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Biomedical subjects

Rongling Wu

Publications and source records attributed to Rongling Wu.

At least 19 recordsLinked to original sources

A statistical model to analyse quantitative trait locus interactions for HIV dynamics from the virus and human genomes.

Viruses can be considered 'parasites' because they cannot survive outside of a host. The progression rate to AIDS caused by human immunodeficiency virus type-1 (HIV-1) is therefore a consequence of HIV-host cell interactions. In this article, we present an innovative statistical model for detecting the effects of genetic interactions on HIV-1 dynamics triggered by different quantitative trait loci (QTL) from the HIV and human genomes. Our model integrates the principles of functional mapping for longitudinal traits and of linkage disequilibrium analysis for high-resolution mapping of QTL within the maximum likelihood context and is implemented with the EM algorithm. We performed Monte Carlo simulation studies to investigate the impacts of different heritability levels and sample sizes on the power to detect interacting QTL. Our model allows for the tests of a number of clinically meaningful hypotheses and provides a powerful tool for unravelling the genetic architecture of HIV-1 dynamics and therefore AIDS progression rate.

Algorithms↗

Functional mapping of quantitative trait loci that interact with the hg mutation to regulate growth trajectories in mice.

The high growth (hg) mutation increases body size in mice by 30-50%. Given the complexity of the genetic regulation of animal growth, it is likely that the effect of this major locus is mediated by other quantitative trait loci (QTL) with smaller effects within a web of gene interactions. In this article, we extend our functional mapping model to characterize modifier QTL that interact with the hg locus during ontogenetic growth. Our model is derived within the maximum-likelihood context, incorporated by mathematical aspects of growth laws and implemented with the EM algorithm. In an F2 population founded by a congenic high growth (HG) line and non-HG line, a highly additive effect due to the hg gene was detected on growth trajectories. Three QTL located on chromosomes 2 and X were identified to trigger significant additive and/or dominant effects on the process of growth. The most significant finding made from our model is that these QTL interact with the hg locus to affect the shapes of the growth process. Our model provides a powerful means for understanding the genetic architecture and regulation of growth rate and body size in mammals.

Age Factors↗

A general model for detecting genetic determinants underlying longitudinal traits with unequally spaced measurements and nonstationary covariance structure.

A mixture model for determining quantitative trait loci (QTL) affecting growth trajectories has been proposed in the literature. In this article, we extend this model to a more general situation in which longitudinal traits for each subject are measured at unequally spaced time intervals, different subjects have different measurement patterns, and the residual correlation within subjects is nonstationary. We derive an EM-simplex hybrid algorithm to estimate the allele frequencies, Hardy-Weinberg disequilibrium, and linkage disequilibrium between QTL in the original population and parameters contained in the growth equation and in the covariance structure. A worked example of head circumference growth in 145 children is used to validate our extended model. A simulation study is performed to examine the statistical properties of the parameter estimation obtained from this example. Finally, we discuss the implications and extensions of our model for detecting QTL that affect growth trajectories.

Algorithms↗

A general framework for statistical linkage analysis in multivalent tetraploids.

In multivalent polyploids, simultaneous pairings among homologous chromosomes at meiosis result in a unique cytological phenomenon-double reduction. Double reduction casts an impact on chromosome evolution in higher plants, but because of its confounded effect on the pattern of gene cosegregation, it complicates linkage analysis and map construction with polymorphic molecular markers. In this article, we have proposed a general statistical model for simultaneously estimating the frequencies of double reduction, the recombination fraction, and optimal parental linkage phases between any types of markers, both fully and partially informative, or dominant and codominant, for a tetraploid species that undergoes only multivalent pairing. This model provides an in-depth extension of our earlier linkage model that was built upon Fisher's classifications for different gamete formation modes during the polysomic inheritance of a multivalent polyploid. By implementing a two-stage hierarchical EM algorithm, we derived a closed-form solution for estimating the frequencies of double reduction through the estimation of gamete mode frequencies and the recombination fraction. We performed different settings of simulation studies to demonstrate the statistical properties of our model for estimating and testing double reduction and the linkage in multivalent tetraploids. As shown by a comparative analysis, our model provides a general framework that covers existing statistical approaches for linkage mapping in polyploids that are predominantly multivalent. The model will have great implications for understanding the genome structure and organization of polyploid species.

