Genome-wide association studies (GWAS) have identified more than 38 larger genetic regions which influence blood levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) ^[1]?^[3]. These studies modeled basic anthropometric confounders, such as sex and age, while leaving out important environmental influences, such as diet and activity. This strategy is statistically suboptimal since the unexplained variation in the phenotype can increase the measurement error and as a result require larger sample sizes to detect a significant effect. Manolio ^[4] argued strongly for modeling of environmental covariates in GWAS and recommended lipid levels as a paradigmatic phenotype for studying the genetic and environmental architecture of quantitative traits.

In order to explore the usefulness of including both environmental and genetic factors in the analysis model, we used lipid measurements from the EUROSPAN study, comprising 3,938 individuals for whom genome-wide SNP data (N_SNP?=?311,388) were available ^[5]. We measured daily intake of food and physical activity at work and at leisure and modeled the influence of those environmental covariates on serum lipid levels in a GWAS. First, data from the Northern Sweden Population Health Study (NSPHS) were used as a discovery cohort to screen for SNPs that displayed the lowest p-values when the model was adjusted for environmental covariates. We then used the other, non-Swedish EUROSPAN cohorts for replication of our strongest associations in a candidate gene association study (CGAS).

We chose a population living in northern Sweden for the selection of candidate loci because it shows strong natural heterogeneity in certain lifestyle factors (e.g. diet, activity), but homogeneity in other environmental aspects such as climate ^[6]. Whereas one group is living a modern, sedentary lifestyle found also in the southern part of Sweden and other western European countries, a subgroup of Swedes follows a traditional, semi-nomadic way of life based on reindeer herding. Reindeer herders typically show higher intake of game meat (reindeer, moose), which has a high protein and low fat content, and lower intake of non-game meat, fish, and dairy products among other, lesser differences. They also exert more physical activity at work to tend their reindeer herds, but less activity at leisure ^[7].

Environmental covariates may either act as moderators, mediators or even suppressors, thereby affecting the discovery of genetic susceptibility loci ^[8],^[9]. Therefore, we conducted a GWAS, modeling genetic and important environmental effects, such as food intake and physical activity, on serum levels of classical lipids. To our knowledge, this is the first GWAS on blood lipid levels modeling environmental factors, in particular major food categories and physical activity, in international cohorts. Our analysis replicated one known locus in the CETP gene ^[1] and identified two other gene loci in the SLC2A2 and HP gene, respectively, involved in energy metabolism but not previously reported to be associated with cholesterol levels.

SLC2A2 encodes the facilitated glucose transporter member 2 (GLUT-2, Solute carrier family 2) and is predominantly expressed in the liver. Mice deficient in GLUT-2 are hyperglycemic and have elevated plasma levels of glucagon and free fatty acids ^[10]. Mutations in GLUT-2 cause the Fanconi-Bickel syndrome (FBS) characterized by hypercholesterolemia and hyperlipidemia ^[11],^[12]. Cerf ^[13] argued that a high-fat diet causes a decreased expression of the GLUT-2 glucose receptor on ?-cell islets. As a result, glucose stimulation of insulin exocytosis is impaired causing hyperglycemia, a clinical hallmark of type 2 diabetes. In addition, Kilpelainen et al. ^[14] found that physical activity moderates the genetic effect of SLC2A2 on type 2 diabetes. These studies suggest that these lifestyle factors could have masked genetic effects in previous, unadjusted GWAS. This is emphasized by the strong increase in statistical significance of the SLC2A2 polymorphisms after adjusting for diet and physical activity, indicating that the examined lifestyle factors modified the effect of this gene. Our supplemental results show that physical activity markedly moderated the genetic effect on total cholesterol.

The HP gene encodes the Haptoglobin-related Protein Precursor (Hp), which binds hemoglobin (Hb) to form a stable Hp-Hb complex and, thereby, prevents Hb-induced oxidative tissue damage. Asleh et al. ^[15] identified severe impairment in the ability of Hp to prevent oxidation caused by glycosylated Hb. Diabetes is also associated with an increase in the non-enzymatic glycosylation of serum proteins, so these authors suggested that there is a specific interaction between diabetes, cardiovascular disease and the Hp genotype. It results from the increased need of rapidly clearing glycosylated Hb-Hp complexes from the subendothelial space before they oxidatively modify low-density lipoprotein to form the atherogenic oxidized low-density lipoprotein. The p-value for association between the HP SNP rs2000999 and total serum cholesterol concentration decreased in the model adjusted for diet and physical activity, suggesting that the genetic effect is moderated by diet and physical activity. Our supporting material points out the moderating role of physical activity in particular.

