Association Between Polymorphisms in GRIK2 Gene and Obsessive-Compulsive Disorder: A Family-Based Study

Several studies support a genetic influence on obsessive-compulsive disorder (OCD) etiology. The role of glutamate as an important neurotransmitter affecting OCD pathophysiology has been supported by neuroimaging, animal model, medication, and initial candidate gene studies. Genes involved in glutamatergic pathways, such as the glutamate receptor, ionotropic, kainate 2 (GRIK2), have been associated with OCD in previous studies. This study examines GRIK2 as a candidate gene for OCD susceptibility in a family-based approach. Probands had full DSM-IV diagnostic criteria for OCD. Forty-seven OCD probands and their parents were recruited from tertiary care OCD specialty clinics from France and USA. Genotypes of single nucleotide polymorphism (SNP) markers and related haplotypes were analyzed using Haploview and FBAT software. The polymorphism at rs1556995 (P = 0.0027; permuted P-value = 0.03) was significantly associated with the presence of OCD. Also, the two marker haplotype rs1556995/rs1417182, was significantly associated with OCD (P = 0.0019, permuted P-value = 0.01). This study supports previously reported findings of association between proximal GRIK2 SNPs and OCD in a comprehensive evaluation of the gene. Further study with independent samples and larger sample sizes is required.

The Sticky Information Macro Model: Beyond Perfect Foresight

Sticky information monetary models have been used in the macroeconomic literature to explain some of the observed features regarding inflation dynamics. In this paper, we explore the consequences of relaxing the rational expectations assumption usually taken in this type of model; in particular, by considering expectations formed through adaptive learning, it is possible to arrive to results other than the trivial convergence to a fixed point long-term equilibrium. The results involve the possibility of endogenous cyclical motion (periodic and a-periodic), which emerges essentially in scenarios of hyperinflation. In low inflation settings, the introduction of learning implies a less severe impact of monetary shocks that, nevertheless, tend to last for additional time periods relative to the pure perfect foresight setup.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Bayesian Model Averaging in the Instrumental Variable Regression Model

This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainty can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very exible and can be easily adapted to analyze any of the di¤erent priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Baysian Model Averaging, Learning and Model Selection

Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

Growth, convergence and public investment : a bayesian model averaging approach

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

Growth, convergence and public investment : a bayesian model averaging approach

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

Genomewide association study of atazanavir pharmacokinetics and hyperbilirubinemia in AIDS Clinical Trials Group protocol A5202.

BACKGROUND: Atazanavir-associated hyperbilirubinemia can cause premature discontinuation of atazanavir and avoidance of its initial prescription. We used genomewide genotyping and clinical data to characterize determinants of atazanavir pharmacokinetics and hyperbilirubinemia in AIDS Clinical Trials Group protocol A5202. METHODS: Plasma atazanavir pharmacokinetics and indirect bilirubin concentrations were characterized in HIV-1-infected patients randomized to atazanavir/ritonavir-containing regimens. A subset had genomewide genotype data available. RESULTS: Genomewide assay data were available from 542 participants, of whom 475 also had data on estimated atazanavir clearance and relevant covariates available. Peak bilirubin concentration and relevant covariates were available for 443 participants. By multivariate analysis, higher peak on-treatment bilirubin levels were found to be associated with the UGT1A1 rs887829 T allele (P=6.4×10), higher baseline hemoglobin levels (P=4.9×10), higher baseline bilirubin levels (P=6.7×10), and slower plasma atazanavir clearance (P=8.6×10). For peak bilirubin levels greater than 3.0 mg/dl, the positive predictive value of a baseline bilirubin level of 0.5 mg/dl or higher with hemoglobin concentrations of 14 g/dl or higher was 0.51, which increased to 0.85 with rs887829 TT homozygosity. For peak bilirubin levels of 3.0 mg/dl or lower, the positive predictive value of a baseline bilirubin level less than 0.5 mg/dl with a hemoglobin concentration less than 14 g/dl was 0.91, which increased to 0.96 with rs887829 CC homozygosity. No polymorphism predicted atazanavir pharmacokinetics at genomewide significance. CONCLUSION: Atazanavir-associated hyperbilirubinemia is best predicted by considering UGT1A1 genotype, baseline bilirubin level, and baseline hemoglobin level in combination. Use of ritonavir as a pharmacokinetic enhancer may have abrogated genetic associations with atazanavir pharmacokinetics.

Non-offensive defence : a criteria model of military credibility

Finnish Defence Studies is published under the auspices of the War College, and the contributions reflect the fields of research and teaching of the College. Finnish Defence Studies will occasionally feature documentation on Finnish Security Policy. Views expressed are those of the authors and do not necessarily imply endorsement by the War College.