A multivariate model for ordinal trait analysis

Many economically important characteristics of agricultural crops are measured as ordinal traits. Statistical analysis of the genetic basis of ordinal traits appears to be quite different from regular quantitative traits. The generalized linear model methodology implemented via the Newton-Raphson algorithm offers improved efficiency in the analysis of such data, but does not take full advantage of the extensive theory developed in the linear model arena. Instead, we develop a multivariate model for ordinal trait analysis and implement an EM algorithm for parameter estimation. We also propose a method for calculating the variance-covariance matrix of the estimated parameters. The EM equations turn out to be extremely similar to formulae seen in standard linear model analysis. Computer simulations are performed to validate the EM algorithm. A real data set is analyzed to demonstrate the application of the method. The advantages of the EM algorithm over other methods are addressed. Application of the method to QTL mapping for ordinal traits is demonstrated using a simulated baclcross (BC) population.

Diverse signals converge at MAPK cascades in plant

Mitogen-activated protein kinases (MAPKs) are important signal transducing enzymes that connects diverse receptors/sensors to a wide range of cellular responses in mammals, yeasts and plants. In recent years, a large number of different components of plant MAPK cascades were isolated. Molecular and biochemical studies have revealed that plant MAPKs play important role in the response to a broad variety of biotic and abiotic stresses, including wounding, pathogen infection, temperature, drought, salinity, but also in the signaling of plant hormones and the cell division. This review briefly summaries the recent research results about the cross-talk and complexity of MAP kinase cascades in plant obtained from functional analyses.

HCHs and DDTs in sediment-dwelling animals from the Yangtze Estuary, China

HCHs and DDTs in sediment-dwelling animals including mollusks and crabs from the Yangtze Estuary were determined by GC-ECD. Levels of t-HCH were in the range of 1.2-5.5 ng g(-1) and averaged 3.5 ng g(-1) in mollusks, while t-DDT concentrations ranged from 26.0 to 68.8 ng g(-1), with a mean of 34.5 ng g(-1). In crabs t-HCH concentrations varied from 2.0 to 25.7 ng g(-1) and averaged 13.8 ng g(-1), whereas the concentrations of t-DDT were in the range of 1.5-24.8 ng g(-1) with a mean value of 5.9 ng g(-1). The HCHs and DDTs levels depend on geographical position and sources, showing the high levels at fresh water area in the estuary, such as XP, CM and LHK sites, and lower at brackish water area, such as FX site, and little difference between species. Results also indicate there was no significant relationship between t-HCH (t-DDT) concentrations and lipid contents both in mollusks and crabs because of non-equilibrium state under a specific estuarine dynamics; smaller individuals accumulated more HCHs and DDTs than larger individuals of mollusks at LHK site, showing different uptake rate for these pesticides; moreover, HCHs and DDTs levels were lower in female crab bodies than male crab bodies suggesting that the release of spawning. BSAFs (Biota- Sediment Accumulation Factors) from sediment-dwelling animals for HCHs and DDTs show a significant "one high with two low" and "one low with two high" effect in the Yangtze Estuary.

Precision placement of instruments for minimally invasive procedures using a “needle driver” robot

Medical practice continues to move toward less invasive procedures. Many of these procedures require the precision placement of a needle in the anatomy. Over the past several years, our research team has been investigating the use of a robotic needle driver to assist the physician in this task. This paper summarizes our work in this area. The robotic system is briefly described, followed by a description of a clinical trial in spinal nerve blockade. The robot was used under joystick control to place a 22 gauge needle in the spines of 10 patients using fluoroscopic imaging. The results were equivalent to the current manual procedure. We next describe our follow-up clinical application in lung biopsy for lung cancer screening under CT fluoroscopy. The system concept is discussed and the results of a phantom study are presented. A start-up company named ImageGuide has recently been formed to commercialize the robot. Their revised robot design is presented, along with plans to install a ceiling-mounted version of the robot in the CT fluoroscopy suite at Georgetown University.

HCHs and DDTs in salt marsh plants (Scirpus) from the Yangtze estuary and nearby coastal areas, China

HCHs and DDTs in salt marsh plants taken from intertidal flats in the Yangtze estuary and coastal area in April and July 2002 were determined by GC-ECD. A significant seasonal effect was observed for HCHs and DDTs in sources and concentration levels in different sample types including above-ground tissues and roots as well as the whole plants and rhizospheric sediments. The results indicated that the concentration of t-HCH was higher in the above-ground tissues than in their roots in April; however, the partitioning of DDTs between contaminated sediments and the roots showed the higher concentrations of t-DDT in their roots. HCHs and DDTs concentration levels were higher in above-ground tissues than in roots in July. BCFs of HCHs and DDTs exhibited lower values with higher levels of contaminants in sediments, and higher values with lower levels in sediments.

Water Clusters on Graphite: Methodology for Quantum Chemical A Priori Prediction of Reaction Rate Constants

The properties, interactions, and reactions of cyclic water clusters (H(2)O)(n=1-5) on model systems for a graphite surface have been studied using pure B3LYP, dispersion-augmented density functional tight binding (DFTB-D), and integrated ONIOM(B3LYP:DFTB-D) methods. Coronene C(24)H(12) as well as polycircumcoronenes C(96)H(24) and C(216)H(36) in monolayer, bilayer, and trilayer arrangements were used as model systems to simulate ABA bulk graphite. Structures, binding energies, and vibrational frequencies of water clusters on mono- and bilayer graphite models have been calculated, and structural changes and frequency shifts due to the water cluster-graphite interactions are discussed. ONIOM(B3LYP:DFTB-D) with coronene and water in the high level and C(96)H(24) in the low level mimics the effect of extended graphite pi-conjugation on the water-graphite interaction very reasonably and suggests that water clusters only weakly interact with graphite surfaces, as suggested by the fact that water is an excellent graphite lubricant. We use the ONIOM(B3LYP:DFTB-D) method to predict rate constants for model pathways of water dissociative adsorption on graphite. Quantum chemical molecular dynamics (QM/MD) simulations of water clusters and water addition products on the C(96)H(24) graphite model are presented using the DFTB-D method. A three-stage strategy is devised for a priori investigations of high temperature corrosion processes of graphite surfaces due to interaction with water molecules and fragments.

