Alison A. Motsinger

CD1d-restricted Human Natural Killer T Cells Are Highly Susceptible to Human Immunodeficiency Virus 1 Infection

Human natural killer (NK) T cells are unique T lymphocytes that express an invariant T cell receptor (TCR) Vα24-Vβ11 and have been implicated to play a role in various diseases. A subset of NKT cells express CD4 and hence are potential targets for human immunodeficiency virus (HIV)-1 infection. We demonstrate that both resting and activated human Vα24+ T cells express high levels of the HIV-1 coreceptors CCR5 and Bonzo (CXCR6), but low levels of CCR7, as compared with conventional T cells. Remarkably NKT cells activated with α-galactosylceramide (α-GalCer)-pulsed dendritic cells were profoundly more susceptible to infection with R5-tropic, but not X4-tropic, strains of HIV-1, compared with conventional CD4+ T cells. Furthermore, resting CD4+ NKT cells were also more susceptible to infection. After initial infection, HIV-1 rapidly replicated and depleted the CD4+ subset of NKT cells. In addition, peripheral blood NKT cells were markedly and selectively depleted in HIV-1 infected individuals. Although the mechanisms of this decline are not clear, low numbers or absence of NKT cells may affect the course of HIV-1 infection. Taken together, our findings indicate that CD4+ NKT cells are directly targeted by HIV-1 and may have a potential role during viral transmission and spread in vivo.

Multifactor dimensionality reduction: An analysis strategy for modelling and detecting gene - gene interactions in human genetics and pharmacogenomics studies

The detection of gene - gene and gene - environment interactions associated with complex human disease or pharmacogenomic endpoints is a difficult challenge for human geneticists. Unlike rare, Mendelian diseases that are associated with a single gene, most common diseases are caused by the non-linear interaction of numerous genetic and environmental variables. The dimensionality involved in the evaluation of combinations of many such variables quickly diminishes the usefulness of traditional, parametric statistical methods. Multifactor dimensionality reduction (MDR) is a novel and powerful statistical tool for detecting and modelling epistasis. MDR is a non-parametric and model-free approach that has been shown to have reasonable power to detect epistasis in both theoretical and empirical studies. MDR has detected interactions in diseases such as sporadic breast cancer, multiple sclerosis and essential hypertension.As this method is more frequently applied, and was gained acceptance in the study of human disease and pharmacogenomics, it is becoming increasingly important that the implementation of the MDR approach is properly understood. As with all statistical methods, MDR is only powerful and useful when implemented correctly. Concerns regarding dataset structure, configuration parameters and the proper execution of permutation testing in reference to a particular dataset and configuration are essential to the methods effectiveness.The detection, characterisation and interpretation of gene - gene and gene - environment interactions are expected to improve the diagnosis, prevention and treatment of common human diseases. MDR can be a powerful tool in reaching these goals when used appropriately.

Drug Transporter and Metabolizing Enzyme Gene Variants and Nonnucleoside Reverse-Transcriptase Inhibitor Hepatotoxicity

This nested case-control study examined relationships between MDR1, CYP2B6, and CYP3A4 variants and hepatotoxicity during antiretroviral therapy with either efavirenz- or nevirapine-containing regimens. Decreased risk of hepatotoxicity was associated with MDR1 3435C-->T (odds ratio, 0.254; P=.021). An interaction between MDR1 and hepatitis B surface antigen status predicted risk with 82% accuracy (P<.001).

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

BackgroundThe identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinsons disease.ResultsWe show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (<1%) or when interactions involved more than three loci. We tested GPNN on a real dataset comprised of Parkinsons disease cases and controls and found a two locus interaction between the DLST gene and sex.ConclusionThese results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.

Multifactor dimensionality reduction for detecting gene–gene and gene–environment interactions in pharmacogenomics studies

In the quest for discovering disease susceptibility genes, the reality of gene-gene and gene-environment interactions creates difficult challenges for many current statistical approaches. In an attempt to overcome limitations with current disease gene detection methods, the multifactor dimensionality reduction (MDR) approach was previously developed. In brief, MDR is a method that reduces the dimensionality of multilocus information to identify polymorphisms associated with an increased risk of disease. This approach takes multilocus genotypes and develops a model for defining disease risk by pooling high-risk genotype combinations into one group and low-risk combinations into another. Cross-validation and permutation testing are used to identify optimal models. While this approach was initially developed for studies of complex disease, it is also directly applicable to pharmacogenomic studies where the outcome variable is drug treatment response/nonresponse or toxicity/no toxicity. MDR is a nonparametric and model-free approach that has been shown to have reasonable power to detect epistasis in both theoretical and empirical studies. This computational technology is described in detail in this review, and its application in pharmacogenomic studies is demonstrated.

Data simulation software for whole-genome association and other studies in human genetics.

