Skip to main content
SpringerLink
Log in

The Future of and Beyond GWAS

  • Chapter
  • First Online:
Genome-Wide Association Studies

Abstract

Although GWAS technologies themselves have become mature, there are still many issues to be solved. One such issue is the missing heritability problem. It is still unknown whether it is sufficient to base the genetic architecture, which is required when attempting to fully explain the heritability, on common markers, or if rare markers, markers other than SNVs, or interactions between the markers must be considered. This may depend on the specific disease types and traits. Simulation methods to estimate the heritability with hypothetical markers have found that the top few thousand markers may explain much of the heritability. However, because of the statistical power issue, whether this is valid will be unclear until the sample size is sufficiently large. Therefore, international meta-analyses to increase power have become popular. Another direction to advance GWAS is to consider molecules other than the genome, which is expected to approach the mechanism of disease with the GWAS results: genomic annotation with omic data, integrated association analysis with multiomics and transomics, in particular expression quantitative loci (eQTL), will be harnessed with GWAS data to focus on disease related genes and markers, and to identify correlation and even causality of the relationships between molecules and diseases. These must be based on different networks of cell types interacting with the environment. Disease phenotype itself could also be considered. These have a complex relationship with each other and cannot be categorized clearly. Rather, such relationships may be used effectively for GWAS and further analyses. Methodological advancement will be needed to solve these complex relationships and dynamics. GWAS applications include drug target discovery and precision medicine – personalized medicine and prevention. To properly achieve these, we need new mathematical methodologies. It is expected that data sharing and utilization of molecular databases will be promoted, and a next generation of mathematical models and methods based on AI will be developed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Flannick J, Florez JC (2016) Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet 17:535–549

    Article  CAS  PubMed  Google Scholar 

  2. Visscher PM, Yang J, Goddard ME (2010) A commentary on ‘common SNPs explain a large proportion of the heritability for human height’ by Yang et al. (2010). Twin Res Hum Genet 13:517–524

    Article  PubMed  Google Scholar 

  3. Yang L et al (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:76–82

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Stahl EA et al (2012) Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat Genet 44:483–489

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Speed D et al (2017) Reevaluation of SNP heritability in complex human traits. Nat Genet 49:986–992

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yang J, Zeng J, Goddard ME, Wray NR, Visscher PM (2017) Concepts, estimation and interpretation of SNP-based heritability. Nat Genet 49:1304–1310

    Article  CAS  PubMed  Google Scholar 

  7. Fuchsberger C et al (2016) The genetic architecture of type 2 diabetes. Nature 536:41–47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Marouli E et al (2017) Rare and low-frequency coding variants alter human adult height. Nature 542:186–190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Surakka I et al (2015) The impact of low-frequency and rare variants on lipid levels. Nat Genet 47:589–597

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zheng HF et al (2015) Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526:112–117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Singh T et al (2017) The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability. Nat Genet 49:1167–1173

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dickson SP et al (2010) Rare variants create synthetic genome-wide associations. PLoS Biol 8:e1000294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hunt KA et al (2013) Negligible impact of rare autoimmune-locus coding-region variants on missing heritability. Nature 498:232–235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. UK10K Consortium et al (2015) The UK10K project identifies rare variants in health and disease. Nature 526:82–90

    Article  CAS  Google Scholar 

  15. Rich SS (2016) Diabetes: still a geneticist’s nightmare. Nature 536:37–38

    Article  CAS  PubMed  Google Scholar 

  16. Lee S et al (2014) Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 95:5–23

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cruchaga C et al (2014) Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature 505:550–554

    Article  CAS  PubMed  Google Scholar 

  18. Liu DJ et al (2014) Meta-analysis of gene-level tests for rare variant association. Nat Genet 46:200–204

    Article  CAS  PubMed  Google Scholar 

  19. Ahlbom A, Alfredsson L (2005) Interaction: a word with two meanings creates confusion. Eur J Epidemiol 20:563–564

    Article  PubMed  Google Scholar 

  20. Cordell HJ, Clayton DG (2002) A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes. Am J Hum Genet 70:124–141

    Article  CAS  PubMed  Google Scholar 

  21. Lenz TL et al (2015) Widespread non-additive and interaction effects within HLA loci modulate the risk of autoimmune diseases. Nat Genet 47:1085–1090

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Weiner DJ et al (2017) Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat Genet 49:978–985

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhu Z et al (2015) Dominance genetic variation contributes little to the missing heritability for human complex traits. Am J Hum Genet 96:377–385

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. CNV and Schizophrenia Working Groups of the Psychiatric Genomics Consortium (2017) Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat Genet 49:27–35

    Article  CAS  Google Scholar 

  25. Sekar A et al (2016) Schizophrenia risk from complex variation of complement component 4. Nature 530:177–183

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chuong EB, Elde NC, Feschotte C (2017) Regulatory activities of transposable elements: from conflicts to benefits. Nat Rev Genet 18:71–86

    Article  CAS  PubMed  Google Scholar 

  27. Hoffmann TJ et al (2017) Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat Genet 49:54–64

    Article  CAS  PubMed  Google Scholar 

  28. https://obamawhitehouse.archives.gov/node/333101

  29. http://www.ukbiobank.ac.uk

  30. Moore C, Sambrook J, Walker M, Tolkien Z, Kaptoge S, Allen D, Mehenny S, Mant J, Di Angelantonio E, Thompson SG et al (2014) The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Trials 363:15

