P, Cooper C, Wickham C, et al. Osteoarthritis of the hip and acetabular dysplasia. Ann Rheum Dis 1991;50:308-10.

5 Neame RL, Muir K, Doherty S, et al. Genetic risk of knee osteoarthritis: a sibling study. Ann Rheum Dis 2004;63:1022-7.

6 Lanyon P, Muir K, Doherty S, et al. Assessment of a genetic contribution to osteoarthritis of the hip: sibling study. Bmj 2000;321:1179-83.

7 Gordon A, Kiss-Toth E, Stockley I, et al. Polymorphisms in the interleukin-1 receptor antagonist and interleukin-6 genes affect risk of osteolysis in patients with total hip arthroplasty. Arthritis Rheum 2008;58:3157-65.

8 WTCCC: www.wtccc.org.uk (date accessed 13 August 2010)

9 TwinsUK: www.twinsUK.ac.uk (date accessed 13 August 2010)

10 Andrew T, Hart DJ, Snieder H, et al. Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women. Twin Res 2001;4:464-77.

11 Teo YY, Inouye M, Small KS, et al. A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics 2007;23:2741-6.

12 Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75.

13 Pettersson F, Morris AP, Barnes MR, et al. Goldsurfer2 (Gs2): a comprehensive tool for the analysis and visualization of genome wide association studies. BMC Bioinformatics 2008;9:138.

22

14 Price AL, Patterson NJ, Plenge RM, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006;38:904-9.

15 GeneSniffer: www.genesniffer.org (date accessed 13 August 2010)

16 International HapMap Consortium. A haplotype map of the human genome. Nature 2005;437:1299-320.

17 Marchini J, Howie B, Myers S, et al. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 2007;39:906-13.

18 Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661-78.

19 Hofman A, Breteler MM, van Duijn CM, et al. The Rotterdam Study: objectives and design update. Eur J Epidemiol. 2007;22:819-29.

20 Richards JB, Rivadeneira F, Inouye M, et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet 2008;371:1505-12.

21 Li Y, Willer C, Sanna S, et al. Genotype imputation. Annu Rev Genomics Hum Genet 2009;10:387-406.

22 Stefansson SE, Jonsson H, Ingvarsson T, et al. Genomewide scan for hand osteoarthritis: a novel mutation in matrilin-3. Am J Hum Genet 2003;72:1448-59.

23 Dawber TR, Meadors GF, Moore FE Jr. Epidemiological approaches to heart disease: the Framingham Study. Am J Public Health Nations Health 1951;41:279-81.

24 Hunter DJ, Demissie S, Cupples LA, et al. A genome scan for joint-specific hand osteoarthritis susceptibility: The Framingham Study. Arthritis Rheum 2004;50:2489-96.

25 Spector TD, Williams FM. The UK Adult Twin Registry (TwinsUK). Twin Res Hum Genet 2006;9:899-906.

26 Metspalu A, Kohler F, Laschinski G, et al. The Estonian Genome Project in the context of European genome research. Dtsch Med Wochenschr 2004;129 Suppl 1:S25-8.

23

27 EGCUT: www.geenivaramu.ee (date accessed 13 August 2010)

28 International Classification of Diseases: www.who.int/classifications/icd/en/ (date accessed 13 August 2010)

29 Illumina: www.illumina.com (date accessed 13 August 2010)

30 Riyazi N, Meulenbelt I, Kroon HM, et al. Evidence for familial aggregation of hand, hip, and spine but not knee osteoarthritis in siblings with multiple joint involvement: the GARP study. Ann Rheum Dis 2005;64:438-43.

31 Heijmans BT, Beekman M, Houwing-Duistermaat JJ, et al. Lipoprotein particle profiles mark familial and sporadic human longevity. PLoS Med 2006;3:e495.

32 Rodriguez-Lopez J, Pombo-Suarez M, Liz M, et al. Lack of association of a variable number of aspartic acid residues in the asporin gene with osteoarthritis susceptibility: case-control studies in Spanish Caucasians. Arthritis Res Ther 2006;8:R55.

33 Velasco J, Zarrabeitia MT, Prieto JR, et al. Wnt pathway genes in osteoporosis and osteoarthritis: differential expression and genetic association study. Osteoporos Int 2010;21:109-18.

34 Fytili P, Giannatou E, Papanikolaou V, et al. Association of repeat polymorphisms in the estrogen receptors alpha, beta, and androgen receptor genes with knee osteoarthritis. Clin Genet 2005;68:268-77.

