Multi-locus interactions predict risk for post-PTCA restenosis: an approach to the genetic analysis of common complex disease

Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis

PURPOSE

Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable.

METHODS

We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index.

RESULTS

This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways.

CONCLUSION

Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

Pathogenesis and Clinical Significance of In-Stent Restenosis in Patients with Diabetes

Diabetes mellitus (DM) is a strong risk factor for the development of cardiovascular diseases such as coronary heart disease, cerebrovascular disease, and peripheral arterial disease (PAD). In the population of people living with DM, PAD is characterised by multi-level atherosclerotic lesions as well as greater involvement of the arteries below the knee. DM is also a factor that significantly increases the risk of lower limb amputation. Percutaneous balloon angioplasty with or without stent implantation is an important method of the treatment for atherosclerotic cardiovascular diseases, but restenosis is a factor limiting its long-term effectiveness. The pathogenesis of atherosclerosis in the course of DM differs slightly from that in the general population. In the population of people living with DM, more attention is drawn to such factors as inflammation, endothelial dysfunction, platelet dysfunction, blood rheological properties, hypercoagulability, and additional factors stimulating vascular smooth muscle cell proliferation. DM is a risk factor for restenosis. The purpose of this paper is to provide a review of the literature and to present the most important information on the current state of knowledge on mechanisms and the clinical significance of restenosis and in-stent restenosis in patients with DM, especially in association with the endovascular treatment of PAD. The role of such processes as inflammation, neointimal hyperplasia and neoatherosclerosis, allergy, resistance to antimitotic drugs used for coating stents and balloons, genetic factors, and technical and mechanical factors are discussed. The information on restenosis collected in this publication may be helpful in planning further research in this field, which may contribute to the formulation of more and more precise recommendations for the clinical practice.

Genetic risk factors for restenosis after percutaneous coronary intervention in Kazakh population

BACKGROUND

After coronary stenting, the risk of developing restenosis is from 20 to 35 %. The aim of the present study is to investigate the association of genetic variation in candidate genes in patients diagnosed with restenosis in the Kazakh population.

METHODS

Four hundred fifty-nine patients were recruited to the study; 91 patients were also diagnosed with diabetes and were excluded from the sampling. DNA was extracted with the salting-out method. The patients were genotyped for 53 single-nucleotide polymorphisms. Genotyping was performed on the QuantStudio 12K Flex (Life Technologies). Differences in distribution of BMI score among different genotype groups were compared by analysis of variance (ANOVA). Also, statistical analysis was performed using R and PLINK v.1.07. Haplotype frequencies and LD measures were estimated by using the software Haploview 4.2.

RESULTS

A logistic regression analysis found a significant difference in restenosis rates for different genotypes. FGB (rs1800790) is significantly associated with restenosis after stenting (OR = 2.924, P = 2.3E-06, additive model) in the Kazakh population. CD14 (rs2569190) showed a significant association in the additive (OR = 0.08033, P = 2.11E-09) and dominant models (OR = 0.05359, P = 4.15E-11). NOS3 (rs1799983) was also highly associated with development of restenosis after stenting in additive (OR = 20.05, P = 2.74 E-12) and recessive models (OR = 22.24, P = 6.811E-10).

CONCLUSIONS

Our results indicate that FGB (rs1800790), CD14 (rs2569190), and NOS3 (rs1799983) SNPs could be genetic markers for development of restenosis in Kazakh population. Adjustment for potential confounder factor BMI gave almost the same results.

Forward LASSO analysis for high-order interactions in genome-wide association study

Previous genome-wide association study (GWAS) focused on low-order interactions between pairwise single-nucleotide polymorphisms (SNPs) with significant main effects. Little is known how high-order interactions effect, especially one among the SNPs without main effects regulates quantitative traits. Within the frameworks of linear model and generalized linear model, the LASSO with coordinate descent step can be used to simultaneously analyze thousands and thousands of SNPs for normal and discrete traits. With consideration of high-order interactions among SNPs, a huge number of genetic effects make the LASSO failing to work under the presented condition of computation. Forward LASSO analysis is, therefore, proposed to shrink most of genetic effects to be zeros stage by stage. Simulation demonstrates that our proposed method could be used instead of the LASSO method for full model in mapping high-order interactions. Application of forward LASSO method is provided to GWAS for carcass traits and meat quality traits in beef cattle.

Novel potential targets for prevention of arterial restenosis: insights from the pre-clinical research

Restenosis is the pathophysiological process occurring in 10-15% of patients submitted to revascularization procedures of coronary, carotid and peripheral arteries. It can be considered as an excessive healing reaction of the vascular wall subjected to arterial/venous bypass graft interposition, endarterectomy or angioplasty. The advent of bare metal stents, drug-eluting stents and of the more recent drug-eluting balloons, have significantly reduced, but not eliminated, the incidence of restenosis, which remains a clinically relevant problem. Biomedical research in pre-clinical animal models of (re)stenosis, despite its limitations, has contributed enormously to the identification of processes involved in restenosis progression, going well beyond the initial dogma of a primarily proliferative disease. Although the main molecular and cellular mechanisms underlying restenosis have been well described, new signalling molecules and cell types controlling the progress of restenosis are continuously being discovered. In particular, microRNAs and vascular progenitor cells have recently been shown to play a key role in this pathophysiological process. In addition, the advanced highly sensitive high-throughput analyses of molecular alterations at the transcriptome, proteome and metabolome levels occurring in injured vessels in animal models of disease and in human specimens serve as a basis to identify novel potential therapeutic targets for restenosis. Molecular analyses are also contributing to the identification of reliable circulating biomarkers predictive of post-interventional restenosis in patients, which could be potentially helpful in the establishment of an early diagnosis and therapy. The present review summarizes the most recent and promising therapeutic strategies identified in experimental models of (re)stenosis and potentially translatable to patients subjected to revascularization procedures.

