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 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 Bayesian B-spline mapping for dynamic quantitative traits

2012 ◽  
Vol 94 (2) ◽  
pp. 85-95 ◽  
Author(s):  
JUN XING ◽  
JIAHAN LI ◽  
RUNQING YANG ◽  
XIAOJING ZHOU ◽  
SHIZHONG XU
Keyword(s):  
Legendre Polynomial ◽  
Legendre Polynomials ◽  
Simulated Dataset ◽  
Model Parameters ◽  
B Spline ◽  
B Splines ◽  
Bayesian Mapping ◽  
Bayesian Shrinkage ◽  
Dynamic Traits ◽  
Over Time

SummaryOwing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.

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 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

scholarly journals Avoiding ascertainment bias in the maximum likelihood inference of phylogenies based on truncated data

2017 ◽  
Author(s):  
Asif Tamuri ◽  
Nick Goldman
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Function ◽  
Sequence Data ◽  
Ascertainment Bias ◽  
Data Matrix ◽  
Morphological Data ◽  
Model Parameters ◽  
Evolutionary Divergence ◽  
Nucleotide Polymorphisms ◽  
Conditional Maximum

AbstractSome phylogenetic datasets omit data matrix positions at which all taxa share the same state. For sequence data this may be because of a focus on single nucleotide polymorphisms (SNPs) or the use of a technique such as restriction site-associated DNA sequencing (RADseq) that concentrates attention onto regions of differences. With morphological data, it is common to omit states that show no variation across the data studied. It is already known that failing to correct for the ascertainment bias of omitting constant positions can lead to overestimates of evolutionary divergence, as the lack of constant sites is explained as high divergence rather than as a deliberate data selection technique. Previous approaches to using corrections to the likelihood function in order to avoid ascertainment bias have either required knowledge of the omitted positions, or have modified the likelihood function to reflect the omitted data. In this paper we indicate that the technique used to date for this latter approach is a conditional maximum likelihood (CML) method. An alternative approach — unconditional maximum likelihood (UML) — is also possible. We investigate the performance of CML and UML and find them to have almost identical performance in the phylogenetic SNP dataset context. We also make some observations about the nucleotide frequencies observed in SNP datasets, indicating that these can differ systematically from the overall equilibrium base frequencies of the substitution process. This suggests that model parameters representing base frequencies should be estimated by maximum likelihood, and not by empirical (counting) methods.

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 Functional Mapping of Quantitative Trait Loci Underlying the Character Process: A Theoretical Framework

Genetics ◽  
2002 ◽  
Vol 161 (4) ◽  
pp. 1751-1762 ◽  
Author(s):  
Chang-Xing Ma ◽  
George Casella ◽  
Rongling Wu
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Critical Role ◽  
Continuous Variable ◽  
Functional Mapping ◽  
Growth Trajectories ◽  
Model Parameters ◽  
Animal Evolution ◽  
Logistic Mapping ◽  
Trait Loci

AbstractUnlike a character measured at a finite set of landmark points, function-valued traits are those that change as a function of some independent and continuous variable. These traits, also called infinite-dimensional characters, can be described as the character process and include a number of biologically, economically, or biomedically important features, such as growth trajectories, allometric scalings, and norms of reaction. Here we present a new statistical infrastructure for mapping quantitative trait loci (QTL) underlying the character process. This strategy, termed functional mapping, integrates mathematical relationships of different traits or variables within the genetic mapping framework. Logistic mapping proposed in this article can be viewed as an example of functional mapping. Logistic mapping is based on a universal biological law that for each and every living organism growth over time follows an exponential growth curve (e.g., logistic or S-shaped). A maximum-likelihood approach based on a logistic-mixture model, implemented with the EM algorithm, is developed to provide the estimates of QTL positions, QTL effects, and other model parameters responsible for growth trajectories. Logistic mapping displays a tremendous potential to increase the power of QTL detection, the precision of parameter estimation, and the resolution of QTL localization due to the small number of parameters to be estimated, the pleiotropic effect of a QTL on growth, and/or residual correlations of growth at different ages. More importantly, logistic mapping allows for testing numerous biologically important hypotheses concerning the genetic basis of quantitative variation, thus gaining an insight into the critical role of development in shaping plant and animal evolution and domestication. The power of logistic mapping is demonstrated by an example of a forest tree, in which one QTL affecting stem growth processes is detected on a linkage group using our method, whereas it cannot be detected using current methods. The advantages of functional mapping are also discussed.

scholarly journals DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1356 ◽  
Author(s):  
Malgorzata Nowicka ◽  
Mark D. Robinson
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Performance Metrics ◽  
Multinomial Distribution ◽  
Joint Modeling ◽  
R Package ◽  
Model Parameters ◽  
Nucleotide Polymorphisms ◽  
Transcript Quantification ◽  
Main Challenge

There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.

scholarly journals Functional Mapping of Phenotypic Plasticity of Staphylococcus aureus Under Vancomycin Pressure

2021 ◽  
Vol 12 ◽  
Author(s):  
Dengcheng Yang ◽  
Xuyang Zheng ◽  
Libo Jiang ◽  
Meixia Ye ◽  
Xiaoqing He ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Phenotypic Plasticity ◽  
Time Course ◽  
Genome Wide Association Study ◽  
Environmental Changes ◽  
Functional Mapping ◽  
Mapping Technique ◽  
Phenotypic Traits ◽  
Nucleotide Polymorphisms ◽  
A Genome

Phenotypic plasticity is the exhibition of various phenotypic traits produced by a single genotype in response to environmental changes, enabling organisms to adapt to environmental changes by maintaining growth and reproduction. Despite its significance in evolutionary studies, we still know little about the genetic control of phenotypic plasticity. In this study, we designed and conducted a genome-wide association study (GWAS) to reveal genetic architecture of how Staphylococcus aureus strains respond to increasing concentrations of vancomycin (0, 2, 4, and 6 μg/mL) in a time course. We implemented functional mapping, a dynamic model for genetic mapping using longitudinal data, to map specific loci that mediate the growth trajectories of abundance of vancomycin-exposed S. aureus strains. 78 significant single nucleotide polymorphisms were identified following analysis of the whole growth and development process, and seven genes might play a pivotal role in governing phenotypic plasticity to the pressure of vancomycin. These seven genes, SAOUHSC_00020 (walR), SAOUHSC_00176, SAOUHSC_00544 (sdrC), SAOUHSC_02998, SAOUHSC_00025, SAOUHSC_00169, and SAOUHSC_02023, were found to help S. aureus regulate antibiotic pressure. Our dynamic gene mapping technique provides a tool for dissecting the phenotypic plasticity mechanisms of S. aureus under vancomycin pressure, emphasizing the feasibility and potential of functional mapping in the study of bacterial phenotypic plasticity.

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