EINVis: A Visualization Tool for Analyzing and Exploring Genetic Interactions in Large‐Scale Association Studies

Epistasis (gene‐gene interaction) detection in large‐scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user‐friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/ .     

A global test for gene‐gene interactions based on random matrix theory

Statistical interactions between markers of genetic variation, or gene‐gene interactions, are believed to play an important role in the etiology of many multifactorial diseases and other complex phenotypes. Unfortunately, detecting gene‐gene interactions is extremely challenging due to the large number of potential interactions and ambiguity regarding marker coding and interaction scale. For many data sets, there is insufficient statistical power to evaluate all candidate gene‐gene interactions. In these cases, a global test for gene‐gene interactions may be the best option. Global tests have much greater power relative to multiple individual interaction tests and can be used on subsets of the markers as an initial filter prior to testing for specific interactions. In this paper, we describe a novel global test for gene‐gene interactions, the global epistasis test (GET), that is based on results from random matrix theory. As we show via simulation studies based on previously proposed models for common diseases including rheumatoid arthritis, type 2 diabetes, and breast cancer, our proposed GET method has superior performance characteristics relative to existing global gene‐gene interaction tests. A glaucoma GWAS data set is used to demonstrate the practical utility of the GET method.     

Application of pharmacokinetic principles to exposure to chemical mixtures

Kinetic interactions among chemicals present in a mixture can influence the relationship between administered and delivered or effective dose; these interactions are distinct from dynamic interactions such as, for example, competition between two chemicals for receptor binding sites. The relationship between administered and effective dose depends on a number of biochemical, physiological, and physical factors such as age, sex, level of physical activity, route of administration, dose pattern, and bioavailability. In addition, interactions among chemicals may limit or increase the effective dose relative to the administered dose for any of the chemicals of a mixture. If the mechanism of the interaction is known, the direction and general magnitude of its effects may be predictable. A variety of potential interactions is briefly reviewed, and selected illustrations of these interactions are given. Two types of interactions likely to be pervasive in mixtures of chemically related bioactive materials are considered in greater detail. These interactions are induction of metabolizing enzymes such as the mixed-function oxidases, and competition of structurally similar chemicals for active sites on metabolizing enzymes. Simulations are presented to illustrate the consequences of these interactions, and examples of their occurrence are given.     

临床药物相互作用机制及其有效利用

药物相互作用的机制十分复杂,有时甚至难以预测,因此药物相互作用已经成为日常临床实践中的一个重要问题。随着多药合用情况的增加,药物相互作用所引起的药物不良反应的发生率也在迅速上升。基于此,文章主要研究了临床药物相互作用机制及其有效利用方法。     

The benefits of respectful interactions: fluid alliancing and inter‐occupational information sharing in primary care

Though inter‐occupational interactions in health care have been the focus of increasing attention, we still know little about how such interactions shape information sharing in clinical settings. This is particularly true in primary care where research on teams and collaboration has been based on individual perceptions of work (using surveys and interviews) rather than observing the interactions that directly mediate the inter‐occupational flow of information. To explore how interactions shape information sharing, we conducted a secondary analysis of ethnographic data from 27 primary care practices. Ease of information sharing among nurses and doctors is linked to the degree to which practices feature respectful interactions, with practices in the sample falling into one of three categories (those with low, uneven, and high degrees of respectful interactions). Those practices with the highest degree of respectful interactions demonstrate what we describe as fluid‐alliancing: flexible interactions between individuals from different occupational groups in which bidirectional information sharing occurs for the benefit of patients and the efficacy of the practice community. We conclude by arguing that this process unlocks the strengths of all practice members, and that leadership should encourage respectful interactions to augment organisational efficacy and the ability of individual practice members to provide quality patient care.     

