gene-environment interaction

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Effect of TNF and LTA polymorphisms on biological markers of response to oxidative stimuli in coal miners: a model of gene-environment interaction. Tumour necrosis factor and lymphotoxin alpha.

Effect of TNF and LTA polymorphisms on biological markers of response to oxidative stimuli in coal miners: a model of gene-environment interaction. Tumour necrosis factor and lymphotoxin alpha.

Assessing gene-environment interaction has proven to be difficult, and few clear examples have been evidenced and replicated. Problems often encountered are the lack of specific hypotheses (candidate interactions), insufficient characterization of exposure, and the availability of sufficiently low level (closer to the gene than clinical disease) phenotype markers. Unraveling biochemical and physiological hierarchies leading from genes to clinical endpoints in complex diseases need new strategies [30]. This study attempted to incorporate a conceptual temporal sequence [31] from environment and genes towards disease. In such an overall framework of etiological factors to severe disease, it is possible to distinguish 1) internal dose (inhaled coal dust), 2) early biologic effect (low-level intermediate phenotype [30], such as enzymatic response to oxidative stimuli, 3) altered structure/function (CT score), and 4) clinical (X-ray confirmed CWP) and severe (nodular form) disease. We presently attempted to apply this framework to study gene-environment interaction and gene- intermediate phenotype interaction in the pathogenesis of CWP.
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Gene-environment interaction in Paget's disease of bone

Gene-environment interaction in Paget's disease of bone

Chapter 3: Materials and methods 3.1 Recruitment of participants and survey This study was approved by the CHU de Québec-Université Laval Ethics Committee and all participants signed a consent form before inclusion in the study. We studied 140 patients with PDB (familial and non-familial forms) and 113 healthy controls from the French-Canadian cohort, who previously answered a general survey on environmental factors and who agreed to participate to this new study (90). An affected participant was diagnosed with PDB if at least two of the following criteria were satisfied: 1) an increase in total serum alkaline phosphatase level and/or 2) a typical aspect of PDB on bone radiographs and/or 3) an abnormal whole-body bone scan. Familial forms are defined by the presence of at least one relative affected with PDB. Controls were unrelated healthy adults without personal or familial history of PDB and with normal total serum alkaline phosphatase levels at inclusion. None of the controls carried any mutation in SQSTM1 gene. Controls were not matched for age and sex with PDB patients. All participants lived in the same geographic area within a 120 km radius of Quebec City. In the new survey, we focused on the history of residence and proximity to sources of indoor and outdoor air pollutants during childhood and adulthood. Outdoor pollution sources were represented by residence close to a highway, an airport, a train, a bus, or a gas station, while indoor pollution sources were represented by frequent exposure to heating combustibles (carbon, wood, and oil) and cigarette smoke exposure at home.
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Gut microbiota-mediated Gene-Environment interaction in the TashT mouse
model of Hirschsprung disease

Gut microbiota-mediated Gene-Environment interaction in the TashT mouse model of Hirschsprung disease

Amplicon sequencing and bioinformatics. Extraction of bacterial DNA was performed using the QIAamp ® Fast DNA Stool Mini Kit (QIAGEN Cat. No. 51604). A total of 26 (2 Forward and 24 Reverse) bar- coded primers 43 , 44 were then used to amplify the V5-V6 region of the 16S rRNA gene using the Feldan PCR kit (Bio Basic Inc) and 100 ng of DNA per sample (final volume of 25 µl). PCR conditions consisted of an initial dena- turation step of 2 min at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 64 °C and 30 s at 72 °C, and completed by a final extension of 5 minutes at 72 °C. PCR reactions also included two negative controls: a water blank with a Forward/Reverse primer pair, and 100 ng of DNA from sample number 1 with only a Forward primer (Table S1). To confirm the efficiency of PCR reactions, 5 µl of each sample was visualized on a 2% agarose gel. DNA content from the remaining 20 µl was normalized using the Invitrogen Sequalprep PCR Cleanup and Normalization Kit, and pooled at equal concentrations (0.8 ng/ µl). Sequencing was performed on an Illumina MiSeq sequencer as previously described 43 , 44 .
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Unconditional analyses can increase efficiency in assessing gene-environment interaction of the case-combined-control design.

Unconditional analyses can increase efficiency in assessing gene-environment interaction of the case-combined-control design.

