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.

Tests for the mean vector under intraclass covariance structure

1981 ◽  
Vol 12 (3-4) ◽  
pp. 237-245 ◽  
Author(s):  
Bernard Clement ◽  
Sukharanyan Chakraborty ◽  
Bimal K. Sinha ◽  
Narayan C. Giri
Keyword(s):  
Covariance Structure ◽  
The Mean ◽  
Mean Vector

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 Monitoring the variability of beltline moulding process using Wilks’s statistic

2014 ◽  
Vol 6 (2) ◽  
Author(s):  
Aishah Mohd Noor ◽  
Maman A. Djauhari
Keyword(s):  
Automotive Industry ◽  
Covariance Structure ◽  
Process Quality ◽  
Customer Demand ◽  
Process Variability ◽  
Moulding Process ◽  
Real Application ◽  
Monitoring Process ◽  
The Mean ◽  
Mean Vector

Due to the increase of the complexity of customer demand on products and services, monitoring process quality is becoming multivariate in nature. In this setting there are two important parameters to be monitored, i.e., the mean vector and the covariance structure which determines the variability of the process. This paper deals with process variability monitoring of beltline moulding process at an automotive industry where the process is in multivariate setting and monitoring process is based on individual observations. Our approach is based on Wilks’s statistic. A real application will be presented and the strength of that statistic, as well as its limitations, will be discussed.

scholarly journals BAYESIAN INFERENCE FOR THE TANGENT PORTFOLIO

2018 ◽  
Vol 21 (08) ◽  
pp. 1850054 ◽  
Author(s):  
DAVID BAUDER ◽  
TARAS BODNAR ◽  
STEPAN MAZUR ◽  
YAREMA OKHRIN
Keyword(s):  
Bayesian Inference ◽  
Point Of View ◽  
Posterior Distributions ◽  
Conditional Distributions ◽  
Linear Combinations ◽  
Conjugate Priors ◽  
Mean And Variance ◽  
The Mean ◽  
Stochastic Representations ◽  
Mean Vector

In this paper, we consider the estimation of the weights of tangent portfolios from the Bayesian point of view assuming normal conditional distributions of the logarithmic returns. For diffuse and conjugate priors for the mean vector and the covariance matrix, we derive stochastic representations for the posterior distributions of the weights of tangent portfolio and their linear combinations. Separately, we provide the mean and variance of the posterior distributions, which are of key importance for portfolio selection. The analytic results are evaluated within a simulation study, where the precision of coverage intervals is assessed.

scholarly journals A statistical model for functional mapping of quantitative trait loci regulating drug response

2004 ◽  
Vol 4 (5) ◽  
pp. 315-321 ◽  
Author(s):  
Y Gong ◽  
Z Wang ◽  
T Liu ◽  
W Zhao ◽  
Y Zhu ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Statistical Model ◽  
Quantitative Trait ◽  
Drug Response ◽  
Functional Mapping ◽  
Trait Loci
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