Algorithms↗

Theoretical basis for the identification of allelic variants that encode drug efficacy and toxicity.

Almost all drugs that produce a favorable response (efficacy) may also produce adverse effects (toxicity). The relative strengths of drug efficacy and toxicity that vary in human populations are controlled by the combined influences of multiple genes and environmental influences. Genetic mapping has proven to be a powerful tool for detecting and identifying specific DNA sequence variants on the basis of the haplotype map (HapMap) constructed from single-nucleotide polymorphisms (SNPs). In this article, we present a novel statistical model for sequence mapping of two different but related drug responses. This model is incorporated by mathematical functions of drug response to varying doses or concentrations and the statistical device used to model the correlated structure of the residual (co)variance matrix. We implement a closed-form solution for the EM algorithm to estimate the population genetic parameters of SNPs and the simplex algorithm to estimate the curve parameters describing the pharmacodynamic changes of different genetic variants and matrix-structuring parameters. Extensive simulations are performed to investigate the statistical properties of our model. The implications of our model in pharmacogenetic and pharmacogenomic research are discussed.

Algorithms↗

A non-stationary model for functional mapping of complex traits.

SUMMARY: Understanding the genetic control of growth is fundamental to agricultural, evolutionary and biomedical genetic research. In this article, we present a statistical model for mapping quantitative trait loci (QTL) that are responsible for genetic differences in growth trajectories during ontogenetic development. This model is derived within the maximum likelihood context, implemented with the expectation-maximization algorithm. We incorporate mathematical aspects of growth processes to model the mean vector and structured antedependence models to approximate time-dependent covariance matrices for longitudinal traits. Our model has been employed to map QTL that affect body mass growth trajectories in both male and female mice of an F2 population derived from the Large and Small mouse strains. The results from this model are compared with those from the autoregressive-based functional mapping approach. Based on results from computer simulation studies, we suggest that these two models are alternative to one another and should be used simultaneously for the same dataset.

Aging↗

Mapping genome-genome epistasis: a high-dimensional model.

MOTIVATION: The proper development of any organ or tissue requires the coordinated expression of its underlying genes that can be located on different genomes present in an organism. For instance, each step in the development of seed for a higher plant is the consequence of gene interactions from the maternal, embryo and endosperm genomes. RESULTS: We present a multivariate statistical model for mapping quantitative trait loci (QTL) by incorporating two important aspects of seed development in plants-QTL interactions derived from different genomes, the maternal, embryo and endosperm, and genetic correlations among phenotypic traits expressed in different genome-specific tissues. This model, which has a high dimensionality, is constructed within the maximum-likelihood context based on a finite mixture model. The implementation of the expectation-maximization algorithm allows for the efficient estimation of QTL positions, their action and interaction effects and pleiotropic effects. The application of this high-dimensional model to a real rice dataset has validated its usefulness. CONCLUSIONS: Our model was derived for self-pollinated plants, but it can be extended to cross-pollinated plants and to animals. With the burgeoning of genetic and genomic data, this high-dimensional model will have many implications for agricultural and evolutionary genetic research. AVAILABILITY: A package of software will be provided from the corresponding author upon request.

Chromosome Mapping↗

A model for estimating joint maternal-offspring effects on seed development in autogamous plants.

We present a statistical model for testing and estimating the effects of maternal-offspring genome interaction on the embryo and endosperm traits during seed development in autogamous plants. Our model is constructed within the context of maximum likelihood implemented with the EM algorithm. Extensive simulations were performed to investigate the statistical properties of our approach. We have successfully identified a quantitative trait locus that exerts a significant maternal-offspring interaction effect on amino acid contents of the endosperm in maize, demonstrating the power of our approach. This approach will be broadly useful in mapping endosperm traits for many agriculturally important crop plants and also make it possible to study the genetic significance of double fertilization in the evolution of higher plants.

Algorithms↗

An algorithm for molecular dissection of tumor progression.