SAS function RANUNI (SAS, version 8, Cary, NC) to assign each of the cases and controls to the different possible E and G categories. General assumptions/requirements For purposes of presentation, we make several assumptions about the study population. We assume that there is no population stratification bias. Second, we assume that baseline disease risks do not differ in the study population. That is, we assume that there are no other factors in addition to G and E that differentially influence risk of disease. To examine the impact of this assumption, however, we examine the effect of a family-specific variable denoted by H on the estimates of the interaction effect (GxE) and the relative efficiencies. H is defined as follows. OR H for a given family was randomly determined from a normal distribution with mean
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en fr Gene-environment interaction in oral cleft etiology INTERACTIONS GENE-ENVIRONNEMENT DANS L'ETIOLOGIE DES FENTES ORALES

n’est observée. Certaines expérimentations montrent pourtant que l’apport en folates influe sur l’activité enzymatique du polymorphisme. La littérature épidémiologique concernant le rôle du polymorphisme MTHFR C677T, quel que soit l’apport en folates de la mère, dans l’étiologie des fentes orales, est encore peu développée. Les résultats de ces études, incluant l’étude présente, sont conflictuels et reflètent un mécanisme complexe intervenant entre le gène MTHFR, l’apport en folates et le risque de fentes orales. D’autres analyses de ce projet montrent le rôle des apports alimentaires en folates dans la prévention des fentes orales. La troisième application participe à une littérature épidémiologique très débattue concernant l’exposition professionnelle des mères aux solvants organiques et le risque de malformations congénitales, en particulier de fentes orales. Elle rapporte une association entre l’exposition professionnelle aux solvants et le risque de fentes orales, bien qu’il soit difficile d’attribuer ce risque à un agent spécifique dû aux expositions multiples à différents solvants. Un risque protecteur est observé entre l’allèle muté du CYP2E1*RsaI porté par l’enfant et le risque de fentes orales non syndromiques. Aucune interaction gène-environnement statistiquement significative n’est observée. De façon générale, peu d’études se sont intéressées aux interactions entre l’exposition professionnelle des mères à des substances chimiques et des gènes impliqués dans le métabolisme de xénobiotiques toxiques, dans la survenue d’une fente orale ou, plus généralement, de malformation congénitale. Ces quelques études, incluant l’étude présente, fournissent des estimations d’interaction très peu puissantes, dûes à une prévalence faible de l’exposition et des polymorphismes d’étude rare.
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Increased power to detect gene-environment interaction using siblings controls.: GxE interaction and siblings controls

Increased power to detect gene-environment interaction using siblings controls.: GxE interaction and siblings controls

INTRODUCTION Interest is increasing in studying gene-environment (GxE) interaction in disease etiology. In general, two types of control groups are used for examining GxE interactions: unrelated (e.g. population-based) or related (e.g. sibling) controls. To date, most studies of GxE interactions have used unrelated controls. This use of unrelated controls, however, has been questioned because of the potential problem of population stratification (1-6). This potential bias from stratification was thus the motivation for some authors to propose the use of related controls as a more appropriate control group for evaluating genetic factors (7, 8). Witte et al. compared a case-control design with at least 1 control per case using population-based controls to a design using sibling (or cousin) controls. The results showed that population-based controls were most efficient for evaluating a genetic main effect, with siblings being the least efficient control group. In contrast, sibling controls were the most efficient group for detecting a GxE interaction effect. This gain in relative efficiency decreased as the frequency of the genetic factor increased (7, 8).
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Gene-environment processes linking peer victimization and physical health problems: A longitudinal twin study

Gene-environment processes linking peer victimization and physical health problems: A longitudinal twin study

