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Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic–based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.

Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (P$_v$), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (P$_m$). Correlations between P$_v$ and P$_m$ were stronger for SNPs with established marginal effects (Spearman's ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When P$_v$ and P$_m$ were compared for all pruned SNPs, only BMI was statistically significant (Spearman's ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the P$_v$ distribution (P$_{binomial}$ <0.05). SNPs from the top 1% of the P$_m$ distribution for BMI had more significant Pv values (P$_{Mann-Whitney}$ = 1.46×10$^{-5}$), and the odds ratio of SNPs with nominally significant (<0.05) P$_m$ and P$_v$ was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (P$_{int}$<0.05) were enriched with nominally significant P$_v$ values (P$_{binomial}$ = 8.63×10$^{-9}$ and 8.52×10$^{-7}$ for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them. ; This research was undertaken as part of a research program supported by the European Commission (CoG-2015_681742_NASCENT), Swedish Research Council (Distinguished Young Researchers Award in Medicine), Swedish HeartLung Foundation, and the Novo Nordisk Foundation, all grants to PWF. DS is supported by the Swedish Research Council International Postdoc Fellowship (4.1-2016-00416). TVV is supported by the Novo Nordisk Foundation Postdoctoral Fellowship within Endocrinology/ Metabolism at International Elite Research Environments via NNF16OC0020698. TWW was supported by the grants "Bundesministerium fur Bildung und Forschung": BMBF-01ER1206, BMBF- 01ER1507. APM is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant WT098017). LAC acknowledges funding for the Framingham Heart Study: This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195 and Contract No. HHSN268201500001I) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This research was partially supported by grant R01-DK089256 from the National Institute of Diabetes and Digestive and Kidney Diseases (MPIs: I.B. Borecki, LAC, K. North). TOK was supported by the Danish Council for Independent Research (DFF—1333-00124) and Sapere Aude program grant (DFF—1331-00730B). RM would like to acknowledge the High Performance Computing Center of University of Tartu. EGCUT was supported by EU H2020 grants 692145, 676550, 654248, 692065, Estonian Research Council Grant IUT20-60, and PerMed I NIASC, EIT—Health and European Union through the European Regional Development Fund (Project No, 2014-2020.4.01.15-0012 GENTRANSMED).

Objective To investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines. Design Pooled analysis of eight cohorts. Setting Multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries. Participants 325 367 adults from the general population recruited mostly in the 1990s or 2000s with detailed lifestyle data. Stratified Cox proportional hazard models were used to analyse the associations between air pollution and mortality. Western Europe-wide land use regression models were used to characterise residential air pollution concentrations of ambient fine particulate matter (PM2.5), nitrogen dioxide, ozone, and black carbon. Main outcome measures Deaths due to natural causes and cause specific mortality. Results Of 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM2.5, nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m3 in PM2.5 was associated with 13% (95% confidence interval 10.6% to 15.5%) increase in natural deaths; the corresponding figure for a 10 µg/m3 increase in nitrogen dioxide was 8.6% (7% to 10.2%). Associations with PM2.5, nitrogen dioxide, and black carbon remained significant at low concentrations. For participants with exposures below the US standard of 12 µg/m3 an increase of 5 µg/m3 in PM2.5 was associated with 29.6% (14% to 47.4%) increase in natural deaths. Conclusions Our study contributes to the evidence that outdoor air pollution is associated with mortality even at low pollution levels below the current European and North American standards and WHO guideline values. These findings are therefore an important contribution to the debate about revision of air quality limits, guidelines, and standards, and future assessments by the Global Burden of Disease.

OBJECTIVE: To investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines. DESIGN: Pooled analysis of eight cohorts. SETTING: Multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries. PARTICIPANTS: 325 367 adults from the general population recruited mostly in the 1990s or 2000s with detailed lifestyle data. Stratified Cox proportional hazard models were used to analyse the associations between air pollution and mortality. Western Europe-wide land use regression models were used to characterise residential air pollution concentrations of ambient fine particulate matter (PM2.5), nitrogen dioxide, ozone, and black carbon. MAIN OUTCOME MEASURES: Deaths due to natural causes and cause specific mortality. RESULTS: Of 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM2.5, nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m3 in PM2.5 was associated with 13% (95% confidence interval 10.6% to 15.5%) increase in natural deaths; the corresponding figure for a 10 µg/m3 increase in nitrogen dioxide was 8.6% (7% to 10.2%). Associations with PM2.5, nitrogen dioxide, and black carbon remained significant at low concentrations. For participants with exposures below the US standard of 12 µg/m3 an increase of 5 µg/m3 in PM2.5 was associated with 29.6% (14% to 47.4%) increase in natural deaths. CONCLUSIONS: Our study contributes to the evidence that outdoor air pollution is associated with mortality even at low pollution levels below the current European and North American standards and WHO guideline values. These findings are therefore an important contribution to the debate about revision of air quality limits, guidelines, and standards, and future assessments by the Global Burden of Disease.