Drug-nutrient interaction in clinical nutrition

Drug-nutrient interactions have been recognized for decades. It is known that improper management of some of these interactions may lead to therapeutic failure or cause serious adverse effects to the patients. While most of the known drug-nutrient interactions involve changes in oral bioavailabilities and absorption of the offending compounds, recent investigations suggest that different mechanisms also exist. A mechanism-derived classification system for drug-nutrient interactions has only recently been developed. This system should facilitate the future research and development of practice guidelines in the identification and management of important interactions.     

Enteral feeding: drug/nutrient interaction

Enteral nutrition support via a feeding tube is the first choice for artificial nutrition. Most patients also require simultaneous drug therapy, with the potential risk for drug-nutrient interactions which may become relevant in clinical practice. During enteral nutrition, drug-nutrient interactions are more likely to occur than in patients fed orally. However, there is a lack of awareness about its clinical significance, which should be recognised and prevented in order to optimise nutritional and pharmacological therapeutic goals of safety and efficacy.Learning objectives:To raise the awareness of potential drug-nutrient interactions and influence on clinical outcomes.To identify factors that can promote drug-nutrient interactions and contribute to nutrition and/or therapeutic failure.To be aware of different types of drug-nutrient interactions.To understand complex underlying mechanisms responsible for drug-nutrient interactions.To learn basic rules for the administration of medications during tube-feeding.     

Analysis of verbal interactions in tutorial groups: a process study

INTRODUCTION: Collaborative learning, including problem-based learning (PBL), is a powerful learning method. Group interaction plays a crucial role in stimulating student learning. However, few studies on learning processes in medical education have examined group interactions. Most studies on collaboration within PBL used self-reported data rather than observational data. We investigated the following types of interactions in PBL tutorial groups: learning-oriented interactions (exploratory questioning, cumulative reasoning and handling conflicts about knowledge); procedural interactions, and irrelevant/off-task interactions. AIM: The central question concerned how much time is spent on the different types of interaction during group sessions and how the types of interaction are distributed over the meeting. METHOD: Four tutorial group sessions in Year 2 of the PBL undergraduate curriculum of Maastricht Medical School were videotaped and analysed. The sessions concerned the reporting phase of the PBL process. We analysed the interactions using a coding scheme distinguishing several verbal interaction types, such as questions, arguments and evaluations. RESULTS: Learning-orientated interactions accounted for 80% of the interactions, with cumulative reasoning, exploratory questioning and handling conflicts about knowledge accounting for about 63%, 10% and 7% of the interactions, respectively. Exploratory questioning often preceded cumulative reasoning. Both types occurred throughout the meeting. Handling conflicts mainly occurred after the first 20 minutes. CONCLUSIONS: Task involvement in the tutorial groups was high. All types of learning-orientated interactions were observed. Relatively little time was spent on exploratory questions and handling conflicts about knowledge. Problem-based learning training should pay special attention to stimulating discussion about contradictory information.     

Efficient designs of gene-environment interaction studies: implications of Hardy-Weinberg equilibrium and gene-environment independence

It is important to investigate whether genetic susceptibility variants exercise the same effects in populations that are differentially exposed to environmental risk factors. Here, we assess the power of four two-stage case-control design strategies for assessing multiplicative gene-environment (G-E) interactions or for assessing genetic or environmental effects in the presence of G-E interactions. We considered a di-allelic single nucleotide polymorphism G and a binary environmental variable E under the constraints of G-E independence and Hardy-Weinberg equilibrium and used the Wald statistic for all tests. We concluded that (i) for testing G-E interactions or genetic effects in the presence of G-E interactions when data for E are fully available, it is preferable to ascertain data for G in a subsample of cases with similar numbers of exposed and unexposed and a random subsample of controls; and (ii) for testing G-E interactions or environmental effects in the presence of G-E interactions when data for G are fully available, it is preferable to ascertain data for E in a subsample of cases that has similar numbers for each genotype and a random subsample of controls. In addition, supplementing external control data to an existing case-control sample leads to improved power for assessing effects of G or E in the presence of G-E interactions. Copyright ? 2012 John Wiley & Sons, Ltd.     

