Linkage analysis for 18 enzyme loci in Pinus rigida Mill.

Summary Estimates of recombination frequency among enzyme loci of pitch pine revealed two new linkages, Mdh3:Pgm2 (=0.01) and Pep1:Mdh4 (=0.38), and confirmed two previously established linkages. Tighter linkage (=0.30) was ruled out for nearly all gene pairs examined. In general, the Bayesian approach used in this study to test for linkage performed better than alternative methods.This work was supported by the School of Natural Resources, College of Agricultural and Life Sciences, University of Wisconsin, Madison, WI, and by McIntyre-Stennis, project no. 142-C385     

Computerized matching of relevés and association tables,with an application to the British National Vegetation Classification

When a new relevé is to be assigned to a pre-existing type, its composition is compared with an association table. Bayesian inference may seem a good way to make the comparison, but presents difficulties. In an alternative approach, three indices of goodness-of-fit are proposed. Compositional satisfaction is a measure of how well the species composition of the relevé fits the constancy classes in the table; it is a minor modification of the Czekanowski coefficient of similarity between observed and expected numbers of species in each constancy class. Dominance satisfaction is a modification of the Czekanowski similarity between the relevé and cover values that might be expected from the association table. Dominance constancy is a weighted mean of the constancy class of the four most abundant species in the relevé. A computer program, TABLEFIT, combines them into a single index. It has been tested on British mire vegetation.     

Probabilistic reconstruction of ancestral protein sequences

Using a maximum-likelihood formalism, we have developed a method with which to reconstruct the sequences of ancestral proteins. Our approach allows the calculation of not only the most probable ancestral sequence but also of the probability of any amino acid at any given node in the evolutionary tree. Because we consider evolution on the amino acid level, we are better able to include effects of evolutionary pressure and take advantage of structural information about the protein through the use of mutation matrices that depend on secondary structure and surface accessibility. The computational complexity of this method scales linearly with the number of homologous proteins used to reconstruct the ancestral sequence.     

A Monte Carlo method for Bayesian analysis of linkage between single markers and quantitative trait loci. I. Methodology

A Bayesian approach to the statistical mapping of Quantitative Trait Loci (QTLs) using single markers was implemented via Markov Chain Monte Carlo (MCMC) algorithms for parameter estimation and hypothesis testing. Parameter estimators were marginal posterior means computed using a Gibbs sampler with data augmentation. Variables sampled included the augmented data (marker-QTL genotypes, polygenic effects), an indicator variable for linkage, and the parameters (allele frequency, QTL substitution effect, recombination rate, polygenic and residual variances). Several MCMC algorithms were derived for computing Bayesian tests of linkage, which consisted of the marginal posterior probability of linkage and the marginal likelihood of the QTL variance associated with the marker.     

Bayesian signal extraction from noisy FT NMR spectra

Summary The statistical interpretation of the histogram representation of NMR spectra is described, leading to an estimation of the probability density function of the noise. The white-noise and Gaussian hypotheses are discussed, and a new estimator of the noise standard deviation is derived from the histogram strategy. The Bayesian approach to NMR signal detection is presented. This approach homogeneously combines prior knowledge, obtained from the histogram strategy, together with the posterior information resulting from the test of presence of a set of reference shapes in the neighbourhood of each data point. This scheme leads to a new strategy in the local detection of NMR signals in 2D and 3D spectra, which is illustrated by a complete peak-picking algorithm.     

Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge

Summary Prior information on gene effects at individual quantitative trait loci (QTL) and on recombination rates between marker loci and QTL is derived. The prior distribution of QTL gene effects is assumed to be exponential with major effects less likely than minor ones. The prior probability of linkage between a marker and another single locus is a function of the number and length of chromosomes, and of the map function relating recombination rate to genetic distance among loci. The prior probability of linkage between a marker locus and a quantitative trait depends additionally on the number of detectable QTL, which may be determined from total additive genetic variance and minimum detectable QTL effect. The use of this prior information should improve linkage tests and estimates of QTL effects.     

Combining parametric and nonparametric models to estimate treatment effects in observational studies

Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. In settings with few discrete-valued confounders, standard models can be employed. However, as the number of confounders increases these models become less feasible as there are fewer observations available for each unique combination of confounding variables. In this paper, we propose a new model for estimating treatment effects in observational studies that incorporates both parametric and nonparametric outcome models. By conceptually splitting the data, we can combine these models while maintaining a conjugate framework, allowing us to avoid the use of Markov chain Monte Carlo (MCMC) methods. Approximations using the central limit theorem and random sampling allow our method to be scaled to high-dimensional confounders. Through simulation studies we show our method can be competitive with benchmark models while maintaining efficient computation, and illustrate the method on a large epidemiological health survey.     

Forest resistance to drought in a humid tropical dipterocarp forest in the Western Ghats of India

Droughts are linked to tree die-offs in the biodiverse humid tropics. We assessed drought response of a Dipterocarp Forest and found a marginal decrease in tree survival, indicating drought resistance. Understory and emergent species were sensitive to drought. Urgent focus is needed to understand drought impacts and plant physiological responses in South Asian forests.