The genetic information of people who smoke present distinctive characteristics. In particular, previous research has revealed differences in patterns of DNA methylation, a type of chemical modification that helps cells switch certain genes on or off. However, most of these studies could not establish for sure whether these changes were caused by smoking, predisposed individuals to smoke, or were driven by underlying genetic variation in the DNA sequence itself.

To investigate this question, van Dongen et al. examined DNA methylation data from the blood cells of over 700 pairs of identical twins. These individuals share the exact same genetic information, making it possible to better evaluate the impact of lifestyle on DNA modifications.

The analyses identified differences in methylation at 13 DNA locations in pairs of twins where one was a current smoker and their sibling had never smoked. Two of the genes code for proteins involved in the response to nicotine, the primary addictive chemical in cigarette smoke. The differences were smaller if one of the twins had stopped smoking, suggesting that quitting can help to reverse some of these changes.

These findings confirm that DNA methylation in blood cells is influenced by cigarette smoke, which could help to better understand smoking-associated diseases. They also demonstrate how useful identical twins studies can be to identify methylation changes that are markers of lifestyle.

Epigenome-wide association studies (EWASs) have identified robust differences in DNA methylation between smokers and non-smokers (Gao et al., 2015; Heikkinen et al., 2022). In a meta-analysis of blood-based DNA methylation studies (N = 15,907 individuals; the largest EWAS of smoking to date), 2623 CpG sites passed the Bonferroni threshold for genome-wide significance in a comparison of current and never smokers (Joehanes et al., 2016). Based on comparison with loci identified in large genome-wide association studies (GWASs), differentially methylated sites were significantly enriched in genes implicated in well-established smoking-associated diseases, such as cancer, cardiovascular disease, inflammatory disease, and lung disease, as well as in genes associated with schizophrenia and educational attainment (Joehanes et al., 2016). It has been hypothesized that smoking-induced methylation changes might also contribute to the addictive effect of smoking (Zillich et al., 2022).

Importantly, smoking-associated DNA methylation levels, as established in human EWA studies, may reflect different mechanisms. They may reflect causal effects of smoking on methylation, causal effects of methylation on smoking behaviour, methylation differences associated with epiphenomena of other exposures that correlate with smoking e.g. alcohol use (Liu et al., 2018) , or they may reflect a shared genetic predisposition to smoking and methylation level. To distinguish these different mechanisms require incisive study designs (Vink et al., 2017). Establishing whether methylation levels in smokers revert to levels of never smokers upon smoking cessation is a first step. A previous study of 2648 former smokers with cross-sectional methylation data from the Framingham Heart Study suggested that methylation levels at most CpGs return to the level of never smokers within 5 years after quitting smoking, but 36 CpGs were still differentially methylated in former smokers, who had quit smoking for 30 years (Joehanes et al., 2016). In the large EWAS meta-analysis of smoking (Joehanes et al., 2016), 185 CpGs were differentially methylated between former and never smokers (compared to 2623 between current and never smokers). In addition, differences between former and never smokers were smaller than between current and never smokers. Reversible DNA methylation patterns may suggest that DNA methylation is reactive to smoking. However, it is also possible that the different methylation level in current smokers reflects a higher genetic liability to smoking behaviour (that makes them more likely to initiate and keep smoking). Similarly, differences between former smokers and never smokers could reflect that smoking has caused a persistent methylation change but can also be driven by genetic factors.

In population-based studies, smoking cases and non-smoking individuals may differ on many aspects, including their genetic predisposition to smoking. On the other hand, monozygotic twins are genetically identical (except for de novo mutations, but these are rare [Jonsson et al., 2021; Ouwens et al., 2018]), share a womb, and are matched on sex, age, and childhood environment. They have been exposed to similar prenatal conditions, which may include second hand smoke from smoking mothers and others. Differences in prenatal environment of monozygotic twins due to for instance unequal vascular supply are also recognized (Hall, 1996; Martin et al., 1997), although it remains to be investigated to what extent the impact of prenatal smoke exposure might differ between monozygotic twins. Smoking discordant monozygotic twin pairs offer a unique opportunity to assess smoking-reactive DNA methylation patterns (Leeuwen et al., 2007; Vink et al., 2017). Despite the large number of previous population-based smoking EWASs, only one previous study compared genome-wide DNA methylation in smoking discordant monozygotic twin pairs (Allione et al., 2015). This study analysed whole-blood Illumina 450k array methylation data from 20 discordant pairs, and reported 22 top loci, many of which had been previously associated with cigarette smoking in previous studies. However, following the correction for multiple testing, none of the differentially methylated loci were statistically significant, and this previous twin study did not examine reversibility of smoking effects, that is, where methylation status changes again following smoking cessation.

