Victor Urrea

Letter to the Editor: Stability of Random Forest importance measures

The goal of this article (letter to the editor) is to emphasize the value of exploring ranking stability when using the importance measures, mean decrease accuracy (MDA) and mean decrease Gini (MDG), provided by Random Forest. We illustrate with a real and a simulated example that ranks based on the MDA are unstable to small perturbations of the dataset and ranks based on the MDG provide more robust results.

Improving strategies for detecting genetic patterns of disease susceptibility in association studies

The analysis of gene interactions and epistatic patterns of susceptibility is especially important for investigating complex diseases such as cancer characterized by the joint action of several genes. This work is motivated by a case-control study of bladder cancer, aimed at evaluating the role of both genetic and environmental factors in bladder carcinogenesis. In particular, the analysis of the inflammation pathway is of interest, for which information on a total of 282 SNPs in 108 genes involved in the inflammatory response is available. Detecting and interpreting interactions with such a large number of polymorphisms is a great challenge from both the statistical and the computational perspectives. In this paper we propose a two-stage strategy for identifying relevant interactions: (1) the use of a synergy measure among interacting genes and (2) the use of the model-based multifactor dimensionality reduction method (MB-MDR), a model-based version of the MDR method, which allows adjustment for confounders.

Model-Based Multifactor Dimensionality Reduction for detecting epistasis in case–control data in the presence of noise

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data‐mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model‐Based Multifactor Dimensionality Reduction (MB‐MDR), a relatively new MDR‐based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB‐MDR to detect gene–gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB‐MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

mbmdr: an R package for exploring gene–gene interactions associated with binary or quantitative traits

SUMMARY We describe mbmdr, an R package for implementing the model-based multifactor dimensionality reduction (MB-MDR) method. MB-MDR has been proposed by Calle et al. as a dimension reduction method for exploring gene-gene interactions in case-control association studies. It is an extension of the popular multifactor dimensionality reduction (MDR) method of Ritchie et al. allowing a more flexible definition of risk cells. In MB-MDR, risk categories are defined using a regression model which allows adjustment for covariates and main effects and, in addition to the classical low risk and high risk categories, MB-MDR considers a third category of indeterminate or not informative cells. An important improvement added to the current mbmdr algorithm with respect to the original MB-MDR formulation in Calle et al. and also to the classical MDR approach, is the extension of the methodology to different outcome types. While MB-MDR was initially proposed for binary traits in the context of case-control studies, the mbmdr package provides options to analyze both binary or quantitative traits for unrelated individuals. AVAILABILITY http://cran.r-project.org/.

FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals.

We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.

An efficient algorithm to perform multiple testing in epistasis screening

BackgroundResearch in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems. In this context, the maxT algorithm is one technique to control the false-positive rate. However, the memory needed by this algorithm rises linearly with the amount of hypothesis tests. Gene-gene interaction studies will require a memory proportional to the squared number of SNPs. A genome-wide epistasis search would therefore require terabytes of memory. Hence, cache problems are likely to occur, increasing the computation time. In this work we present a new version of maxT, requiring an amount of memory independent from the number of genetic effects to be investigated. This algorithm was implemented in C++ in our epistasis screening software MBMDR-3.0.3. We evaluate the new implementation in terms of memory efficiency and speed using simulated data. The software is illustrated on real-life data for Crohn’s disease.ResultsIn the case of a binary (affected/unaffected) trait, the parallel workflow of MBMDR-3.0.3 analyzes all gene-gene interactions with a dataset of 100,000 SNPs typed on 1000 individuals within 4 days and 9 hours, using 999 permutations of the trait to assess statistical significance, on a cluster composed of 10 blades, containing each four Quad-Core AMD Opteron(tm) Processor 2352 2.1 GHz. In the case of a continuous trait, a similar run takes 9 days. Our program found 14 SNP-SNP interactions with a multiple-testing corrected p-value of less than 0.05 on real-life Crohn’s disease (CD) data.ConclusionsOur software is the first implementation of the MB-MDR methodology able to solve large-scale SNP-SNP interactions problems within a few days, without using much memory, while adequately controlling the type I error rates. A new implementation to reach genome-wide epistasis screening is under construction. In the context of Crohn’s disease, MBMDR-3.0.3 could identify epistasis involving regions that are well known in the field and could be explained from a biological point of view. This demonstrates the power of our software to find relevant phenotype-genotype higher-order associations.

Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

Elevated humoral response to cytomegalovirus in HIV-infected individuals with poor CD4+ T-cell immune recovery

Some HIV-infected c-ART-suppressed individuals show incomplete CD4+ T-cell recovery, abnormal T-cell activation and higher mortality. One potential source of immune activation could be coinfection with cytomegalovirus (CMV). IgG and IgM levels, immune activation, inflammation and T-cell death in c-ART-suppressed individuals with CD4+ T-cell counts >350 cells/μL (immunoconcordant, n = 133) or <350 cells/μL (immunodiscordant, n = 95) were analyzed to evaluate the effect of CMV humoral response on immune recovery. In total, 27 HIV-uninfected individuals were included as controls. In addition, the presence of CMV IgM antibodies was retrospectively analyzed in 58 immunoconcordant individuals and 66 immunodiscordant individuals. Increased CMV IgG levels were observed in individuals with poor immune reconstitution (p = 0.0002). Increased CMV IgG responses were significantly correlated with lower nadir and absolute CD4+ T-cell counts. In contrast, CMV IgG responses were positively correlated with activation (HLA-DR+) and death markers in CD4+ T-cells and activated memory CD8+ T-cells (CD45RA-CD38+). Longitudinal subanalysis revealed an increased frequency of IgM+ samples in individuals with poor CD4+ T-cell recovery, and an association was observed between retrospective IgM positivity and the current level of IgG. The magnitude of the humoral immune response to CMV is associated with nadir CD4+ T-cell counts, inflammation, immune activation and CD4+ T-cell death, thus suggesting that CMV infection may be a relevant driving force in the increased morbidity/mortality observed in HIV+ individuals with poor CD4+ T-cell recovery.

Impact of intensification with raltegravir on HIV-1-infected individuals receiving monotherapy with boosted PIs

Abstract Background Monotherapy with ritonavir-boosted PIs (PI/r) has been used to simplify treatment of HIV-1-infected patients. In previous studies raltegravir intensification evidenced ongoing viral replication and reduced T cell activation, preferentially in subjects receiving PI-based triple ART. However, data about low-level viral replication and its consequences in patients receiving PI/r monotherapy are scarce. Methods We evaluated the impact of 24 weeks of intensification with raltegravir on markers of viral persistence, cellular immune activation and inflammation biomarkers in 33 patients receiving maintenance PI/r monotherapy with darunavir or lopinavir boosted with ritonavir. ClinicalTrials.gov identifier: NCT01480713. Results The addition of raltegravir to PI/r monotherapy resulted in a transient increase in 2-LTR (long-terminal repeat) circles in a significant proportion of participants, along with decreases in CD8+ T cell activation levels and a temporary increase in the expression of the exhaustion marker CTLA-4 in peripheral T lymphocytes. Intensification with raltegravir also reduced the number of samples with intermediate levels of residual viraemia (10–60 HIV-1 RNA copies/mL) compared with samples taken during PI/r monotherapy. However, there were no changes in cell-associated HIV-1 DNA in peripheral CD4+ T cells or soluble inflammatory biomarkers (CD14, IP-10, IL-6, C-reactive protein and D-dimer). Conclusions Intensification of PI/r monotherapy with raltegravir revealed persistent low-level viral replication and reduced residual viraemia in some patients during long-term PI/r monotherapy. The concomitant change in T cell phenotype suggests an association between active viral production and T cell activation. These results contribute to understanding the lower efficacy rates of PI/r monotherapies compared with triple therapies in clinical trials.

Dynamics of CD4 and CD8 T-Cell Subsets and Inflammatory Biomarkers during Early and Chronic HIV Infection in Mozambican Adults

During primary HIV infection (PHI), there is a striking cascade response of inflammatory cytokines and many cells of the immune system show altered frequencies and signs of extensive activation. These changes have been shown to have a relevant role in predicting disease progression; however, the challenges of identifying PHI have resulted in a lack of critical information about the dynamics of early pathogenic events. We studied soluble inflammatory biomarkers and changes in T-cell subsets in individuals at PHI (n = 40), chronic HIV infection (CHI, n = 56), and HIV-uninfected (n = 58) recruited at the Manhiça District Hospital in Mozambique. Plasma levels of 49 biomarkers were determined by Luminex and ELISA. T-cell immunophenotyping was performed by multicolor flow cytometry. Plasma HIV viremia, CD4, and CD8 T cell counts underwent rapid stabilization after PHI. However, several immunological parameters, including Th1-Th17 CD4 T cells and activation or exhaustion of CD8 T cells continued decreasing until more than 9 months postinfection. Importantly, no sign of immunosenescence was observed over the first year of HIV infection. Levels of IP-10, MCP-1, BAFF, sCD14, tumor necrosis factor receptor-2, and TRAIL were significantly overexpressed at the first month of infection and underwent a prompt decrease in the subsequent months while, MIG and CD27 levels began to increase 1 month after infection and remained overexpressed for almost 1 year postinfection. Early levels of soluble biomarkers were significantly associated with subsequently exhausted CD4 T-cells or with CD8 T-cell activation. Despite rapid immune control of virus replication, the stabilization of the T-cell subsets occurs months after viremia and CD4 count plateau, suggesting persistent immune dysfunction and highlighting the potential benefit of early treatment initiation that could limit immunological damage.