Analysis of gene-gene interactions

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

Bayesian epistasis association mapping via SNP imputation

Genetic mutations may interact to increase the risk of human complex diseases. Mapping of multiple interacting disease loci in the human genome has recently shown promise in detecting genes with little main effects. The power of interaction association mapping, however, can be greatly influenced by the set of single nucleotide polymorphism (SNP) genotyped in a case-control study. Previous imputation methods only focus on imputation of individual SNPs without considering their joint distribution of possible interactions. We present a new method that simultaneously detects multilocus interaction associations and imputes missing SNPs from a full Bayesian model. Our method treats both the case-control sample and the reference data as random observations. The output of our method is the posterior probabilities of SNPs for their marginal and interacting associations with the disease. Using simulations, we show that the method produces accurate and robust imputation with little overfitting problems. We further show that, with the type I error rate maintained at a common level, SNP imputation can consistently and sometimes substantially improve the power of detecting disease interaction associations. We use a data set of inflammatory bowel disease to demonstrate the application of our method.

Lipopolysaccharide, CD14 and Toll-like receptors: an emerging link between innate immunity and atherosclerotic disease

Atherosclerosis and its clinical complications are now understood to be an inflammatory syndrome in which an ongoing systemic inflammatory response is combined with the accumulation of immune cells in the atherosclerotic plaque. Both arms of the immune system, innate and adaptive, have been implicated in contributing to essentially all stages of atherosclerosis, from initiation to progression and, ultimately, atherothrombotic complications. Innate immunity is the first line of defense against invading microorganisms. The recognition units of the innate immune system are designed to respond to molecular patterns shared by a variety of infectious microorganisms, such as bacterial lipopolysaccharide. Numerous basic and clinical studies have provided evidence that responsiveness to lipopolysaccharide may be correlated to the risk of atherosclerotic disease. The molecular basis of this connection appears to lie in Toll-like receptors that are expressed on cells of the innate immune system, bind to lipopolysaccharide, and thus determine the strength of antibacterial immune responses in the host. Variations in the function of Toll-like receptors and their signaling pathways are now suspected to play a critical role in determining the risk of atherosclerosis. This review summarizes recent research advances exploring the role of innate immunity, particularly lipopolysaccharide, CD14 and Toll-like receptors, in the initiation and development of atherosclerotic disease.

Lack of association between the cholesteryl ester transfer protein gene--TaqIB polymorphism and coronary restenosis following percutaneous transluminal coronary angioplasty and stenting: a pilot study

BACKGROUND

The most widely studied variation at the cholesteryl ester transfer protein (CETP) gene locus is a silent base change called the Thermobius aquaticus IB (TaqIB) polymorphism. TaqIB has been shown to affect levels/activity of CETP, plasma levels of high-density lipoprotein cholesterol (HDL-C), and to contribute to the risk of developing atherosclerosis and coronary heart disease (CHD). Ongoing studies are investigating possible associations between CETP gene polymorphisms and the development of coronary restenosis following percutaneous transluminal coronary angioplasty (PTCA) and stenting.

METHODS AND RESULTS

The primary objective of the present study was to investigate the frequency of TaqIB-polymorphism, and a possible association with post-PTCA coronary restenosis, in 204 Greek patients who had undergone PTCA and stenting. As a secondary objective, the analysis was extended to explore possible interacting or additive effects by various CHD risk factors, and a deletion in the alpha(2B)-adrenergic receptor gene. The frequency of TaqIB was 54%, similar to the frequency of the polymorphism in a group of 35 healthy controls.

CONCLUSIONS

The results from this study do not indicate that the TaqIB variation at the CETP gene locus is a significant predictor for assessing the risk of developing coronary restenosis following PTCA and stenting. This result was not affected when considering any one of the additionally studied factors.

Detecting disease-associated genotype patterns

Background

In addition to single-locus (main) effects of disease variants, there is a growing consensus that gene-gene and gene-environment interactions may play important roles in disease etiology. However, for the very large numbers of genetic markers currently in use, it has proven difficult to develop suitable and efficient approaches for detecting effects other than main effects due to single variants.

Results

We developed a method for jointly detecting disease-causing single-locus effects and gene-gene interactions. Our method is based on finding differences of genotype pattern frequencies between case and control individuals. Those single-nucleotide polymorphism markers with largest single-locus association test statistics are included in a pattern. For a logistic regression model comprising three disease variants exerting main and epistatic interaction effects, we demonstrate that our method is vastly superior to the traditional approach of looking for single-locus effects. In addition, our method is suitable for estimating the number of disease variants in a dataset. We successfully apply our approach to data on Parkinson Disease and heroin addiction.

Conclusion

Our approach is suitable and powerful for detecting disease susceptibility variants with potentially small main effects and strong interaction effects. It can be applied to large numbers of genetic markers.

Analysis of gene-gene interactions

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

Association between polymorphisms in the beta2-adrenoceptor gene and migraine in women

OBJECTIVE

To investigate the role of three common polymorphisms in the beta2-adrenoceptor gene in migraine.

BACKGROUND

Migraine has been associated with increased risk of cardiovascular disease and asthma in which beta2-adrenoceptors play an important role; beta-adrenoceptor antagonists are used in migraine prevention. However, the role of variants in the beta2-adrenoceptor gene in migraine is unclear.

METHODS

Association study among 23,753 white women, participating in the Women's Health Study, for whom we had information on migraine at baseline and genotype status of the polymorphisms rs1042713 (Gly16Arg), rs1042714 (Gln27Glu), rs1800888 (Thr164Ile). Migraine was self-reported and we distinguished between any history of migraine, active migraine with and without aura, and prior migraine (history of migraine but not active migraine) in our analyses.