Genome-wide association studies have become a reality in the study of the genetics of complex disease. This technology provides a wealth of genomic information on patient samples, from which we hope to learn novel biology and detect important genetic and environmental factors for disease processes. Because strategies for analyzing these data have not kept pace with the laboratory methods that generate the data it is unlikely that these advances will immediately lead to an improved understanding of the genetic contribution to common human disease and drug response. Currently, no single analytical method will allow us to extract all information from a whole-genome association study. Thus, many novel methods are being proposed and developed. It will be vital for the success of these new methods, to have the ability to simulate datasets consisting of polymorphisms throughout the genome with realistic linkage disequilibrium patterns. Within these datasets, we can embed genetic models of disease whereby we can evaluate the ability of novel methods to detect these simulated effects. This paper describes a new software package, genomeSIM, for the simulation of large-scale genomic data in population based case-control samples. It allows for single SNP, as well as gene-gene interaction models to be associated with disease risk. We describe the algorithm and demonstrate its utility for future genetic studies of whole-genome association.

Genetic Basis for Adverse Events after Smallpox Vaccination

Identifying genetic factors associated with the development of adverse events might allow screening before vaccinia virus administration. Two independent clinical trials of the smallpox vaccine (Aventis Pasteur) were conducted in healthy, vaccinia virus-naive adult volunteers. Volunteers were assessed repeatedly for local and systemic adverse events (AEs) associated with the receipt of vaccine and underwent genotyping for 1,442 singlenucleotide polymorphisms (SNPs). In the first study, 36 SNPs in 26 genes were associated with systemic AEs (P <or= .05); these 26 genes were tested in the second study. In the final analysis, 3 SNPs were consistently associated with AEs in both studies. The presence of a nonsynonymous SNP in the methylenetetrahydrofolate reductase (MTHFR)gene was associated with the risk ofAEin both trials (odds ratio [OR], 2.3 [95% confidence interval {CI}, 1.1-5.2] [P = .04] and OR, 4.1 [95% CI, 1.4 -11.4] [P<.01]). Two SNPs in the interferon regulatory factor-1 (IRF1) gene were associated with the risk of AE in both sample sets (OR, 3.2 [95% CI, 1.1-9.8] [P = .03] andOR, 3.0 [95% CI, 1.1- 8.3] [P = .03]). Genetic polymorphisms in genes expressing an enzyme previously associated with adverse reactions to a variety of pharmacologic agents (MTHFR) and an immunological transcription factor (IRF1) were associated with AEs after smallpox vaccination in 2 independent study samples.

Immunogenetics of CD4 Lymphocyte Count Recovery during Antiretroviral Therapy: An AIDS Clinical Trials Group Study

During antiretroviral therapy, CD4 lymphocyte count increases are modest in some patients despite virologic control. We explored whether polymorphisms in genes important for T cell expansion, survival, and apoptosis are associated with the magnitude of CD4 lymphocyte count recovery during antiretroviral therapy. We studied treatment-naive individuals who achieved sustained control of plasma viremia (<400 HIV-1 RNA copies/mL) for at least 48 weeks after initiation of antiretroviral therapy and compared genotypes among individuals who had an increase of either <200 or > or =200 CD4 cells/mm3 from baseline. A total of 137 single-nucleotide polymorphisms across 17 genes were characterized in 873 study participants. In multivariate analyses that controlled for clinical variables, polymorphisms in genes encoding tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), TNF- alpha , Bcl-2-interacting molecule (Bim), interleukin (IL)-15, and IL-15 receptor alpha chain (IL-15R alpha ) were associated with the magnitude of the increase in CD4 lymphocyte count, as were haplotypes in genes encoding interferon- alpha , IL-2, and IL-15R alpha (P < .05, for each). Multifactor dimensionality reduction identified a gene-gene interaction between IL-2/IL-15 receptor common beta chain and IL-2/IL-7/IL-15 receptor common gamma chain. Immune recovery during antiretroviral therapy is a complex phenotype that is influenced by multiple genetic variants. Future studies should validate these tentative associations and define underlying mechanisms.

Novel methods for detecting epistasis in pharmacogenomics studies

The importance of gene-gene and gene-environment interactions in the underlying genetic architecture of common, complex phenotypes is gaining wide recognition in the field of pharmacogenomics. In epidemiological approaches to mapping genetic variants that predict drug response, it is important that researchers investigate potential epistatic interactions. In the current review, we discuss data-mining tools available in genetic epidemiology to detect such interactions and appropriate applications. We survey several classes of novel methods available and present an organized collection of successful applications in the literature. Finally, we provide guidance as to how to incorporate these novel methods into a genetic analysis. The overall goal of this paper is to aid researchers in developing an analysis plan that accounts for gene-gene and gene-environment in their own work.

Genetic programming neural networks: A powerful bioinformatics tool for human genetics

The identification of genes that influence the risk of common, complex disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of genetic and gene-environment combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene and gene-environment interactions. The goal of this study was to compare the power of GPNN to stepwise logistic regression (SLR) and classification and regression trees (CART) for identifying gene-gene and gene-environment interactions. SLR and CART are standard methods of analysis for genetic association studies. Using simulated data, we show that GPNN has higher power to identify gene-gene and gene-environment interactions than SLR and CART. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions in studies of human disease.