Ageing happens to us all, and as the cabaret singer Maurice Chevalier pointed out, "old age is not that bad when you consider the alternative". Yet, the growing ageing population of most developed countries presents challenges to healthcare systems and government finances. For many older people, long periods of ill health are part of the end of life, and so a better understanding of ageing could offer the opportunity to prolong healthy living into old age.

Ageing is complex and takes a long time to study – a lifetime in fact. This makes it difficult to discern its causes, among the countless possibilities based on an individual’s genes, behaviour or environment. While thousands of regions in an individual’s genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle. Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average.

The DNA of over 500,000 people was read to reveal the specific ‘genetic fingerprints’ of each participant. Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan. Five of these regions were new and had not been linked to lifespan before. Across the twelve as a whole several were known to be involved in Alzheimer’s disease, smoking-related cancer or heart disease. Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average.

Many factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone. Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease.

Human lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance. The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998; Herskind et al., 1996; McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018). One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin.

Despite this modest heritability, extensive research has gone into genome-wide association studies (GWAS) finding genetic variants influencing human survival, using a variety of trait definitions and study designs (Deelen et al., 2011; Sebastiani et al., 2012; Beekman et al., 2013; Broer et al., 2015; Joshi et al., 2016; Pilling et al., 2016; Zeng et al., 2016; Pilling et al., 2017). GWAS have primarily focused on extreme cases of long-livedness (longevity) – individuals surviving past a certain age threshold – and scanning for differences in genetic variation from controls. While this case-control design has the advantage of focusing on highly statistically-informative individuals, who also often exhibit extreme healthspan and have potentially unique genetic attributes (Sebastiani et al., 2013; Sebastiani et al., 2016), the exceptional nature of the phenotype precludes collection of large samples, and differences in definitions of longevity complicate meta-analysis. As a result, only two robustly replicated, genome-wide significant associations (near APOE and FOXO3) have been made to date (Broer et al., 2015; Deelen et al., 2014).

An alternative approach is to study lifespan as a quantitative trait in the general population and use survival models (such as Cox proportional hazards [Cox, 1972]) to allow long-lived survivors to inform analysis. However, given the incidence of mortality in middle-aged subjects is low, studies have shifted to the use of parental lifespans with subject genotypes (an instance of Wacholder’s kin-cohort method [Wacholder et al., 1998]), circumventing the long wait associated with studying age at death in a prospective study (Joshi et al., 2016; Pilling et al., 2016). In addition, the recent increase in genotyped population cohorts around the world, and in particular the creation of UK Biobank (Bycroft et al., 2017), has raised GWAS sample sizes to hundreds of thousands of individuals, providing the statistical power necessary to detect genetic effects on mortality.

A third approach is to gather previously published GWAS on risk factors thought to possibly affect lifespan, such as smoking behaviour and cardiovascular disease (CVD), and estimate their actual independent, causal effects on mortality using Mendelian Randomisation. These causal estimates can then be used in a Bayesian framework to inform previously observed SNP associations with lifespan (McDaid et al., 2017).

Here, we blend these three approaches to studying lifespan and perform the largest GWAS on human lifespan to date. First, we leverage data from UK Biobank and 26 independent European-heritage population cohorts (Joshi et al., 2017) to carry out a GWAS of parental survival, quantified using Cox models. We then supplement this with data from 58 GWAS on mortality risk factors to conduct a Bayesian prior-informed GWAS (iGWAS). Finally, we use publicly available case-control longevity GWAS statistics to compare the genetics of lifespan and longevity and provide collective replication of our lifespan GWAS results.

We also examine the diseases associated with lifespan-altering variants and the effect of known disease variants on lifespan, to provide insight into the interplay between lifespan and disease. Finally, we use our GWAS results to implicate specific genes, biological pathways, and cell types, and use our findings to create and test whole-genome polygenic scores for survival.

