IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0150975.html
   My bibliography  Save this article

Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test

Author

Listed:
  • Zhenchuan Wang
  • Qiuying Sha
  • Shuanglin Zhang

Abstract

The joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods use all of the traits for testing the association between multiple traits and a single variant. However, those methods for association studies may lose power in the presence of a large number of noise traits. In this paper, we propose an “optimal” maximum heritability test (MHT-O) to test the association between multiple traits and a single variant. MHT-O includes a procedure of deleting traits that have weak or no association with the variant. Using extensive simulation studies, we compare the performance of MHT-O with MHT, Trait-based Association Test uses Extended Simes procedure (TATES), SUM_SCORE and MANOVA. Our results show that, in all of the simulation scenarios, MHT-O is either the most powerful test or comparable to the most powerful test among the five tests we compared.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0150975
    DOI: 10.1371/journal.pone.0150975
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0150975
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0150975&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0150975?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. Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    2. Benjamin M Neale & Manuel A Rivas & Benjamin F Voight & David Altshuler & Bernie Devlin & Marju Orho-Melander & Sekar Kathiresan & Shaun M Purcell & Kathryn Roeder & Mark J Daly, 2011. "Testing for an Unusual Distribution of Rare Variants," PLOS Genetics, Public Library of Science, vol. 7(3), pages 1-8, March.
    3. B. Devlin & Kathryn Roeder, 1999. "Genomic Control for Association Studies," Biometrics, The International Biometric Society, vol. 55(4), pages 997-1004, December.
    4. 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.
    5. Lange Christoph & van Steen Kristel & Andrew Toby & Lyon Helen & DeMeo Dawn L & Raby Benjamin & Murphy Amy & Silverman Edwin K & MacGregor Alex & Weiss Scott T & Laird Nan M, 2004. "A Family-Based Association Test for Repeatedly Measured Quantitative Traits Adjusting for Unknown Environmental and/or Polygenic Effects," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-29, August.
    6. Matthew Stephens, 2013. "A Unified Framework for Association Analysis with Multiple Related Phenotypes," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-19, July.
    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. 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.
    2. 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.
    3. 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.
    4. Lei Zhang & Yu-Fang Pei & Jian Li & Christopher J Papasian & Hong-Wen Deng, 2009. "Univariate/Multivariate Genome-Wide Association Scans Using Data from Families and Unrelated Samples," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-12, August.
    5. Dominic Holland & Oleksandr Frei & Rahul Desikan & Chun-Chieh Fan & Alexey A Shadrin & Olav B Smeland & V S Sundar & Paul Thompson & Ole A Andreassen & Anders M Dale, 2020. "Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model," PLOS Genetics, Public Library of Science, vol. 16(5), pages 1-30, May.
    6. Vincent Michaud & Eulalie Lasseaux & David J. Green & Dave T. Gerrard & Claudio Plaisant & Tomas Fitzgerald & Ewan Birney & Benoît Arveiler & Graeme C. Black & Panagiotis I. Sergouniotis, 2022. "The contribution of common regulatory and protein-coding TYR variants to the genetic architecture of albinism," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    7. Parsa Akbari & Dragana Vuckovic & Luca Stefanucci & Tao Jiang & Kousik Kundu & Roman Kreuzhuber & Erik L. Bao & Janine H. Collins & Kate Downes & Luigi Grassi & Jose A. Guerrero & Stephen Kaptoge & Ju, 2023. "A genome-wide association study of blood cell morphology identifies cellular proteins implicated in disease aetiology," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    8. Ebrahimi, Nader, 2008. "Simultaneous control of false positives and false negatives in multiple hypotheses testing," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 437-450, March.
    9. 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.
    10. Gang Zheng & Zhaohai Li & Mitchell H. Gail & Joseph L. Gastwirth, 2010. "Impact of Population Substructure on Trend Tests for Genetic Case–Control Association Studies," Biometrics, The International Biometric Society, vol. 66(1), pages 196-204, March.
    11. Sandosh Padmanabhan & Olle Melander & Toby Johnson & Anna Maria Di Blasio & Wai K Lee & Davide Gentilini & Claire E Hastie & Cristina Menni & Maria Cristina Monti & Christian Delles & Stewart Laing & , 2010. "Genome-Wide Association Study of Blood Pressure Extremes Identifies Variant near UMOD Associated with Hypertension," PLOS Genetics, Public Library of Science, vol. 6(10), pages 1-11, October.
    12. Jakris Eu-ahsunthornwattana & E Nancy Miller & Michaela Fakiola & Wellcome Trust Case Control Consortium 2 & Selma M B Jeronimo & Jenefer M Blackwell & Heather J Cordell, 2014. "Comparison of Methods to Account for Relatedness in Genome-Wide Association Studies with Family-Based Data," PLOS Genetics, Public Library of Science, vol. 10(7), pages 1-20, July.
    13. 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.
    14. Jianzhong Ma & Christopher I Amos, 2010. "Theoretical Formulation of Principal Components Analysis to Detect and Correct for Population Stratification," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-14, September.
    15. Claire L Simpson & Robert Wojciechowski & Konrad Oexle & Federico Murgia & Laura Portas & Xiaohui Li & Virginie J M Verhoeven & Veronique Vitart & Maria Schache & S Mohsen Hosseini & Pirro G Hysi & Le, 2014. "Genome-Wide Meta-Analysis of Myopia and Hyperopia Provides Evidence for Replication of 11 Loci," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-19, September.
    16. Lei Zhang & Aaron J Bonham & Jian Li & Yu-Fang Pei & Jie Chen & Christopher J Papasian & Hong-Wen Deng, 2009. "Family-Based Bivariate Association Tests for Quantitative Traits," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-9, December.
    17. Matthieu Bouaziz & Christophe Ambroise & Mickael Guedj, 2011. "Accounting for Population Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-13, December.
    18. Aditi Shendre & Howard W Wiener & Marguerite R Irvin & Bradley E Aouizerat & Edgar T Overton & Jason Lazar & Chenglong Liu & Howard N Hodis & Nita A Limdi & Kathleen M Weber & Stephen J Gange & Degui , 2017. "Genome-wide admixture and association study of subclinical atherosclerosis in the Women’s Interagency HIV Study (WIHS)," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-23, December.
    19. Li Shaoyu & Lu Qing & Fu Wenjiang & Romero Roberto & Cui Yuehua, 2009. "A Regularized Regression Approach for Dissecting Genetic Conflicts that Increase Disease Risk in Pregnancy," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-28, October.
    20. Warrington Nicole M. & Tilling Kate & Howe Laura D. & Paternoster Lavinia & Pennell Craig E. & Wu Yan Yan & Briollais Laurent, 2014. "Robustness of the linear mixed effects model to error distribution assumptions and the consequences for genome-wide association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 1-21, 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:pone00:0150975. 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.