    Google Scholar 

  31. Astle WJ et al (2016) The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167:1415–1429

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pasaniuc B, Price AL (2017) Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet 18:117–127

    Article  CAS  PubMed  Google Scholar 

  33. Visscher PM et al (2017) Am J Hum Genet 101:5–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Iyer MK et al (2015) The landscape of long noncoding RNAs in the human transcriptome. Nat Genet 47:199–208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Liu SJ et al (2017) CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 355:aah7111

    Article  CAS  PubMed  Google Scholar 

  36. Yue F et al (2014) A comparative encyclopedia of DNA elements in the mouse genome. Nature 515:355–364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mumbach MR et al (2017) Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat Genet 49:1602–1612

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Huang H et al (2017) Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547:173–178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yang J et al (2017) A scalable Bayesian method for integrating functional information in genome-wide association studies. Am J Hum Genet 101:404–416

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. He Z, Xu B, Lee S, Ionita-Laza I (2017) Unified sequence-based association tests allowing for multiple functional annotations and meta-analysis of noncoding variation in metabochip data. Am J Hum Genet 101:340–352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Trynka G et al (2015) Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex-trait loci. Am J Hum Genet 97:139–152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Sveinbjornsson G et al (2016) Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nat Genet 48:314–317

    Article  CAS  PubMed  Google Scholar 

  43. Stranger BE et al (2007) Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315:848–853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhang X et al (2015) Identification of common genetic variants controlling transcript isoform variation in human whole blood. Nat Genet 47:345–352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Long T et al (2017) Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet 49:568–578

    Article  CAS  PubMed  Google Scholar 

  46. Ishigaki K et al (2017) Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis. Nat Genet 49:1120–1125

    Article  CAS  PubMed  Google Scholar 

  47. Wahl S et al (2017) Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541:81–86

    Article  CAS  PubMed  Google Scholar 

  48. Hu JX, Thomas CE, Brunak S (2016) Network biology concepts in complex disease comorbidities. Nat Rev Genet 17:615–629

    Article  CAS  PubMed  Google Scholar 

  49. Robinson MR et al (2017) Genotype-covariate interaction effects and the heritability of adult body mass index. Nat Genet 49:1174

    Article  CAS  PubMed  Google Scholar 

  50. Wang K, Gaitsch H, Poon H, Cox NJ, Rzhetsky A (2017) Classification of common human diseases derived from shared genetic and environmental determinants. Nat Genet 49:1319–1325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cox NJ (2017) Reaching for the next branch on the biobank tree of knowledge. Nat Genet 49:1295–1296

    Article  CAS  PubMed  Google Scholar 

  52. Cortes A et al (2017) Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank. Nat Genet 49:1311–1318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bulik-Sullivan BK et al (2015) LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47:291–295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Fortune MD et al (2015) Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat Genet 47:839–846

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Liley J, Todd JA, Wallace C (2017) A method for identifying genetic heterogeneity within phenotypically defined disease subgroups. Nat Genet 49:310–316

    Article  CAS  PubMed  Google Scholar 

  56. Dewey FE et al (2016) Inactivating variants in ANGPTL4 and risk of coronary artery disease. N Engl J Med 374:1123–1133

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Yom-Tov E (2016) Crowdsourced Health. The MIT Press, Cambridge, MA

    Book  Google Scholar 

  58. Neff G, Nafus D (2016) Self-tracking. The MIT Press, Cambridge, MA

    Book  Google Scholar 

  59. Dewey FE et al (2016) Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR Study. Science 354(6319). https://doi.org/10.1126/science.aaf6814

    Article  CAS  PubMed  Google Scholar 

  60. Abul-Husn NS et al. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 354(6319). https://doi.org/10.1126/science.aaf7000

    Article  CAS  PubMed  Google Scholar 

  61. https://www.ga4gh.org

  62. Khera AV et al (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50:1219–1224

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Zhou X, Carbonetto P, Stephens M (2013) Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet 9:e1003264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44:512–525

    Article  PubMed  PubMed Central  Google Scholar 

  65. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37:658–665

    Article  PubMed  PubMed Central  Google Scholar 

  66. Cyranoski D (2017) Chinese AI company plans to mine health data faster than rivals. Nature 541:141–142

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tsunoda Laboratory (Medical Science Mathematics), Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan

    Tatsuhiko Tsunoda

  2. Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan

    Tatsuhiko Tsunoda

  3. Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Tatsuhiko Tsunoda

Authors
  1. Tatsuhiko Tsunoda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tatsuhiko Tsunoda .

Editor information

Editors and Affiliations

  1. Graduate School of Science, The University of Tokyo, Tokyo, Japan

    Tatsuhiko Tsunoda

  2. BioResource Research Center, Tokyo Medical and Dental University, Tokyo, Japan

    Toshihiro Tanaka

  3. Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan

    Yusuke Nakamura

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tsunoda, T. (2019). The Future of and Beyond GWAS. In: Tsunoda, T., Tanaka, T., Nakamura, Y. (eds) Genome-Wide Association Studies. Springer, Singapore. https://doi.org/10.1007/978-981-13-8177-5_8

Download citation

Publish with us

Policies and ethics

Search

Navigation