doctor. The response rate to requests for the CSQ among RA self-reports was 77%, and 96% to requests for medical records. Population for analysis For cases and controls, we excluded women who reported any cancer (except non-melanoma skin cancer) at baseline or during follow-up. Each participant with RA was matched by year of birth, race/ethnicity, menopausal status and postmenopausal hormone use to a single healthy woman in the same cohort without RA. To minimise potential population stratification, we limited the analyses to Caucasian women. DNA extraction and amplification DNA was extracted from buffy coats and processed via the QIAmp (Qiagen, Chatsworth, California, USA) 96-spin blood kit protocol as previously described.27 All genomic DNA samples had an aliquot put through a whole genome amplification protocol using the GenomPhi DNA amplification kit (GE Healthcare, Piscataway, New Jersey, USA) to yield high quality DNA sufficient for HLA genotyping. Seropositive phenotyping We collected information on RF from medical records reviewed from the date of RA diagnosis. We did not have records from later in the disease course, or information on CCP as cases were diagnosed prior to its widespread use. For a subset of 180 NHS and 41 NHSII RA cases, plasma samples were collected in 1989 and a second set of plasma samples collected in 2000. In all, 98 samples were collected before RA diagnosis (incident samples) and 123 were collected after diagnosis (prevalent samples). We used the DIASTAT CCP (Axis-Shield Diagnostics, Dundee, UK) second-generation test, a semiquantitative/qualitative ELISA for detection of IgG CCP antibodies. A CCP antibody titre .5 U/ml was considered positive according to the manufacturer’s estab- lished threshold. Since prior work from Sweden has demon- strated gene–environment interactions between HLA-SE and smoking for RF-positive13 and CCP-positive RA,19 we created a combination phenotype based on RF results from the medical record supplemented by CCP results from plasma samples where available, as ‘‘ever seropositive’’ versus ‘‘never seropositive’’. HLA-SE determination Low-resolution HLA-DRB1 genotyping was performed using polymerase chain reaction with sequence specific primers (PCR- SSP) using OLERUP SSP kits (Qiagen, West Chester, Pennsylvania, USA). We used primers to amplify DNA samples that contained sequences for HLA-DRB1*04, *01,*10 and *14, along with consensus primers and appropriate positive and negative control samples. For samples with positive two-digit HLA signals, sequence specific primers were used for high- resolution four-digit shared epitope allele detection of DRB1*0401, *0404, *0405, *0101, *0102, *1402 and *1001. OLERUP SSP computer software (Qiagen) was used to determine four-digit HLA types. Covariates Information was collected via prospective biennial subject questionnaires regarding diseases, lifestyle and health practices. Reproductive covariates were chosen based on associations between reproductive factors and the RA risk in this cohort.28 Lifetime smoking history was collected at baseline and updated data concerning current smoking and number of cigarettes smoked a day were collected every 2 years. Data on smoking, Ann Rheum Dis 2010;69:54–60. doi:10.1136/ard.2008.102962 Extended report parity, total duration of breast feeding, menopausal status and postmenopausal hormone use were selected from the cycle prior to the RA diagnosis date (or index date in controls). Smoking was categorised as: (1) never versus ever and (2) pack-years of smoking (product of years of smoking and packs of cigarettes per day). Pack-years were dichotomised as never or light smoking versus heavy smoking (10 vs .10 pack-years based on epidemiological data from this cohort that demonstrate increased RA risk with .10 pack-years of smoking.14 We further investigated three smoking categories (never, past, current) and three categories of pack-years ((10, 10–20 and .20 pack-years). Statistical methods We verified the Hardy–Weinberg equilibrium for each genotype among controls in each nested case-control dataset. We calculated means with standard deviation and medians with range for continuous covariates stratified by cohort and case/ control status. For categorical covariates, we calculated frequen- cies and percentages. SAS V.9.1 was used for all analyses (SAS Institute, Cary, North Carolina, USA). Distributions for HLA-SE among cases and controls were compared using the x2 test of independence. Conditional logistic regression analyses, condi- tioning on matching factors and adjusting for age at menarche, menstrual regularity, parity, breastfeeding duration, menopausal status and postmenopausal hormone use, tested the association between HLA-SE alleles and RA risk in a general model and in a dominant model. Unconditional logistic regression analyses, adjusting for matching factors and covariates, were used to examine the risks of seropositive and seronegative RA. Analyses of interaction We used an additive models of interaction based on disease rates connected to the ‘‘pie model’’.29 Rothman showed that independent risk factors adhere to an additive model and that biological interaction results in departure from additivity of the disease rates (see supplementary material). To test for additive interactions we followed the methods outlined by Lundberg30 and Andersson,31 using a 262 factorial design to calculate the attributable proportion due to interaction (AP), the relative excess risk due to interaction (RERI) and the synergy index (SI). A p value of ,0.05 for AP was considered as departure from an additive model of association. For models where HLA-SE, pack- years of smoking and smoking status were categorised into three categories, we calculated indices of additive interaction for each stratum of exposure compared to the referent category of non-exposure. The 95% confidence intervals (CIs) were calculated using the delta method as described previously,32 which is a straightforward Taylor expansion of the variances and covariances to derive a probability distribution. Multiplicative interaction was assessed by adding an interaction variable (HLA-SE* smoking) to the regression models. A p value of ,0.05 was considered as evidence for departure from a multiplicative model of association. RESULTS A total of 439 pairs of Caucasian women, each pair being 1 RA case and a matched control, were included. The cases in the NHS had a mean (SD) age of 56.7 (9.4) years, compared to 43.1 (5.1) years in the younger NHSII cohort, due to the different ages targeted for enrolment in each of the cohorts (table 1). Otherwise, the cases were similar in terms of RA characteristics with 61% seropositive RA in NHS and 65% seropositive in NHSII. 55