(36.8 nm), and very small VLDL (31.3 nm); intermediate-density lipoprotein (IDL) (28.6 nm); three LDL subclasses: large LDL (25.5 nm), medium-size LDL (23.0 nm), and small LDL (18.7 nm); and four HDL subclasses: very large HDL (14.3 nm), large HDL (12.1 nm), medium size HDL (10.9 nm), and small HDL (8.7 nm). Statistical methods Triglyceride concentrations were natural log transformed prior to analysis. BMI and WHR were transformed to normality using inverse-normal transformation of ranks. For analyses where sex was the epidemiological variable of interest, the phenotypes were defined as the rank-inverse normal transformed residuals resulting from the regression of the lipid measurement on age and age2. For the other analyses, the phenotypes were defined as the inverse normal transformed residuals resulting from the regression of the lipid measurement on age, age2, and sex. Associations between the transformed residuals and epidemio- logical risk factors/SNPs were tested using linear regression models under the assumption of an additive (allelic trend) model of genotypic effect. The models regressed phenotypes on epidemio- logical factor, SNP, and epidemiological factor6SNP terms TransformðresidualsÞ*EzSNPzE|SNP and tested if the effect for E6SNP was 0 using 1 df Wald tests. In family-based cohorts, linear mixed modeling was implemented to control for relatedness among samples [21]. Analysis software used by the individual cohorts is described in Table S1A and S1B. The interaction terms from the regression analyses were meta- analyzed using inverse variance weighted fixed-effects models [22]. Prior to meta-analysis, genomic control correction factors (lGC) [23], calculated from all imputed SNPs, were applied on a per- study basis to correct for residual bias possibly caused by population sub-structure. Meta-analyses were performed by two independent analysts using METAL (http://www.sph.umich.edu/ csg/abecasis/Metal/index.html) and the R [24] package MetA- BEL (part of the GenABEL suite, http://www.genabel.org/). All results were concordant, reflecting a robust analysis. Results were selected for in silico replication if the meta-analysis P-value was less than 1026. Results passing the threshold of suggestive genome- wide association (P-value #561027) were selected for further replication by direct genotyping. The commonly accepted genome wide level of significance (561028) reflects the estimated testing burden of one million independent SNPs in samples of European ancestry [25]. To address the multiple testing arising from testing interactions with multiple risk factors, we set the genome wide significance threshold to 561028/3 = 1.6761028 corresponding to three principal components explaining 97.8% of the total variation of the risk factors (Table S5). Pathway analysis. The functional analyses were generated through the use of Ingenuity Pathways Analysis (Ingenuity Systems, www.ingenuity.com).’’ The Functional Analysis identified the biological functions and/or diseases that were most significant to the data set. Molecules which met the P-value cutoff of 0.01 for the rs6448771 – expression association in dataset of 54 Finnish individuals with both genotype and adipose tissue expression data, and were associated with biological functions and/or diseases in Ingenuity’s Knowledge Base were considered for the analysis. Right-tailed Fisher’s exact test was used to calculate a P-value determining the probability that each biological function and/or disease assigned to that data set is Genome-Wide Screen for G6E Interactions on Lipids due to chance alone and Benjamini-Hochberg multiple test correction [26] was applied. Supporting Information Figure S1 Effect of waist-to-hip ratio on total cholesterol as a function of rs6448771 genotypes. The bars in the plot are the effect estimates from three meta-analyzed linear models where total cholesterol (TC) has been explained using waist-to-hip ratio (WHR). The analyses were ran in three strata based on the rs6448771 genotypes. The whiskers in the plot correspond to the confidence intervals of the effect estimates. (DOC) Table S1 Cohort characteristics. The number of study subjects with available phenotype and genotype (lower line) and summary statistics (upper line) for every cohort and trait. For continuous traits mean (standard deviation) is presented. For dichotomous traits number of individuals with phenotype present (%) is presented. TC: total cholesterol (mmol/l); HDL- C: high-density lipoprotein cholesterol (mmol/l); LDL-C: low- density lipoprotein cholesterol (mmol/l); TG: triglycerides (mmol/l); BMI: body-mass index; WHR: waist-to-hip ratio; NA: not available. (DOC) Table S2 Loci having P-value,161026 in Stage 1 analyses and replication of the SNPs. Best SNP per locus having P- value,161026 in the Stage 1 analysis combining 19 cohorts. The bolded number is the genome-wide significant P-value. N: number of individuals; SE: standard error of the effect estimate, Beta; LDL-C: low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; ALC: alcohol usage (drinker/abstainer); WHR: waist- to-hip ratio; BMI: body mass index; SMO: smoking (current/not);; SMOq: semi-quantitative smoking (0: 0 cigarettes/day; 1: .0 and #10 cigarettes/day; 2: .10 and #20 cigarettes/day; 3: .20 and #30 cigarettes/day; 4: .30 cigarettes/day); ALCq: semi-quanti- tative alcohol (0: 0 g/day; 1: .0 and #10 g/day; 2: .10 and #20 g/day; 3: .20 and #40 g/day; 4: .40 g/day). (DOC) Table S3 Effect of rs6448771 on total cholesterol (TC) by waist- to-hip ratio (WHR) tertiles and effect of WHR on TC by SNP genotype classes. Section A shows the combined effect of waist-to- hip ratio (WHR) on total cholesterol (TC) stratified by the rs6448771 genotype class from five Finnish cohorts (FINRISK, NFBC1966, YFS, Genmets and HBCS, combined number of individuals is 12,782) and section B shows the combined effect of the SNP on TC stratified by WHR tertiles from the same cohorts. The limit values for the waist-to-hip ratio (WHR) tertiles have been calculated using WHR values from all five datasets. Both analyses were ran using untransformed and standardized scales and were adjusted with age, age2 and sex. Beta: effect estimate; CI: confidence interval. (DOC) Table S4 Details of GWA data in discovery and replication cohorts. QC: quality control; MAF: minor allele frequency; HWE: Hardy-Weinberg equilibrium. (DOC) Table S5 Proportions of variance explained by principal components. Principal components analysis (PCA) was run for the seven risk factors used in the screening. PC: Principal Component. (DOC) PLoS Genetics | www.plosgenetics.org 7 October 2011 | Volume 7 | Issue 10 | e1002333