Functional Mapping of Multiple Dynamic Traits

Jiguo Cao, Liangliang Wang, Zhongwen Huang, Junyi Gai, Rongling Wu

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)60-75
Number of pages16
JournalJournal of Agricultural, Biological, and Environmental Statistics
Volume22
Issue number1
DOIs
StatePublished - Mar 1 2017

Fingerprint

Quantitative Trait Loci
Biological Phenomena
Internal-External Control
Soybeans
Soybean
soybean
quantitative trait loci
Developmental Genes
Genetic Linkage
chromosome mapping
Chromosome Mapping
Microsatellites
Profile Likelihood
Mixture Distribution
biological processes
Microsatellite Repeats
Biomass
soybeans
Likelihood Methods
Multivariate Models

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Environmental Science(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

Cao, Jiguo ; Wang, Liangliang ; Huang, Zhongwen ; Gai, Junyi ; Wu, Rongling. / Functional Mapping of Multiple Dynamic Traits. In: Journal of Agricultural, Biological, and Environmental Statistics. 2017 ; Vol. 22, No. 1. pp. 60-75.
@article{73a07bb243614cde9eb357649cd10ec7,
title = "Functional Mapping of Multiple Dynamic Traits",
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.",
author = "Jiguo Cao and Liangliang Wang and Zhongwen Huang and Junyi Gai and Rongling Wu",
year = "2017",
month = "3",
day = "1",
doi = "10.1007/s13253-016-0275-0",
language = "English (US)",
volume = "22",
pages = "60--75",
journal = "Journal of Agricultural, Biological, and Environmental Statistics",
issn = "1085-7117",
publisher = "Springer New York",
number = "1",

}

Functional Mapping of Multiple Dynamic Traits. / Cao, Jiguo; Wang, Liangliang; Huang, Zhongwen; Gai, Junyi; Wu, Rongling.

In: Journal of Agricultural, Biological, and Environmental Statistics, Vol. 22, No. 1, 01.03.2017, p. 60-75.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Functional Mapping of Multiple Dynamic Traits

AU - Cao, Jiguo

AU - Wang, Liangliang

AU - Huang, Zhongwen

AU - Gai, Junyi

AU - Wu, Rongling

PY - 2017/3/1

Y1 - 2017/3/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85006783303&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006783303&partnerID=8YFLogxK

U2 - 10.1007/s13253-016-0275-0

DO - 10.1007/s13253-016-0275-0

M3 - Article

AN - SCOPUS:85006783303

VL - 22

SP - 60

EP - 75

JO - Journal of Agricultural, Biological, and Environmental Statistics

JF - Journal of Agricultural, Biological, and Environmental Statistics

SN - 1085-7117

IS - 1

ER -