Applying the kin-cohort method in a GWAS and mortality risk factor iGWAS across UK Biobank and the LifeGen meta-analysis, we identified 11 novel genome-wide significant associations with lifespan and replicated six previously discovered loci. We also replicated long-standing longevity SNPs near APOE, FOXO3, and 5q33.3/EBF1 – albeit with smaller effect sizes in the latter two cases – but found evidence of no association (at effect sizes originally published) with lifespan for more recently published longevity SNPs near IL6, ANKRD20A9P, USP42, and TMTC2. Conversely, all individual variants identified in our analyses showed directionally consistent effects in a meta-analysis of two European-ancestry studies of extreme longevity, and a test of association of a polygenic risk score of the variants was highly significant in the longevity dataset (p < 1.5 × 10⁻⁵).

Our findings validate the results of a previous Bayesian analysis performed on a subset (N = 116,279) of the present study’s discovery sample (McDaid et al., 2017), which highlighted two loci which are now genome-wide significant in conventional GWAS in the present study’s larger sample. iGWAS thus appears to be an effective method able to identify lifespan-associated variants in smaller samples than standard GWAS, albeit relying on known biology.

With the curious exception of a locus near HTT (the Huntington's disease gene), all lead SNPs are known to associate with autoimmune, cardiometabolic, neuropsychiatric, or smoking-related disease, and it is plausible these are the major pathways through which the variants affect lifespan. Whole-genome polygenic risk scores showed similar associations with disease, excluding late-onset disorders such as Alzheimer’s and Parkinson’s, where polygenic risk scores for extended lifespan increased risk (of survival to age at onset) of the disease.

Genetic variants affecting lifespan were enriched for pathways involving the transport, homeostasis and metabolism of lipoprotein particles, validating previous reports (McDaid et al., 2017). We also identified new pathways including vesicle transport, metabolism of acylglycerol and sterols, and synaptic and dendritic function. We discovered genomic regions with epigenetic marks determining cell differentiation into foetal brain and DLPC cells were enriched for genetic variants affecting lifespan. Finally, we showed that we can use our GWAS results to construct a polygenic risk score, which makes 3 to 5 year distinctions in life expectancy at birth between individuals from the score’s top and bottom deciles.

Despite studying over 1 million lives, our standard GWAS only identified 12 variants influencing lifespan at genome-wide significance. This contrasts with height (another highly polygenic trait) where a study of around 250,000 individuals by Wood et al., 2014. found 423 loci. This difference can partly be explained by the much lower heritability of lifespan (0.12; Kaplanis et al., 2018) (cf. 0.8 for height [Wood et al., 2014]), consistent with evolution having a stronger influence on the total heritability of traits more closely related to fitness and limiting effect sizes. In addition, the use of indirect genotypes (the kin-cohort method) reduces the effective sample size to 1/4 for the parent-offspring design.

When considering these limitations, we calculate our study was equal in power to a height study of only around 23,224 individuals, were lifespan to have a similar genetic architecture to height (see Materials and methods). Under this assumption, we would require a sample size of around 10 million parents (or equivalently 445,000 nonagenarian cases, with even more controls) to detect a similar number of loci as Wood et al. At the same time, our inability to replicate several previous borderline significant longevity and lifespan findings suggests research into survival in general requires substantial increases in power to robustly identify loci.

Meta-analysis of mothers and fathers, permitting common or sex-specific effect sizes, of course, doubled effective sample size, with slight attenuation to reflect the observed correlation (~10%) between father and mother traits (consistent with previous studies [Kaplanis et al., 2018]). This correlation indicates the presence of assortative mating on traits which correlate with lifespan (as lifespan itself is of course not observed until later), or post-pairing environmental convergence. We note that in principle, assortative mating could lead to allelic correlations at causal loci for the contributing traits, causing departures from Hardy-Weinberg equilibrium, and increasing the genotypic variance and thus power to detect association. However, in practice, at least for lifespan, the effects are too small for the effect to be material.

The association of lifespan variants with well-known, life-shortening diseases (cardiovascular, autoimmune, smoking-related diseases and lung cancer; Mathers et al., 2018) is not surprising, but the paucity of associations with other forms of cancer – without pleiotropic effects on CVD – is. This paucity suggests cancer deaths may often be due to (perhaps many) rarer variants or environmental exposures, although effect sizes might simply be slightly below our cut-off threshold to detect. Disappointingly, the variants and pathways we identified do not appear to underpin a generalised form of ageing independent of disease.

