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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Risk factor interactions and genetic effects associated with post-operative atrial fibrillation.

Postoperative Atrial Fibrillation (PoAF) is the most common arrhythmia after heart surgery, and continues to be a major cause of morbidity. Due to the complexity of this condition, many genes and/or environmental factors may play a role in susceptibility. Previous findings have shown several clinical and genetic risk factors for the development of PoAF. The goal of this study was to determine whether interactions among candidate genes and a variety of clinical factors are associated with PoAF. We applied the Multifactor Dimensionality Reduction (MDR) method to detect interactions in a sample of 940 adult subjects undergoing elective procedures of the heart or great vessels, requiring general anesthesia and sternotomy or thoracotomy, where 255 developed PoAF. We took a random sample of controls matched to the 255 AF cases for a total sample size of 510 individuals. MDR is a powerful statistical approach used to detect gene-gene or gene-environment interactions in the presence or absence of statistically detectable main effects in pharmacogenomics studies. We chose polymorphisms in three (IL-6, ACE, and ApoE) candidate genes, all previously implicated in PoAF risk, and a variety of environmental factors for analysis. We detected a single locus effect of IL-6 which is able to correctly predict disease status with 58.8% (p<0.001) accuracy. We also detected an interaction between history of AF and length of hospital stay that predicted disease status with 68.34% (p<0.001) accuracy. These findings demonstrate the utility of novel computational approaches for the detection of disease susceptibility genes. While each of these results looks interesting, they only explain part of PoAF susceptibility. It will be important to collect a larger set of candidate genes and environmental factors to better characterize the development of PoAF. Applying this approach, we were able to elucidate potential associations with postoperative atrial fibrillation.

Multilocus genetic interactions and response to efavirenz-containing regimens: an adult AIDS clinical trials group study.

OBJECTIVE: For the HIV-1 reverse transcriptase inhibitor efavirenz, variant drug transporter gene ABCB1 may predict virologic response but not plasma efavirenz exposure. Conversely, variant drug metabolizing enzyme gene CYP2B6 predicts greater plasma efavirenz exposure but not virologic response. We examined whether long-term responses to efavirenz, and/or plasma efavirenz exposure, are better predicted by multilocus genetic interactions than by individual polymorphisms. MATERIALS AND METHODS: We studied antiretroviral-naïve study participants randomized to receive efavirenz (with or without nelfinavir) plus two nucleoside analogues in study ACTG 384, and who had DNA available for analysis. Participants were followed up for up to 3 years. Nine single nucleotide polymorphisms in ABCB1, CYP2B6, CYP3A4, CYP3A5 and CYP2C19 were identified. Gene-gene interactions were identified using multifactor dimensionality reduction. RESULTS: Among 340 efavirenz recipients, higher efavirenz AUC24 h values were associated with a single locus model involving CYP2B6 516G>T (73% accuracy; P<0.001). This was also the best model among blacks (69% accuracy; P<0.001), whereas among whites the best model involved a gene-gene interaction between CYP2B6 516G>T and ABCB1 2677G>T (82% accuracy, P<0.001). Among 155 participants who received efavirenz without nelfinavir, virologic failure was associated with a two-locus interaction between ABCB1 2677G>T and CYP2B6 516G>T (65% accuracy, P<0.001). Toxicity failure was best predicted by an interaction between ABCB1 2677G>T and ABCB1 3435C>T (71% accuracy, P<0.001). CONCLUSIONS: Multilocus genetic interactions between variant drug metabolism and transporter genes may predict efavirenz pharmacokinetics and treatment responses. This finding may have implications for better individualizing antiretroviral therapy.

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.

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).