Genome-Wide Screen for G6E Interactions on Lipids (ERC) Genetics of Mental Illness (230374); Rutgers University Cell and DNA Repository cooperative agreement (NIMH U24 MH068457-06); Collaborative study of the genetics of DZ twinning (NIH R01D0042157-01A); the Genetic Association Information Network, a public–private partnership between the NIH and Pfizer, Affymetrix, and Abbott Laboratories. The generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) project nr. 050-060-810. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The LifeLines Cohort Study, and generation and management of GWAS genotype data for the LifeLines Cohort Study is supported by the Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. PREVEND genetics is supported by the Dutch Kidney Foundation (Grant E033), the EU project grant GENECURE (FP-6 LSHM CT 2006 037697), the National Institutes of Health (grant LM010098), The Netherlands organisation for health research and development (NWO VENI grant 916.761.70), and the Dutch Inter UniversityCardiology Institute Netherlands (ICIN). EGCUT was supported by the Estonian Ministry of E&R (SF0180142s08), EU FP7 OPENGENE (#245536) and ENGAGE (201413), by EurRDF grant to the Centre of Excellence in Genomics, Estonian Biocentre and University of Tartu and by Estoinian Research Infrastructure’s Roadmap. Genmets was supported through funds from The European Community’s Seventh Framework Programme (FP7/2007–2013), BioSHaRE Consortium, grant agreement 261433. VS was supported by the Sigrid Juselius Foundation, Finnish Foundation for Cardiovascular research, and the Finnish Academy (grant number 129494). The CoLaus study received financial contributions from GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, and the Swiss National Science Foundation (33CSCO-122661). EPIC–Norfolk is supported by programme grants from the Medical Research Council UK (G9502233, G0300128) and Cancer Research UK (C865/A2883). Adipose Tissue eQTL dataset has been funded by Finnish Foundation for Cardiovascular Research, Helsinki University, Sigrid Juselius Foundation Central Hospital Research Foundation. Metabonomic datasets are supported by the Academy of Finland (grant number 137870 to PS) and the Responding to Public Health Challenges Research Programme of the Academy of Finland (grant number 129429 to MA-K), the Finnish Cardiovascular Research Foundation (MA-K), and the Jenny and Antti Wihuri Foundation (AJK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: One of the replication cohorts used in the study is partially funded by GlaxoSmithKline. * E-mail: samuli.ripatti@fimm.fi . These authors contributed equally to this work. " These authors also contributed equally to this work. { Deceased. Introduction Serum lipids are important determinants of cardiovascular disease and related morbidity [1]. The heritability of circulating lipid levels is estimated to be 40%–60% and recent genome-wide association (GWA) studies implicated a total of 95 loci associated with serum high-density lipoprotein cholesterol (HDL-C), low- density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglyceride (TG) levels [2]. Currently identified common variants explain 10%–12% of the total variation in lipid levels, corre- sponding to ,25% of the trait heritability [2]. Epidemiological risk factors, such as alcohol consumption, smoking, physical activity, diet and body composition are known to affect lipid levels [3–5]. These risk factors also show moderate to high heritabilities, and over 120 loci with genome-wide significant association have been identified (http://www.genome.gov/ 26525384). To better understand the biological processes modifying lipid levels, several twin studies [6–8] and candidate gene studies [9–14] have tested for interactions between genes and epidemiological risk factors. Interactions between genes and modifiable risk factors might help us develop new lifestyle interventions targeted to susceptible individuals based on their genetic information. The effects of genetic loci and risk factors have been studied widely separately, but to date no GWA studies for interactions on lipids have been reported. Results We conducted a genome-wide screen for interactions between 2.5 million genetic markers and sex, lifestyle factors (smoking and alcohol consumption), and body composition (BMI and WHR) in association to serum lipid levels (TC, TG, HDL-C, and LDL-C) in 18 population-based cohorts (max N = 32,225; Table S1A, Text S1). We defined interaction as a departure from a linear statistical model allowing for the additive main effects of both the SNP and the epidemiological risk factor. 18 SNPs with suggestive interactions for at least one of the trait – epidemiological factor combinations (P-value for the interaction ,1026) in stage 1 analyses were taken forward to stage 2 analysis in eight additional cohorts (max N = 14,889; Table S1B, Text S1). In inverse variance meta-analyses combining the results from stage 1 and stage 2 (Table S2), the interaction between rs6448771 in chromosome 4p15 and WHR on TC (Figure 1) was statistically genome-wide significant (stage 1 and 2 combined P = 9.0861029). This interaction was tested in stage 3 in two further cohorts (N = 7,813; Table S1C, Text S1), which showed an effect to the same direction. After combining results from all three stages (total N = 43,903), the P-value for interaction was 4.7961029. The association between WHR and TC was strongest for individuals carrying two G alleles of rs6448771, for whom a one standard deviation (sd) difference in WHR corresponds to 0.19 sd difference (confidence interval 0.13–0.25) in TC concentration, while for individuals homozygous for the A allele the difference was 0.12 sd (confidence interval 0.09–0.16) (Table S3A, Figure S1). The effect corresponds to 0.5% and 0.2% of the total variance explained in a cohort of young individuals (YFS, mean age = 37.6) and an old cohort (HBCS, mean age = 61.49), respectively. Additionally, when looking at the effect of the SNP on TC in WHR tertiles, the estimates differed in a way that the estimated SNP effect is higher for the individuals with higher WHR (Table S3B). The SNP did not have a direct effect on either TC or WHR (P = 0.46 and P = 0.51, respectively, Figure 1). The SNP rs6448771 is located 249 kb downstream of the protocadherin 7 (PCDH7) gene. Since the polymorphisms associated with complex phenotypes often influence gene expression, we examined whether individuals carrying different genotypes of rs6448771 have variation in their transcript profiles. As WHR reflects adipose tissue function, we selected 54 individuals from Finnish dyslipidemic families with available fat biopsies and GWA data. We used linear regression to find genes that were differentially expressed in adipose tissue depending on the rs6448771 genotype. We found two potential candidate genes with nominally significant cis-eQTL effects, PCDH7 (P = 0.027, distance from the rs6448771 250 kb) and CCKAR (P = 0.017, distance from the SNP 4.9 Mb). The region with CCKAR has previously been linked with obesity [15]. PLoS Genetics | www.plosgenetics.org 3 October 2011 | Volume 7 | Issue 10 | e1002333