RESULTS

At baseline 4339 women reported any history of migraine. Of these, 3041 had active migraine (1221 migraine with aura, 1820 migraine without aura) and 1298 prior migraine. No migraine was reported by 19,414 women. Genotype- and haplotype-based analyses did not show an association of any of the gene variants tested with any history of migraine. The multivariable-adjusted odds ratios (ORs) (95% confidence intervals) for any history of migraine in the additive model were 1.0 (0.96-1.05) for rs1042713, 1.0 (0.95-1.05) for rs1042714, and 0.84 (0.68-1.05) for rs1800888. In the haplotype analysis the ORs ranged from 0.83 (0.67-1.03) to 1.01 (0.94-1.07) with Gly16-Glu27-Thr164 as the reference. We also did not find associations in the genotype- and haplotype-based analyses within migraine-specific subgroups.

CONCLUSIONS

Our results do not support a role of 3 investigated polymorphisms in the beta2-adrenoceptor gene in migraine pathophysiology.

Fine-scale genetic mapping using independent component analysis

The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process. Given this model, we propose a new fine-scale genetic mapping method based on independent component analysis. The proposed method outputs both independent associated groups of SNPs in addition to specific associated SNPs with the phenotype. It is applied to a clinical data set for the Schizophrenia disease with 368 individuals and 42 SNPs. It is also applied to a simulation study to investigate in more depth its performance. The obtained results demonstrate the novel characteristics of the proposed method compared to other genetic mapping methods. Finally, we study the robustness of the proposed method with missing genotype values and limited sample sizes.

Variable selection in logistic regression for detecting SNP-SNP interactions: the rheumatoid arthritis example

Many complex disease traits are observed to be associated with single nucleotide polymorphism (SNP) interactions. In testing small-scale SNP-SNP interactions, variable selection procedures in logistic regressions are commonly used. The empirical evidence of variable selection for testing interactions in logistic regressions is limited. This simulation study was designed to compare nine variable selection procedures in logistic regressions for testing SNP-SNP interactions. Data on 10 SNPs were simulated for 400 and 1000 subjects (case/control ratio=1). The simulated model included one main effect and two 2-way interactions. The variable selection procedures included automatic selection (stepwise, forward and backward), common 2-step selection, AIC- and SC-based selection. The hierarchical rule effect, in which all main effects and lower order terms of the highest-order interaction term are included in the model regardless of their statistical significance, was also examined. We found that the stepwise variable selection without the hierarchical rule, which had reasonably high authentic (true positive) proportion and low noise (false positive) proportion, is a better method compared to other variable selection procedures. For testing interactions, the hierarchical rule effect was obvious. The procedure without the hierarchical rule requires fewer terms in testing interactions, so it can accommodate more SNPs than the procedure with the hierarchical rule. For testing interactions, the procedures without the hierarchical rule had higher authentic proportion and lower noise proportion compared with ones with the hierarchical rule. These variable selection procedures were also applied and compared in a rheumatoid arthritis study.

Analysis of multiple SNPs in genetic association studies: comparison of three multi-locus methods to prioritize and select SNPs

Nonparametric approaches have been developed that are able to analyze large numbers of single nucleotide polymorphisms (SNPs) in modest sample sizes. These approaches have different selection features and may not provide similar results when applied to the same dataset. Therefore, we compared the results of three approaches (set association, random forests and multifactor dimensionality reduction [MDR]) to select from a total of 93 candidate SNPs a subset of SNPs that are important in determining high-density lipoprotein (HDL)-cholesterol levels. The study population consisted of a random sample from a Dutch monitoring project for cardiovascular disease risk factors and was dichotomized into cases (low HDL-cholesterol, n = 533) and non-cases (high HDL-cholesterol, n = 545) based on gender-specific median values for HDL cholesterol. Clearly, all three approaches prioritized three SNPs as important (CETP Taq1B, CETP-629 C/A and LPL Ser447X). Two SNPs with weaker main effects were additionally prioritized by random forests (APOC3 3175 G/C and CCR2 Val62Ile), whereas MTHFR 677 C/T was selected in combination with CETP Taq1B as best model by MDR. Obtained p-values for the selected models were significant for the set association approach (p =.0019), random forests (p<.01) and MDR (p<.02). In conclusion, the application of a combination of multi-locus methods is a useful approach in genetic association studies to select a well-defined set of important SNPs for further statistical and epidemiological interpretation, providing increased confidence and more information compared with the application of only one method.

Bayesian inference of epistatic interactions in case-control studies

Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small-scale studies, we here propose a method, denoted 'bayesian epistasis association mapping' (BEAM), for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible.

Detection of gene x gene interactions in genome-wide association studies of human population data

Empirical evidence supporting the commonality of gene x gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop methods to handle this important subject. Nonparametric methods have generated intense interest because of their capacity to handle high-dimensional data. Genome-wide association analysis of large-scale SNP data is challenging mathematically and computationally. In this paper, we describe major issues and questions arising from this challenge, along with methodological implications. Data reduction and pattern recognition methods seem to be the new frontiers in efforts to detect gene x gene interactions comprehensively. Currently, there is no single method that is recognized as the 'best' for detecting, characterizing, and interpreting gene x gene interactions. Instead, a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene x gene interactions in human data.