Here, we analyse unique data from a large cohort of monozygotic twin pairs. This cohort is sufficiently large to include current smoking discordant and concordant pairs, as well as pairs discordant for former smoking (Figure 1). These groups allow identification of loci that are reactive to smoking, and examination of the extent to which effects are reversible upon quitting smoking. Monozygotic pairs in which both twins are current smokers, but who differ in quantity smoked, enable examination of the effects of smoking intensity. Finally, concordant pairs who never smoked allow assessment of the amount of DNA methylation variation at smoking-reactive loci that is due to non-genetic sources of variation other than smoking. In secondary enrichment analyses, we examined whether smoking-reactive methylation patterns are enriched (1) at loci detected in previous EWASs of other traits and exposures, (2) at loci detected in a previous large GWAS meta-analysis of smoking initiation (Liu et al., 2019) – these loci are presumed to have a causal effect on smoking behaviour, and (3) within Gene Ontology and Kegg pathways. Finally, we examined the relationship between DNA methylation and RNA transcript levels in blood for smoking-reactive loci.

Figure 1

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Descriptives of the smoking-discordant and concordant monozygotic twin pairs are given in Table 1. In twin pairs discordant for current smoking status (i.e. one twin a current smoker at the time of blood sampling and the other never initiated regular smoking, N = 53 pairs, mean age = 33 years), the smoking twin on average smoked 8.9 cigarettes per day at the time of blood sampling, and had an average smoking history equivalent to 6.8 packyears. The EWAS analysis in pairs discordant for current smoking status identified 13 epigenome-wide significant (p < 1.20 × 10⁻⁷) DMPs (Figure 3a). Genome-wide test statistics were not inflated (Supplementary file 2). Absolute differences in methylation ranged from 2.5% to 13% (0.025–0.13 on the methylation β-value scale), with a mean of 5.4% (Table 2). Eight of the 13 CpGs (61.5%) showed lower methylation in the current smoking twins compared to their non-smoking twins. Pair-level methylation β-values are shown in Figure 3—figure supplement 1 and illustrate large consistency in the direction of effect. For example, at top CpG site cg05575921, for 51 out of the 53 pairs, the smoking twin had a lower methylation level than the non-smoking twin. At 11 of the 13 CpGs, the methylation difference in smoking discordant monozygotic twin pairs was smaller (on average 19.0%, range = 2.2–37.5%) compared to the methylation difference reported previously in an EWAS meta-analysis of smoking (Joehanes et al., 2016). At two CpGs, the methylation difference in smoking discordant monozygotic twins was larger (on average 24.6%).

Figure 3 with 1 supplement see all

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In twin pairs discordant for former smoking (N = 72 pairs, mean age = 41 years), the twins, who used to smoke, had quit smoking on average 14 years ago (standard deviation [SD] = 11.4, range = 0.04–50 years), while the other twins had never initiated regular smoking. In this group, no epigenome-wide significant DMPs were identified, and within-pair differences at the 13 significant DMPs identified in the previous analysis were diminished (average reduction: 81%, range = 61–96%; Figure 3b, Table 2). By contrast, in twin pairs of which one twin was a current smoker at blood draw and the co-twin had quit smoking (on average 9 years, ago, SD = 10.2, range = 0.02–40 years, N = 66 pairs), the reduction of within-pair differences at the 13 top CpGs was much smaller (on average, 31%, range 15–52%; Figure 3b, Supplementary file 3), and 5 of the 13 DMPs identified by comparing current and never smoking twins were also epigenome-wide significant in this group. Furthermore, five additional epigenome-wide CpGs were identified in current/former smoking discordant pairs (Supplementary file 4). Figure 3b illustrates the pattern of within-pair differences at the 13 top DMPs identified in current/never discordant monozygotic pairs: largest differences in current/never smoking discordant pairs, smaller differences in former/never discordant pair, and current/former discordant pairs are intermediate. Differences are smallest within smoking concordant pairs. This pattern is in line with smoking-associated methylation patterns in blood cells being to a large extent reversible upon quitting smoking.