A penalized maximum likelihood method for estimating epistatic effects of QTL

Although epistasis is an important phenomenon in the genetics and evolution of complex traits, epistatic effects are hard to estimate. The main problem is due to the overparameterized epistatic genetic models. An epistatic genetic model should include potential pair-wise interaction effects of all loci. However, the model is saturated quickly as the number of loci increases. Therefore, a variable selection technique is usually considered to exclude those interactions with negligible effects. With such techniques, we may run a high risk of missing some important interaction effects by not fully exploring the extremely large parameter space of models. We develop a penalized maximum likelihood method. The method developed here adopts a penalty that depends on the values of the parameters. The penalized likelihood method allows spurious QTL effects to be shrunk towards zero, while QTL with large effects are estimated with virtually no shrinkage. A simulation study shows that the new method can handle a model with a number of effects 15 times larger than the sample size. Simulation studies also show that results of the penalized likelihood method are comparable to the Bayesian shrinkage analysis, but the computational speed of the penalized method is orders of magnitude faster.

Ion drag force in plasmas at high electronegativity

The electric as well as the positive- and negative-ion drag forces on an isolated dust grain in an electronegative plasma are studied for large negative-ion densities, when the negative ions are not Boltzmann distributed. The investigation is carried out for submicrometer dust particles, so that the theory of Coulomb scattering is applicable for describing ion-dust interaction. Among the forces acting on the dust grain, the negative-ion drag force is found to be important. The effects of the negative-ion density, neutral-gas pressure, and dust-grain size on the forces are also considered. It is shown that by increasing the density of the negative ions one can effectively manipulate the dust grains. Our results imply that both dust voids and balls can be formed.

A Monte Carlo algorithm for computing the IBD matrices using incomplete marker information

Identity-By-Descent (IBD) is a general measurement of the relationship between two groups of genes. If the two groups consist of two homologous genes, one from each individual, the IBD is called the coancestry between the two individuals. Coancestry is an important concept in both population and quantitative genetics. It is the probability that both genes are copies of the same gene in the genealogy. The average coancestry value at a random locus in a population reflects the level of population diversity, effective population size, the level of inbreeding and other attributes. Coancestry is also the building block for the covariance structure used to estimate the additive genetic variance component for a quantitative trait. There are many other types of IBD matrices, depending on the natures of the genes included in each group, and these IBD matrices vary from locus to locus. Molecular markers distributed along the genome provide information that can be used to infer these locus-specific IBD matrices. As a result, we can estimate and test the variance components of a quantitative trait contributed by these loci using the inferred IBD matrices. In this study, we develop the concept of locus-specific epistatic IBD matrices and a Monte Carlo method to infer these IBD matrices. The method is suitable for large pedigrees with arbitrary complexity and various levels of missing marker information. With these locus-specific IBD matrices, we are ready to search for quantitative trait loci along the genome in complicated pedigrees.

A quantitative genetics model for viability selection

Viability selection will change gene frequencies of loci controlling fitness. Consequently, the frequencies of marker loci linked to the viability loci will also change. In genetic mapping, the change of marker allelic frequencies is reflected by the departure from Mendelian segregation ratio. The non-Mendelian segregation of markers has been used to map viability loci along the genome. However, current methods have not been able to detect the amount of selection (s) and the degree of dominance (h) simultaneously. We developed a method to detect both s and h using an F2 mating design under the classical fitness model. We also developed a quantitative genetics model for viability selection by proposing a continuous liability controlling the viability of individuals. With the liability model, mapping viability loci has been formulated as mapping quantitative trait loci. As a result, nongenetic systematic environmental effects can be easily incorporated into the model and subsequently separated from the genetic effects of the viability loci. The quantitative genetic model has been verified with a series of Monte Carlo simulation experiments.

An EM algorithm for mapping quantitative resistance loci

Many disease resistance traits in plants have a polygenic background and the disease phenotypes are modified by environmental factors. As a consequence, the phenotypic values usually show a quantitative variation. The phenotypes of such disease traits, however, are often measured in discrete but ordered categories. These traits are called ordinal traits. In terms of disease resistance, they are called quantitative resistance traits, as opposed to qualitative resistance traits, and are controlled by the quantitative resistance loci (QRL). Classical quantitative trait locus mapping methods are not optimal for ordinal trait analysis because the assumption of normal distribution is violated. Methods for mapping binary trait loci are not suitable either because there are more than two categories in ordinal traits. We developed a maximum likelihood method to map these QRL. The method is implemented via a multicycle expectation-conditional-maximization (ECM) algorithm under the threshold model, where we can estimate both the QRL effects and the thresholds that link the disease liability and the categorical phenotype. The method is verified in simulated data under various combinations of the parameters. An SAS program is available to implement the multicycle ECM algorithm. The program can be downloaded from our website at www.statgen.ucr.edu.