Identifying Gene–Environment and Gene–Gene Interactions Using a Progressive Penalization Approach

In genomic studies, identifying important gene–environment and gene–gene interactions is a challenging problem. In this study, we adopt the statistical modeling approach, where interactions are represented by product terms in regression models. For the identification of important interactions, we adopt penalization, which has been used in many genomic studies. Straightforward application of penalization does not respect the “main effect, interaction” hierarchical structure. A few recently proposed methods respect this structure by applying constrained penalization. However, they demand very complicated computational algorithms and can only accommodate a small number of genomic measurements. We propose a computationally fast penalization method that can identify important gene–environment and gene–gene interactions and respect a strong hierarchical structure. The method takes a stagewise approach and progressively expands its optimization domain to account for possible hierarchical interactions. It is applicable to multiple data types and models. A coordinate descent method is utilized to produce the entire regularized solution path. Simulation study demonstrates the superior performance of the proposed method. We analyze a lung cancer prognosis study with gene expression measurements and identify important gene–environment interactions.     

Nutrient interaction with drugs and other xenobiotics

Drugs and other xenobiotics (foreign compounds) are consumed with food, presenting the potential for interactions that may modify the biological activity of both nutrients and xenobiotics. The complexities associated with the biological fate of both classes of compounds yield a multitude of potential interactions, making the prediction of specific interactions a complicated task. However, the similarities between the biological processes involved in the biological fate of both xenobiotics and nutrients allow a logical approach to the understanding and prediction of interactions. For example, for either a xenobiotic or nutrient to produce a systemic effect, it must be absorbed. There are similarities between the factors that influence the availability and absorption of both xenobiotics and nutrients. It is at these points of similarity that interactions are most likely to occur. This is also true for the other major processes involved with the biological fate of both xenobiotics and nutrients, such as distribution, metabolism, and excretion. An understanding of these processes and the most likely points of interaction is the first step in understanding and acquiring predictive ability of the potential interactions between nutrients and drugs, as well as other xenobiotics.     

The influence of Korean preschool teachers’ work environments and self-efficacy on children’s peer play interactions: the mediating effect of teacher–child interactions

ABSTRACT

The present study investigated the structural relationships between Korean preschool teachers’ work environments, self-efficacy, and children’s peer play interactions; it also examined the mediating influence of teacher–child interactions on these relationships. This study performed SEM analysis on 1193 teachers’ survey responses from the 2014 Panel Study on Korean Children. The results showed that, first, teachers’ self-efficacy not only directly influenced teacher–child interactions but also influenced children’s peer play interactions through teacher–child interactions. Second, teachers’ work environments not only directly influenced teacher–child interactions, but also influenced children’s peer play interactions (mediated by teacher–child interactions). Third, teacher–child interactions had a direct effect on children’s peer play interactions. The paper concludes with a discussion of the implications of the influence of Korean preschool teachers’ work environments and self-efficacy on children’s peer play interactions, mediated by teacher–child interactions.     

A maximum likelihood method for studying gene-environment interactions under conditional independence of genotype and exposure

Given the biomedical interest in gene-environment interactions along with the difficulties inherent in gathering genetic data from controls, epidemiologists need methodologies that can increase precision of estimating interactions while minimizing the genotyping of controls. To achieve this purpose, many epidemiologists suggested that one can use case-only design. In this paper, we present a maximum likelihood method for making inference about gene-environment interactions using case-only data. The probability of disease development is described by a logistic risk model. Thus the interactions are model parameters measuring the departure of joint effects of exposure and genotype from multiplicative odds ratios. We extend the typical inference method derived under the assumption of independence between genotype and exposure to that under a more general assumption of conditional independence. Our maximum likelihood method can be applied to analyse both categorical and continuous environmental factors, and generalized to make inference about gene-gene-environment interactions. Moreover, the application of this method can be reduced to simply fitting a multinomial logistic model when we have case-only data. As a consequence, the maximum likelihood estimates of interactions and likelihood ratio tests for hypotheses concerning interactions can be easily computed. The methodology is illustrated through an example based on a study about the joint effects of XRCC1 polymorphisms and smoking on bladder cancer. We also give two simulation studies to show that the proposed method is reliable in finite sample situation.     

