IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0012264.html
   My bibliography  Save this article

Epistasis: Obstacle or Advantage for Mapping Complex Traits?

Author

Listed:
  • Koen J F Verhoeven
  • George Casella
  • Lauren M McIntyre

Abstract

Identification of genetic loci in complex traits has focused largely on one-dimensional genome scans to search for associations between single markers and the phenotype. There is mounting evidence that locus interactions, or epistasis, are a crucial component of the genetic architecture of biologically relevant traits. However, epistasis is often viewed as a nuisance factor that reduces power for locus detection. Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches. We demonstrate, both theoretically and via simulation, that the expected power to detect loci when fitting full models is often larger when these loci act epistatically than when they act additively. Additionally, we show that the power for single locus detection may be improved in cases of epistasis compared to the additive model. Our exploration of a two step model selection procedure shows that identifying the true model is difficult. However, this difficulty is certainly not exacerbated by the presence of epistasis, on the contrary, in some cases the presence of epistasis can aid in model selection. The impact of allele frequencies on both power and model selection is dramatic.

Suggested Citation

  • Koen J F Verhoeven & George Casella & Lauren M McIntyre, 2010. "Epistasis: Obstacle or Advantage for Mapping Complex Traits?," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-12, August.
  • Handle: RePEc:plo:pone00:0012264
    DOI: 10.1371/journal.pone.0012264
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012264
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0012264&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0012264?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Park, Mira & Lee, Jae Won & Kim, Choongrak, 2007. "Correspondence analysis approach for finding allele associations in population genetic study," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3145-3155, March.
    2. Magnus Nordborg & Detlef Weigel, 2008. "Next-generation genetics in plants," Nature, Nature, vol. 456(7223), pages 720-723, December.
    3. Karl W. Broman & Terence P. Speed, 2002. "A model selection approach for the identification of quantitative trait loci in experimental crosses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 641-656, October.
    4. Rachel B. Brem & John D. Storey & Jacqueline Whittle & Leonid Kruglyak, 2005. "Genetic interactions between polymorphisms that affect gene expression in yeast," Nature, Nature, vol. 436(7051), pages 701-703, August.
    5. Santiago F. Elena & Richard E. Lenski, 1997. "Test of synergistic interactions among deleterious mutations in bacteria," Nature, Nature, vol. 390(6658), pages 395-398, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Frommlet, Florian & Ruhaltinger, Felix & Twaróg, Piotr & Bogdan, Małgorzata, 2012. "Modified versions of Bayesian Information Criterion for genome-wide association studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1038-1051.
    2. Zak-Szatkowska, Malgorzata & Bogdan, Malgorzata, 2011. "Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2908-2924, November.
    3. Blasius, J. & Greenacre, M. & Groenen, P.J.F. & van de Velden, M., 2009. "Special issue on correspondence analysis and related methods," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3103-3106, June.
    4. Frommlet Florian & Ljubic Ivana & Arnardóttir Helga Björk & Bogdan Malgorzata, 2012. "QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-26, May.
    5. Zehua Chen & Jianbin Liu, 2009. "Mixture Generalized Linear Models for Multiple Interval Mapping of Quantitative Trait Loci in Experimental Crosses," Biometrics, The International Biometric Society, vol. 65(2), pages 470-477, June.
    6. Gilmour, A.R., 2007. "Mixed model regression mapping for QTL detection in experimental crosses," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3749-3764, May.
    7. Andreas Wagner, 2001. "Estimating Coarse Gene Network Structure from Large-Scale Gene Perturbation Data," Working Papers 01-09-051, Santa Fe Institute.
    8. Huang, B. Emma & Shah, Rohan & George, Andrew W., 2012. "dlmap: An R Package for Mixed Model QTL and Association Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i06).
    9. Marit Ackermann & Mathieu Clément-Ziza & Jacob J Michaelson & Andreas Beyer, 2012. "Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-8, July.
    10. Sean Myles & Jer-Ming Chia & Bonnie Hurwitz & Charles Simon & Gan Yuan Zhong & Edward Buckler & Doreen Ware, 2010. "Rapid Genomic Characterization of the Genus Vitis," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    11. Zheyang Wu & Hongyu Zhao, 2009. "Statistical Power of Model Selection Strategies for Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 5(7), pages 1-14, July.
    12. Erich Dolejsi & Bernhard Bodenstorfer & Florian Frommlet, 2014. "Analyzing Genome-Wide Association Studies with an FDR Controlling Modification of the Bayesian Information Criterion," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    13. Liberman, Uri & Feldman, Marcus, 2008. "On the evolution of epistasis III: The haploid case with mutation," Theoretical Population Biology, Elsevier, vol. 73(2), pages 307-316.
    14. Sourav Bandyopadhyay & Ryan Kelley & Nevan J Krogan & Trey Ideker, 2008. "Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-8, April.
    15. C. M. Kendziorski & M. Chen & M. Yuan & H. Lan & A. D. Attie, 2006. "Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping," Biometrics, The International Biometric Society, vol. 62(1), pages 19-27, March.
    16. Andreas Wagner, 2015. "Causal Drift, Robust Signaling, and Complex Disease," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-29, March.
    17. Yawei He & Zehua Chen, 2016. "The EBIC and a sequential procedure for feature selection in interactive linear models with high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 155-180, February.
    18. Chun Wang, 2021. "Using Penalized EM Algorithm to Infer Learning Trajectories in Latent Transition CDM," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 167-189, March.
    19. repec:jss:jstsof:28:i02 is not listed on IDEAS
    20. Małgorzata Bogdan & Florian Frommlet & Przemysław Biecek & Riyan Cheng & Jayanta K. Ghosh & R.W. Doerge, 2008. "Extending the Modified Bayesian Information Criterion (mBIC) to Dense Markers and Multiple Interval Mapping," Biometrics, The International Biometric Society, vol. 64(4), pages 1162-1169, December.
    21. Erhardt Vinzenz & Bogdan Malgorzata & Czado Claudia, 2010. "Locating Multiple Interacting Quantitative Trait Loci with the Zero-Inflated Generalized Poisson Regression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-27, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0012264. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.