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Search Results: 1 - 10 of 43976 matches for " Rongling Wu "
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A Bayesian Algorithm for Functional Mapping of Dynamic Complex Traits
Tian Liu,Rongling Wu
Algorithms , 2009, DOI: 10.3390/a2020667
Abstract: Functional mapping of dynamic traits measured in a longitudinal study was originally derived within the maximum likelihood (ML) context and implemented with the EM algorithm. Although ML-based functional mapping possesses many favorable statistical properties in parameter estimation, it may be computationally intractable for analyzing longitudinal data with high dimensions and high measurement errors. In this article, we derive a general functional mapping framework for quantitative trait locus mapping of dynamic traits within the Bayesian paradigm. Markov chain Monte Carlo techniques were implemented for functional mapping to estimate biologically and statistically sensible parameters that model the structures of time-dependent genetic effects and covariance matrix. The Bayesian approach is useful to handle difficulties in constructing confidence intervals as well as the identifiability problem, enhancing the statistical inference of functional mapping. We have undertaken simulation studies to investigate the statistical behavior of Bayesian-based functional mapping and used a real example with F2 mice to validate the utilization and usefulness of the model.
A joint model for nonparametric functional mapping of longitudinal trajectory and time-to-event
Min Lin, Rongling Wu
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-138
Abstract: We present a joint statistical model for functional mapping of dynamic traits in which the event times and longitudinal traits are taken to depend on a common set of genetic mechanisms. By fitting the Legendre polynomial of orthogonal properties for the time-dependent mean vector, our model does not rely on any curve, which is different from earlier parametric models of functional mapping. This newly developed nonparametric model is demonstrated and validated by an example for a forest tree in which stemwood growth and the time to first flower are jointly modelled.Our model allows for the detection of specific QTL that govern both longitudinal traits and developmental processes through either pleiotropic effects or close linkage, or both. This model will have great implications for integrating longitudinal and event data to gain better insights into comprehensive biology and biomedicine.Although there has been a upsurge of interest in jointly modelling longitudinal and event data during the last decade [1-9], no statistical models have been developed to characterize the shared genetic basis for these two types of traits. In biomedicine, the identification of specific genetic variants responsible for an HIV patient's time-dependent CD4 count and for the time to onset of AIDS symptoms can help to design individualized drugs to control this patient's progression to AIDS. Similarly, in studies of prostate cancer, a shared genetic basis between prostate specific antigen, repeatedly measured for patients following treatment for prostate cancer, and the time to disease recurrence can be used to make optimal treatment schedules for patients. In plants, knowledge about whether the genetic loci for reproductive behaviors, such as the time to first flower and the time to form seeds, also govern growth rates and sizes of plants helps to understand the etiology of plant's adaptation to the environment in which they are grown.The genetic mapping of quantitative trait loci (QTL) t
A statistical model for the identification of genes governing the incidence of cancer with age
Kiranmoy Das, Rongling Wu
Theoretical Biology and Medical Modelling , 2008, DOI: 10.1186/1742-4682-5-7
Abstract: Age is thought to be the largest single risk factor for developing cancer [1,2]. A considerable body of data suggests that the incidence of cancer increases exponentially with age [3-7], although death from cancer may decline at very old age. This age-dependent rise in cancer incidence is characteristic of multicellular organisms that contain a large proportion of mitotic cells. For those organisms composed primarily of postmitotic cells, such as Drosophila melanogaster (flies) and Caenorhabditis elegans (worms), no cancer will develop. Elucidation of the causes of increasing cancer incidence with age in multicellular organisms can help to design a strategy for primary cancer prevention. The association between cancer