Our finding that lifespan genetics are enriched for lipid metabolism genes is in line with expectations, given lipid metabolites – especially cholesterol metabolites – have well-established effects on atherosclerosis, type 2 diabetes, Alzheimer’s disease, osteoporosis, and age-related cancers (Zarrouk et al., 2014). Pilling et al. (2017) implicated nicotinic acetylcholine receptor pathways in human lifespan, which we detected at nominal significance (p = 2 × 10⁻⁴) but not at 5% FDR correction (q = 0.0556). Instead we highlighted more general synapse and dendrite pathways and identified foetal brain and DLPC cells as important in ageing. The DLPC is involved in smoking addiction (Hayashi et al., 2013), dietary self-control (Lowe et al., 2014), and is susceptible to neurodegeneration (Morrison and Baxter, 2012), which could explain why genetic variation for lifespan is specifically enriched in these cells, mediated through smoking-related, cardiometabolic, and neuropsychiatric disease.

Much work has been done implicating FOXO3 as an ageing gene in model organisms (Kenyon et al., 1993; Hwangbo et al., 2004), however we found the association in humans at that locus may be driven by expression of SESN1 (admittedly a finding restricted to peripheral blood tissue). SESN1 is a gene connected to the FOXO3 promoter via chromatin interactions and is involved in the response to reactive oxygen species and mTORC1 inhibition (Donlon et al., 2017). While fine-mapping studies have specifically found genetic variation within the locus causes differential expression of FOXO3 itself (Flachsbart et al., 2017; Grossi et al., 2018), this does not rule out the effect of co-expression of SESN1. More powered tissue-specific expression data and experimental work on SESN1 vs. FOXO3 could elucidate the causal mechanism. For now, results from model organisms seem to leave the preponderance of evidence for FOXO3.

Our results suggest disease-associated lifespan variants reduce the chances of extreme long-livedness, but remain agnostic as to the more interesting two-part question: are there longevity variants that have little effect on lifespan in the normal range (Sebastiani et al., 2016), and if so, do they control underlying ageing processes? We note, the genetic overlap between lifespan and extreme long-livedness is high (0.73), but not complete (McDaid et al., 2017). Regardless of this, only a small part of the heritability of both lifespan and longevity has thus far been explained by GWAS. It thus remains plausible that an enlarged long-livedness or lifespan study will find variants controlling the rate of ageing and associated pathways. Curiously, we find little evidence of SNPs of large deleterious effect on lifespan acting with antagonistic pleiotropy on other fitness and developmental component traits, despite long-standing theoretical suggestions to the contrary (Williams, 1957). However, we did not examine mortality before the age of 40, or mortality of individuals without offspring (by definition as we were examining parental lifespans), which may exhibit this phenomenon. For the time being, our findings that the improved polygenic risk score for lifespan was associated with an increased prevalence of Alzheimer's disease, Parkinson's disease, and prostate and breast cancer, means we appear to be predominantly measuring a propensity for longer life through avoidance of early disease-induced mortality, rather than healthy ageing or fertility costs.

Whilst it has previously been shown that transcriptomic age calculated based on age-related genes is meaningful in the sense that its deviation from the chronological age is associated with biological features linked to ageing (Peters et al., 2015), the role of these genes in ageing was unclear. A gene might change expression with age because (i) it is a biological clock (higher expression tracking biological ageing, but not influencing ageing or disease); (ii) it is a response to the consequences of ageing (e.g. a protective response to CVD); (iii) it is an indicator of selection bias: if low expression is life-shortening, older people with low expression tend to be eliminated from the study, hence the average expression level of older age groups is higher. However, our results now show that the differential expression of many of the age-related genes discovered by Peters et al., 2015 are not only biomarkers of ageing, but are also enriched for direct effects on lifespan.