Trick or treat: the effect of placebo on the power of pharmacogenetic association studies

The genetic mapping of drug-response traits is often characterised by a poor signal-to-noise ratio that is placebo related and which distinguishes pharmacogenetic association studies from classical case-control studies for disease susceptibility. The goal of this study was to evaluate the statistical power of candidate gene association studies under different pharmacogenetic scenarios, with special emphasis on the placebo effect. Genotype/phenotype data were simulated, mimicking samples from clinical trials, and response to the drug was modelled as a binary trait. Association was evaluated by a logistic regression model. Statistical power was estimated as a function of the number of single nucleotide polymorphisms (SNPs) genotyped, the frequency of the placebo 'response', the genotype relative risk (GRR) of the response polymorphism, the strategy for selecting SNPs for genotyping, the number of individuals in the trial and the ratio of placebo-treated to drugtreated patients. We show that: (i) the placebo 'response' strongly affects the statistical power of association studies -- even a highly penetrant drug-response allele requires at least a 500-patient trial in order to reach 80 per cent power, several-fold more than the value estimated by standard tools that are not calibrated to pharmacogenetics; (ii) the power of a pharmacogenetic association study depends primarily on the penetrance of the response genotype and, when this penetrance is fixed, power decreases for larger placebo effects; (iii) power is dramatically increased when adding markers; (iv) an optimal study design includes a similar number of placebo- and drugtreated patients; and (v) in this setting, straightforward haplotype analysis does not seem to have an advantage over single marker analysis.

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

Present status of outcome prediction of invasive coronary treatment by using genetic markers

A growing number of studies suggest that the outcome after invasive coronary treatment may be in part genetically determined. Here, we review the present status of outcome prediction of invasive coronary treatments by using genetic markers. Although some studies found an association between one or another genetic marker with one or another clinical endpoint, many other studies found no such relations; to date, none of the genetic markers that have been investigated in association studies are used in routine clinical practice to prospectively assess the prognosis following invasive coronary treatment or to decide upon therapeutic strategies. Many associations between genetic markers and certain clinical endpoints were initially reported in small studies but could not be confirmed in larger ones. Some of these discrepancies may be explained by publication bias. Some genetic variants may have true effects on clinical endpoints, which, albeit biologically interesting, do not bear much clinical relevance. On the other hand, many-if not most-studies that have been published to date are more or less grossly underpowered and very rarely report on the results of an a priori power analysis. Thus, there is still a need for further high-quality studies designed to investigate the specific contribution of genetic factors to the outcome after invasive coronary interventions.

Family-based association analysis implicates IL-4 in susceptibility to Kawasaki disease

Several compelling lines of evidence suggest an important influence of genetic variation in susceptibility to Kawasaki disease (KD), an acute vasculitis that causes coronary artery aneurysms in children. We performed a family-based genotyping study to test for association between KD and 58 genes involved in cardiovascular disease and inflammation. By analysis of a cohort of 209 KD trios using the transmission disequilibrium test, we documented the asymmetric transmission of five alleles including the interleukin-4 (IL-4) C(-589)T allele (P=0.03). Asymmetric transmission of the IL-4 C(-589)T was replicated in a second, independent cohort of 60 trios (P=0.05, combined P=0.002). Haplotypes of alleles in IL-4, colony-stimulating factor 2 (CSF2), IL-13, and transcription factor 7 (TCF7), all located in the interleukin gene cluster on 5q31, were also asymmetrically transmitted. The reported associations of KD with atopic dermatitis and allergy, elevated serum IgE levels, eosinophilia, and increased circulating numbers of monocyte/macrophages expressing the low-affinity IgE receptor (FCepsilonR2) may be related to effects of IL-4. Thus, the largest family-based genotyping study of KD patients to date suggests that genetic variation in the IL-4 gene, or regions linked to IL-4, plays an important role in KD pathogenesis and disease susceptibility.

Family history, diabetes and extension of coronary atherosclerosis are strong predictors of adverse events after PTCA: A one-year follow-up study

BACKGROUND AND AIM

In this study we addressed some open questions in patients with coronary artery disease (CAD). First, we analysed which of the traditional risk factors was associated with the spreading of coronary stenosis and second, we aimed to identify if any variable was predictive of post-percutaneous transluminal coronary angioplasty (PTCA) clinical events.

METHODS AND RESULTS

We collected a consecutive series of patients with CAD (n=301) and in the subgroup of patients undergoing PTCA (n=135) we performed a prospective one-year follow-up study recording cardiovascular morbidity and total mortality. According to the extension of coronary atherosclerosis, we found a significant relationship with the prevalence of diabetes in men and with plasma HDL-cholesterol concentrations in women. The follow-up was completed in 95% of patients; we did not document any death whereas clinical events were registered in 16% of patients. At univariate analysis, we found that patients with clinical events had a higher prevalence of family history of CAD (43% vs 14%, p<0.005), diabetes (52% vs 21%, p<0.005) and multivessel disease (52% vs 35%, p<0.05). Multivariate analysis (logistic regression) confirmed that family history of CAD (OR 4.6, 95% CI 1.7-12.8, p<0.005), diabetes (OR 4.0, 95% CI 1.5-10.6, p<0.01) and multivessel disease (OR 2.8, 95% CI 1.1-7.4, p<0.05) were the only variables predictive of clinical events.

CONCLUSIONS

In this study, factors associated with the spreading of coronary stenosis were different according to the gender. Moreover, the presence of diabetes and multivessel disease had a negative impact on the long-term prognosis of patients undergoing PTCA. In addition, the family history of CAD represented in our study a strong predictor of clinical events. We suggest that in the management of post-PTCA patients, the role of individual baseline clinical characteristics must be taken into account and that subjects with a family history of premature CAD, diabetes and a wide extension of coronary disease represent those with the highest risk.

Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two or more DNA sequence variations. We review here this approach and then discuss how it can be used to model biochemical and metabolic data in the context of genetic studies of human disease susceptibility.

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations? Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast and digital organisms will be a useful place to start.

