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  1. Journal of Agricultural, Biological, and Environmental Statistics
  2. Journal of Agricultural, Biological, and Environmental Statistics : Volume 22
  3. Journal of Agricultural, Biological, and Environmental Statistics : Volume 22, Issue 1, March 2017
  4. Functional Mapping of Multiple Dynamic Traits
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Journal of Agricultural, Biological, and Environmental Statistics : Volume 22
Journal of Agricultural, Biological, and Environmental Statistics : Volume 22, Issue 4, December 2017
Journal of Agricultural, Biological, and Environmental Statistics : Volume 22, Issue 3, September 2017
Journal of Agricultural, Biological, and Environmental Statistics : Volume 22, Issue 2, June 2017
Journal of Agricultural, Biological, and Environmental Statistics : Volume 22, Issue 1, March 2017
Residual Variance–Covariance Modelling in Analysis of Multivariate Data from Variety Selection Trials
Smooth additive mixed models for predicting aboveground biomass
The Bayesian Group Lasso for Confounded Spatial Data
Functional Mapping of Multiple Dynamic Traits
Comparison of Models Analyzing a Small Number of Observed Meningitis Cases in Navrongo, Ghana
“Extending R”, by John M. Chambers : Paperback $69.95, May 24, 2016, Chapman and Hall/CRC, 364 pages, 7 B/W Illustrations
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Hidden Markov Models for Time Series: An Introduction Using R, 2nd Edition, by  Walter Zucchini, Iain L. MacDonald, and Roland Langrock
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Journal of Agricultural, Biological, and Environmental Statistics : Volume 7
Journal of Agricultural, Biological, and Environmental Statistics : Volume 6

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Functional Mapping of Multiple Dynamic Traits

Content Provider Springer Nature Link
Author Cao, Jiguo Wang, Liangliang Huang, Zhongwen Gai, Junyi Wu, Rongling
Copyright Year 2016
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.
Starting Page 60
Ending Page 75
Page Count 16
File Format PDF
ISSN 10857117
Journal Journal of Agricultural, Biological, and Environmental Statistics
Volume Number 22
Issue Number 1
e-ISSN 15372693
Language English
Publisher Springer US
Publisher Date 2016-12-20
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword B-spline Functional mapping Multivariate model Quantitative trait loci Soybean Statistics for Life Sciences, Medicine, Health Sciences Agriculture Monitoring/Environmental Analysis Biostatistics
Content Type Text
Resource Type Article
Subject Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty
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