There is increasing interest in polygenic risk scores, and their potential clinical utility for some diseases appears to be similar to some Mendelian mutations (albeit such monogenic tests are usually only applied in the context of family history; Khera et al., 2018). At first sight, the magnitude of the distinctions in our genetic lifespan score (5 years of life between top and bottom deciles, for both the parent and subject generations) are quite small compared with variability in individual lifespans. However, these distinctions are potentially material at a group level, for example, actuarially. The implied distinction in price (14%; Methods) is greater than some recently reported annuity profit margins (8.9%) (Legal General Group PLC, 2017). In our view, the legal and ethical frameworks (at least in the UK [Association of British Insurers and UK Government, 2014]) are presently under-developed for genome-wide scores, whether for disease or lifespan and this needs to be urgently addressed. At the same time, although material in isolation, our lifespan associations may only have practical utility in many applications if they provide additional information than that provided by conventional clinical risk measures (e.g. the Framingham score [D'Agostino et al., 2008]). Such an assessment has been beyond the scope of this work, in part as such risk measures are not readily available for the parents (rather than subjects) studied.

One limitation of our study was the power reduction caused by the exclusion of relatives in our study, rather than linear mixed modelling (LMM) with a term for kinship as measured by the genomic relationship matrix (GRM) (Pilling et al., 2017; Loh et al., 2015). However, as the correct adjustment is not derivable under the kin-cohort method, we felt this was the best approach. To see that the normal adjustment is not correct, consider two siblings. The phenotypes under study are of course identical (as the parents are the same), but the expected correlation under the mixed model would only be 50% of the heritability. Simply excluding siblings, however, is not sufficient. For example, consider two offspring subjects who are first cousins descended from two full brothers. The GRM entry in this situation is 12.5% whilst the appropriate relatedness factor for the father trait is 50% and the mother 0%. Exclusion of relatives thus appears the most straightforward solution, although if a pedigree were available, not just a GRM, accurate LMM might have been feasible.

The analysis of parent lifespans has enabled us to probe mortality for a generation whose lives are mostly complete and attain increased power in a survival GWAS. However, changes in the environment (and thus the relative importance of each genetic susceptibility, for example following the smoking ban) inevitably mean we have less certainty about associations with prospective lifespans for the present generation of middle-aged people, or a different population (with perhaps different relative importance of disease or even overall heritability of lifespan). The 21% reduction in the effect size of the association between our PRS for the UK offspring generation supports this idea, although the estimated contrast in hazard ratios across the deciles was not reduced, which may be a statistical artefact or due to the different periods of life probed. The lower explanatory power of the PRS in Estonia may reflect the differing alleles and LD patterns between the UK training data and the Estonian test data, but also the different environments, in particular the sources of mortality in that country in the Soviet, and early post-Soviet era.

In conclusion, recent genomic susceptibility to death in the normal age range seems rooted in modern diseases: Alzheimer’s, CVD and lung cancer; in turn arising from our modern – long-lived, obesogenic and tobacco-laden – environment, however the keys to the distinct traits of ageing and extreme longevity remain elusive. At the same time, genomic information alone can now make material distinctions at a group level in variations in expected length of life, although the limited individual accuracy of these distinctions is far from reaching genetic determinism of that most (self-) interesting of traits – your lifespan.

Individual phenotypic and genetic data is available from UK Biobank upon application: https://www.ukbiobank.ac.uk/register-apply/. Phenotypes used in this work include subject age, sex, ethnicity, relatedness, genotyping batch, array, and principal components, as well as parental age and alive/dead status. Also included are self-reported diseases of subjects and their families. A full description of our application can be found at https://www.ukbiobank.ac.uk/2015/02/dr-james-wilson-university-of-edinburgh-centre-for-population-sciences/. The results that support our findings, in particular, the GWAS summary statistics we generated for >1 million parental lifespans in this study are available at http://dx.doi.org/10.7488/ds/2463. Gene expression data used in the age-related gene analysis is being made available by the eQTLGen Consortium, see http://www.eqtlgen.org/ and Võsa, U. et al. Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. bioRxiv 447367 (2018). Single tissue gene expression data used in the SMR-Heidi analysis can be found on the GTEx website https://gtexportal.org/home/datasets, under GTEx_Analysis_v7_eQTL.tar.gz. Source data files for main figures and tables are included as supplementary material, other than GWAS summary statistics.

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Author details

1. Paul RHJ Timmers

   Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Writing—original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5197-1267

2. Ninon Mounier

   1. Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland
   2. Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   Formal analysis, Investigation, Visualization, Writing—original draft

   Competing interests

   No competing interests declared

3. Kristi Lall

   1. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
   2. Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia

   Contribution

   Formal analysis, Investigation, Writing—original draft

   Competing interests

   No competing interests declared

4. Krista Fischer

   1. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
   2. Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia

   Contribution

   Conceptualization, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing—original draft

   Competing interests

   No competing interests declared

5. Zheng Ning

   Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Formal analysis, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

6. Xiao Feng

   State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China

   Contribution

   Formal analysis, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

7. Andrew D Bretherick

   MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Software, Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

8. David W Clark

   Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Software, Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

9. eQTLGen Consortium

   Competing interests

   No competing interests declared

   1. M Agbessi, Ontario Institute for Cancer Research, Toronto, Canada
   2. H Ahsan, Department of Public Health Sciences, University of Chicago, Chicago, United States
   3. I Alves, Ontario Institute for Cancer Research, Toronto, Canada
   4. A Andiappan, Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
   5. P Awadalla, Ontario Institute for Cancer Research, Toronto, Canada
   6. A Battle, Department of Computer Science, Johns Hopkins University, Baltimore, United States
   7. MJ Bonder, Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands
   8. D Boomsma, Vrije Universiteit, Amsterdam, The Netherlands
   9. M Christiansen, Cardiovascular Health Research Unit, University of Washington, Seattle, United States
   10. A Claringbould, Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands
   11. P Deelen, Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands
   12. J van Dongen, Vrije Universiteit, Amsterdam, The Netherlands
   13. T Esko, Estonian Genome Center, University of Tartu, Tartu, Estonia
   14. M Favé, Ontario Institute for Cancer Research, Toronto, Canada
   15. L Franke, Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands
   16. T Frayling, Exeter Medical School, University of Exeter, Exeter, United Kingdom
   17. SA Gharib, Department of Medicine, University of Washington, Seattle, United States
   18. G Gibson, School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
   19. G Hemani, MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   20. R Jansen, Vrije Universiteit, Amsterdam, The Netherlands
   21. A Kalnapenkis, Estonian Genome Center, University of Tartu, Tartu, Estonia
   22. S Kasela, Estonian Genome Center, University of Tartu, Tartu, Estonia
   23. J Kettunen, University of Helsinki, Helsinki, Finland
   24. Y Kim, Department of Computer Science, Johns Hopkins University, Baltimore, United States
   25. H Kirsten, Institut für Medizinische Informatik, Statistik und Epidemiologie, LIFE – Leipzig ResearchCenter for Civilization Diseases, Universität Leipzig, Leipzig, Germany
   26. P Kovacs, IFB Adiposity Diseases, Universität Leipzig, Leipzig, Germany
   27. K Krohn, Interdisciplinary Center for Clinical Research, Faculty of Medicine, Universität Leipzig, Leipzig, Germany
   28. J Kronberg-Guzman, Estonian Genome Center, University of Tartu, Tartu, Estonia
   29. V Kukushkina, Estonian Genome Center, University of Tartu, Tartu, Estonia
   30. Z Kutalik, Lausanne University Hospital, Lausanne, Switzerland
   31. M Kähönen, Department of Clinical Physiology and Faculty of Medicine and Life Sciences, Tampere University Hospital and University of Tampere, Tampere, Finland
   32. B Lee, Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
   33. T Lehtimäki, Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
   34. M Loeffler, Institut für Medizinische Informatik, Statistik und Epidemiologie, LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
   35. U Marigorta, School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
   36. A Metspalu, Estonian Genome Center, University of Tartu, Tartu, Estonia
   37. J van Meurs, Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
   38. L Milani, Estonian Genome Center, University of Tartu, Tartu, Estonia
   39. M Müller-Nurasyid, Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
   40. M Nauck, Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
