IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0106918.html
   My bibliography  Save this article

Testing Genetic Association by Regressing Genotype over Multiple Phenotypes

Author

Listed:
  • Kai Wang

Abstract

Complex disorders are typically characterized by multiple phenotypes. Analyzing these phenotypes jointly is expected to be more powerful than dealing with one of them at a time. A recent approach (O'Reilly et al. 2012) is to regress the genotype at a SNP marker on multiple phenotypes and apply the proportional odds model. In the current research, we introduce an explicit expression for the score test statistic and its non-centrality parameter that determines its power. Same simulation studies as those reported in Galesloot et al. (2014) were conducted to assess its performance. We demonstrate by theoretical arguments and simulation studies that, despite its potential usefulness for multiple phenotypes, the proportional odds model method can be less powerful than regular methods for univariate traits. We also introduce an implementation of the proposed score statistic in an R package named iGasso.

Suggested Citation

  • Kai Wang, 2014. "Testing Genetic Association by Regressing Genotype over Multiple Phenotypes," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-9, September.
    • Handle: RePEc:plo:pone00:0106918
    DOI: 10.1371/journal.pone.0106918
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0106918
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0106918&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0106918?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. Paul F O’Reilly & Clive J Hoggart & Yotsawat Pomyen & Federico C F Calboli & Paul Elliott & Marjo-Riitta Jarvelin & Lachlan J M Coin, 2012. "MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-1, May.
    2. Tessel E Galesloot & Kristel van Steen & Lambertus A L M Kiemeney & Luc L Janss & Sita H Vermeulen, 2014. "A Comparison of Multivariate Genome-Wide Association Methods," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, 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. Jose A Seoane & Colin Campbell & Ian N M Day & Juan P Casas & Tom R Gaunt, 2014. "Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-13, October.
    2. Heejung Shim & Daniel I Chasman & Joshua D Smith & Samia Mora & Paul M Ridker & Deborah A Nickerson & Ronald M Krauss & Matthew Stephens, 2015. "A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    3. Huanhuan Zhu & Shuanglin Zhang & Qiuying Sha, 2018. "A novel method to test associations between a weighted combination of phenotypes and genetic variants," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    4. Aline Camporez Crispim & Matthew John Kelly & Simone Eliza Facioni Guimarães & Fabyano Fonseca e Silva & Marina Rufino Salinas Fortes & Raphael Rocha Wenceslau & Stephen Moore, 2015. "Multi-Trait GWAS and New Candidate Genes Annotation for Growth Curve Parameters in Brahman Cattle," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    5. Xue Yuan & Zhang Sanguo & Wang Jinjuan & Ding Juan & Li Qizhai, 2019. "A powerful test for ordinal trait genetic association analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-9, April.
    6. Robert J Ellis & Zhiyan Duan & Ye Wang, 2014. "Quantifying Auditory Temporal Stability in a Large Database of Recorded Music," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-24, December.
    7. Yang, Chiao-Yu & Lei, Lihua & Ho, Nhat & Fithian, William, 2022. "BONuS: Multiple Multivariate Testing with a Data-Adaptive Test Statistic," Research Papers 4031, Stanford University, Graduate School of Business.
    8. Lin Zhang & Lei Sun, 2022. "A generalized robust allele‐based genetic association test," Biometrics, The International Biometric Society, vol. 78(2), pages 487-498, June.
    9. Zihuai He & Erin K Payne & Bhramar Mukherjee & Seunggeun Lee & Jennifer A Smith & Erin B Ware & Brisa N Sánchez & Teresa E Seeman & Sharon L R Kardia & Ana V Diez Roux, 2015. "Association between Stress Response Genes and Features of Diurnal Cortisol Curves in the Multi-Ethnic Study of Atherosclerosis: A New Multi-Phenotype Approach for Gene-Based Association Tests," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-15, May.
    10. Nan Lin & Yun Zhu & Ruzong Fan & Momiao Xiong, 2017. "A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-33, October.
    11. Zhenchuan Wang & Qiuying Sha & Shuanglin Zhang, 2016. "Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    12. Young Lee & Suyeon Park & Sanghoon Moon & Juyoung Lee & Robert C. Elston & Woojoo Lee & Sungho Won, 2014. "On the Analysis of a Repeated Measure Design in Genome-Wide Association Analysis," IJERPH, MDPI, vol. 11(12), pages 1-21, November.
    13. Michael C Turchin & Matthew Stephens, 2019. "Bayesian multivariate reanalysis of large genetic studies identifies many new associations," PLOS Genetics, Public Library of Science, vol. 15(10), pages 1-18, October.

    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:pone00:0106918. 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.