Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The importance of cohort studies in the post-GWAS era

Abstract

The past decade has seen enormous success of wide-scale genetic studies in identifying genetic variants that modify individuals’ predisposition to common diseases. However, the interpretation and functional understanding of these variants lag far behind. In this Perspective, we discuss opportunities for using large-scale cohort studies to investigate the downstream molecular effects of SNPs at different ‘omics’ data levels. We point to the pivotal role of population cohorts in establishing causality and advancing drug discovery. In particular, we focus on the breadth-versus-depth concepts of population studies, on data harmonization, and on the challenges, ethical aspects and future perspectives of cohort studies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Scheme of a cohort study in which a subset of the extensive population cohort is selected for deep multi-omics and single-cell phenotypes.

References

  1. 1.

    Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Ozaki, K. et al. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat. Genet. 32, 650–654 (2002).

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    Article  PubMed Central  Google Scholar 

  7. 7.

    Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Cortes, A. & Brown, M. A. Promise and pitfalls of the Immunochip. Arthritis Res. Ther. 13, 101 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48, 134–143 (2016).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Chiang, C. et al. The impact of structural variation on human gene expression. Nat. Genet. 49, 692–699 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Visel, A. et al. Targeted deletion of the 9p21 non-coding coronary artery disease risk interval in mice. Nature 464, 409–412 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Annotation of the non-coding genome. Nature  https://doi.org/10.1038/nature14309 (2015).

  14. 14.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Li, Y. et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 167, 1099–1110.e14 (2016).

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Scholtens, S. et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 44, 1172–1180 (2015).

    Article  PubMed  Google Scholar 

  19. 19.

    Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  PubMed  Google Scholar 

  21. 21.

    Mahmood, S. S., Levy, D., Vasan, R. S. & Wang, T. J. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 383, 999–1008 (2014).

    Article  PubMed  Google Scholar 

  22. 22.

    Splansky, G. L. et al. The Third Generation Cohort of the National Heart, Lung, and Blood Institute’s Framingham Heart Study: design, recruitment, and initial examination. Am. J. Epidemiol. 165, 1328–1335 (2007).

    Article  PubMed  Google Scholar 

  23. 23.

    Folkersen, L. et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet. 13, e1006706 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Yao, C. et al. Genome-wide association study of plasma proteins identifies putatively causal genes, proteins, and pathways for cardiovascular disease. Preprint at https://www.biorxiv.org/content/early/2017/05/12/136523/ (2017).

  25. 25.

    Rosenquist, J. N. et al. Cohort of birth modifies the association between FTO genotype and BMI. Proc. Natl. Acad. Sci. USA 112, 354–359 (2015).

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Moayyeri, A., Hammond, C. J., Hart, D. J. & Spector, T. D. The UK Adult Twin Registry (TwinsUK Resource). Twin Res. Hum. Genet. 16, 144–149 (2013).

    Article  PubMed  Google Scholar 

  27. 27.

    Power, C. & Elliott, J. Cohort profile: 1958 British birth cohort (National Child Development Study). Int. J. Epidemiol 35, 34–41 (2006).

    Article  PubMed  Google Scholar 

  28. 28.

    Holle, R., Happich, M., Löwel, H. & Wichmann, H. E. KORA: a research platform for population based health research. Gesundheitswesen 67 (Suppl. 1), S19–S25 (2005). 

    Article  PubMed  Google Scholar 

  29. 29.

    Völzke, H. et al. Cohort profile: the study of health in Pomerania. Int. J. Epidemiol. 40, 294–307 (2011).

    Article  PubMed  Google Scholar 

  30. 30.

    Pilia, G. et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2, e132 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Sabatti, C. et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat. Genet. 41, 35–46 (2009).

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Würtz, P. et al. Metabolic signatures of adiposity in young adults: Mendelian randomization analysis and effects of weight change. PLoS Med. 11, e1001765 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Colditz, G. A., Philpott, S. E. & Hankinson, S. E. The impact of the Nurses’ Health Study on population health: prevention, translation, and control. Am. J. Public Health 106, 1540–1545 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Netea, M. G. et al. Understanding human immune function using the resources from the Human Functional Genomics Project. Nat. Med. 22, 831–833 (2016).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Bonder, M. J. et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat. Genet. 49, 131–138 (2017).