   41. M Nivard, Vrije Universiteit, Amsterdam, The Netherlands
   42. B Penninx, Vrije Universiteit, Amsterdam, The Netherlands
   43. M Perola, National Institute for Health and Welfare, University of Helsinki, Helsinki, Finland
   44. N Pervjakova, Estonian Genome Center, University of Tartu, Tartu, Estonia
   45. B Pierce, Department of Public Health Sciences, University of Chicago, Chicago, United States
   46. J Powell, Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
   47. H Prokisch, Institute of Human Genetics, Helmholtz Zentrum München, München, Germany
   48. BM Psaty, Departments of Epidemiology, Medicine, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, United States
   49. O Raitakari, Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital and University of Turku, Turku, Finland
   50. S Ring, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
   51. S Ripatti, University of Helsinki, Helsinki, Finland
   52. O Rotzschke, Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
   53. S Ruëger, Lausanne University Hospital, Lausanne, Switzerland
   54. A Saha, Department of Computer Science, Johns Hopkins University, Baltimore, United States
   55. M Scholz, Institut für Medizinische InformatiK, Statistik und Epidemiologie, LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
   56. K Schramm, Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
   57. I Seppälä, Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
   58. M Stumvoll, Department of Medicine, Universität Leipzig, Leipzig, Germany
   59. P Sullivan, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
   60. A Teumer, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
   61. J Thiery, Institute for Laboratory Medicine, LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
   62. L Tong, Department of Public Health Sciences, University of Chicago, Chicago, United States
   63. A Tönjes, Department of Medicine, Universität Leipzig, Leipzig, Germany
   64. J Verlouw, Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
   65. PM Visscher, Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
   66. U Võsa, Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands
   67. U Völker, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
   68. H Yaghootkar, Exeter Medical School, University of Exeter, Exeter, United Kingdom
   69. J Yang, Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
   70. B Zeng, School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
   71. F Zhang, Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
   72. M Agbessi, Ontario Institute for Cancer Research, Toronto, Canada
   73. H Ahsan, Department of Public Health Sciences, University of Chicago, Chicago, United States
   74. I Alves, Ontario Institute for Cancer Research, Toronto, Canada
   75. A Andiappan, Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
   76. P Awadalla, Ontario Institute for Cancer Research, Toronto, Canada
   77. A Battle, Department of Computer Science, Johns Hopkins University, Baltimore, United States
   78. MJ Bonder, Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands
   79. D Boomsma, Vrije Universiteit, Amsterdam, The Netherlands
   80. M Christiansen, Cardiovascular Health Research Unit, University of Washington, Seattle, United States
   81. A Claringbould, Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands
   82. P Deelen, Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands
   83. J van Dongen, Vrije Universiteit, Amsterdam, The Netherlands
   84. T Esko, Estonian Genome Center, University of Tartu, Tartu, Estonia
   85. M Favé, Ontario Institute for Cancer Research, Toronto, Canada
   86. L Franke, Department of Genetics University, Medical Centre Groningen, Groningen, The Netherlands
   87. T Frayling, Exeter Medical School, University of Exeter, Exeter, United Kingdom
   88. SA Gharib, Department of Medicine, University of Washington, Seattle, United States
   89. G Gibson, School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
   90. G Hemani, MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   91. R Jansen, Vrije Universiteit, Amsterdam, The Netherlands
   92. A Kalnapenkis, Estonian Genome Center, University of Tartu, Tartu, Estonia
   93. S Kasela, Estonian Genome Center, University of Tartu, Tartu, Estonia
   94. J Kettunen, University of Helsinki, Helsinki, Finland
   95. Y Kim, Department of Computer Science, Johns Hopkins University, Baltimore, United States
   96. H Kirsten, Institut für Medizinische Informatik, Statistik und Epidemiologie, LIFE – Leipzig ResearchCenter for Civilization Diseases, Universität Leipzig, Leipzig, Germany
   97. P Kovacs, IFB Adiposity Diseases, Universität Leipzig, Leipzig, Germany
   98. K Krohn, Interdisciplinary Center for Clinical Research, Faculty of Medicine, Universität Leipzig, Leipzig, Germany
   99. J Kronberg-Guzman, Estonian Genome Center, University of Tartu, Tartu, Estonia
   100. V Kukushkina, Estonian Genome Center, University of Tartu, Tartu, Estonia
   101. Z Kutalik, Lausanne University Hospital, Lausanne, Switzerland