We also observed a highly significant association between rs1532624 in CETP and HDL-C levels. The CETP protein catalyzes the transfer of insoluble cholesteryl esters among lipoprotein particles. Variation in CETP is known to affect the susceptibility to atherosclerosis and other cardiovascular diseases ^[16]. Adjustment for diet and physical activity in our model caused an increase of the p-value of this SNP. Our supporting results indicate that the genetic effect is mediated by diet or by physical activity in a similar way.

This study also has some limitations. First, we are aware that our candidate gene association approach covers only a very small fraction of all genomic loci, which is one of the potential reasons why some classical lipid-influencing genes, such as APOE, are not represented in our candidate SNP list. Therefore, our approach is not comprehensive and may have failed to identify other relevant lifestyle-sensitive genetic variants. Nonetheless, we decided to apply this approach to make the best out of the available lifestyle data. Second, our study provides only limited information on the role of individual lifestyle factors for a genetic variant. However, in this study we aimed at amplifying genetic effects by adjusting for a maximum amount of environmental variance in a single model and, therefore, we neglected some of these aspects here. Third, we did not model genetic covariates in known lipid-relevant genes which may also moderate the effect of other genetic predictors. This is due to the focus of this paper on gene-environment relationships.

In summary, we have demonstrated that modeling environmental factors, in particular major food categories and physical activity, can improve statistical power and lead to the discovery of novel susceptibility loci. Such models also provide an understanding of the complex interplay of genetic and environmental factors affecting human quantitative traits. Inclusion of environmental covariates represents a much needed next step in the quest to model the complete environmental and genetic architecture of complex traits.

The authors have declared that no competing interests exist.

The European Special Populations Research Network (EUROSPAN) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947). High-throughput genome-wide association analysis of the data was supported by joint grant from Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). Lipidomic analysis was supported by the European Commission FP7 grant LipidomicNet (2007-202272). The NSPHS study was supported by grants from the Swedish Natural Sciences Research Council, the European Commission through EUROSPAN, the Foundation for Strategic Research (SSF), and the Linneaus Centre for Bioinformatics (LCB). The ORCADES study was supported by the Scottish Executive Health Department and the Royal Society. DNA extractions were performed at the Wellcome Trust Clinical Research Facility in Edinburgh. The VIS study in the Croatian island of Vis was supported through the grants from the Medical Research Council UK to HC, AW, and IR and the Ministry of Science, Education, and Sport of the Republic of Croatia to IR (number 108-1080315-0302). The MICROS study was supported by the Ministry of Health and Department of Educational Assistance, University and Research of the Autonomous Province of Bolzano and the South Tyrolean Sparkasse Foundation. The ERF study was supported by grants from the NWO, Erasmus MC, and the Centre for Medical Systems Biology (CMSB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We would like to thank the many colleagues who contributed to collection and phenotypic characterization of the samples, genotyping and analysis of the GWAS data, as well as lipid species analysis. We would also like to acknowledge those who agreed to participate in these studies.

NSPHS: We are grateful for the contribution of samples from the Medical Biobank in Ume? and for the contribution of the district nurse Svea Hennix. ORCADES: We would like to acknowledge the invaluable contributions of Lorraine Anderson and the research nurses in Orkney, the administrative team in Edinburgh and the people of Orkney. VIS: We collectively thank a large number of individuals for their individual help in organizing, planning and carrying out the field work related to the project and data management: Professor Pavao Rudan and the staff of the Institute for Anthropological Research in Zagreb, Croatia (organization of the field work, anthropometric and physiological measurements, and DNA extraction); Professor Ariana Vorko-Jovic and the staff and medical students of the Andrija ?tampar School of Public Health of the Faculty of Medicine, University of Zagreb, Croatia (questionnaires, genealogical reconstruction and data entry); Dr Branka Salzer from the biochemistry lab ?Salzer?, Croatia (measurements of biochemical traits); local general practitioners and nurses (recruitment and communication with the study population); and the employees of several other Croatian institutions who participated in the field work, including but not limited to the University of Rijeka and Split, Croatia; Croatian Institute of Public Health; Institutes of Public Health in Split and Dubrovnik, Croatia. SNP Genotyping of the Vis samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, WGH, Edinburgh. MICROS: We thank the primary care practitioners Raffaela Stocker, Stefan Waldner, Toni Pizzecco, Josef Plangger, Ugo Marcadent and the personnel of the Hospital of Silandro (Department of Laboratory Medicine) for their participation and collaboration in the research project. ERF: We are grateful to all patients and their relatives, general practitioners, and neurologists for their contributions and to P. Veraart for her help in genealogy, Jeannette Vergeer for the supervision of the laboratory work and P. Snijders for his help in data collection. UPPMAX: The computations were performed on UPPMAX (http://www.uppmax.uu.se) resources under Project p2008027. Further information about The European Special Populations Research Network (EUROSPAN) consortium is available at http//www.eurospan.org.