Distributions of within-pair differences in smoking discordant and concordant pairs for the top 1000 CpGs of the EWAS in discordant pairs are shown in Figure 4a. The distributions illustrate that differences are largest, as expected, within monozygotic twin pairs discordant for current smoking (current/never smoking pairs), followed by discordant current/former smoking discordant pairs, followed by former/never smoking discordant monozygotic twin pairs. Monozygotic pairs concordant for current smoking also show notable within-pair differences at these CpGs that are substantially larger compared to monozygotic pairs concordant for never smoking (Figure 4a). This could be explained by within-pair differences in the number of cigarettes smoked by monozygotic twins who were concordant for current smoking. The twin correlations in current smoking monozygotic twin pairs were r = 0.50, p = 2.2 × 10⁻⁶ for cigarettes per day (Figure 4b), r = 0.43, p = 3.2 × 10⁻⁴ for packyears, and r = 0.58, p = 1.6 × 10⁻⁸, for plasma cotinine levels, respectively. Within-pair differences in DNA methylation at the 13 top CpGs correlated with within-pair differences in the number of cigarettes smoked per day (mean absolute r = 0.38, range [for different CpGs]: –0.56 to 0.41; Table 3, Figure 4c),with within-pair differences in packyears (mean absolute r = 0.46, range: −0.65 to 0.42; Table 3), but did not correlate strongly with within-pair differences in plasma cotinine level (mean absolute r = 0.14, range: −0.23 to 0.28, Table 3). In twin pairs discordant for former smoking, within-pair differences in DNA methylation at the 13 top CpGs were weakly correlated with time since quitting smoking (mean r = −0.11, range = −0.28 to 0.05, Supplementary file 5). Based on scatterplots of the within-pair methylation differences against time since quitting smoking (Figure 4d), we hypothesized that the lack of a strong correlation with time since quitting smoking might be explained by most of the reversal taking place within the first years after quitting smoking. We therefore repeated the analysis restricting to those pairs of which the smoking twin had quit smoking less than 5 years ago (N = 15 pairs). In this group, within-pair differences in DNA methylation at the 13 top CpGs were on average more strongly correlated with time since quitting smoking (mean r = −0.16, range = −0.48 to 0.23) but the sample size was greatly reduced and correlations were non-significant.

Figure 4

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All 13 differentially methylated CpGs identified in current smoking discordant pairs and all 10 CpGs identified in former smoking discordant pairs have been previously associated with smoking. To study the overlap of methylation differences in smoking discordant twin pairs with loci that have a causal effect on smoking, we considered the largest GWAS meta-analysis of smoking phenotypes, the meta-analysis of smoking initiation by the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) (Liu et al., 2019). Three of the 13 epigenome-wide significant DMPs detected in smoking discordant monozygotic pairs (cg13411554, cg00336149, and cg21188533 in CACNA1D) are located within 1 Mb of a GWAS locus associated with smoking initiation. The methylation sites within 1 Mb of genome-wide significant SNPs from the GWAS overall did not show a stronger signal in the within-pair EWAS of smoking discordant monozygotic pairs compared to other genome-wide methylation sites (β = −0.002, se = 0.004, p = 0.56, Figure 3c).

We tested for enrichment of methylation sites previously associated with 680 traits reported in the EWAS atlas (Li et al., 2019), among the top differentially methylated loci in smoking discordant pairs, which showed strong enrichment of smoking-related traits (Supplementary file 6). Enrichment analysis based on Kegg pathways showed one significantly enriched pathway; Dopaminergic Synapse (hsa04728; Supplementary file 7), with three of the top differentially methylated loci in smoking discordant monozygotic pairs mapping to this pathway: CACNA1D, GNG12, and ARRB1. No significant enrichment was seen in GO pathways after multiple testing correction (Supplementary file 8).

To examine potential functional consequences of top DMPs, we used previously published data on whole-blood DNA methylation and RNA-sequencing (n = 2101 samples). At 4 of the 13 CpGs, DNA methylation level in blood was associated with the expression level of nearby genes (Table 4). At three CpGs, a higher methylation level correlated with lower expression level. None of the 13 CpGs overlapped with six genes that were differentially expressed in monozygotic pairs discordant for current smoking (Vink et al., 2017).

