scholarly journals Functional Mapping of Dynamic Traits with Robust t-Distribution

PLoS ONE ◽  
2011 ◽  
Vol 6 (9) ◽  
pp. e24902 ◽  
Author(s):  
Cen Wu ◽  
Gengxin Li ◽  
Jun Zhu ◽  
Yuehua Cui
Keyword(s):  
Functional Mapping ◽  
T Distribution ◽  
Dynamic Traits

scholarly journals E-Index for Differentiating Complex Dynamic Traits

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Jiandong Qi ◽  
Jianfeng Sun ◽  
Jianxin Wang
Keyword(s):  
Functional Mapping ◽  
Developmental Trajectory ◽  
Model Parameters ◽  
Nucleotide Polymorphisms ◽  
Complex Dynamic ◽  
Index Vector ◽  
Underlying Network ◽  
Dynamic Trait ◽  
Daunting Challenge ◽  
Dynamic Traits

While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement,E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show thatE-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile,E-index vector is proposed as well to capture more subtle differences of developmental patterns.

scholarly journals 3FunMap: full-sib family functional mapping of dynamic traits

2011 ◽  
Vol 27 (14) ◽  
pp. 2006-2008 ◽  
Author(s):  
Chunfa Tong ◽  
Zhong Wang ◽  
Bo Zhang ◽  
Jisen Shi ◽  
Rongling Wu
Keyword(s):  
Functional Mapping ◽  
Dynamic Traits

scholarly journals An optimal strategy for functional mapping of dynamic trait loci

2010 ◽  
Vol 92 (1) ◽  
pp. 55-62 ◽  
Author(s):  
TIANBO JIN ◽  
JIAHAN LI ◽  
YING GUO ◽  
XIAOJING ZHOU ◽  
RUNQING YANG ◽  
...  
Keyword(s):  
Leaf Age ◽  
Covariance Structure ◽  
Functional Mapping ◽  
Significant Qtls ◽  
Trait Loci ◽  
Dynamic Trait ◽  
The Mean ◽  
Mapping Models ◽  
Mean Vector ◽  
Dynamic Traits

SummaryAs an emerging powerful approach for mapping quantitative trait loci (QTLs) responsible for dynamic traits, functional mapping models the time-dependent mean vector with biologically meaningful equations and are likely to generate biologically relevant and interpretable results. Given the autocorrelation nature of a dynamic trait, functional mapping needs the implementation of the models for the structure of the covariance matrix. In this article, we have provided a comprehensive set of approaches for modelling the covariance structure and incorporated each of these approaches into the framework of functional mapping. The Bayesian information criterion (BIC) values are used as a model selection criterion to choose the optimal combination of the submodels for the mean vector and covariance structure. In an example for leaf age growth from a rice molecular genetic project, the best submodel combination was found between the Gaussian model for the correlation structure, power equation of order 1 for the variance and the power curve for the mean vector. Under this combination, several significant QTLs for leaf age growth trajectories were detected on different chromosomes. Our model can be well used to study the genetic architecture of dynamic traits of agricultural values.

scholarly journals Functional Mapping of Multiple Dynamic Traits

2016 ◽  
Vol 22 (1) ◽  
pp. 60-75 ◽  
Author(s):  
Jiguo Cao ◽  
Liangliang Wang ◽  
Zhongwen Huang ◽  
Junyi Gai ◽  
Rongling Wu
Keyword(s):  
Functional Mapping ◽  
Dynamic Traits

scholarly journals A nonlinear mixed-effect mixture model for functional mapping of dynamic traits

Heredity ◽  
2008 ◽  
Vol 101 (4) ◽  
pp. 321-328 ◽  
Author(s):  
W Hou ◽  
H Li ◽  
B Zhang ◽  
M Huang ◽  
R Wu
Keyword(s):  
Mixture Model ◽  
Functional Mapping ◽  
Mixed Effect ◽  
Nonlinear Mixed Effect ◽  
Dynamic Traits

MULTIVARIATE SKEW t -DISTRIBUTION

Statistics ◽  
2003 ◽  
Vol 37 (4) ◽  
pp. 1-1
Author(s):  
A. K. GUPTA
Keyword(s):  
T Distribution

Categorizing Exam Results: t-distribution

2017 ◽  
Vol 1 (11) ◽  
pp. 1-10
Author(s):  
Suresh Akella ◽  
◽  
Girija Akella ◽  
Keyword(s):  
T Distribution

Sebaran Peluang Acak Kontinu, Distribusi Normal, Distribusi Normal Baku, Distribusi T, Distribusi Chi Square, dan Distribusi F

2020 ◽  
Author(s):  
Ahmad Sudi Pratikno
Keyword(s):  
Hypothesis Test ◽  
Standard Normal Distribution ◽  
Process Data ◽  
Nominal Data ◽  
Chi Square ◽  
Stable Curve ◽  
Standard Normal ◽  
Research Findings ◽  
F Distribution ◽  
T Distribution

In statistics, there are various terms that may feel unfamiliar to researcher who is not accustomed to discussing it. However, despite all of many functions and benefits that we can get as researchers to process data, it will later be interpreted into a conclusion. And then researcher can digest and understand the research findings. The distribution of continuous random opportunities illustrates obtaining opportunities with some detection of time, weather, and other data obtained from the field. The standard normal distribution represents a stable curve with zero mean and standard deviation 1, while the t distribution is used as a statistical test in the hypothesis test. Chi square deals with the comparative test on two variables with a nominal data scale, while the f distribution is often used in the ANOVA test and regression analysis.

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