Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

REMOVING TECHNICAL VARIABILITY IN RNA-SEQ DATA USING CONDITIONAL QUANTILE NORMALIZATION

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade’s worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show RNA-seq data demonstrates unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find GC-content has a strong sample specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here we describe statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization (CQN) algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content, and quantile normalization to correct for global distortions.

Cyclic nucleotide specific phosphodiesterases of Leishmania major

Background Leishmania represent a complex of important human pathogens that belong to the systematic order of the kinetoplastida. They are transmitted between their human and mammalian hosts by different bloodsucking sandfly vectors. In their hosts, the Leishmania undergo several differentiation steps, and their coordination and optimization crucially depend on numerous interactions between the parasites and the physiological environment presented by the fly and human hosts. Little is still known about the signalling networks involved in these functions. In an attempt to better understand the role of cyclic nucleotide signalling in Leishmania differentiation and host-parasite interaction, we here present an initial study on the cyclic nucleotide-specific phosphodiesterases of Leishmania major. Results This paper presents the identification of three class I cyclic-nucleotide-specific phosphodiesterases (PDEs) from L. major, PDEs whose catalytic domains exhibit considerable sequence conservation with, among other, all eleven human PDE families. In contrast to other protozoa such as Dictyostelium, or fungi such as Saccharomyces cerevisiae, Candida ssp or Neurospora, no genes for class II PDEs were found in the Leishmania genomes. LmjPDEA contains a class I catalytic domain at the C-terminus of the polypeptide, with no other discernible functional domains elsewhere. LmjPDEB1 and LmjPDEB2 are coded for by closely related, tandemly linked genes on chromosome 15. Both PDEs contain two GAF domains in their N-terminal region, and their almost identical catalytic domains are located at the C-terminus of the polypeptide. LmjPDEA, LmjPDEB1 and LmjPDEB2 were further characterized by functional complementation in a PDE-deficient S. cerevisiae strain. All three enzymes conferred complementation, demonstrating that all three can hydrolyze cAMP. Recombinant LmjPDEB1 and LmjPDEB2 were shown to be cAMP-specific, with Km values in the low micromolar range. Several PDE inhibitors were found to be active against these PDEs in vitro, and to inhibit cell proliferation. Conclusion The genome of L. major contains only PDE genes that are predicted to code for class I PDEs, and none for class II PDEs. This is more similar to what is found in higher eukaryotes than it is to the situation in Dictyostelium or the fungi that concomitantly express class I and class II PDEs. Functional complementation demonstrated that LmjPDEA, LmjPDEB1 and LmjPDEB2 are capable of hydrolyzing cAMP. In vitro studies with recombinant LmjPDEB1 and LmjPDEB2 confirmed this, and they demonstrated that both are completely cAMP-specific. Both enzymes are inhibited by several commercially available PDE inhibitors. The observation that these inhibitors also interfere with cell growth in culture indicates that inhibition of the PDEs is fatal for the cell, suggesting an important role of cAMP signalling for the maintenance of cellular integrity and proliferation.

Detection of latent tuberculosis in immunosuppressed patients with autoimmune diseases: performance of a Mycobacterium tuberculosis antigen-specific interferon gamma assay

OBJECTIVE: To analyse the performance of a new M. tuberculosis-specific interferon gamma (IFNgamma) assay in patients with chronic inflammatory diseases who receive immunosuppressive drugs, including tumour necrosis factor alpha (TNFalpha) inhibitors. METHODS: Cellular immune responses to the M. tuberculosis-specific antigens ESAT-6, CFP-10, TB7.7 were prospectively studied in 142 consecutive patients treated for inflammatory rheumatic conditions. Results were compared with tuberculin skin tests (TSTs). Association of both tests with risk factors for latent M. tuberculosis infection (LTBI) and BCG vaccination were determined and the influence of TNFalpha inhibitors, corticosteroids, and disease modifying antirheumatic drugs (DMARDs) on antigen-specific and mitogen-induced IFNgamma secretion was analysed. RESULTS: 126/142 (89%) patients received immunosuppressive therapy. The IFNgamma assay was more closely associated with the presence of risk factors (odds ratio (OR) = 23.8 (95% CI 5.14 to 110) vs OR = 2.77 (1.22 to 6.27), respectively; p = 0.009), but less associated with BCG vaccination than the TST (OR = 0.47 (95% CI 0.15 to 1.47) vs OR = 2.44 (0.74 to (8.01), respectively; p = 0.025). Agreement between the IFNgamma assay and TST results was low (kappa = 0.17; 95% CI 0.02 to 0.32). The odds for a positive IFNgamma assay strongly increased with increasing prognostic relevance of LTBI risk factors. Neither corticosteroids nor conventional DMARDs significantly affected IFNgamma responses, but the odds for a positive IFNgamma assay were decreased in patients treated with TNFalpha inhibitors (OR = 0.21 (95% CI 0.07 to 0.63), respectively; p = 0.006). CONCLUSIONS: These results demonstrate that the performance of the M. tuberculosis antigen-specific IFNgamma ELISA is better than the classic TST for detection of LTBI in patients receiving immunosuppressive therapy for treatment of systemic autoimmune disorders.

Application of conventional and real-time fluorescent ITS1 rDNA PCR for detection of Besnoitia besnoiti infections in bovine skin biopsies

Besnoitia besnoiti, an apicomplexan protozoan parasite, is the causative agent of bovine besnoitiosis. This infection may dramatically affect body condition, and, in males, lead to irreversible infertility. While identification of clinical cases and their histopathological confirmation is relatively simple to carry out, finding subclinical forms of infection is more difficult, thus a more sensitive test for the identification of the etiological agent may be an appropriate diagnostic tool. We have developed the ITS1 rDNA-sequence-based conventional and real-time PCR which are highly sensitive and specific for the detection of B. besnoiti infection in cattle. A recombinant internal positive control was introduced to assess possible sample-related inhibitory effects during the amplification reaction and, in order to prevent false-positive results, a pre-PCR treatment of potentially contaminating dU-containing PCR product with uracil-DNA-glycosylase (UDG) was followed.