Previous EWASs have identified robust differences in DNA methylation between smokers and non-smokers at a number of loci. These differences may reflect true smoking-reactive DNA methylation patterns, but can also be driven by (genetic) confounding or reverse causation. We exploited a strong within-family design, that is, the discordant monozygotic twin design (Bell and Spector, 2012), to identify smoking-reactive loci. By analysing whole-blood genome-wide DNA methylation patterns in 53 monozygotic pairs discordant for current smoking, we found 13 CpGs with a difference in methylation level between the current smoking twin and the twin who never smoked. All 13 CpGs have been previously associated with smoking in unrelated individuals and in line with previous studies that compared unrelated smokers and controls (Joehanes et al., 2016), our data from monozygotic pairs discordant for former smoking also indicate that methylation patterns are to a large extent reversible upon smoking cessation. We further showed that differences in smoking level exposure for monozygotic twins who are both current smokers but differ in the number of cigarettes they smoke are reflected in their DNA methylation profiles.

The strongest smoking-associated loci typically detected in human blood EWAS are genes involved in detoxification pathways of aromatic hydrocarbons, such as AHRR and CYP1A1 (Gao et al., 2015), of which AHRR was also present among the top differentially methylated loci in our analysis of discordant twin pairs. Mainstream tobacco smoke is a mixture of thousands of chemicals (Rodgman and Perfetti, 2008). Although the effects of many of the compounds present in cigarette smoke are unknown, several mechanisms have been described through which cigarette smoking may affect global or gene-specific DNA methylation levels. These include DNA damage induced by certain compounds such as arsenic, chromium, formaldehyde, polycyclic aromatic hydrocarbons, and nitrosamines that all cause double-stranded breaks (Smith and Hansch, 2000) (which causes increased methylation near repaired DNA) (Mortusewicz et al., 2005; Cuozzo et al., 2007), hypoxia induced by carbon monoxide (Olson, 1984) (causing global CpG island demethylation by disrupting methyl donor availability), and modulation of the expression level or activity of DNA-binding proteins, such as transcription factors (Lee and Pausova, 2013). Nicotine, presumed to be the major addictive compound in cigarette smoke (although other putative addictive compounds have also been described [Talhout et al., 2011]), has gene regulatory effects. Binding of nicotine to nicotinic acetylcholine receptors causes downstream activation of cAMP response element-binding protein, which is a key transcription factor for many genes (Shen and Yakel, 2009). In mouse brain, nicotine downregulates the DNA methyl transferase gene Dnmt (Satta et al., 2008). Previous EWAS studies based on blood cotinine levels, as a biomarker for nicotine exposure, and based on a polygenic scores for nicotine metabolism, reported differentially methylated CpGs that largely overlap with CpGs found in EWAS of smoking status (Gupta et al., 2019; Lee et al., 2016). Furthermore, E-cigarette-based nicotine exposure of mice has been shown to cause DNA methylation changes in white blood cells (Peng et al., 2022).

Importantly, effects of smoking on DNA methylation in brain cells have been hypothesized to contribute to addiction (Zillich et al., 2022), but it is largely unknown to what extent addiction-related DNA methylation dynamics are captured in other tissues such as blood. Nicotinic receptors are especially abundant in the central and peripheral nervous system, but are also present in other tissues. In peripheral blood, nicotinic receptors are present on lymphocytes and polymorphonuclear cells (Benhammou et al., 2000), suggesting that EWA studies performed on blood cells might capture nicotine-reactive methylation patterns. Interesting in this regard is our finding that among the top differentially methylated CpGs in smoking discordant pairs are multiple CpGs in CACNA1D and GNG12, which encode subunits of a calcium voltage-gated channel and G protein, respectively; proteins that interact with the nicotinic acetylcholine receptor, and the related enrichment of Kegg pathway dopaminergic neuron. Methylation levels at these CpGs might be reactive to nicotine exposure. Furthermore, the CpGs in CACNA1D are in proximity of a GWAS locus for smoking initiation, suggesting that this might be a locus that is not only reactive to smoking exposure, but may also contribute to smoking behaviour. Although it remains to be established if the epigenetic and genetic variation at this locus are functionally connected (i.e. have the same downstream consequences on gene expression), these results suggest that these CpGs can be interesting candidates for further studies into peripheral biomarkers of smoking addiction. Since we applied a discordant monozygotic twin design, the methylation differences identified at this locus in our study cannot be driven by mQTL effects of the SNPs associated with smoking. The data from monozygotic pairs discordant for former smoking indicate that methylation patterns are to a large extent reversible upon smoking cessation, which is in line with DNA methylation patterns being reactive to smoking. Nevertheless, our findings do not rule out that the possibility that reverse causation (DNA methylation driving smoking behaviour) might also contribute to the (maintenance of) smoking discordance in smoking discordant monozygotic twin pairs. Future analyses combining DNA methylation and genetic data from monozygotic and dizygotic twins may be applied to examine bidirectional causal associations between DNA methylation and smoking (Minică et al., 2018).

