Polymorphism modulates symptomatic response to antiarrhythmic drug therapy in patients with lone atrial fibrillation.

BACKGROUND: The angiotensin-converting enzyme (ACE) deletion allele, ACE D, is associated with increased ACE activity and adverse outcomes in cardiovascular disease. Although activation of the renin-angiotensin-aldosterone system (RAAS) now appears to play a role in the pathophysiology of atrial fibrillation (AF), it remains to be determined if ACE genotype impacts response to conventional AAD therapy in patients with AF. OBJECTIVES: The purpose of this study was to investigate whether response to antiarrhythmic drug (AAD) therapy in patients with AF is modulated by the ACE I/D polymorphism. METHODS: We studied 213 patients (147 men, 66 women; ages 52 +/- 15 years) prospectively enrolled in the Vanderbilt AF Registry. AAD therapy outcome was defined prospectively as response if there was a >or=75% reduction in symptomatic AF burden or nonresponse if AF burden was unchanged, necessitating a change in drugs or therapy. RESULTS: Lone AF (age <65 years, no identifiable cause) was present in 72 (34%) patients, whereas hypertension was the most common underlying disease in the remaining 141 (41%). AF was paroxysmal in 170 (80%) and persistent in 43 (20%). The frequencies of the DD, ID, and II genotypes were in Hardy-Weinberg equilibrium. Lone AF and DD/ID genotypes were highly significant predictors of failure of drug therapy (P <.005). In patients with lone AF, failure of drug response was 5%, 41%, and 47% in patients with II, ID, and DD genotypes, respectively, (P <.005, II vs. ID/DD). CONCLUSIONS: These results provide further evidence for a role of RAAS activation in the pathophysiology of AF and point to a potential role for stratification of therapeutic approaches by ACE genotype.

Genetic variation in the mitochondrial enzyme carbamyl-phosphate synthetase I predisposes children to increased pulmonary artery pressure following surgical repair of congenital heart defects: a validated genetic association study.

Increased pulmonary artery pressure (PAP) can complicate the postoperative care of children undergoing surgical repair of congenital heart defects. Endogenous NO regulates PAP and is derived from arginine supplied by the urea cycle. The rate-limiting step in the urea cycle is catalyzed by a mitochondrial enzyme, carbamoyl-phosphate synthetase I (CPSI). A well-characterized polymorphism in the gene encoding CPSI (T1405N) has previously been implicated in neonatal pulmonary hypertension. A consecutive modeling cohort of children (N=131) with congenital heart defects requiring surgery was prospectively evaluated to determine key factors associated with increased postoperative PAP, defined as a mean PAP>20 mmHg for at least 1h during the 48h following surgery measured by an indwelling pulmonary artery catheter. Multiple dimensionality reduction (MDR) was used to both internally validate observations and develop optimal two-variable through five-variable models that were tested prospectively in a validation cohort (N=41). Unconditional logistic regression analysis of the modeling cohort revealed that age (OR=0.92, p=0.01), CPSI T1405N genotype (AC vs. AA: OR=4.08, p=0.04, CC vs. AA: OR=5.96, p=0.01), and Down syndrome (OR=5.25, p=0.04) were independent predictors of this complex phenotype. MDR predicted that the best two-variable model consisted of age and CPSI T1405N genotype (p<0.001). This two-variable model correctly predicted 73% of the outcomes from the validation cohort. A five-variable model that added race, gender and Down's syndrome was not significantly better than the two-variable model. In conclusion, the CPSI T1405N genotype appears to be an important new factor in predicting susceptibility to increased PAP following surgical repair of congenital cardiac defects in children.

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 method's 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.

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.

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

BACKGROUND: The 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 Parkinson's disease. RESULTS: We 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 Parkinson's disease cases and controls and found a two locus interaction between the DLST gene and sex. CONCLUSION: These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.

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).

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.

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

Hemochromatosis gene polymorphisms, mitochondrial haplogroups, and peripheral lipoatrophy during antiretroviral therapy.