Genetics and diet--gene interactions: involvement, confidence and knowledge of dietitians

Diet-gene interactions have become the focus of much research in recent years. However, little is known about UK dietitians' involvement, confidence and knowledge in genetics and diet-gene interactions. A validated postal questionnaire sent to a randomly selected sample of 600 dietitians in the UK resulted in 390 responses (65 %). Most dietitians had no involvement in eleven activities relating to genetics and diet-gene interactions and lacked confidence in undertaking such activities. However, a significant positive association was found between involvement and confidence for all activities tested (P < 0.0001). A mean knowledge score of 41 % (sd 19) indicated generally low levels of knowledge in genetics and diet-gene interactions. Knowledge scores were higher for those who reported discussing the genetic basis of disease or discussing how diet-gene interactions affected risk (P < 0.05). For the majority of activities, dietitians who reported higher confidence had higher knowledge scores. Given the importance of interactions between genetics and nutrition in preventing and managing disease, this study identifies a need to increase the involvement, confidence and knowledge in genetics and diet-gene interactions of dietitians in the UK.     

Drug‐Nutrient Interactions

Drug‐nutrient interactions are defined as physical, chemical, physiologic, or pathophysiologic relationships between a drug and a nutrient. The causes of most clinically significant drug‐nutrient interactions are usually multifactorial. Failure to identify and properly manage drug‐nutrient interactions can lead to very serious consequences and have a negative impact on patient outcomes. Nevertheless, with thorough review and assessment of the patient's history and treatment regimens and a carefully executed management strategy, adverse events associated with drug‐nutrient interactions can be prevented. Based on the physiologic sequence of events after a drug or a nutrient has entered the body and the mechanism of interactions, drug‐nutrient interactions can be categorized into 4 main types. Each type of interaction can be managed using similar strategies. The existing data that guide the clinical management of most drug‐nutrient interactions are mostly anecdotal experience, uncontrolled observations, and opinions, whereas the science in understanding the mechanism of drug‐nutrient interactions remains limited. The challenge for researchers and clinicians is to increase both basic and higher level clinical research in this field to bridge the gap between the science and practice. The research should aim to establish a better understanding of the function, regulation, and substrate specificity of the nutrient‐related enzymes and transport proteins present in the gastrointestinal tract, as well as assess how the incidence and management of drug‐nutrient interactions can be affected by sex, ethnicity, environmental factors, and genetic polymorphisms. This knowledge can help us develop a true personalized medicine approach in the prevention and management of drug‐nutrient interactions.     

Sample size needed to detect gene-gene interactions using association designs

It is likely that many complex diseases result from interactions among several genes, as well as environmental factors. The presence of such interactions poses challenges to investigators in identifying susceptibility genes, understanding biologic pathways, and predicting and controlling disease risks. Recently, Gauderman (Am J Epidemiol 2002;155:478-84) reported results from the first systematic analysis of the statistical power needed to detect gene-gene interactions in association studies. However, Gauderman used different statistical models to model disease risks for different study designs, and he assumed a very low disease prevalence to make different models more comparable. In this article, assuming a logistic model for disease risk for different study designs, the authors investigate the power of population-based and family-based association designs to detect gene-gene interactions for common diseases. The results indicate that population-based designs are more powerful than family-based designs for detecting gene-gene interactions when disease prevalence in the study population is moderate.     