The main strength of our study is the use of the discordant monozygotic twin design to examine the effects of smoking, because it rules out genetic confounding, as well as many other confounding factors. The value of studying smoking effects against an identical genetic background is clear if one considers that one of the most strongly associated genetic variants for nicotine dependence is located in the DNA methyltransferase gene DNMT3B (Hancock et al., 2018). This strongly implies a role for DNA methylation in nicotine addiction, but it also suggests that horizontal genetic pleiotropy might contribute to associations between DNA methylation and smoking in ordinary case–control EWASs, where differences in DNA methylation between unrelated smokers and non-smokers may reflect differences in genotype. Our analysis had adequate power to detect large effects (i.e., the top hits identified in typical smoking EWAS) (Tsai and Bell, 2015). These reflect only a small proportion, however, of all smoking-associated sites. In our analysis of 53 monozygotic twin pairs discordant for current versus never smoking, we detected 13 CpGs at genome-wide significance, which represent 0.5% of the total number of CpGs (2623) detected in the smoking meta-analysis of unrelated individuals (2433 current verus 6956 never smokers) (Joehanes et al., 2016). The within-pair difference in smoking discordant monozygotic pairs was smaller compared to the effect size reported previously based on the comparison of unrelated smokers and non-smokers. Larger sample sizes are required to achieve adequate power to detect smaller effects. While the pattern of within-pair differences in current/never, current/former and former/never discordant monozygotic twin pairs was clearly in line with reversal of methylation patterns following smoking cessation, we did not find a strong correlation between within-pair differences in DNA methylation and time since quitting smoking in former smoking discordant pairs. If most reversal takes place gradually in the first view years after smoking cessation, it might require larger sample sizes of twin pairs discordant for recently quitting smoking to detect such a correlation. Larger samples sizes may be achieved by combining data from multiple twin cohorts in a meta-analysis.

Common limitations that apply to many EWA studies including ours are that we only analysed DNA methylation data from blood and that the technique used to measure DNA methylation only covers a small subset of all CpG sites in the genome. Another limitation is that information on smoking was obtained through self-report. We previously described smoking misclassification in this cohort based on blood levels of cotinine (van Dongen et al., 2018), a biomarker for nicotine exposure, that has been measured in a subset of the cohort (Bot et al., 2013), which indicated a low misclassification rate. Plasma cotinine levels were available for 591 individuals classified as never smokers by self-report. Five of these individuals (0.8%) had cotinine levels ≥15 ng/ml, which is indicative of smoking, and thus indicates a misclassification of smoking status. In the current paper, we further showed that the correlation between cotinine levels in concordant current smoking pairs was similar to the correlation between self-reported number of cigarettes per day.

The HumanMethylation450 BeadChip data from the NTR are available as part of the Biobank-based Integrative Omics Studies (BIOS) Consortium in the European Genome-phenome Archive (EGA), under the accession code EGAD00010000887, https://ega-archive.org/datasets/EGAD00010000887 (Study ID EGAS00001001077, Title: The mission of the BIOS Consortium is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch Biobanks, contact: The BIOS Consortium: Biobank-based Integrative Omics Studies, Contact person: Rick Jansen). The OMICs data and additional phenotype data are available upon request via the BBMRI-NL BIOS consortium (https://www.bbmri.nl/acquisition-use-analyze/bios). All NTR data can be requested by bona fida researchers (https://ntr-data-request.psy.vu.nl/). Because of the consent given by study participants the data cannot be made publicly available. The pipeline for DNA methylation-array analysis developed by the Biobank-based Integrative Omics Study (BIOS) consortium is available here: https://molepi.github.io/DNAmArray_workflow/ copy archived at Sinke, 2020 (https://doi.org/10.5281/zenodo.3355292). The code for the EWAS analysis in monozygotic twin pairs is included in Source code 1.