and age can be explained by one or two of the physiological causes [8], i.e., a more prolonged exposure to carcinogens in older individuals [9] and an increasingly favorable environment for the induction of neoplasms in senescent cells [10]. These two possible causes lead older humans to accumulate effects of mutational load, increased epigenetic gene silencing, telomere dysfunction, and altered stromal milieu [2].As a complex biological phenomena, susceptibility to cancer and its age-dependent increase is thought to include mixed genetic and environmental components [11-13]. The use of candidate gene approaches or association studies has led to the identification of specific genetic variants for cancer risk and their interactions with other genes and with environment, such as lifestyle. A more powerful method for cancer gene identification is to scan the complete genome for polymorphisms that confer increased risk [11]. Genome-wide identification of cancer genes has been conducted in laboratory mice by mapping individual quantitative trait loci (QTLs) for tumor susceptibility or resistance [12-14]. As a model system for studying human cancer, mice have been useful for elucidating the genetic architecture of cancer through the control of environmental
A Haplotype Block Model for Fine Mapping of Quantitative Trait Loci Regulating HIV-1 Pathogenesis
Yun Zhu,Wei Hou,Rongling Wu
Computational and Mathematical Methods in Medicine , 2003, DOI: 10.1080/10273660412331319486
Abstract: The dynamic change of human immunodeficiency virus type-1 (HIV-1) particles that cause AIDS displays considerable variation from patients to patients. It is likely that such variation in HIV-1 pathogenesis is correlated with the genetic architecture of hosts. Traditional genetic analysis of HIV-1 infection is based on various biochemical approaches, but it has been little successful because HIV-1 dynamics, as a complex trait, is under polygenic control and sensitive to environmental changes. Here, we present a novel model for integrating mathematical functions for HIV-1 dynamics that have been well constructed into a multivariate mixture model for genetic mapping. This integrative mapping model on the foundation of linkage disequilibrium (LD)-based haplotype block analysis provides unique power to precisely detect human quantitative trait loci (QTL) determining HIV-1 dynamics and facilitates positional cloning of target QTL. The model allows for a number of hypothesis tests for the effects of the dynamic QTL on the virion clearance rate, the infected cell life-span and the average viral generation time in vivo, all of which provide theoretical principles to guide the development of efficient gene therapy strategies.
A Computational Approach for Functional Mapping of Quantitative Trait Loci That Regulate Thermal Performance Curves
John Stephen Yap, Chenguang Wang, Rongling Wu
PLOS ONE , 2007, DOI: 10.1371/journal.pone.0000554
Abstract: Whether and how thermal reaction norm is under genetic control is fundamental to understand the mechanistic basis of adaptation to novel thermal environments. However, the genetic study of thermal reaction norm is difficult because it is often expressed as a continuous function or curve. Here we derive a statistical model for dissecting thermal performance curves into individual quantitative trait loci (QTL) with the aid of a genetic linkage map. The model is constructed within the maximum likelihood context and implemented with the EM algorithm. It integrates the biological principle of responses to temperature into a framework for genetic mapping through rigorous mathematical functions established to describe the pattern and shape of thermal reaction norms. The biological advantages of the model lie in the decomposition of the genetic causes for thermal reaction norm into its biologically interpretable modes, such as hotter-colder, faster-slower and generalist-specialist, as well as the formulation of a series of hypotheses at the interface between genetic actions/interactions and temperature-dependent sensitivity. The model is also meritorious in statistics because the precision of parameter estimation and power of QTLdetection can be increased by modeling the mean-covariance structure with a small set of parameters. The results from simulation studies suggest that the model displays favorable statistical properties and can be robust in practical genetic applications. The model provides a conceptual platform for testing many ecologically relevant hypotheses regarding organismic adaptation within the Eco-Devo paradigm.