headaches) show considerable genetic influence (Holloway, Yang, & Holgate, 2010; Ligthart, Nyholt, Penninx, & Boomsma, 2010; Thomsen, Van Der Sluis, Kyvik, Skytthe, & Backer, 2010). As proposed by Shostak (2003), environ- mental stressors and genetic vulnerability factors may not act independently but rather interact with each other to produce ill physical health. Shanahan and Hofer (2005) have described two forms of geneenvironment interaction (G  E) that may occur in this context. Specifically, in line with a diathesis-stress process of G  E, an environmental stressor such as peer victimization may trigger or exacerbate the effect of genetic vulnerabilities, such that health prob- lems are mainly observed in genetically vulnerable youth. For instance, findings from two molecular genetic studies suggest that the deleterious effect of peer victimization on depression symptoms is especially pronounced in early ad- olescents carrying two 5-HTTLPR short alleles, which in- creases their vulnerability to developing depression (Benjet, Thompson, & Gotlib, 2010; Sugden et al., 2010). Alternatively, one may observe a suppression process of G  E. This occurs when a stressful environmental experi- ence reduces the role of genetic factors in explaining interindividual differences in health problems, such that many victims exhibit health problems, irrespective of their genetic vulnerabilities. Such a suppression process of G  E may be especially likely in younger victimized children, who often have not yet developed the social and emotional skills to cope with peer harassment (Smith, Shu, & Madsen, 2001). Support for this notion comes from findings that genetic vulnerability explained a large part of depression symptoms during kindergarten only in popular children, who presumably face little peer-related stress. In contrast, children who were rejected by their peers showed depressive symptoms regardless of their genetic disposition (Brendgen et al., 2009). It is still unknown, however, whether G  E can also be found in the link of peer victimization and phys- ical health problems and whether such an interaction is in line with a trigger or a suppression process of G  E.
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The importance of gene–environment interactions in human obesity

The importance of gene–environment interactions in human obesity

Introduction Over the past three decades, the prevalence of obesity has reached epidemic proportions throughout the world (1). This recent epidemic cannot be explained by sudden changes in the human population gene pool and has been mainly attributed to lifestyle modifications (2). Over-nutrition and decline in physical activity are the two “usual suspects”, but additional factors (reduced gut microflora diversity, sleep debt, endocrine disruptors, reduction in variability of ambient temperatures) have emerged as significant contributors to the escalating prevalence of obesity (3). If obesity is a multifactorial disorder that requires environmental influences to manifest, some individuals are more susceptible than others to weight gain in an obesity-prone environment, and who becomes obese at the individual level is largely determined by genetic factors (4). Technological and methodological breakthroughs in the last twenty years have led to important progress in the elucidation of the genetic architecture of obesity (5). The first two genes (LEP and MKKS) associated with a Mendelian non- syndromic or syndromic form of obesity were identified in 1997 and 2000 (6, 7). Seven years later, the first common variant (located in the intron 1 of the FTO gene) reproducibly associated with polygenic obesity was identified (8, 9). At the time we are writing, over 40 monogenic obesity loci (with or without syndromic features) and 130 polygenic obesity loci have been described, and this list is destined to grow over the coming years (5). In parallel with successful gene identification efforts, the number of studies on gene-environment interactions has grown rapidly (10). In the first segment of this review, we summarize the findings supporting gene-environment interaction in obesity from heritability, monogenic and polygenic studies and provide a biological hypothesis to explain these statistical interactions. The final section will outline methodological challenges associated with GEI studies, provide potential solutions to these issues based on existing evidence and highlight future directions of GEI research.
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Power comparison of different methods to detect genetic effects and gene-environment interactions.

Power comparison of different methods to detect genetic effects and gene-environment interactions.

2 Abstract Identifying gene-environment (GxE) interactions has become a crucial issue in the past decades. Different methods have been proposed to test for GxE interactions in the framework of linkage or association testing. Their respective performances have however rarely been compared. Using GAW15 simulated data, we compare here the power of four methods: one based on affected sib-pairs that tests for linkage and interaction (the mean interaction test) and three methods that test for association and/or interaction: a case-control test, a case-only test and a log-linear approach based on case-parent trios. Results show that for the particular model of interaction between tobacco use and locus B simulated here, the mean interaction test has poor power to detect either the genetic effect or the interaction. The association studies, i.e. the log- linear-modeling approach and the case-control method, are more powerful to detect the genetic effect (power of 78% and 95% respectively) and taking into account interaction moderately increases the power (increase of 9% and 3% respectively). The case-only design exhibits a 95% power to detect gene-environment interaction but the type I error rate is increased.
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Candidate gene-environment interactions.

Candidate gene-environment interactions.