35 Orwoll E, Blank JB, Barrett-Connor E, et al. Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study--a large observational study of the determinants of fracture in older men. Contemp Clin Trials 2005;26:569-85.

36 Cummings SR, Nevitt MC, Browner WS, et al. Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group. N Engl J Med 1995;332:767-73.

37 Southam L, Rodriguez-Lopez J, Wilkins JM, et al. An SNP in the 5'-UTR of GDF5 is associated with osteoarthritis susceptibility in Europeans and with in vivo differences in allelic expression in articular cartilage. Hum Mol Genet 2007;16:2226-32.

38 Diggle PJ, Liang KY, Zeger SL et al. Analysis of longitudinal data: Oxford University Press 1994.

24

39 Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. Bmj 2003;327:557-60.

40 Johnson AD, Handsaker RE, Pulit SL, et al. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008;24:2938-9.

41 Chapman K, Mustafa Z, Irven C, et al. Osteoarthritis-susceptibility locus on chromosome 11q, detected by linkage. Am J Hum Genet 1999;65:167-74.

42 Loughlin J, Dowling B, Mustafa Z, et al. Refined linkage mapping of a hip osteoarthritis susceptibility locus on chromosome 2q. Rheumatology (Oxford) 2002;41:955-6.

43 Chapman K, Mustafa Z, Dowling B, et al, Finer linkage mapping of primary hip osteoarthritis susceptibility on chromosome 11q in a cohort of affected female sibling pairs. Arthritis Rheum 2002;46:1780-1783.

44 Loughlin J, Mustafa Z, Irven C, et al. Stratification analysis of an osteoarthritis genome screen-suggestive linkage to chromosomes 4, 6, and 16. Am J Hum Genet 1999;65:1795-8.

45 Forster T, Chapman K, Marcelline L, et al. Finer linkage mapping of primary osteoarthritis susceptibility loci on chromosomes 4 and 16 in families with affected women. Arthritis Rheum 2004;50:98-102.

46 Loughlin J, Mustafa Z, Dowling B, et al. Finer linkage mapping of a primary hip osteoarthritis susceptibility locus on chromosome 6. Eur J Hum Genet 2002;10:562-8.

47 Leppavuori J, Kujala U, Kinnunen J, et al. Genome scan for predisposing loci for distal interphalangeal joint osteoarthritis: evidence for a locus on 2q. Am J Hum Genet 1999;65:1060-7.

48 Demissie S, Cupples LA, Myers R, et al. Genome scan for quantity of hand osteoarthritis: the Framingham Study. Arthritis Rheum 2002;46:946-52.

49 Livshits G, Kato BS, Zhai G, et al. Genomewide linkage scan of hand osteoarthritis in female twin pairs showing replication of quantitative trait loci on chromosomes 2 and 19. Ann Rheum Dis 2007;66:623-7.

50 Greig C, Spreckley K, Aspinwall R, et al. Linkage to nodal osteoarthritis: quantitative and qualitative analyses of data from a

25

whole-genome screen identify trait-dependent susceptibility loci. Ann Rheum Dis 2006;65:1131-8.

51 Williams FM, Kato BS, Livshits G, et al. Lumbar disc disease shows linkage to chromosome 19 overlapping with a QTL for hand OA. Ann Rheum Dis 2008;67:117-9.

52 Meulenbelt I, Min JL, Bos et al. Identification of DIO2 as a new susceptibility locus for symptomatic osteoarthritis. Hum Mol Genet 2008;17:1867-75.

53 CCRaVAT: www.sanger.ac.uk/resources/software/ccravat-qutie/ (date accessed 13 August 2010)

54 Morris AP, Zeggini E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 2010;34:188-93.

55 Nagelkerke NJD. A note on a general definition of the coefficient of determination. Biometrika 1991;78:691-2.

56 International Schizophrenia Consortium, Purcell SM, Wray NR, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009;460:748-52.

57 Miyamoto Y, Mabuchi A, Shi D, et al. A functional polymorphism in the 5' UTR of GDF5 is associated with susceptibility to osteoarthritis. Nat Genet 2007;39:529-33.

58 Evangelou E, Chapman K, Meulenbelt I, et al. Large-scale analysis of association between GDF5 and FRZB variants and osteoarthritis of the hip, knee, and hand. Arthritis Rheum 2009;60:1710-21.

59 Kerkhof HJ, Lories RJ, Meulenbelt I, et al. A genome-wide association study identifies an osteoarthritis susceptibility locus on chromosome 7q22. Arthritis Rheum 2010;62:499-510.

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