The volumetric growth of tumor cells as a function of time is most often likely to be a complex trait, controlled by the combined influences of multiple genes and environmental influences. Genetic mapping has proven to be a powerful tool for detecting and identifying specific genes affecting complex traits, i.e., quantitative trait loci (QTL), based on polymorphic markers. In this article, we present a novel statistical model for genetic mapping of QTL governing tumor growth trajectories in humans. In principle, this model is a combination of functional mapping proposed to map function-valued traits and linkage disequilibrium mapping designed to provide high resolution mapping of QTL by making use of recombination events created at a historic time. We implement an EM-simplex hybrid algorithm for parameter estimation, in which a closed-form solution for the EM algorithm is derived to estimate the population genetic parameters of QTL including the allele frequencies and the coefficient of linkage disequilibrium, and the simplex algorithm incorporated to estimate the curve parameters describing the dynamic changes of cancer cells for different QTL genotypes. Extensive simulations are performed to investigate the statistical properties of our model. Through a number of hypothesis tests, our model allows for cutting-edge studies aimed to decipher the genetic mechanisms underlying cancer growth, development and differentiation. The implications of our model in gene therapy for cancer research are discussed.

Algorithms↗

A statistical model for high-resolution mapping of quantitative trait loci determining HIV dynamics.

Are there specific genes that control the pathogenesis of HIV infection? This question, which is of fundamental importance in designing personalized strategies of gene therapy to control HIV infection, can be examined by genetic mapping approaches. In this article, we present a new statistical model for unravelling the genetic mechanisms for the dynamic change of HIV that causes AIDS by marker-based linkage disequilibrium (LD) analyses. This new model is the extension of our functional mapping theory to integrate viral load trajectories within a genetic mapping framework. Earlier studies of HIV dynamics have led to various mathematical functions for modelling the kinetic curves of plasma virions and CD4 lymphocytes in HIV patients. Through incorporating these functions into the LD-based mapping procedure, we can identify and map individual quantitative trait loci (or QTL) responsible for viral pathogenesis. We derive a closed-form solution for estimating QTL allele frequency and marker-QTL linkage disequilibrium in the context of EM algorithm and implement the simplex algorithm to estimate the mathematical parameters describing the curve shapes of HIV pathogenesis. We performed different simulation scenarios based on currently used clinical designs in AIDS/HIV research to illustrate the utility and power of our model for genetic mapping of HIV dynamics. The implications of our model for genetic and genomic research into AIDS pathogenesis are discussed.

Acquired Immunodeficiency Syndrome↗

A mechanistic model for genetic machinery of ontogenetic growth.

Two different genetic mechanisms can be proposed to explain variation in growth trajectories. The allelic sensitivity hypothesis states that growth trajectory is controlled by the time-dependent expression of alleles at the deterministic quantitative trait loci (dQTL) formed during embryogenesis. The gene regulation hypothesis states that the differentiation in growth process is due to the opportunistic quantitative trait loci (oQTL) through their mediation with new developmental signals. These two hypotheses of genetic control have been elucidated in the literature. Here, we propose a new statistical model for discerning these two mechanisms in the context of growth trajectories by integrating growth laws within a QTL-mapping framework. This model is developed within the maximum-likelihood context, implemented with a grid approach for estimating the genomic positions of the deterministic and opportunistic QTL and the simplex algorithm for estimating the growth curve parameters of the genotypes at these QTL and the parameters modeling the residual (co)variance matrix. Our model allows for extensive hypothesis tests for the genetic control of growth processes and developmental events by these two types of QTL. The application of this new model to an F(2) progeny in mice leads to the detection of deterministic and opportunistic QTL on chromosome 1 for mouse body mass growth. The estimates of QTL positions and effects from our model are broadly in agreement with those by traditional interval-mapping approaches. The implications of this model for biological and biomedical research are discussed.

Algorithms↗

Phenotypic plasticity of fine root growth increases plant productivity in pine seedlings.