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Author details

1. Jenny van Dongen

   1. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
   2. Amsterdam Public Health Research Institute, Amsterdam, Netherlands
   3. Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, Netherlands

   Contribution

   Conceptualization, Formal analysis, Writing - original draft

   For correspondence

   j.van.dongen@vu.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2063-8741

2. Gonneke Willemsen

   1. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
   2. Amsterdam Public Health Research Institute, Amsterdam, Netherlands

   Contribution

   Data curation, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

3. BIOS Consortium

   Contribution

   Data curation, Funding acquisition, Methodology, Software

   Competing interests

   No competing interests declared

   1. Bastiaan T Heijmans, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   2. Peter AC ’t Hoen, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
   3. Joyce van Meurs, Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands
   4. Aaron Isaacs, Department of Genetic Epidemiology, Erasmus MC, Rotterdam, Netherlands
   5. Rick Jansen, Department of Psychiatry, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, Netherlands
   6. Lude Franke, Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
   7. Dorret I Boomsma, Department of Biological Psychology, VU University Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, Netherlands
   8. René Pool, Department of Biological Psychology, VU University Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, Netherlands
   9. Jenny van Dongen, Department of Biological Psychology, VU University Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, Netherlands
   10. Jouke J Hottenga, Department of Biological Psychology, VU University Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, Netherlands
   11. Marleen MJ van Greevenbroek, Department of Internal Medicine and School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
   12. Coen DA Stehouwer, Department of Internal Medicine and School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
   13. Carla JH van der Kallen, Department of Internal Medicine and School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
   14. Casper G Schalkwijk, Department of Internal Medicine and School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
   15. Cisca Wijmenga, Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
   16. Sasha Zhernakova, Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
   17. Ettje F Tigchelaar, Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
   18. P Eline Slagboom, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   19. Marian Beekman, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   20. Joris Deelen, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   21. Diana van Heemst, Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
   22. Jan H Veldink, Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
   23. Leonard H van den Berg, Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
   24. Cornelia M van Duijn, Department of Genetic Epidemiology, Erasmus MC, Rotterdam, Netherlands
   25. Bert A Hofman, Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
   26. André G Uitterlinden, Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands
   27. P Mila Jhamai, Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands
   28. Michael Verbiest, Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands
   29. H Eka D Suchiman, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   30. Marijn Verkerk, Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands
   31. Ruud van der Breggen, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   32. Jeroen van Rooij, Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands
   33. Nico Lakenberg, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   34. Hailiang Mei, Sequence Analysis Support Core, Leiden University Medical Center, Leiden, Netherlands
   35. Maarten van Iterson, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   36. Michiel van Galen, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
   37. Jan Bot, SURFsara, Amsterdam, Netherlands
   38. Dasha V Zhernakova, Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
   39. Peter van ’t Hof, Sequence Analysis Support Core, Leiden University Medical Center, Leiden, Netherlands
   40. Patrick Deelen, Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
   41. Irene Nooren, SURFsara, Amsterdam, Netherlands
   42. Bastiaan T Heijmans, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   43. Matthijs Moed, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   44. Martijn Vermaat, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
   45. René Luijk, Molecular Epidemiology Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   46. Marc Jan Bonder, Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
   47. Freerk van Dijk, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
   48. Wibowo Arindrarto, Sequence Analysis Support Core, Leiden University Medical Center, Leiden, Netherlands
   49. Szymon M Kielbasa, Medical Statistics Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   50. Morris A Swertz, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
   51. Erik W van Zwet, Medical Statistics Section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, Netherlands
   52. Peter-Bram’t Hoen, Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands

4. Eco JC de Geus

   1. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
   2. Amsterdam Public Health Research Institute, Amsterdam, Netherlands

   Contribution

   Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

5. Dorret I Boomsma

   1. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
   2. Amsterdam Public Health Research Institute, Amsterdam, Netherlands
   3. Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, Netherlands

   Contribution

   Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

6. Michael C Neale

   Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, United States

   Contribution

   Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

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