A multilocus likelihood approach to joint modeling of linkage, parental diplotype and gene order in a full-sib family
Qing Lu, Yuehua Cui, Rongling Wu
BMC Genetics , 2004, DOI: 10.1186/1471-2156-5-20
Abstract: We formulate a general model of simultaneously estimating linkage, parental diplotype and gene order through multi-point analysis in a full-sib family. Our model is based on a multinomial mixture model taking into account different diplotypes and gene orders, weighted by their corresponding occurring probabilities. The EM algorithm is implemented to provide the maximum likelihood estimates of the linkage, parental diplotype and gene order over any type of markers.Through simulation studies, this model is found to be more computationally efficient compared with existing models for linkage mapping. We discuss the extension of the model and its implications for genome mapping in outcrossing species.The construction of genetic linkage maps based on molecular markers has become a routine tool for comparative studies of genome structure and organization and the identification of loci affecting complex traits in different organisms [1]. Statistical methods for linkage analysis and map construction have been well developed in inbred line crosses [2] and implemented in the computer packages MAPMAKER [3], CRI-MAP [4], JOINMAP [5] and MULTIMAP [6]. Increasing efforts have been made to develop robust tools for analyzing marker data in outcrossing organisms [7-12], in which inbred lines are not available due to the heterozygous nature of these organisms and/or long-generation intervals.Genetic analyses and statistical methods in outcrossing species are far more complicated than in species that can be selfed to produce inbred lines. There are two reasons for this. First, the number of marker alleles and the segregation pattern of marker genotypes may vary from locus to locus in outcrossing species, whereas an inbred line-initiated segregating population, such as an F2 or backcross, always has two alleles and a consistent segregation ratio across different markers. Second, linkage phases among different markers are not known a priori for outbred parents and, therefore, an algorith
Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies
Li Zhang,Zhong Wang,Rongling Wu
Frontiers in Genetics , 2012, DOI: 10.3389/fgene.2012.00002
Abstract: Haplotype analysis has been increasingly used to study the genetic basis of human diseases, but models for characterizing genetic interactions between haplotypes from different chromosomal regions have not been well developed in the current literature. In this article, we describe a statistical model for testing haplotype-haplotype interactions for human diseases with a case-control genetic association design. The model is formulated on a contingency table in which cases and controls are typed for the same set of molecular markers. By integrating well-established quantitative genetic principles, the model is equipped with a capacity to characterize physiologically meaningful epistasis arising from interactions between haplotypes from different chromosomal regions. The model allows the partition of epistasis into different components due to additive × additive, additive × dominance, dominance × additive, and dominance × dominance interactions. We derive the EM algorithm to estimate and test the effects of each of these components on differences in the pattern of genetic variation between cases and controls and, therefore, examine their role in the pathogenesis of human diseases. The method was further extended to investigate gene-environment interactions expressed at the haplotype level. The statistical properties of the models were investigated through simulation studies and its usefulness and utilization validated by analyzing the genetic association of sarcoidosis from a human genetics project.
Genetic mapping of complex traits by minimizing integrated square errors
Song Wu, Guifang Fu, Yunmei Chen, Zhong Wang, Rongling Wu
BMC Genetics , 2012, DOI: 10.1186/1471-2156-13-20
Abstract: Here, we derive a robust statistical approach for QTL mapping that accommodates a certain degree of misspecification of the true model by incorporating integrated square errors into the genetic mapping framework. A hypothesis testing is formulated by defining a new test statistics - energy difference.Simulation studies were performed to investigate the statistical properties of this approach and compare these properties with those from traditional maximum likelihood and non-parametric QTL mapping approaches. Lastly, analyses of real examples were conducted to demonstrate the usefulness and utilization of the new approach in a practical genetic setting.Genetic mapping of quantitative trait loci, or QTLs, plays prominent roles in understanding the genetic basis of many phenotypic variations [1-4]. Depending on the biological nature of the organism and trait studied, several types of mapping populations generated from different experimental crosses can be constructed to map the QTL of interest. Among those, backcross and F2 intercross are probably two of the most widely used techniques and have been applied in many areas, such as maize and mice studies [5-7]. These experimental crosses separate individual gene components, including QTLs, in a controlled manner, which serves as a foundation for QTL mapping. The basic question is how to efficiently and effectively associate a quantitative trait with its corresponding QTLs and subsequently determine their locations and genetic effects through QTL-linked genetic markers. The past two decades have seen tremendous statistical methodological development in this area [8-16]. Usually, one significant assumption required to derive these statistical methods is that the phenotypic values of a trait can be modelled by a known parametric distribution, such as a normal distribution. By estimating the parameters that define the phenotypic distribution of each genotype at a putative QTL and testing their differences among different QTL
