Apo-E genotypes and cardiovascular diseases: a sensitivity study using cross-validatory criteria

The Apolipoprotein-E (Apo-E) gene, a gene that produces proteins which help to regulate lipid levels in the bloodstream, is of interest in the study of cardiovascular diseases. An approach to making inferences about the genetic effects of the Apo-E gene has been developed by Glickman and Gagnon (2002). The framework describes the role of genetic and risk factors on the onset ages of multiple diseases, and accounts for the possibility that an individual was censored for reasons related to the diseases of interest. The framework also allows for missing genetic information, so that subjects censored prior to genetic sampling, and therefore missing such information, may still be included in the analysis. We apply an extension to this framework to the original cohort of the Framingham Heart Study for measuring the effects of different Apo-E genotypes on the onset age of various cardiovascular disease events. In particular, we compare the fit of univariate versus multivariate onset age components to the model, whether to incorporate health covariates measured at baseline or at a point later in the study, and whether to assume a heritability model for Apo-E genotype frequencies. The results of the best fitting model are presented.     

Direct observation and unambiguous inference

In science, it sometimes occurs that an event is directly observed, and on other occasions that it is not directly observed but one can make the unambiguous inference that it has occurred. Is there any difference concerning the analysis of data arising from these two situations? In this note we show that there is such a difference in one case arising frequently in genetics. The difference derives from the fact that the ability to make the unambiguous inference arises only from a restricted form of data.     

Fungi Evolution Revisited: Application of the Penalized Likelihood Method to a Bayesian Fungal Phylogeny Provides a New Perspective on Phylogenetic Relationships and Divergence Dates of Ascomycota Groups

The depiction of evolutionary relationships within phylum Ascomycota is still controversial because of unresolved branching orders in the radiation of major taxa. Here we generated a dataset of 166 small subunit (18S) rDNA sequences, representative of all groups of Fungi and used as input in a Bayesian phylogenetic analysis. This phylogeny suggests that Discomycetes are a basal group of filamentous Ascomycetes and probably maintain ancestor characters since their representatives are intermingled among other filamentous fungi. Also, we show that the evolutionary rate heterogeneity within Ascomycota precludes the assumption of a global molecular clock. Accordingly, we used the penalized likelihood method, and for calibration we included a 400 million-year-old Pyrenomycete fossil considering two distinct scenarios found in the literature, one with an estimated date of 1576 Myr for the plant–animal–fungus split and the other with an estimated date of 965 Myr for the animal–fungus split. Our data show that the current classification of the fossil as a Pyrenomycete is not compatible with the second scenario. Estimates under the first scenario are older than dates proposed in previous studies based on small subunit rDNA sequences but support estimates based on multiprotein analysis, suggesting that the radiation of the major Ascomycota groups occurred into the Proterozoic era. Reviewing Editor: Dr. Nicolas Galtier     

Phylogenetic relationships within the Laridae (Charadriiformes: Aves) inferred from mitochondrial markers

Gulls (Aves: Laridae) constitute a recent and cosmopolite family of well-studied seabirds for which a robust phylogeny is needed to perform comparative and biogeographical analyses. The present study, the first molecular phylogeny of all Larids species (N=53), is based on a combined segment of mtDNA (partial cytochrome b and control region). We discuss our phylogenetic tree in the light of previous suggestions based on morphological, behavioral, and plumage characters. Although the phylogeny is not fully resolved, it highlights several robust species groups and confirms or identifies for the first time some sister relationships that had never been suggested before. The Dolphin Gull (Leucophaeus scoresbii) for instance, is identified as the sister species of the Grey Gull (Larus modestus) and the Ross's Gull (Rhodostethia rosea) forms a monophyletic group with the Little Gull (Larus minutus). Our results clearly demonstrate that the genus Larus as currently defined is not monophyletic, since current taxonomy of gulls is based on the use of convergent phenotypic characters. We propose a new systematic arrangement that better reflects their evolutionary history.     

Likelihood methods for treatment noncompliance and subsequent nonresponse in randomized trials

While several new methods that account for noncompliance or missing data in randomized trials have been proposed, the dual effects of noncompliance and nonresponse are rarely dealt with simultaneously. We construct a maximum likelihood estimator (MLE) of the causal effect of treatment assignment for a two-armed randomized trial assuming all-or-none treatment noncompliance and allowing for subsequent nonresponse. The EM algorithm is used for parameter estimation. Our likelihood procedure relies on a latent compliance state covariate that describes the behavior of a subject under all possible treatment assignments and characterizes the missing data mechanism as in Frangakis and Rubin (1999, Biometrika 86, 365-379). Using simulated data, we show that the MLE for normal outcomes compares favorably to the method-of-moments (MOM) and the standard intention-to-treat (ITT) estimators under (1) both normal and non-normal data, and (2) departures from the latent ignorability and compound exclusion restriction assumptions. We illustrate methods using data from a trial to compare the efficacy of two antipsychotics for adults with refractory schizophrenia.     

A stochastic model to analyze clonal data on multi-type cell populations

This article presents a stochastic model designed to analyze experimental data on the development of cell clones composed of two (or more) distinct types of cells. The proposed model is an extension of the traditional multi-type Bellman-Harris branching stochastic process allowing for nonidentical time-to-transformation distributions defined for different cell types. A simulated pseudo likelihood method has been developed for the parametric statistical inference from experimental data on cell clones under the proposed model. The method uses simulation-based approximations of the means and the variance-covariance matrices of cell counts. The proposed estimator for the vector of unknown parameters is strongly consistent and asymptotically normal under mild regularity conditions, while its variance-covariance matrix is estimated by the parametric bootstrap. A Monte Carlo Wald test is proposed for the test of hypotheses. Finite sample properties of the estimator have been studied by computer simulations. The model and associated methods of parametric inference have been applied to the analysis of proliferation and differentiation of cultured O-2A progenitor cells that play a key role in the development of the central nervous system. It follows from this analysis that the time to division of the progenitor cell and the time to its differentiation (into an oligodendrocyte) are not identically distributed. This biological finding suggests that a molecular event determining the type of cell transformation is more likely to occur at the start rather than at the end of the mitotic cycle.     

Bayesian inference for stochastic kinetic models using a diffusion approximation

This article is concerned with the Bayesian estimation of stochastic rate constants in the context of dynamic models of intracellular processes. The underlying discrete stochastic kinetic model is replaced by a diffusion approximation (or stochastic differential equation approach) where a white noise term models stochastic behavior and the model is identified using equispaced time course data. The estimation framework involves the introduction of m- 1 latent data points between every pair of observations. MCMC methods are then used to sample the posterior distribution of the latent process and the model parameters. The methodology is applied to the estimation of parameters in a prokaryotic autoregulatory gene network.