IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1005556.html
   My bibliography  Save this article

Fast and general tests of genetic interaction for genome-wide association studies

Author

Listed:
  • Mattias Frånberg
  • Rona J Strawbridge
  • Anders Hamsten
  • PROCARDIS consortium
  • Ulf de Faire
  • Jens Lagergren
  • Bengt Sennblad

Abstract

A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two ‘tag’ variants in the LPA locus (p = 2.42 ⋅ 10−09) as well as replicate the interaction (p = 6.97 ⋅ 10−07). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.Author summary: Interaction between organic molecules forms the basis of all biological systems. The availability of high-throughput genotyping and sequencing platforms enables us to cost-effectively genotype a large number of individuals. For sufficiently large datasets it is possible to reconstruct the genetic dependencies that underlie complex traits and diseases. However, there is a need for efficient statistical methodologies that can tackle the large sample size and computational resources required to study interaction. In this work we provide theory that reduces the required computational resources, and enable multiple research groups to effectively combine their results.

Suggested Citation

  • Mattias Frånberg & Rona J Strawbridge & Anders Hamsten & PROCARDIS consortium & Ulf de Faire & Jens Lagergren & Bengt Sennblad, 2017. "Fast and general tests of genetic interaction for genome-wide association studies," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-29, June.
  • Handle: RePEc:plo:pcbi00:1005556
    DOI: 10.1371/journal.pcbi.1005556
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005556
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005556&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1005556?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. Andrew R. Wood & Marcus A. Tuke & Mike A. Nalls & Dena G. Hernandez & Stefania Bandinelli & Andrew B. Singleton & David Melzer & Luigi Ferrucci & Timothy M. Frayling & Michael N. Weedon, 2014. "Another explanation for apparent epistasis," Nature, Nature, vol. 514(7520), pages 3-5, October.
    2. Mattias Frånberg & Karl Gertow & Anders Hamsten & PROCARDIS consortium & Jens Lagergren & Bengt Sennblad, 2015. "Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests," PLOS Genetics, Public Library of Science, vol. 11(9), pages 1-24, September.
    3. Guillaume Paré & Nancy R Cook & Paul M Ridker & Daniel I Chasman, 2010. "On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study," PLOS Genetics, Public Library of Science, vol. 6(6), pages 1-10, June.
    4. Daryl Pregibon, 1980. "Goodness of Link Tests for Generalized Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 15-24, March.
    5. Masao Ueki & Heather J Cordell, 2012. "Improved Statistics for Genome-Wide Interaction Analysis," PLOS Genetics, Public Library of Science, vol. 8(4), pages 1-19, April.
    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. Anika Reichert & Rowena Jacobs, 2018. "The impact of waiting time on patient outcomes: Evidence from early intervention in psychosis services in England," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1772-1787, November.
    2. Fabio M. Manenti & Stefano Comino & Marialaura Parisi, 2005. "From Planning to Mature: on the Determinants of Open Source Take-Off," Industrial Organization 0507006, University Library of Munich, Germany, revised 29 Sep 2005.
    3. Norma B. Coe & Jing Guo & R. Tamara Konetzka & Courtney Harold Van Houtven, 2019. "What is the marginal benefit of payment‐induced family care? Impact on Medicaid spending and health of care recipients," Health Economics, John Wiley & Sons, Ltd., vol. 28(5), pages 678-692, May.
    4. Antony W. Dnes & Raymond Swaray, 2020. "Criminalizing price‐fixing," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(8), pages 1417-1430, December.
    5. Salmon, Claire & Tanguy, Jeremy, 2016. "Rural Electrification and Household Labor Supply: Evidence from Nigeria," World Development, Elsevier, vol. 82(C), pages 48-68.
    6. Forte, Francesco & Magazzino, Cosimo & Mantovani, Michela, 2010. "On the failure of European planning for less developed regions. The case of Calabria," MPRA Paper 25527, University Library of Munich, Germany.
    7. Jones, A.M, 2010. "Models For Health Care," Health, Econometrics and Data Group (HEDG) Working Papers 10/01, HEDG, c/o Department of Economics, University of York.
    8. Hayam Wahba, 2010. "How do institutional shareholders manipulate corporate environmental strategy to protect their equity value? A study of the adoption of ISO 14001 by Egyptian firms," Business Strategy and the Environment, Wiley Blackwell, vol. 19(8), pages 495-511, December.
    9. Caballer-Tarazona, Vicent & Guadalajara-Olmeda, Natividad & Vivas-Consuelo, David, 2019. "Predicting healthcare expenditure by multimorbidity groups," Health Policy, Elsevier, vol. 123(4), pages 427-434.
    10. McWay, Ryan & Nchare, Karim & Sun, Pu, 2023. "Replication and Sensitivity Analysis of "Market Access and Quality Up-grading: Evidence from Four Field Experiments": A Comment on Bold et al. (2022b)," I4R Discussion Paper Series 72, The Institute for Replication (I4R).
    11. Jarko Fidrmuc & Pavel Ciaian & d'Artis Kancs & Jan Pokrivcak, 2013. "Credit Constraints, Heterogeneous Firms and Loan Defaults," Annals of Economics and Finance, Society for AEF, vol. 14(1), pages 53-68, May.
    12. Thériault, Marius & Le Berre, Iwan & Dubé, Jean & Maulpoix, Adeline & Vandersmissen, Marie-Hélène, 2020. "The effects of land use planning on housing spread: A case study in the region of Brest, France," Land Use Policy, Elsevier, vol. 92(C).
    13. Simon Tyler & Kate Gunn & Adrian Esterman & Bob Clifford & Nicholas Procter, 2022. "Suicidal Ideation in the Australian Construction Industry: Prevalence and the Associations of Psychosocial Job Adversity and Adherence to Traditional Masculine Norms," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    14. Willy, Daniel Kyalo & Holm-Müller, Karin, 2013. "Social influence and collective action effects on farm level soil conservation effort in rural Kenya," Ecological Economics, Elsevier, vol. 90(C), pages 94-103.
    15. Ann L. Owen, 2010. "Grades, Gender, and Encouragement: A Regression Discontinuity Analysis," The Journal of Economic Education, Taylor & Francis Journals, vol. 41(3), pages 217-234, June.
    16. Lee, Andy H. & Zhao, Yuejen, 1997. "Assessing influence on the goodness-of-link in generalized linear models," Statistics & Probability Letters, Elsevier, vol. 31(4), pages 351-358, February.
    17. Zhang, Wenyang & Li, Degui & Xia, Yingcun, 2015. "Estimation in generalised varying-coefficient models with unspecified link functions," Journal of Econometrics, Elsevier, vol. 187(1), pages 238-255.
    18. Anirban Basu & Bhakti V. Arondekar & Paul J. Rathouz, 2006. "Scale of interest versus scale of estimation: comparing alternative estimators for the incremental costs of a comorbidity," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1091-1107, October.
    19. Tor Iversen & Eline Aas & Gunnar Rosenqvist & Unto Häkkinen & on behalf of the EuroHOPE study group, 2015. "Comparative Analysis of Treatment Costs in EUROHOPE," Health Economics, John Wiley & Sons, Ltd., vol. 24(S2), pages 5-22, December.
    20. Comino, Stefano & Manenti, Fabio M. & Parisi, Maria Laura, 2007. "From planning to mature: On the success of open source projects," Research Policy, Elsevier, vol. 36(10), pages 1575-1586, December.

    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:pcbi00:1005556. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.