Identifying interacting SNPs using Monte Carlo logic regression

Interactions are frequently at the center of interest in single-nucleotide polymorphism (SNP) association studies. When interacting SNPs are in the same gene or in genes that are close in sequence, such interactions may suggest which haplotypes are associated with a disease. Interactions between unrelated SNPs may suggest genetic pathways. Unfortunately, data sets are often still too small to definitively determine whether interactions between SNPs occur. Also, competing sets of interactions could often be of equal interest. Here we propose Monte Carlo logic regression, an exploratory tool that combines Markov chain Monte Carlo and logic regression, an adaptive regression methodology that attempts to construct predictors as Boolean combinations of binary covariates such as SNPs. The goal of Monte Carlo logic regression is to generate a collection of (interactions of) SNPs that may be associated with a disease outcome, and that warrant further investigation. As such, the models that are fitted in the Markov chain are not combined into a single model, as is often done in Bayesian model averaging procedures. Instead, the most frequently occurring patterns in these models are tabulated. The method is applied to a study of heart disease with 779 participants and 89 SNPs. A simulation study is carried out to investigate the performance of the Monte Carlo logic regression approach.

Rationale and study design of the CardioGene Study: genomics of in-stent restenosis

BACKGROUND AND AIMS

in-stent restenosis is a major limitation of stent therapy for atherosclerosis coronary artery disease. The CardioGene Study is an ongoing study of restenosis in bare mental stents (BMS) for the treatment of coronary artery disease. The overall goal is to understand the genetic determinants of the responses to vascular injury that result in the development of restenosis in some patients but not in others. Gene expression profiling at transcriptional and translational levels provides global assessment of gene activity after vascular injury and mechanistic insight. Furthermore, the delineation of genetic biomarkers would be of value in the clinical setting of risk-stratify patients prior to stent therapy. Prospective risk stratification would allow for the rational selection of specialized treatments against the development of in-stent restenosis (ISR), such as drug-eluting stents.

SETTING

Patients are enrolled at two sites in the US with high-volume cardiac catheterization facilities: the William Beaumont Hospital in Royal Oak, MI, USA, and the Mayo Clinic in Rochester, MN, USA.

STUDY DESIGN

Two complementary study designs are used to understand the molecular mechanisms of restenosis and the genetic biomarkers predictive of restenosis. First, 350 patients are enrolled prospectively at the time of stent implantation. Blood is sampled prior to stent placement and afterwards at 2 weeks and 6 months. The clinical outcome of restenosis is determined 6 and 12 months after stent placement. The primary outcome is clinical restenosis at 6 months. The major secondary outcome is clinical restenosis at 12 months. Second, a corollary case-control analysis will be carried out with the enrollment of an additional 250 cases with a history of recurrent restenosis after treatment with BMS. Controls for this analysis are derived from the prospective cohort.

PATIENTS AND METHODS

Consecutive patients presenting to the cardiac catheterization laboratory are screened, informed about the study and enrolled after signing the consent form. Enrollment has been completed for the prospective cohort, and enrollment of the additional group is ongoing. A standardized questionnaire is used to collect clinical data primarily through direct patient interview to assess medical history, medication use, functional status, family history, environmental factors, and social history. Further data are abstracted from the medical charts and catheterization reports. A total of 276 clinical variables are collected per individual at baseline, and 49 variables are collected at each of the 6- and 12-month follow-up visits. A Clinical Events Committee adjudicates clinical outcomes. Blood samples are processed at each clinical enrollment site using standardized operating procedures. From each blood sample, several aliquots are prepared and stored of peripheral blood mononuclear cells, granulocytes, platelets, serum, and plasma. Additionally, a portion of each patient's leukocytes is cryopreserved for future cell-line creation. Samples are frozen and shipped to the National Heart, Lung and Blood Institute (NHLBI). Additional materials generated in the analysis of the samples at the NHLBI are frozen and stored, including isolated genomic DNA, total RNA, reverse transcribed cDNA libraries and labeled RNA hybridization mixtures used in microarray analysis. Per individual in the prospective cohort, high-quality transcript profiles of peripheral blood mononuclear cells at each time of blood sampling are obtained using Affymetrix U133A microarrays (Affymetrix, Santa Clara, CA, USA). Per chip, this yields 495,930 features per individual per time of sampling. This represents expression levels for 22,283 genes per patients oer time of blood sampling, including 14,500 well-characterized human genes. Proteomics of plasma is performed with multidimensional liquid chromatography and tandem mass spectrometry. Protein expression is examined similarly to mRNA expression as a measure of gene expression. Genotyping is performed in two manners. First, those genes showing differential expression at the levels of mRNA and protein are investigated using a candidate gene approach. Specific variants in known gene regulatory regions, such as promoters, are sought initially, as those variants may explain differences in expression level. Second, a genome-wide scan is used to identify genetic loci that are associated with ISR. Those regions identified are further examined for genes that show differential expression in the mRNA microarray profiling or proteomics investigations. These genes are finely investigated for candidate SNPs and other gene variants. Complementary genomic and proteomic approaches are expected to be robust. Integration of data sets is accomplished using a variety of informatics tools, organization of gene expression into functional pathways, and investigation of physical maps of up- and downregulated sets of genes.

CONCLUSIONS

The CardioGene Study is designed to understand ISR. Global gene and protein expression profiling define molecular phenotypes of patients. Well-defined clinical phenotypes will be paired with genomic data to define analyses aimed to achieve several goals. These include determining blood gene and protein expression in patients with ISR, investigating the genetic basis of ISR, developing predictive gene and protein biomarkers, and the identification of new targets for treatment.