This work was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the e:Med research and funding concept (grant 01ZX1313A-2014). The ADVANCE study was supported by a grant from the Reynold's Foundation and NHLBI grant HL087647. Sample collection in the Cardiogenics Consortium (http://www.cardiogenics.eu/web/) was funded by the 6th Framework Program of the European Union (LSHM-CT-2006-037593). We thank all the participants and clinicians involved in the recruitment process at Cambridge and Leicester (UK), Luebeck and Regensburg (Germany), and Paris (France). CATHGEN was supported by NIH grants HL095987 and HL101621. The Cleveland Clinic Gene Bank study was funded by P01HL076491 (to S.L.H). EGCUT was supported by Estonian Research Council grant no. IUT20-60 and Research Roadmap grant no. 3.2.0304.11-0312 and by University Tartu grant no. ARENG SP1GV. The FGENTCARD-Functional Genomic diagnostic tools for coronary artery disease project was funded by an EU FP6 award. We thank the patients for agreeing to participate in the study. We thank Sonia Youhanna, Nour Moukalled and Bariaa Khalil for their help with subject recruitment and data collection. The work of FINCAVAS was supported by the Competitive Research Funding of the Tampere University Hospital (Grant 9M048 and 9N035), the Finnish Cultural Foundation, the Finnish Foundation for Cardiovascular Research, the Emil Aaltonen Foundation, Finland, and the Tampere Tuberculosis Foundation. The authors thank the staff of the Department of Clinical Physiology for collecting the exercise test data. The GerMIFS studies were supported by grants from the German Federal Ministry of Education and Research (BMBF) within the framework of NGFN and NGFN-plus (Atherogenomics) and e:Med research and funding concept (e:AtheroSysMed, grant 01ZX1313A-2014), the Fondation Leducq (CADgenomics: Understanding CAD Genes, 12CVD02), and the European Union Sixth Framework Programme FP6 (under grant agreement FP6-LIFESCIHEALTH (Cardiogenics)) and the Seventh Framework Programme FP7/2007-2013 under grant agreement n° HEALTH-F2-2013-601456 (CVgenes-at-target). The Heart Protection Study (HPS) (ISRCTN48489393) was supported by the UK Medical Research Council (MRC), British Heart Foundation, Merck and Co (manufacturers of simvastatin), and Roche Vitamins Ltd (manufacturers of vitamins). Genotyping was supported by a grant to Oxford University and CNG from Merck and Co. Jemma C. Hopewell acknowledges support from the British Heart Foundation (FS/14/55/30806). HPS acknowledges the National Blood Service (NBS) donors and UK Twin study for using as population controls. A full list of the investigators who contributed to the generation of the NBS data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 07611. The UK Twin study was funded by the Wellcome Trust; European Community"s Seventh Framework Programme (FP7/2007–2013). The Helsinki Sudden Death Study (HSDS) was financially supported by EU's 7th Framework Programme (grant no. 201668 for AtheroRemo), the Tampere University Foundation, the Tampere University Hospital Medical Funds (grants X51001, 9M048 and 9N035 for Terho Lehtimäki, the Emil Aaltonen Foundation (Terho Lehtimäki, the Finnish Foundation of Cardiovascular Research (Terho Lehtimäki, Pekka J. Karhunen), the Pirkanmaa Regional Fund of the Finnish Cultural Foundation, the Yrjö Jahnsson Foundation, and the Tampere Tuberculosis Foundation (Terho Lehtimäki). LIFE-Heart is a part of the LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and by means of the Free State of Saxony within the framework of the excellence initiative. The LOLIPOP study is supported by the National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre Imperial College Healthcare NHS Trust, the British Heart Foundation (SP/04/002), the Medical Research Council (G0601966, G0700931), the Wellcome Trust (084723/Z/08/Z), the NIHR (RP-PG-0407-10371), European Union FP7 (EpiMigrant, 279143) and Action on Hearing (G51). We thank the participants and research staff who made the study possible. LURIC was supported by the 7th Framework Program (integrated project AtheroRemo, grant agreement number 201668 and RiskyCAD, grant agreement number 305739) of the European Union and by the INTERREG IV Oberrhein Program (Project A28, Genetic mechanisms of cardiovascular diseases) with support from the European Regional Development Fund (ERDF) and the Wissenschaftsoffensive TMO. We extend our appreciation to the participants of the LURIC study and thank the LURIC study team who were either temporarily or permanently involved in patient recruitment as well as sample and data handling, in addition to the laboratory staff at the Ludwigshafen General Hospital and the Universities of Freiburg and Ulm, Germany. The MIGen study was funded by R01HL087676 from the US National Heart, Lung, and Blood Institute. The Mount Sinai IPM Biobank Program is supported by The Andrea and Charles Bronfman Philanthropies. It was in part supported by NHGRI U01HG007417. OHGS_A2, OHGS_B2, and OHGS_C2 were funded by Canadian Institutes of Health Research (# MOP-2380941 to R.M.), (#MOP82810, MOP77682 to R.R., A.F.S. & R.M.); Canada Foundation for Innovation (#11966 to R.R., R.M. & A.F.S.; Heart & Stroke Foundation of Canada (#NA6001, #NA6650 to R.M). PIVUS was supported by Knut and Alice Wallenberg Foundation (Wallenberg Academy Fellow), European Research Council (ERC Starting Grant), Swedish Diabetes Foundation (grant no. 2013-024), Swedish Research Council (grant no. 2012-1397), and Swedish Heart-Lung Foundation (20120197). We thank the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se) for excellent genotyping. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project b2011036. PROCARDIS was supported by the European Community Sixth Framework Program (LSHM-CT- 2007-037273), AstraZeneca, the British Heart Foundation, the Swedish Research Council, the Knut and Alice Wallenberg Foundation, the Swedish Heart-Lung Foundation, the Torsten and Ragnar Söderberg Foundation, the Strategic Cardiovascular Program of Karolinska Institutet and Stockholm County Council, the Foundation for Strategic Research and the Stockholm County Council (560283). Research in SDS was partly supported by NIH grants -R01DK082766 funded by the National Institute of Diabetes and Digestive and Kidney Diseases and NOT-HG-11-009 funded by National Genome Research Institute, and VPR Bridge grant from University of Oklahoma Health Sciences Center, Oklahoma City, USA. Recruitment for THISEAS was partially funded by a research grant (PENED 2003) from the Greek General Secretary of Research and Technology; we thank all the dieticians and clinicians for their contribution to the project. TwinGene was supported by grants from the Ministry for Higher Education, the Swedish Research Council (M-2005-1112 and 2009-2298), GenomEUtwin (EU/QLRT-2001-01254; QLG2-CT-2002-01254), NIH grant DK U01-066134, Knut and Alice Wallenberg Foundation (Wallenberg Academy Fellow), European Research Council (ERC Starting Grant), Swedish Diabetes Foundation (grant no. 2013-024), Swedish Research Council (grant no. 2012-1397), and Swedish Heart-Lung Foundation (20120197). We thank the SNP&SEQ Technology Platform in Uppsala (www. genotyping.se) for excellent genotyping. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project b2011036. ULSAM was supported by Knut and Alice Wallenberg Foundation (Wallenberg Academy Fellow), European Research Council (ERC Starting Grant), Swedish Diabetes Foundation (grant no. 2013-024), Swedish Research Council (grant no. 2012-1397), and Swedish Heart-Lung Foundation (20120197). We thank the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se) for excellent genotyping. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project b2011036. Recruitment for the WTCCC study was funded by the British Heart Foundation and genotyping by the Wellcome Trust. Themistocles L. Assimes was supported by an NIDDK career development award DK088942. Panos Deloukas's work forms part of the research themes contributing to the translational research portfolio of Barts Cardiovascular Biomedical Research Unit which is supported and funded by the National Institute for Health Research. Analysis was partly supported by BHF grant (to Panos Deloukas) RG/14/5/30893. Martin Farrall and Hugh Watkins acknowledge the support of the Wellcome Trust core award (090532/Z/09/Z) and Martin Farrall, Hugh Watkins and Theodosios Kyriakou, the BHF Centre of Research Excellence. Anuj Goel, Hugh Watkins and Theodosios Kyriakou acknowledge European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no. HEALTH-F2-2013-601456 (CVGenes@Target) & and Anuj Goel, the Wellcome Trust Institutional strategic support fund. The UK Twin study was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007- 2013). PoBI samples from the Wellcome Trust funded People of the British Isles project. Sekar Kathiresan is supported by the Donovan Family Foundation, Fondation Leducq, MGH Research Scholar Award, and R01 HL107816. Andrew P. Morris is a Wellcome Trust Senior Fellow in Basic Biomedical Science, funded under grant WT098017. Christopher P. Nelson and Nilesh J. Samani are funded by the British Heart Foundation and Nilesh J. Samani is a UK NIUHR Senior Investigator. Christopher P. Nelson and Nilesh J. Samani are funded by the British Heart Foundation and Nilesh J. Samani is a UK NIUHR Senior Investigator. Samuli Ripatti was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No. 213506 and 129680), Academy of Finland (Grant No. 251217 and 285380), the Finnish foundation for Cardiovascular Research, the Sigrid Juselius Foundation and the European Community's Seventh Framework Programme (FP7/2007-2013) through the BioSHaRE-EU (Biobank Standardisation and Harmonisation for Research Excellence in the European Union) project, grant agreement 261433. Alexandre F. R. Stewart is supported by operating grants from the Canadian Institute of Health Research and Natural Sciences and Engineering Research Council of Canada. Hong-Hee Won is supported by a postdoctoral award from the American Heart Association (15POST23280019).