Estimating the Number of Essential Genes in a Genome by Random Transposon Mutagenesis

We describe a Bayesian method for estimating the number of essential genes in a genome, on the basis of data on viable mutants for which a single transposon was inserted after a random TA site in a genome,potentially disrupting a gene. The prior distribution for the number of essential genes was taken to be uniform. A Gibbs sampler was used to estimate the posterior distribution. The method is illustrated with simulated data. Further simulations were used to study the performance of the procedure.

Estimating Patterns of CD4+ Lymphocyte Decline Using Data from a Prevalent Cohort of HIV Infected Individuals

In natural history studies of chronic disease, it is of interest to understand the evolution of key variables that measure aspects of disease progression. This is particularly true for immunological variables in persons infected with the Human Immunodeficiency Virus (HIV). The natural timescale for such studies is time since infection. However, most data available for analysis arise from prevalent cohorts, where the date of infection is unknown for most or all individuals. As a result, standard curve fitting algorithms are not immediately applicable. Here we propose two methods to circumvent this difficulty. The first uses repeated measurement data to provide information not only on the level of the variable of interest, but also on its rate of change, while the second uses an estimate of the expected time since infection. Both methods are based on the principal curves algorithm of Hastie and Stuetzle, and are applied to data from a prevalent cohort of HIV-infected homosexual men, giving estimates of the average pattern of CD4+ lymphocyte decline. These methods are applicable to natural history studies using data from prevalent cohorts where the time of disease origin is uncertain, provided certain ancillary information is available from external sources.

Estimating Multiple Tumor Transition Rates Based on Data from Survival/Sacrifice Experiments

In this paper, we focus on the model for two types of tumors. Tumor development can be described by four types of death rates and four tumor transition rates. We present a general semi-parametric model to estimate the tumor transition rates based on data from survival/sacrifice experiments. In the model, we make a proportional assumption of tumor transition rates on a common parametric function but no assumption of the death rates from any states. We derived the likelihood function of the data observed in such an experiment, and an EM algorithm that simplified estimating procedures. This article extends work on semi-parametric models for one type of tumor (see Portier and Dinse and Dinse) to two types of tumors.

Estimation of Transition Probabilities in Animal Carcinogenicity Experiments

This paper discusses estimation of the tumor incidence rate, the death rate given tumor is present and the death rate given tumor is absent using a discrete multistage model. The model was originally proposed by Dewanji and Kalbfleisch (1986) and the maximum likelihood estimate of the tumor incidence rate was obtained using EM algorithm. In this paper, we use a reparametrization to simplify the estimation procedure. The resulting estimates are not always the same as the maximum likelihood estimates but are asymptotically equivalent. In addition, an explicit expression for asymptotic variance and bias of the proposed estimators is also derived. These results can be used to compare efficiency of different sacrifice schemes in carcinogenicity experiments.

A unifying approach for haplotype analysis of quantitative traits in family-based association studies: Testing and estimating gene-environment interactions with complex exposure variables

We propose robust and e±cient tests and estimators for gene-environment/gene-drug interactions in family-based association studies. The methodology is designed for studies in which haplotypes, quantitative pheno- types and complex exposure/treatment variables are analyzed. Using causal inference methodology, we derive family-based association tests and estimators for the genetic main effects and the interactions. The tests and estimators are robust against population admixture and strati¯cation without requiring adjustment for confounding variables. We illustrate the practical relevance of our approach by an application to a COPD study. The data analysis suggests a gene-environment interaction between a SNP in the Serpine gene and smok- ing status/pack years of smoking that reduces the FEV1 volume by about 0.02 liter per pack year of smoking. Simulation studies show that the pro- posed methodology is su±ciently powered for realistic sample sizes and that it provides valid tests and effect size estimators in the presence of admixture and stratification.

Dephosphorylation of p-ERK1/2 in relation to tumor remission after HER-2 and Raf1 blocking therapy in a conditional mouse tumor model

Several studies have shown that HER-2/neu (erbB-2) blocking therapy strategies can cause tumor remission. However, the responsible molecular mechanisms are not yet known. Both ERK1/2 and Akt/PKB are critical for HER-2-mediated signal transduction. Therefore, we used a mouse tumor model that allows downregulation of HER-2 in tumor tissue by administration of anhydrotetracycline (ATc). Switching-off HER-2 caused a rapid tumor remission by more than 95% within 7 d of ATc administration compared to the volume before switching-off HER-2. Interestingly, HER-2 downregulation caused a dephosphorylation of p-ERK1/2 by more than 80% already before tumor remission occurred. Levels of total ERK protein were not influenced. In contrast, dephosphorylation of p-Akt occurred later, when the tumor was already in remission. These data suggest that in our HER-2 tumor model dephosphorylation of p-ERK1/2 may be more critical for tumor remission than dephosphorylation of p-Akt. To test this hypothesis we used a second mouse tumor model that allows ATc controlled expression of BXB-Raf1 because the latter constitutively signals to ERK1/2, but cannot activate Akt/PKB. As expected, downregulation of BXB-Raf1 in tumor tissue caused a strong dephosphorylation of p-ERK1/2, but did not decrease levels of p-Akt. Interestingly, tumor remission after switching-off BXB-Raf1 was similarly efficient as the effect of HER-2 downregulation, despite the lack of p-Akt dephosphorylation. In conclusion, two lines of evidence strongly suggest that dephosphorylation of p-ERK1/2 and not that of p-Akt is critical for the rapid tumor remission after downregulation of HER-2 or BXB-Raf1 in our tumor model: (i) dephosphorylation of p-ERK1/2 but not that of p-Akt precedes tumor remission after switching-off HER-2 and (ii) downregulation of BXB-Raf1 leads to a similarly efficient tumor remission as downregulation of HER-2, although no p-Akt dephosphorylation was observed after switching-off BXB-Raf1.

Semiparametric Estimation in General Repeated Measures Problems

This paper considers a wide class of semiparametric problems with a parametric part for some covariate effects and repeated evaluations of a nonparametric function. Special cases in our approach include marginal models for longitudinal/clustered data, conditional logistic regression for matched case-control studies, multivariate measurement error models, generalized linear mixed models with a semiparametric component, and many others. We propose profile-kernel and backfitting estimation methods for these problems, derive their asymptotic distributions, and show that in likelihood problems the methods are semiparametric efficient. While generally not true, with our methods profiling and backfitting are asymptotically equivalent. We also consider pseudolikelihood methods where some nuisance parameters are estimated from a different algorithm. The proposed methods are evaluated using simulation studies and applied to the Kenya hemoglobin data.