Haplotype analysis of the beta2 adrenergic receptor gene and risk of myocardial infarction in humans

Polymorphisms in the beta2 adrenergic receptor (ADRB2), in particular G16R, Q27E, and T164I, have been implicated in the pathogenesis of cardiovascular and metabolic phenotypes. However, no prospective, genetic-epidemiological data are available on the risk of cardiovascular disease associated with these variants. Using DNA samples collected at baseline in a prospective cohort of 14,916 initially healthy American men, we evaluated the G16R, Q27E, and T164I polymorphisms among 523 individuals who subsequently developed myocardial infarction and among 2092 individuals who remained free of reported cardiovascular events during follow-up. The haplotype frequency distribution was significantly different among cases and controls (chi(2)(7d.f.) = 20.92, P = 0.0039). Haplotype-based logistic regression, adjusting for age, smoking, and randomized treatment group, indicated that G16-Q27-I164 (odds ratio 0.178, 95% C.I. 0.043-0.737, P = 0.017) and (non-G16-Q27)-T164 (odds ratio 1.235, 95% C.I. 1.031-1.480, P = 0.022) haplotypes were significantly associated with altered risk of myocardial infarction. These findings remained after further adjustment for BMI, history of hypertension, and presence or absence of diabetes. In conclusion, variation in haplotype frequencies for the beta2 adrenergic receptor gene was found to be associated with risk of myocardial infarction.

Polymorphisms in the Myocilin Promoter Unrelated to the Risk and Severity of Primary Open-Angle Glaucoma

PURPOSE

To investigate the proximal 2.5 kb promoter in the myocilin (MYOC) gene for mutations in Chinese patients with primary open-angle glaucoma (POAG).

PATIENTS AND METHODS

We screened for sequence alterations in the MYOC promoter in 88 unrelated Chinese patients with POAG and 94 unrelated individuals without glaucoma, aged 50 years or above, as control subjects. In addition, the specific MYOC.mt1 polymorphism was determined in a total of 212 POAG patients and 221 control subjects. The relationships between POAG phenotype and the identified polymorphisms were studied by univariate analysis, multivariable logistic regression analysis, and haplotype analysis.

RESULTS

All polymorphisms identified in this study followed Hardy-Weinberg equilibrium (P > 0.12) both in POAG patients and controls. Both univariate and multivariable logistic regression analyses showed no polymorphism that was significantly associated with the risk of POAG, P > 0.08 and P > 0.044 respectively. Haplotype analysis further indicated no association of MYOC promoter polymorphisms with the susceptibility for POAG (P > 0.1). On the other hand, there was no difference of POAG phenotypes among different genotypes of MYOC.mt1 (P > 0.31).

CONCLUSIONS

In this study on the Chinese population, polymorphisms in the MYOC promoter are not related to the risk of POAG. There is no association between the MYOC.mt1 promoter polymorphism with the severity of POAG.

Polymorphisms of adrenergic receptors: variations on a theme

Recent studies have revealed that most of the adrenergic receptor genes are polymorphic, leading to changes in the amino sequence of the encoded receptor. The variations occur in multiple functional regions of the receptors, and appear as haplotypes with other coding and noncoding polymorphisms in their genes. The consequences of such genetic variability have been explored in recombinant cell-based systems and in human studies. Adrenergic receptor polymorphisms have been shown to alter receptor binding, G-protein coupling, regulation, and expression compared with their allelic counterparts. Here, the genetic and molecular characterization of these polymorphisms is reviewed, as well as their potential impact on pharmacogenetics, disease risk, and disease modification.

The ubiquitous nature of epistasis in determining susceptibility to common human diseases

There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.

Threonine for alanine substitution in the eotaxin (CCL11) gene and the risk of incident myocardial infarction

Recent studies suggest that the chemokine eotaxin may participate in atherosclerosis. Threonine (T) for alanine (A) substitution at amino acid 23 in the eotaxin gene (CCL11) has been associated with risk of developing allergic-inflammatory disorders. However, no genetic-epidemiological data are available on the risk of cardiovascular disease associated with this polymorphism. Using DNA samples collected at baseline in a prospective cohort of 14,916 initially healthy American men, we evaluated the A23T polymorphism among 523 individuals who subsequently developed myocardial infarction (MI) and among 2092 individuals who remained free of reported cardiovascular disease over a mean follow-up period of 13.2 years. The T23 allele was significantly associated with risk of myocardial infarction (odds ratio (OR) in an age and smoking adjusted recessive model of inheritance, 1.86; 95% confidence interval (CI), 1.15-3.01; P = 0.012). This risk effect remained statistically significant in analyses further controlling for body mass index, history of hypertension, the presence of diabetes, and randomized treatment assignment (OR, 1.95; 95% CI, 1.19-3.18; P = 0.008). In this cohort, a T for A substitution at amino acid 23 in the eotaxin gene is associated with increased risk for incident myocardial infarction. If confirmed in other cohorts, these data support the emerging hypothesis that eotaxin participates in atherosclerosis.

Primary hypercholesterolemia: genetic causes and treatment of five monogenic disorders

Coronary heart disease is a major cause of death in Europe and the USA. Insudation of atherogenic lipoproteins, including low-density lipoprotein (LDL), into the artery wall is integral to atherosclerosis. It is clear that numerous genetic loci contribute to increased plasma levels of LDL. However, five specific monogenic disorders, three of which have been reported recently, are known to increase LDL. These are familial hypercholesterolemia (LDL receptor gene: LDLR); familial ligand-defective apoB- 100 (apoB gene: APOB); autosomal recessive hypercholesterolemia (ARH gene); sitosterolemia (ABCG5 or ABCG8 genes) and cholesterol 7alpha-hydroxylase deficiency (CYP7A1 gene). This review relates the mechanisms underlying these five disorders with specific therapeutic interventions.

Genetic predictive factors in restenosis

Restenosis is still the main drawback of percutaneous transluminal coronary angioplasty (PTCA). It is thought to be a multifactorial process where recoil of the vessel, neointimal proliferation and thrombus formation are thought to play a role. Until now it has proven difficult to predict restenosis on clinical and procedural grounds, however, genetic epidemiology might provide more insights. In this review several genetic variables, i.e. polymorphisms that were determined in relation to restenosis are described. The single nucleotide polymorphisms (SNPs) described in the literature so far involve; the renin-angiotensin system, platelet aggregation, the inflammatory response, matrix metalloproteinases, smooth muscle cell proliferation, lipids and oxidative stress and nitric oxide. Nowadays DNA-microarrays have been developed which make it possible to test 50 or 60 polymorphisms at once. However, the risk of error due to multiple testing should be kept in mind. The results of the studies described should be interpreted with care. Many of the published studies are of relatively small sample size, which sometimes show more positive outcomes than the larger studies, this is possibly due to publication bias towards more positive results. The small sample size studies also exhibit wide confidence intervals. On the other hand, one must take into account that the process of restenosis is a multifactorial one and it is likely that multiple genes are involved. Thus, relatively small odds ratios relating to single gene contribution to restenosis can be of paramount importance when encompassed in the overall picture. Although still much research has to be done, stratification according to genetic make-up may enable tailoring of the interventional treatment to the individual patient.

Set association analysis of SNP case-control and microarray data

Common heritable diseases ("complex traits") are assumed to be due to multiple underlying susceptibility genes. While genetic mapping methods for Mendelian disorders have been very successful, the search for genes underlying complex traits has been difficult and often disappointing. One of the reasons may be that most current gene-mapping approaches are still based on conventional methodology of testing one or a few SNPs at a time. Here, we demonstrate a simple strategy that allows for the joint analysis of multiple disease-associated SNPs in different genomic regions. Our set-association method combines information over SNPs by forming sums of relevant single-marker statistics. As previously hypothesized, we show here that this approach successfully addresses the "curse of dimensionality" problem--too many variables should be estimated with a comparatively small number of observations. We also report results of simulation studies showing that our method furnishes unbiased and accurate significance levels. Power calculations demonstrate good power even in the presence of large numbers of nondisease associated SNPs. We extended our method to microarray expression data, where expression levels for large numbers of genes should be compared between two tissue types. In applications to such data, our approach turned out to be highly efficient.

Analysis of gene-gene interactions

The goal of this unit is to introduce gene-gene interactions or epistasis as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common; then, it reviews several statistical and computational methods for detecting and characterizing genes whose effects are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits since most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

The use of animal models in the study of complex disease: all else is never equal or why do so many human studies fail to replicate animal findings?

The study of the genetics of complex human disease has met with limited success. Many findings with candidate genes fail to replicate despite seemingly overwhelming physiological data implicating the genes. In contrast, animal model studies of the same genes and disease models usually have more consistent results. We propose that one important reason for this is the ability to control genetic background in animal studies. The fact that controlling genetic background can produce more consistent results suggests that the failure to replicate human findings in the same diseases is due to variation in interacting genes. Hence, the contrasting nature of the findings from the different study designs indicates the importance of non-additive genetic effects on human disease. We discuss these issues and some methodological approaches that can detect multilocus effects, using hypertension as a model disease. This article contains supplementary material, which may be viewed at the BioEssays website at http://www.interscience.wiley.com/jpages/0265-9247/suppmat/index.html.

TP53 haplotype-based analysis and incidence of post-angioplasty restenosis

The tumor suppressor gene product, in particular tumor suppressor protein p53 (TP53), has been suggested to play a role in post-angioplasty restenosis. However, no genetic-epidemiological studies relating to TP53 gene polymorphism(s) and the incidence of post-angioplasty restenosis are available. TP53 11951_11966dup16bp, R72P, and 13494G>A polymorphisms were characterized in a cohort of 779 patients, of whom 342 cases had developed restenosis (as defined by >50% loss of lumen compared with immediate post-procedure results) at repeat quantitative coronary angiography at six months post angioplasty. The haplotype-frequency distribution was marginally different between cases and controls with restenosis risk (chi(2)(7df)=13.08, P=0.070). Multivariable haplotype-based logistic regression indicated that haplotypes 16bp(-) -P72-G13494 [corrected], and 16bp(+) -P72-A13494 [corrected] exhibit protective effects on restenosis risk (odds ratio=0.58, 95%CI=0.40-0.83, P=0.0033; odds ratio=0.69, 95%CI=0.48-0.99, P=0.049, respectively). Multivariable haplotype-based linear regression again showed similar, significant association with degree of lumen loss. The present findings indicate protective effects of TP53 16bp(-) -P72-G13494 [corrected], and 16bp(+) -P72-A13494 [corrected] haplotypes in the incidence of restenosis after angioplasty. Furthermore, our study demonstrates that a haplotype-based approach can be more informative than a single-marker or marker-by-marker analysis.