Interaction Effects in Health State Valuation Studies: An Optimal Scaling Approach

ObjectivesThis study aimed to introduce a parsimonious modeling approach that enables the estimation of interaction effects in health state valuation studies.MethodsInstead of supplementing a main-effects model with interactions between each and every level, a more parsimonious optimal scaling approach is proposed. This approach is based on the mapping of health state levels onto domain-specific continuous scales. The attractiveness of health states is then determined by the importance-weighted optimal scales (ie, main effects) and the interactions between these domain-specific scales (ie, interaction effects). The number of interaction terms only depends on the number of health domains. Therefore, interactions between dimensions can be included with only a few additional parameters. The proposed models with and without interactions are fitted on 3 valuation data sets from 2 different countries, that is, a Dutch latent-scale discrete choice experiment (DCE) data set with 3699 respondents, an Australian time trade-off data set with 400 respondents, and a Dutch DCE with duration data set with 788 respondents.ResultsImportant interactions between health domains were found in all 3 applications. The results confirm that the accumulation of health problems within health states has a decreasing marginal effect on health state values. A similar effect is obtained when so-called N3 or N5 terms are included in the model specification, but the inclusion of 2-way interactions provides superior model fits.ConclusionsThe proposed interaction model is parsimonious, produces estimates that are straightforward to interpret, and accommodates the estimation of interaction effects in health state valuation studies with realistic sample size requirements. Not accounting for interactions is shown to result in biased value sets, particularly in stand-alone DCE with duration studies.     

Twin brothers with autism and their intra-pair interactions in a pre-school special education class

The present study focused on the intra-pair interaction of a pair of dizygotic twins with autism in pre-school age and explored: (a) the type of play and the level of symbolic play the twins prefer and are most frequently engaged with; (b) the attitudes the twins usually display; (c) the most frequent initiator of interactions; and (d) the way the quality of interaction is being affected by the above factors. The twins’ interactions were observed in their classroom environment. Observational data were triangulated with data from a semi-structured interview with the twins’ mother, their teacher, and the school psychologist. Results indicated a number of intra-pair differences in the twins’ interactions. The triangulation revealed that the mother, the teacher, and the school psychologist had a slightly different understanding of these interactions. The need for a more systematic and consistent assessment of intra-pair interactions, coordinated by specialists, is highlighted.     

Occupational exposure limits in the context of solvent mixtures, consumption of ethanol, and target tissue dose

Individuals are exposed to mixtures, and never to single chemicals. Depending on the composition of the elements of mixtures, significant toxicological interactions between the components may occur. These interactions are complex and often difficult to predict, ranging from synergistic to additive and subadditive interactions. The nature of the interactions needs to be evaluated as the target tissue dose of the active form of each chemical. PBPK modeling is an effective tool for determining the target tissue dose and evaluating these interactions when data are available for model development. Some of the interactions are pharmacokinetic in nature, affecting the disposition of other chemicals in the body. Other interactions can be pharmacodynamic in nature, altering the effects that other chemicals have on the organism. For many organic solvents, these interactions occur principally at the level of the metabolizing enzyme, cytochrome P-450 2E1 (CYP2E1). Many solvents are known to induce or inhibit CYP2E1, or both. Mixtures may be comprised of concomitant exposures to chemicals or from components encountered separately on-the-job, off-the-job, through the diet, and otherwise. Examples of mixtures where the exposure to separate components occurs off the job will be discussed, with special emphasis on ethanol consumption as a modifier of solvent pharmacokinetics. The present practice of the linear extrapolation of the toxicity of individual mixture components in the interpretation of occupational exposure limits will also be critiqued.     

Timing of supplementation of selenium and isoflavones determines prostate cancer risk factor reduction in rats

Drug-drug interactions have become an important issue in health care. It is now realized that many drug-drug interactions can be explained by alterations in the metabolic enzymes that are present in the liver and other extra-hepatic tissues. Many of the major pharmacokinetic interactions between drugs are due to hepatic cytochrome P450 (P450 or CYP) enzymes being affected by previous administration of other drugs. After coadministration, some drugs act as potent enzyme inducers, whereas others are inhibitors. However, reports of enzyme inhibition are very much more common. Understanding these mechanisms of enzyme inhibition or induction is extremely important in order to give appropriate multiple-drug therapies. In future, it may help to identify individuals at greatest risk of drug interactions and adverse events.