A Bayesian Framework for Functional Mapping through Joint Modeling of Longitudinal and Time-to-Event Data
Kiranmoy Das,Runze Li,Zhongwen Huang,Junyi Gai,Rongling Wu
International Journal of Plant Genomics , 2012, DOI: 10.1155/2012/680634
Abstract: The most powerful and comprehensive approach of study in modern biology is to understand the whole process of development and all events of importance to development which occur in the process. As a consequence, joint modeling of developmental processes and events has become one of the most demanding tasks in statistical research. Here, we propose a joint modeling framework for functional mapping of specific quantitative trait loci (QTLs) which controls developmental processes and the timing of development and their causal correlation over time. The joint model contains two submodels, one for a developmental process, known as a longitudinal trait, and the other for a developmental event, known as the time to event, which are connected through a QTL mapping framework. A nonparametric approach is used to model the mean and covariance function of the longitudinal trait while the traditional Cox proportional hazard (PH) model is used to model the event time. The joint model is applied to map QTLs that control whole-plant vegetative biomass growth and time to first flower in soybeans. Results show that this model should be broadly useful for detecting genes controlling physiological and pathological processes and other events of interest in biomedicine. 1. Introduction To study biology, a classic approach is dimension reduction in which a biological phenomenon or process is dissected into several discrete features over time and space. Most efforts in the past decades have been made to understand biological details of individual features and then use knowledge from each feature to draw an inference about biology as a whole. There has been increasing recognition of the limitation of this approach because it fails to detect a rule that governs the transition from one feature to next, thus leading to a significant loss of information behind the development of a biological trait. More recently, tremendous developments in statistics and computer science have enabled scientists to model and compute the dynamic behavior of a biological phenomenon and construct a comprehensive view of how a cell, tissue, or organ grows and develops across the time-space scale. A statistical dynamic model, called functional mapping, is one of the products of such developments [1, 2]. The merit of functional mapping lies in its biological relevance to study the tempo-spatial pattern of change for the trait and further predict the physiological or pathological status of trait phenotype. Functional mapping has proven to be powerful for elucidating the dynamic genetic architecture of
A computational model for functional mapping of genes that regulate intra-cellular circadian rhythms
Tian Liu, Xueli Liu, Yunmei Chen, Rongling Wu
Theoretical Biology and Medical Modelling , 2007, DOI: 10.1186/1742-4682-4-5
Abstract: We present a statistical model for mapping and characterizing specific genes or quantitative trait loci (QTL) that affect variations in rhythmic responses. This model integrates a system of differential equations into the framework for functional mapping, allowing hypotheses about the interplay between genetic actions and periodic rhythms to be tested. A simulation approach based on sustained circadian oscillations of the clock proteins and their mRNAs has been designed to test the statistical properties of the model.The model has significant implications for probing the molecular genetic mechanism of rhythmic oscillations through the detection of the clock QTL throughout the genome.Rhythmic phenomena are considered to involve a mechanism, ubiquitous among organisms populating the earth, for responding to daily and seasonal changes resulting from the planet's rotation and its orbit around the sun [1]. All these periodic responses are recorded in a circadian clock that allows the organism to anticipate rhythmic changes in the environment, thus equipping it with regulatory and adaptive machinery [2]. It is well recognized that circadian rhythms operate at all levels of biological organization, approximating a twenty-four hour period, or more accurately, the alternation between day and night [3]. Although there is a widely accepted view that the normal functions of biological processes are strongly correlated with the genes that control them, the detailed genetic mechanisms by which circadian behavior is generated and mediated are poorly understood [4].Several studies have identified various so-called circadian clock genes and clock-controlled transcription factors through mutants in animal models [5,6]. These genes have implications for clinical trials: their identification holds great promise for determining optimal times for drug administration based on an individual patient's genetic makeup. It has been suggested that drug administration at the appropriate body tim
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