IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0201186.html
   My bibliography  Save this article

Testing an optimally weighted combination of common and/or rare variants with multiple traits

Author

Listed:
  • Zhenchuan Wang
  • Qiuying Sha
  • Shurong Fang
  • Kui Zhang
  • Shuanglin Zhang

Abstract

Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.

Suggested Citation

  • Zhenchuan Wang & Qiuying Sha & Shurong Fang & Kui Zhang & Shuanglin Zhang, 2018. "Testing an optimally weighted combination of common and/or rare variants with multiple traits," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
  • Handle: RePEc:plo:pone00:0201186
    DOI: 10.1371/journal.pone.0201186
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201186
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0201186&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0201186?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. 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.
    2. Qiong Yang & Yuanjia Wang, 2012. "Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies," Journal of Probability and Statistics, Hindawi, vol. 2012, pages 1-13, 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. 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.
    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. Jianjun Zhang & Qiuying Sha & Guanfu Liu & Xuexia Wang, 2019. "A gene based approach to test genetic association based on an optimally weighted combination of multiple traits," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-17, August.
    4. Dennis Meer & Oleksandr Frei & Tobias Kaufmann & Alexey A. Shadrin & Anna Devor & Olav B. Smeland & Wesley K. Thompson & Chun Chieh Fan & Dominic Holland & Lars T. Westlye & Ole A. Andreassen & Anders, 2020. "Understanding the genetic determinants of the brain with MOSTest," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    5. Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
    6. 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.
    7. 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.
    8. 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:pone00:0201186. 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.