IL-1 cluster genes and occurrence of post-percutaneous transluminal coronary angioplasty restenosis: a prospective, angiography-based evaluation

A polymorphic marker of the gene encoding the interleukin-1 (IL-1) receptor antagonist has been recently reported to be associated with risk of coronary artery disease. However, no prospective studies of the IL-1 gene cluster in relation to the occurrence of restenosis, a major complication of percutaneous transluminal coronary angioplasty (PTCA), have so far been conducted. We had the opportunity to investigate this question in a large, prospective cohort characterized by quantitative coronary angiography in all subjects. The IL1A A114S, IL1B -511C>T, IL1B 3953T>C, IL1RI exon1B T>C, and IL1RN VNTR (intron 2) polymorphisms were characterized in a cohort of 779 patients of whom 342 ("cases") had developed restenosis (as defined by >50% loss of lumen compared to immediate post-procedure results) at repeat angiography at 6 months post-PTCA. All observed genotype frequencies were in Hardy-Weinberg equilibrium. Frequencies for the rare alleles were: IL1A S, 0.29 (cases), 0.28 (controls); IL1B T, 0.31 (cases), and 0.33 (controls); IL1B C, 0.23 (cases), 0.24 (controls); IL1RI C, 0.34 (cases), 0.35 (controls); and IL1RN 2, 0.29 (cases), 0.29 (controls), respectively. There was no evidence for an association between genotype and restenosis or degree of lumen loss (adjusted for covariables). Our data indicate that the common variants in the IL-1 cluster genes are not associated with incidence of restenosis after PTCA.

Mathematical multi-locus approaches to localizing complex human trait genes

Statistical analysis methods for gene mapping originated in counting recombinant and non-recombinant offspring, but have now progressed to sophisticated approaches for the mapping of complex trait genes. Here, we outline new statistical methods that capture the simultaneous effects of multiple gene loci and thereby achieve a more global view of gene action and interaction than is possible by traditional gene-by-gene analysis. We aim to show that the work of statisticians goes far beyond the running of computer programs.

Leucine 7 to proline 7 polymorphism of the preproneuropeptide Y gene is not associated with restenosis after coronary stenting

PURPOSE

To identify if an association exists between the leucine 7 (Leu7) to proline 7 (Pro7) polymorphism located in the signal peptide of the preproneuropeptide Y (preproNPY) gene and restenosis after coronary stenting. The Pro7 allele of the preproNPY gene affects the plasma levels of human neuropeptide Y, a potent mitogen of vascular smooth muscle cells.

METHODS

A population of 1850 consecutive patients with symptomatic coronary artery disease undergoing coronary stent implantation was enrolled in a study that featured angiography at 6 months and genotype determination. The primary endpoint was angiographically documented restenosis (> or =50% diameter stenosis) at 6 months. The secondary endpoint was the clinical outcome at 1 year (death, myocardial infarction, target vessel revascularization). Genotyping was based on the polymerase chain reaction with fluorescent allele-specific oligonucleotide probes (TaqMan method).

RESULTS

The carrier frequency of the rare Pro7 allele was 6.2%. Baseline, lesion-related, angiographic, and procedural parameters were similar in the patients with the Leu7/Leu7 genotype and carriers of the Pro7 allele (i.e., subjects with genotype Leu7/Pro7 or Pro7/Pro7). Restenosis rates at 6 months did not differ significantly between the groups (33% versus 30%, p=0.54). In addition, no relationship of the polymorphism with the clinical outcomes at 1 year was observed.

CONCLUSION

Our results suggest that the Leu7 to Pro7 polymorphism of the preproNPY gene is not associated with angiographic restenosis or adverse clinical events after stent placement in coronary arteries.

A method for the assessment of disease associations with single-nucleotide polymorphism haplotypes and environmental variables in case-control studies

The rough draft of the human genome map has been used to identify most of the functional genes in the human genome, as well as to identify nucleotide variations, known as "single-nucleotide polymorphisms" (SNPs), in these genes. By use of advanced biotechnologies, researchers are beginning to genotype thousands of SNPs from biological samples. Among the many possible applications, one of them is the study of SNP associations with complex human diseases, such as cancers or coronary heart diseases, by using a case-control study design. Through the gathering of environmental risk factors and other lifestyle factors, such a study can be effectively used to investigate interactions between genes and environmental factors in their associations with disease phenotype. Earlier, we developed a method to statistically construct individuals' haplotypes and to estimate the distribution of haplotypes of multiple SNPs in a defined population, by use of estimating-equation techniques. Extending this idea, we describe here an analytic method for assessing the association between the constructed haplotypes along with environmental factors and the disease phenotype. This method is also robust to the model assumptions and is scalable to a large number of SNPs. Asymptotic properties of estimations in the method are proved theoretically and are tested for finite sample sizes by use of simulations. To demonstrate the use of the method, we applied it to assess the possible association between apolipoprotein CIII (six coding SNPs) and restenosis by using a case-control data set. Our analysis revealed two haplotypes that may reduce the risk of restenosis.

Pharmacogenetics and pharmacogenomics: recent developments, their clinical relevance and some ethical, social, and legal implications

In recent debates on novel procedures of molecular medicine pharmacogenomics is attracting more and more attention as a genotype-based approach for improving safety and efficacy of the use of therapeutic substances. Promoted by basic knowledge generated in the field of medical genomics, facilitated by novel technological tools for mapping genetic variation in individuals, and supported by results of initial clinical studies linking specific genotypes to metabolic characteristics of individuals important for assessing drug response, procedures of pharmacogenetics and pharmacogenomics now are starting to impact significantly on clinical research and development and medical practice. In this situation assessing the goals, risk, and benefits of pharmacogenetics and pharmacogenomics is essential for the medically successful, ethically justifiable, and socially acceptable implementation of genotype-based diagnosis and pharmacotherapy. We discuss the current state of the art in pharmacogenetics and pharmacogenomics and introduce a model for evidence based assessment of its goals, risk, and benefits. We differentiate here between pragmatic and normative issues in the development of pharmacogenomics in order to contrast prevailing, insufficiently interest-based modes of public technology assessment with the evidence-based mode that can be established as part of clinical study design. Finally, we provide a framework for the analysis of social accountability that can be used for technology development and technology assessment with regard to pharmacogenomics in particular and molecular medicine in general.