BACKGROUND: The plastic response of fine roots to a changing environment is suggested to affect the growth and form of a plant. Here we show that the plasticity of fine root growth may increase plant productivity based on an experiment using young seedlings (14-week old) of loblolly pine. We use two contrasting pine ecotypes, "mesic" and "xeric", to investigate the adaptive significance of such a plastic response. RESULTS: The partitioning of biomass to fine roots is observed to reduce with increased nutrient availability. For the "mesic" ecotype, increased stem biomass as a consequence of more nutrients may be primarily due to reduced fine-root biomass partitioning. For the "xeric" ecotype, the favorable influence of the plasticity of fine root growth on stem growth results from increased allocation of biomass to foliage and decreased allocation to fine roots. An evolutionary genetic analysis indicates that the plasticity of fine root growth is inducible, whereas the plasticity of foliage is constitutive. CONCLUSIONS: Results promise to enhance a fundamental understanding of evolutionary changes of tree architecture under domestication and to design sound silvicultural and breeding measures for improving plant productivity.

Adaptation, Physiological↗

A unifying statistical model for QTL mapping of genotype x sex interaction for developmental trajectories.

Most organisms display remarkable differences in morphological, anatomical, and developmental features between the two sexes. It has been recognized that these sex-dependent differences are controlled by an array of specific genetic factors, mediated through various environmental stimuli. In this paper, we present a unifying statistical model for mapping quantitative trait loci (QTL) that are responsible for sexual differences in growth trajectories during ontogenetic development. This model is derived within the maximum likelihood context, incorporated by sex-stimulated differentiation in growth form that is described by mathematical functions. A typical structural model is implemented to approximate time-dependent covariance matrices for longitudinal traits. This model allows for a number of biologically meaningful hypothesis tests regarding the effects of QTL on overall growth trajectories or particular stages of development. It is particularly powerful to test whether and how the genetic effects of QTL are expressed differently in different sexual backgrounds. Our model has been employed to map QTL affecting body mass growth trajectories in both male and female mice of an F2 population derived from the large (LG/J) and small (SM/J) mouse strains. We detected four growth QTL on chromosomes 6, 7, 11, and 15, two of which trigger different effects on growth curves between the two sexes. All the four QTL display significant genotype-sex interaction effects on the timing of maximal growth rate in the ontogenetic growth of mice. The implications of our model for studying the genetic architecture of growth trajectories and its extensions to some more general situations are discussed.

Animals↗

A multilocus likelihood approach to joint modeling of linkage, parental diplotype and gene order in a full-sib family.

BACKGROUND: Unlike a pedigree initiated with two inbred lines, a full-sib family derived from two outbred parents frequently has many different segregation types of markers whose linkage phases are not known prior to linkage analysis. RESULTS: 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. CONCLUSIONS: 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.

Alleles↗

FunMap: functional mapping of complex traits.

SUMMARY: FunMap is a Web-based user interface designed to map quantitative trait loci (QTL) affecting function-valued traits or infinite-dimensional traits in well-structured pedigrees or natural populations. User input includes three files: longitudinal trait data, marker genotypes and/or a linkage map. This software allows for a systematic genome-wide scan and significance test of QTL throughout the map. The dynamic change of QTL effects during the time course of growth is automatically drawn, from which specific biological hypotheses regarding the genetic control mechanisms of growth and development can be tested. AVAILABILITY: http://web.biostat.ufl.edu/~cma/genetics/software.html

Chromosome Mapping↗

A mixed polyploid model for linkage analysis in outcrossing tetraploids using a pseudo-test backcross design.

Based on how chromosomes pair at meiosis, the nature of polyploids can be described by bivalent polyploids, multivalent polyploids, and mixed polyploids. In bivalent polyploids, only two chromosomes pair, during which two more similar chromosomes have a higher pairing probability (preferential pairing) than two less similar chromosomes, whereas in multivalent polyploids more than two chromosomes pair at a time, which results in double reduction. Preferential chromosome pairings and double reduction affect the frequencies of gamete formation and, therefore, linkage analysis of polymorphic markers in bivalent and multivalent polyploids, respectively. For mixed polyploids, in which both bivalent and multivalent formations occur simultaneously, linkage analysis is affected by both preferential pairings and double reduction. In this study, we develop a hierarchical maximum likelihood model for discerning gamete genotypes derived from different pairing mechanisms and different formation modes. The first-stage model in the hierarchy is formulated to characterize the relative frequencies of bivalent and multivalent pairing configurations in terms of the preferential pairing factor. The second-stage model is derived to rule out identical gamete genotypes into their different formation modes with relative probabilities determined by the recombination fraction. The first-stage pairing mechanism and second-stage formation mode are integrated to provide the simultaneous maximum likelihood estimates of the preferential pairing factor, the frequency of double reduction, and the recombination fraction, by implementing the EM algorithm. We performed extensive simulation studies to demonstrate the statistical properties of our hierarchical model for linkage analysis in tetraploids. The implications of our model for polyploid linkage mapping are discussed.