   102. M Kähönen, Department of Clinical Physiology and Faculty of Medicine and Life Sciences, Tampere University Hospital and University of Tampere, Tampere, Finland
   103. B Lee, Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
   104. T Lehtimäki, Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
   105. M Loeffler, Institut für Medizinische Informatik, Statistik und Epidemiologie, LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
   106. U Marigorta, School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
   107. A Metspalu, Estonian Genome Center, University of Tartu, Tartu, Estonia
   108. J van Meurs, Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
   109. L Milani, Estonian Genome Center, University of Tartu, Tartu, Estonia
   110. M Müller-Nurasyid, Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
   111. M Nauck, Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
   112. M Nivard, Vrije Universiteit, Amsterdam, The Netherlands
   113. B Penninx, Vrije Universiteit, Amsterdam, The Netherlands
   114. M Perola, National Institute for Health and Welfare, University of Helsinki, Helsinki, Finland
   115. N Pervjakova, Estonian Genome Center, University of Tartu, Tartu, Estonia
   116. B Pierce, Department of Public Health Sciences, University of Chicago, Chicago, United States
   117. J Powell, Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
   118. H Prokisch, Institute of Human Genetics, Helmholtz Zentrum München, München, Germany
   119. BM Psaty, Departments of Epidemiology, Medicine, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, United States
   120. O Raitakari, Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital and University of Turku, Turku, Finland
   121. S Ring, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
   122. S Ripatti, University of Helsinki, Helsinki, Finland
   123. O Rotzschke, Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
   124. S Ruëger, Lausanne University Hospital, Lausanne, Switzerland
   125. A Saha, Department of Computer Science, Johns Hopkins University, Baltimore, United States
   126. M Scholz, Institut für Medizinische InformatiK, Statistik und Epidemiologie, LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
   127. K Schramm, Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
   128. I Seppälä, Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
   129. M Stumvoll, Department of Medicine, Universität Leipzig, Leipzig, Germany
   130. P Sullivan, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
   131. A Teumer, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
   132. J Thiery, Institute for Laboratory Medicine, LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany
   133. L Tong, Department of Public Health Sciences, University of Chicago, Chicago, United States
   134. A Tönjes, Department of Medicine, Universität Leipzig, Leipzig, Germany
   135. J Verlouw, Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
   136. PM Visscher, Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
   137. U Võsa, Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands
   138. U Völker, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
   139. H Yaghootkar, Exeter Medical School, University of Exeter, Exeter, United Kingdom
   140. J Yang, Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
   141. B Zeng, School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
   142. F Zhang, Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia

10. Xia Shen

    1. Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
    2. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
    3. State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China

    Contribution

    Conceptualization, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing—original draft

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4390-1979

11. Tõnu Esko

    1. Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
    2. Broad Institute of Harvard and MIT, Cambridge, United States

    Contribution

    Resources, Supervision, Funding acquisition, Writing—original draft

    Competing interests

    Reviewing Editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0003-1982-6569

12. Zoltán Kutalik

    1. Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland
    2. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Contribution

    Conceptualization, Resources, Software, Investigation, Methodology, Writing—original draft

    Competing interests

    No competing interests declared

13. James F Wilson

    1. Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
    2. MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Writing—original draft

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5751-9178

14. Peter K Joshi

    1. Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
    2. Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland

    Contribution

    Conceptualization, Supervision, Validation, Methodology, Writing—original draft, Writing—review and editing

    For correspondence

    peter.joshi@ed.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6361-5059