BACKGROUND: Antiretroviral therapy (ART)-associated lipoatrophy involves mitochondrial dysfunction. Iron metabolism impacts mitochondrial function and oxidative stress. Mitochondrial haplogroups and hemochromatosis gene (HFE) polymorphisms have been associated with ART-induced neuropathy. We assessed relationships between these variants and lipoatrophy. METHODS: The AIDS Clinical Trials Group 384 study randomized ART-naive individuals to receive didanosine-stavudine or zidovudine-lamivudine, combined with efavirenz and/or nelfinavir. Substudy A5005s evaluated fat distribution by dual-energy X-ray absorptiometry (DEXA). We characterized HFE polymorphisms 845G>A and 187C>G and European mitochondrial haplogroups in A5005s participants who consented to genetic analyses. RESULTS: Among 96 participants (58% were white, and 10% were female) with baseline and 48 or 64 week DEXA data, the median limb fat change was -8.8% (interquartile range, -28.7% to +15.6%). HFE 187C/G heterozygotes (n = 23) had less limb fat loss than 187C/C homozygotes (n = 71) (+6.1% vs. -12.5%; P = .02) and were less likely to develop lipoatrophy after adjustment for age, sex, race, and ART randomization (odds ratio, 0.31; 95% confidence interval, 0.10-0.95; P = .04). Among non-Hispanic white participants, median limb fat change was +26.1% among 5 participants with mitochondrial haplogroup J, compared with -9.7% among 49 participants with other mitochondrial haplogroups (P = .07). CONCLUSIONS: HFE 187C>G and, possibly, mitochondrial haplogroup J gave relative protection against lipoatrophy during ART in A5005s. These associations should be replicated in other studies.

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.

Human genomic association studies: a primer for the infectious diseases specialist.

Medical science is undergoing a genomic revolution. In coming years, insights from human genomic research will increasingly influence all aspects of infectious diseases, ranging from fundamental laboratory research and clinical care to epidemiology and global health. Infectious disease specialists unfamiliar with genomic methods and computational techniques may shy away from publications that involve human genomics analyses. In this article, we discuss selected aspects of study design and statistical analysis in this area, emphasizing important pitfalls that may compromise the validity of some studies. Our goal is to provide the infectious diseases specialist with information that will aid in the critical evaluation of publications that include human genomic analyses.

Exploring epistasis in candidate genes for rheumatoid arthritis.

The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data.

Linkage Disequilibrium in Genetic Association Studies Improves the Performance of Grammatical Evolution Neural Networks.

One of the most important goals in genetic epidemiology is the identification of genetic factors/features that predict complex diseases. The ubiquitous nature of gene-gene interactions in the underlying etiology of common diseases creates an important analytical challenge, spurring the introduction of novel, computational approaches. One such method is a grammatical evolution neural network (GENN) approach. GENN has been shown to have high power to detect such interactions in simulation studies, but previous studies have ignored an important feature of most genetic data: linkage disequilibrium (LD). LD describes the non-random association of alleles not necessarily on the same chromosome. This results in strong correlation between variables in a dataset, which can complicate analysis. In the current study, data simulations with a range of LD patterns are used to assess the impact of such correlated variables on the performance of GENN. Our results show that not only do patterns of strong LD not decrease the power of GENN to detect genetic associations, they actually increase its power.

A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction.