Algorithms↗

Phytohormones and shoot growth in a three-generation hybrid poplar family.

Hybrid vigor for secondary growth in poplar has been linked with increased gibberellin (GA) concentration in cambial tissue, but the relationship between concentrations of GAs and hybrid vigor of primary growth has not been investigated. We explored associations between concentrations of GAs, abscisic acid (ABA) and indoleacetic acid (IAA) and shoot extension in a hybrid family of Populus deltoides Bartr. ex Marsh. (Clone ILL-129) and P. trichocarpa Torr. & Gray (Clone 93-968) and two F1 and 67 F2 genotypes. Rapidly elongating subapical internodes from shoots of 4-year-old clonal saplings were selected for hormone analysis. The F1 hybrids displayed heterosis (hybrid vigor) for primary growth as a result of the complementation of dominance for increased internode length from the P. trichocarpa parent and dominance for increased diameter from the P. deltoides parent. Internodes from the faster-elongating shoots of the P. trichocarpa parent had a fourfold higher concentration of bioactive GA1 and higher concentrations of GA20, GA44, GA29 and GA8 than the P. deltoides parent. However, the two fast-growing F1 hybrids had low concentrations of all five GAs, with concentrations similar to those of the slower-elongating P. deltoides parent. Concentrations of ABA and IAA were correlated with GA concentrations and there was thus no evidence of a consistent promoting or inhibiting effect of ABA or IAA within the F1 family. These results indicate that heterosis for internode growth was not primarily regulated by hormone concentration. The segregating population of F2 hybrids was analyzed to assess the possible utility of hormone screening as a selection tool. The internodes of most of the F2 hybrids were smaller than those of their F1 parents and the larger P. trichocarpa parent. Among the F2 hybrids, mature internode length varied fourfold, and was correlated with lengths of young, subapical internodes from which hormones were analyzed. In these internodes, GA1 concentrations were negatively correlated with length (r = 0.41), diameter (0.33) and mass (0.50). Indoleacetic acid concentration was negatively correlated only with diameter (r = 0.37) and positively correlated with GA1 (r = 0.46), whereas ABA concentration was not correlated with any parameter. Thus, in the F2 population, variation in hormone concentration was not primarily responsible for the variation in shoot growth, indicating that selection for individuals with high GA concentration may not be an effective strategy for identifying vigorous hybrid genotypes.

Abscisic Acid↗

Functional mapping of quantitative trait loci underlying growth trajectories using a transform-both-sides logistic model.

The incorporation of developmental control mechanisms of growth has proven to be a powerful tool in mapping quantitative trait loci (QTL) underlying growth trajectories. A theoretical framework for implementing a QTL mapping strategy with growth laws has been established. This framework can be generalized to an arbitrary number of time points, where growth is measured, and becomes computationally more tractable, when the assumption of variance stationarity is made. In practice, however, this assumption is likely to be violated for age-specific growth traits due to a scale effect. In this article, we present a new statistical model for mapping growth QTL, which also addresses the problem of variance stationarity, by using a transform-both-sides (TBS) model advocated by Carroll and Ruppert (1984, Journal of the American Statistical Association 79, 321-328). The TBS-based model for mapping growth QTL cannot only maintain the original biological properties of a growth model, but also can increase the accuracy and precision of parameter estimation and the power to detect a QTL responsible for growth differentiation. Using the TBS-based model, we successfully map a QTL governing growth trajectories to a linkage group in an example of forest trees. The statistical and biological properties of the estimates of this growth QTL position and effect are investigated using Monte Carlo simulation studies. The implications of our model for understanding the genetic architecture of growth are discussed.

Biometry↗