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Functional Mapping of Multiple Dynamic Traits

Author

Listed:
  • Jiguo Cao

    (Simon Fraser University)

  • Liangliang Wang

    (Simon Fraser University)

  • Zhongwen Huang

    (Henan Institute of Science and Technology
    Soybean Research Institute of Nanjing Agricultural University)

  • Junyi Gai

    (Soybean Research Institute of Nanjing Agricultural University)

  • Rongling Wu

    (Pennsylvania State University)

Abstract

Many biological phenomena undergo developmental changes in time and space. Functional mapping, which is aimed at mapping genes that affect developmental patterns, is instrumental for studying the genetic architecture of biological changes. Often biological processes are mediated by a network of developmental and physiological components and, therefore, are better described by multiple phenotypes. In this article, we develop a multivariate model for functional mapping that can detect and characterize quantitative trait loci (QTLs) that simultaneously control multiple dynamic traits. Because the true genotypes of QTLs are unknown, the measurements for the multiple dynamic traits are modeled using a mixture distribution. The functional means of the multiple dynamic traits are estimated using the nonparametric regression method, which avoids any parametric assumption on the functional means. We propose the profile likelihood method to estimate the mixture model. A likelihood ratio test is exploited to test for the existence of pleiotropic effects on distinct but developmentally correlated traits. A simulation study is implemented to illustrate the finite sample performance of our proposed method. We also demonstrate our method by identifying QTLs that simultaneously control three dynamic traits of soybeans. The three dynamic traits are the time-course biomass of the leaf, the stem, and the root of the whole soybean. The genetic linkage map is constructed with 950 microsatellite markers. The new model can aid in our comprehension of the genetic control mechanisms of complex dynamic traits over time.

Suggested Citation

  • Jiguo Cao & Liangliang Wang & Zhongwen Huang & Junyi Gai & Rongling Wu, 2017. "Functional Mapping of Multiple Dynamic Traits," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 60-75, March.
    • Handle: RePEc:spr:jagbes:v:22:y:2017:i:1:d:10.1007_s13253-016-0275-0
    DOI: 10.1007/s13253-016-0275-0
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    References listed on IDEAS

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    1. John Doebley & Adrian Stec & Lauren Hubbard, 1997. "The evolution of apical dominance in maize," Nature, Nature, vol. 386(6624), pages 485-488, April.
    2. Kiranmoy Das & Michael J. Daniels, 2014. "A semiparametric approach to simultaneous covariance estimation for bivariate sparse longitudinal data," Biometrics, The International Biometric Society, vol. 70(1), pages 33-43, March.
    3. Andrea Gallavotti & Qiong Zhao & Junko Kyozuka & Robert B. Meeley & Matthew K. Ritter & John F. Doebley & M. Enrico Pè & Robert J. Schmidt, 2004. "The role of barren stalk1 in the architecture of maize," Nature, Nature, vol. 432(7017), pages 630-635, December.
    4. Jie Yang & Rongling Wu & George Casella, 2009. "Nonparametric Functional Mapping of Quantitative Trait Loci," Biometrics, The International Biometric Society, vol. 65(1), pages 30-39, March.
    5. Xihong Lin & Raymond J. Carroll, 2006. "Semiparametric estimation in general repeated measures problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 69-88, February.
    6. Jabu S. Sithole & Peter W. Jones, 2007. "Bivariate Longitudinal Model for Detecting Prescribing Change in Two Drugs Simultaneously with Correlated Errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 339-352.
    7. Huai Wang & Tina Nussbaum-Wagler & Bailin Li & Qiong Zhao & Yves Vigouroux & Marianna Faller & Kirsten Bomblies & Lewis Lukens & John F. Doebley, 2005. "The origin of the naked grains of maize," Nature, Nature, vol. 436(7051), pages 714-719, August.
    8. Zhao Wei & Hou Wei & Littell Ramon C. & Wu Rongling, 2005. "Structured Antedependence Models for Functional Mapping of Multiple Longitudinal Traits," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-28, November.
    9. Renhua Li & Shirng-Wern Tsaih & Keith Shockley & Ioannis M Stylianou & Jon Wergedal & Beverly Paigen & Gary A Churchill, 2006. "Structural Model Analysis of Multiple Quantitative Traits," PLOS Genetics, Public Library of Science, vol. 2(7), pages 1-12, July.
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    Cited by:

    1. Jiguo Cao & Kunlaya Soiaporn & Raymond J. Carroll & David Ruppert, 2019. "Modeling and Prediction of Multiple Correlated Functional Outcomes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(1), pages 112-129, March.

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