Multifactor dimensionality reduction (MDR) was developed as a method for detecting statistical patterns of epistasis. The overall goal of MDR is to change the representation space of the data to make interactions easier to detect. It is well known that machine learning methods may not provide robust models when the class variable (e.g. case-control status) is imbalanced and accuracy is used as the fitness measure. This is because most methods learn patterns that are relevant for the larger of the two classes. The goal of this study was to evaluate three different strategies for improving the power of MDR to detect epistasis in imbalanced datasets. The methods evaluated were: (1) over-sampling that resamples with replacement the smaller class until the data are balanced, (2) under-sampling that randomly removes subjects from the larger class until the data are balanced, and (3) balanced accuracy [(sensitivity+specificity)/2] as the fitness function with and without an adjusted threshold. These three methods were compared using simulated data with two-locus epistatic interactions of varying heritability (0.01, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4) and minor allele frequency (0.2, 0.4) that were embedded in 100 replicate datasets of varying sample sizes (400, 800, 1600). Each dataset was generated with different ratios of cases to controls (1 : 1, 1 : 2, 1 : 4). We found that the balanced accuracy function with an adjusted threshold significantly outperformed both over-sampling and under-sampling and fully recovered the power. These results suggest that balanced accuracy should be used instead of accuracy for the MDR analysis of epistasis in imbalanced datasets.

Complex gene-gene interactions in multiple sclerosis: a multifactorial approach reveals associations with inflammatory genes.

The complex inheritance involved in multiple sclerosis (MS) risk has been extensively investigated, but our understanding of MS genetics remains rudimentary. In this study, we explore 51 single nucleotide polymorphisms (SNPs) in 36 candidate genes from the inflammatory pathway and test for gene-gene interactions using complementary case-control, discordant sibling pair, and trio family study designs. We used a sample of 421 carefully diagnosed MS cases and 96 unrelated, healthy controls; discordant sibling pairs from 146 multiplex families; and 275 trio families. We used multifactor dimensionality reduction to explore gene-gene interactions. Based on our analyses, we have identified several statistically significant models including both main effect models and two-locus, three-locus, and four-locus epistasis models that predict MS disease risk with between approximately 61% and 85% accuracy. These results suggest that significant epistasis, or gene-gene interactions, may exist even in the absence of statistically significant individual main effects.

ABCB1 and GST polymorphisms associated with TP53 status in breast cancer.

BACKGROUND AND OBJECTIVE: Many environmental and genetic factors influence the development of chemoresistance. The goal of this study was to characterize the genetic variation in the ABCB1, GSTM1, GSTT1 and GSTP1 genes, as well as the haplotype structure in the ABCB1 gene. METHODS: Variants in these genes were studied in 109 healthy controls and 93 breast cancer cases, both of Caucasian origin. The cases were analyzed in relation to TP53 mutation status and response to doxorubicin. Both single and multiple single nucleotide polymorphism analyses were performed. RESULTS: Chi-square analyses revealed a significant association between TP53 mutation status and both the GA genotype of ABCB1 exon 11 (Ser400Asn) and the GG genotype of GSTP1 (Ile105Val; P<0.01 and P<0.05, respectively). Multifactor dimensionality reduction showed that carriers of the combined GG genotype for GSTP1 and the GG for ABCB1 exon 11 had the highest chance of acquiring a mutation in the TP53 gene (P<0.02). Haplotype analysis of ABCB1 revealed a significantly different distribution of haplotypes between the breast cancer cases and the controls (P<0.01). A specific haplotype association to TP53 mutation (P<0.01) distant metastases (P<0.05) and estrogen receptor status (P<0.05) was also observed in the case group. CONCLUSION: An association between polymorphisms in GSTP1 and ABCB1 and risk of acquiring intratumoral TP53 mutations suggests the existence of putative predisposing genotype backgrounds. The degree of linkage disequilibrium in the ABCB1 gene was higher in healthy individuals, whereas haplotypes in the cases seemed degenerated by a number of low frequency variants. This observation may either point to the existence of a protective haplotype in the controls or may underline the importance of the accumulation of low frequency variants as susceptibility factors.

Discussing gene-gene interaction: warning--translating equations to English may result in jabberwocky.

Interest in mapping susceptibility alleles for complex diseases, which do not follow a classic single-gene segregation pattern, has driven interest in methods that account for, or use information from one locus when mapping another. Our discussion group examined methods related to epistasis or gene x gene interaction. The goal of modeling gene x gene interaction varied across groups; some papers tried to detect gene x gene interaction while others tried to exploit it to map genes. Most of the 10 papers summarized here applied newly created or newly modified statistical methods related to gene x gene interaction, while two groups primarily examined computational issues. As is often the case, comparisons are complicated by little overlap in the data used across the papers, and further complicated by the fact that the available data may not have been ideal for some gene x gene interaction methods. However, the main difficulty in comparing and contrasting methods across the papers is the lack of a consistent statistical definition of gene x gene interaction. But despite these issues, two clear trends emerged across the analyses: First, the methods for quantitative trait gene x gene interaction appeared to perform very well, even in families initially ascertained as affected sib pairs; and second, dichotomous trait gene x gene interaction methods failed to produce consistent results. The difficulty of using (primarily) affected sib pair data in a gene x gene interaction analysis is explored.

The effect of reduction in cross-validation intervals on the performance of multifactor dimensionality reduction.

Multifactor Dimensionality Reduction (MDR) was developed to detect genetic polymorphisms that present an increased risk of disease. Cross-validation (CV) is an important part of the MDR algorithm, as it prevents over-fitting and allows the predictive ability of a model to be evaluated. CV is a computationally intensive step in the MDR algorithm. Traditionally, MDR has been implemented using 10-fold CV. In order to reduce computation time and therefore allow MDR analysis to be applied to larger datasets, we evaluated the possibility of eliminating or reducing the number of CV intervals used for analysis. We found that eliminating CV made final model selection impossible, but that reducing the number of CV intervals from ten to five caused no loss of power, thereby reducing the computation time of the algorithm by half. The validity of this reduction was confirmed with data from an Alzheimer's disease (AD) study.

Clinical applications of whole-genome association studies: future applications at the bedside.

Until now, performing whole-genome association studies has been an unattainable, but highly desirable, goal for geneticists. With the recent advent of high-throughput genotyping platforms, this goal is now a reality for geneticists today and for clinicians in the not-so-distant future. This review will cover a broad range of topics to provide an overview of this emerging branch of genetics, and will provide references to more specific sources. Specifically, this review will cover the technologies available today and in the near future, the specific types of whole-genome association studies, the benefits and limitations of these studies, the applications to complex disease-gene interactions, diagnostic devices, therapeutics, and finally, we will describe the 5-year perspective and key issues.

Human natural killer T cells are heterogeneous in their capacity to reprogram their effector functions.

BACKGROUND: Natural killer T (NKT) cells are a subset of T cells that help potentiate and regulate immune responses. Although human NKT cell subsets with distinct effector functions have been identified, it is unclear whether the effector functions of these subsets are imprinted during development or can be selectively reprogrammed in the periphery. RESULTS: We found that neonatal NKT cells are predominantly CD4+ and express higher levels of CCR7 and CD62L and lower levels of CD94 and CD161 than adult CD4+ or CD4- NKT cell subsets. Accordingly, neonatal NKT cells were more flexible than adult CD4+ NKT cells in their capacity to acquire Th1- or Th2-like functions upon either cytokine-mediated polarization or ectopic expression of the Th1 or Th2 transcription factors T-bet and GATA-3, respectively. Consistent with their more differentiated phenotype, CD4- NKT cells were predominantly resistant to functional reprogramming and displayed higher cytotoxic function. In contrast to conventional T cells, neither the expression of CXCR3 nor the cytotoxic capacity of neonatal NKT cells could be reprogrammed. CONCLUSIONS AND SIGNIFICANCE: Together, these results suggest that neonatal CD4+, adult CD4+, and adult CD4- NKT may represent unique states of maturation and that some functions of human NKT cells may be developmentally imprinted, while others are acquired similar to conventional T cell subsets during peripheral maturation and differentiation. Given the potent immuno-regulatory functions of NKT cells, these findings have important implications for the development of novel NKT cell-based therapeutics and vaccines.