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Blanchet, L. et al. Factors that influence the volatile organic compound content in human breath. J. Breath Res. 11, 016013 (2017).

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Aguirre-Gamboa, R. et al. Differential effects of environmental and genetic Factors on T and B cell immune traits. Cell Rep. 17, 2474–2487 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Ter Horst, R. et al. Host and environmental factors influencing individual human cytokine responses. Cell 167, 1111–1124.e13 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Schirmer, M. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 167, 1897 (2016).

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Tigchelaar, E. F. et al. Gut microbiota composition associated with stool consistency. Gut 65, 540–542 (2016).

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Imhann, F. et al. Proton pump inhibitors affect the gut microbiome. Gut 65, 740–748 (2016).

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Imhann, F. et al. Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut 67, 108–119 (2016).

    Article  PubMed  Google Scholar 

  47. 47.

    Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412 (2016).

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK Twins. Cell Host Microbe 19, 731–743 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    He, T. et al. Effects of yogurt and bifidobacteria supplementation on the colonic microbiota in lactose-intolerant subjects. J. Appl. Microbiol. 104, 595–604 (2007).

    PubMed  Google Scholar 

  51. 51.

    Romero, J. R. & Wolf, P. A. Epidemiology of stroke: legacy of the Framingham Heart Study. Glob. Heart 8, 67–75 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  PubMed Central  Google Scholar 

  54. 54.

    Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Graham, J. W. Missing data analysis: making it work in the real world. Annu. Rev. Psychol. 60, 549–576 (2009).

    Article  PubMed  Google Scholar 

  56. 56.

    Wang, C., Butts, C. T., Hipp, J. R., Jose, R. & Lakon, C. M. Multiple imputation for missing edge data: a predictive evaluation method with application to add health. Soc. Networks 45, 89–98 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Brandsma, M. et al. How to kickstart a national biobanking infrastructure: experiences and prospects of BBMRI-NL. Nor. Epidemiol. 21, 143–148 (2012).

    Google Scholar 

  60. 60.

    van Leeuwen, E. M. et al. Genome of the Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels. Nat. Commun. 6, 6065 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Sperber, A. D. et al. The global prevalence of IBS in adults remains elusive due to the heterogeneity of studies: a Rome Foundation working team literature review. Gut 66, 1075–1082 (2017).

    Article  PubMed  Google Scholar 

  62. 62.

    Savage, N. The measure of a man. Cell 169, 1159–1161 (2017).

    CAS  Article  PubMed  Google Scholar 

  63. 63.

    Wallace, S. E., Walker, N. M. & Elliott, J. Returning findings within longitudinal cohort studies: the 1958 birth cohort as an exemplar. Emerg. Themes Epidemiol. 11, 10 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Tian, C. et al. Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections. Nat. Commun. 8, 599 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

C.W. is funded by a European Research Council (ERC) advanced grant (FP/2007-2013/ERC grant 2012-322698), a Netherlands Organization for Scientific Research (NWO) Spinoza prize (NWO SPI 92-266), the NWO Gravitation Netherlands Organ-on-Chip Initiative (024.003.001), the Stiftelsen Kristian Gerhard Jebsen foundation (Norway) and the RuG investment agenda grant Personalized Health. A.Z. is supported by a Rosalind Franklin Fellowship (University of Groningen), an ERC starting grant (715772) and an NWO VIDI grant (2016-178.056), and is also funded by CardioVasculair Onderzoek Nederland (CVON 2012-03). We thank K. Mc Intyre and J. Senior for editorial assistance, and J. Fu for help with graphics for Fig. 1.

Author information

Affiliations

Authors

Contributions

C.W. and A.Z. jointly conceived and wrote the manuscript.

Corresponding authors

Correspondence to Cisca Wijmenga or Alexandra Zhernakova.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wijmenga, C., Zhernakova, A. The importance of cohort studies in the post-GWAS era. Nat Genet 50, 322–328 (2018). https://doi.org/10.1038/s41588-018-0066-3

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing