Advertisement

GWA-Portal: Genome-Wide Association Studies Made Easy

  • Ümit SerenEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1761)

Abstract

Genome-wide association studies (GWAS) are an effective method for investigating the genetics of natural phenotypic variation in many different model organisms.

Here we present GWA-Portal, an interactive web application that enables researchers to upload their phenotypes and easily carry out GWAS directly in the browser. We will present all the steps needed—from uploading the phenotype to interpreting the results—using a published root phenotype.

Key words

Genome-wide association studies GWAS 1001 genomes Natural variation Manhattan plots Population genetics Web application Portal 

Notes

Acknowledgment

GWA-Portal was developed in the course of the transPLANT project, which was funded by the European Commission within its 7th Framework Programme, under the thematic area “Infrastructures,” contract number 283496.

Supplementary material

Video tutorial From phenotype to GWAS - a video walkthrough of GWA-Portal's main features

(MP4 220755 kb)

References

  1. 1.
    Atwell S, Huang YS, Vilhjalmsson BJ, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone AM, TT H, Jiang R, Muliyati NW, Zhang X, Amer MA, Baxter I, Brachi B, Chory J, Dean C, Debieu M, de Meaux J, Ecker JR, Faure N, Kniskern JM, Jones JD, Michael T, Nemri A, Roux F, Salt DE, Tang C, Todesco M, Traw MB, Weigel D, Marjoram P, Borevitz JO, Bergelson J, Nordborg M (2010) Genome-wide association study of 107 phenotypes in Arabidopsis Thaliana inbred lines. Nature 465(7298):627–631.  https://doi.org/10.1038/nature08800CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Brachi B, Morris GP, Borevitz JO (2011) Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol 12(10):232.  https://doi.org/10.1186/gb-2011-12-10-232CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Todesco M, Balasubramanian S, TT H, Traw MB, Horton M, Epple P, Kuhns C, Sureshkumar S, Schwartz C, Lanz C, Laitinen RA, Huang Y, Chory J, Lipka V, Borevitz JO, Dangl JL, Bergelson J, Nordborg M, Weigel D (2010) Natural allelic variation underlying a major fitness trade-off in Arabidopsis Thaliana. Nature 465(7298):632–636.  https://doi.org/10.1038/nature09083CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Genomes Consortium. Electronic address mngoaa, Genomes C (2016) 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis Thaliana. Cell 166(2):481–491.  https://doi.org/10.1016/j.cell.2016.05.063CrossRefGoogle Scholar
  5. 5.
    Seren U, Vilhjalmsson BJ, Horton MW, Meng D, Forai P, Huang YS, Long Q, Segura V, Nordborg M (2012) GWAPP: a web application for genome-wide association mapping in Arabidopsis. Plant Cell 24(12):4793–4805.  https://doi.org/10.1105/tpc.112.108068CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Meijon M, Satbhai SB, Tsuchimatsu T, Busch W (2014) Genome-wide association study using cellular traits identifies a new regulator of root development in Arabidopsis. Nat Genet 46(1):77–81.  https://doi.org/10.1038/ng.2824CrossRefPubMedGoogle Scholar
  7. 7.
    Horton MW, Hancock AM, Huang YS, Toomajian C, Atwell S, Auton A, Muliyati NW, Platt A, Sperone FG, Vilhjalmsson BJ, Nordborg M, Borevitz JO, Bergelson J (2012) Genome-wide patterns of genetic variation in worldwide Arabidopsis Thaliana accessions from the RegMap panel. Nat Genet 44(2):212–216.  https://doi.org/10.1038/ng.1042CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Long Q, Rabanal FA, Meng D, Huber CD, Farlow A, Platzer A, Zhang Q, Vilhjalmsson BJ, Korte A, Nizhynska V, Voronin V, Korte P, Sedman L, Mandakova T, Lysak MA, Seren U, Hellmann I, Nordborg M (2013) Massive genomic variation and strong selection in Arabidopsis Thaliana lines from Sweden. Nat Genet 45(8):884–890.  https://doi.org/10.1038/ng.2678CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Box GEP, Cox DR (1964) An analysis of transformations. J R Stat Soc Series 26(2):211–252Google Scholar
  10. 10.
    Wilcoxon F (1946) Individual comparisons of grouped data by ranking methods. J Econ Entomol 39:269CrossRefPubMedGoogle Scholar
  11. 11.
    Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42(4):348–354.  https://doi.org/10.1038/ng.548CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42(4):355–360.  https://doi.org/10.1038/ng.546CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178(3):1709–1723.  https://doi.org/10.1534/genetics.107.080101CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Cwiek-Kupczynska H, Altmann T, Arend D, Arnaud E, Chen D, Cornut G, Fiorani F, Frohmberg W, Junker A, Klukas C, Lange M, Mazurek C, Nafissi A, Neveu P, van Oeveren J, Pommier C, Poorter H, Rocca-Serra P, Sansone SA, Scholz U, van Schriek M, Seren U, Usadel B, Weise S, Kersey P, Krajewski P (2016) Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods 12:44.  https://doi.org/10.1186/s13007-016-0144-4CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Korte A, Farlow A (2013) The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9:29.  https://doi.org/10.1186/1746-4811-9-29CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Vilhjalmsson BJ, Nordborg M (2013) The nature of confounding in genome-wide association studies. Nat Rev Genet 14(1):1–2.  https://doi.org/10.1038/nrg3382CrossRefPubMedGoogle Scholar
  17. 17.
    Platt A, Vilhjalmsson BJ, Nordborg M (2010) Conditions under which genome-wide association studies will be positively misleading. Genetics 186(3):1045–1052.  https://doi.org/10.1534/genetics.110.121665CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC)ViennaAustria

Personalised recommendations