Hypothesis testing for the genetic background of quantitative traits

The testing of Bayesian point null hypotheses on variance component models have resulted in a tough assignment for which no clear and generally accepted method exists. In this work we present what we believe is a succeeding approach to such a task. It is based on a simple reparameterization of the model in terms of the total variance and the proportion of the additive genetic variance with respect to it, as well as on the explicit inclusion on the prior probability of a discrete component at origin. The reparameterization was used to bypass an arbitrariness related to the impropriety of uninformative priors onto unbounded variables while the discrete component was necessary to overcome the zero probability assigned to sets of null measure by the usual continuous variable models. The method was tested against computer simulations with appealing results.     

Some insights into protein structural class prediction

It has been quite clear that the success rate for predicting protein structural class can be improved significantly by using the algorithms that incorporate the coupling effect among different amino acid components of a protein. However, there is still a lot of confusion in understanding the relationship of these advanced algorithms, such as the least Mahalanobis distance algorithm, the component-coupled algorithm, and the Bayes decision rule. In this communication, a simple, rigorous derivation is provided to prove that the Bayes decision rule introduced recently for protein structural class prediction is completely the same as the earlier component-coupled algorithm. Meanwhile, it is also very clear from the derivative equations that the least Mahalanobis distance algorithm is an approximation of the component-coupled algorithm, also named as the covariant-discriminant algorithm introduced by Chou and Elrod in protein subcellular location prediction (Protein Engineering, 1999; 12:107-118). Clarification of the confusion will help use these powerful algorithms effectively and correctly interpret the results obtained by them, so as to conduce to the further development not only in the structural prediction area, but in some other relevant areas in protein science as well.     

Semiparametric regression modeling with mixtures of Berkson and classical error, with application to fallout from the Nevada test site

We construct Bayesian methods for semiparametric modeling of a monotonic regression function when the predictors are measured with classical error. Berkson error, or a mixture of the two. Such methods require a distribution for the unobserved (latent) predictor, a distribution we also model semiparametrically. Such combinations of semiparametric methods for the dose response as well as the latent variable distribution have not been considered in the measurement error literature for any form of measurement error. In addition, our methods represent a new approach to those problems where the measurement error combines Berkson and classical components. While the methods are general, we develop them around a specific application, namely, the study of thyroid disease in relation to radiation fallout from the Nevada test site. We use this data to illustrate our methods, which suggest a point estimate (posterior mean) of relative risk at high doses nearly double that of previous analyses but that also suggest much greater uncertainty in the relative risk.     

On meta-analytic assessment of surrogate outcomes

We discuss the strengths and weaknesses of the meta-analytic approach to estimating the effect of a new treatment on a true clinical outcome measure, T, from the effect of treatment on a surrogate response, S. The meta-analytic approach (see Daniels and Hughes (1997) 16, 1965-1982) uses data from a series of previous studies of interventions similar to the new treatment. The data are used to estimate relationships between summary measures of treatment effects on T and S that can be used to infer the magnitude of the effect of the new treatment on T from its effects on S. We extend the class of models to cover a broad range of applications in which the parameters define features of the marginal distribution of (T, S). We present a new bootstrap procedure to allow for the variability in estimating the distribution that governs the between-study variation. Ignoring this variability can lead to confidence intervals that are much too narrow. The meta-analytic approach relies on quite different data and assumptions than procedures that depend, for example, on the conditional independence, at the individual level, of treatment and T, given S (see Prentice (1989) 8, 431-440). Meta-analytic calculations in this paper can be used to determine whether a new study, based only on S, will yield estimates of the treatment effect on T that are precise enough to be useful. Compared to direct measurement on T, the meta-analytic approach has a number of limitations, including likely serious loss of precision and difficulties in defining the class of previous studies to be used to predict the effects on T for a new intervention.     

From Africa via Europe to South America: migrational route of a species‐rich genus of Neotropical lowland rain forest trees (Guatteria,Annonaceae)

Aim Several recent studies have suggested that a substantial portion of today’s plant diversity in the Neotropics has resulted from the dispersal of taxa into that region rather than by vicariance. In general, three routes have been documented for the dispersal of taxa onto the South American continent: (1) via the North Atlantic Land Bridge, (2) via the Bering Land Bridge, or (3) from Africa directly onto the continent. Here a species‐rich genus of Neotropical lowland rain forest trees (Guatteria, Annonaceae) is used as a model to investigate these three hypotheses. Location The Neotropics. Methods The phylogenetic relationships within the long‐branch clade of Annonaceae were reconstructed (using maximum parsimony, maximum likelihood and Bayesian inference) in order to gain insight in the phylogenetic position of Guatteria. Furthermore, Bayesian molecular dating and Bayesian dispersal–vicariance (Bayes‐DIVA) analyses were undertaken. Results Most of the relationships within the long‐branch clade of Annonaceae were reconstructed and had high support. However, the relationship between the Duguetia clade, the Xylopia–Artabotrys clade and Guatteria remained unclear. The stem node age estimate of Guatteria ranged between 49.2 and 51.3 Ma, whereas the crown node age estimate ranged between 11.4 and 17.8 Ma. For the ancestral area of Guatteria and its sister group, the area North America–Africa was reconstructed in 99% of 10,000 DIVA analyses, while South America–North America was found just 1% of the time. Main conclusions The estimated stem to crown node ages of Guatteria in combination with the Bayes‐DIVA analyses imply a scenario congruent with an African origin followed by dispersal across the North Atlantic Land Bridge in the early to middle Eocene and further dispersal into North and Central America (and ultimately South America) in the Miocene. The phylogenetically and morphologically isolated position of the genus is probably due to extinction of the North American and European stem lineages in the Tertiary.     

Bayesian Hierarchical Functional Data Analysis Via Contaminated Informative Priors

Summary .  A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.     

Proton transport in carbonic anhydrase: Insights from molecular simulation

This article reviews the insights gained from molecular simulations of human carbonic anhydrase II (HCA II) utilizing non-reactive and reactive force fields. The simulations with a reactive force field explore protein transfer and transport via Grotthuss shuttling, while the non-reactive simulations probe the larger conformational dynamics that underpin the various contributions to the rate-limiting proton transfer event. Specific attention is given to the orientational stability of the His64 group and the characteristics of the active site water cluster, in an effort to determine both of their impact on the maximal catalytic rate. The explicit proton transfer and transport events are described by the multistate empirical valence bond (MS-EVB) method, as are alternative pathways for the excess proton charge defect to enter/leave the active site. The simulation results are interpreted in light of experimental results on the wild-type enzyme and various site-specific mutations of HCA II in order to better elucidate the key factors that contribute to its exceptional efficiency.