2/6 10/03/10 One of the limitations of interdisciplinarity is the misunderstanding regarding specific words and concepts from one discipline to the other (1). One way to circumvent this aspect is to incorporate the vocabulary of other disciplines, when it corresponds to the appropriate concept. Gene environment interaction is a popular topic, for which there has been to date,more reviews than established findings. There has been numerous attempts to represent what types of interactions could occur (2). Geneticists have proposed the term candidate genes to infer there was a specific hypothesis, usually regarding the function of the gene, justifying its study for a given disease, whereas genomewide comparisons have been called "agnostic" (3), which etymologically means without knowledge. In that context, testing a candidate gene-environment interaction is to test a hypothesis, based on knowledge (4).
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Limit cycles in piecewise-affine gene network models with multiple interaction loops

Limit cycles in piecewise-affine gene network models with multiple interaction loops

1 Preliminaries It has been discovered in the 1960’s that some proteins can regulate (i.e. ac- tivate or inhibit) the expression of genes in a living organism. Since proteins are the product of gene expression, feedback appears to be at the core of this process. Moreover, the unprecedented developments of cell biology in the last decades has led to a view where gene regulation involves huge numbers of ele- ments (genes, mRNAS, proteins ...), interacting in a nonlinear way. It is thus clear today that mathematical models and tools are required to analyse these complex systems. Several classes of models have been proposed to describe gene regulation. Although stochastic models undoubtedly have a major role to play, we will focus in this note on deterministic models. They can be divided in two classes : models based on differential equations [16], and discrete models based on a representation by a finite number of states [13, 19]. Both classes have their complementary advantages, the most obvious being relative to the accuracy vs. tractability dilemma. First proposed by L. Glass [4], piecewise linear (in fact piecewise affine) models appear to be an efficient intermediate between the two previous classes. Thus, the sometimes called ’Glass systems’ have been both used to model real gene networks [3, 17], and studied mathematically. The present paper is more related to this second type of work.
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Progress in the epidemiological understanding of gene-environment interactions in major diseases: cancer.

Progress in the epidemiological understanding of gene-environment interactions in major diseases: cancer.

Les modèles classiques de cancérogenèse supposent l'existence de plusieurs évènements mutationnels successifs sur un même clone cellulaire, qui le font progressivement échapper aux mécanismes assurant la régulation de la division cellulaire et le maintien de l'intégrité du génome. La chaîne d'évènements qui conduit d'une exposition environnementale au cancer fait potentiellement intervenir des centaines de gènes polymorphes codant pour des protéines impliquées soit dans le transport et le métabolisme des xénobiotiques, soit dans la réparation, soit dans la réponse immunitaire ou l’inflammation, avec des fréquences d’allèles variants suffisantes pour que l’on puisse envisager de contraster des groupes de sujets ayant des prédispositions différentes au cancer en cas d’exposition. D'autres gènes également polymorphes peuvent intervenir dans la propension individuelle à développer un cancer dans certaines conditions d'exposition (gènes de la pigmentation, gènes des récepteurs hormonaux…). Les caractères multifactoriel et multi–étape du cancer créent des conditions théoriques d’interaction statistique, puisque certains facteurs interviennent à des étapes complémentaires du processus cancéreux. La réalité est que ces interactions ne sont visibles que si les associations sont assez fortes, que les expositions sont bien mesurées en considérant des périodes d’exposition pertinentes, et que la multiplicité des facteurs n’est pas trop importante. De fait, la recherche d'une interaction statistique entre exposition et gènes
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Limit cycles in piecewise-affine gene network models with multiple interaction loops

Limit cycles in piecewise-affine gene network models with multiple interaction loops

1 Preliminaries It has been discovered in the 1960’s that some proteins can regulate (i.e. ac- tivate or inhibit) the expression of genes in a living organism. Since proteins are the product of gene expression, feedback appears to be at the core of this process. Moreover, the unprecedented developments of cell biology in the last decades has led to a view where gene regulation involves huge numbers of ele- ments (genes, mRNAS, proteins ...), interacting in a nonlinear way. It is thus clear today that mathematical models and tools are required to analyse these complex systems. Several classes of models have been proposed to describe gene regulation. Although stochastic models undoubtedly have a major role to play, we will focus in this note on deterministic models. They can be divided in two classes : models based on differential equations [16], and discrete models based on a representation by a finite number of states [13, 19]. Both classes have their complementary advantages, the most obvious being relative to the accuracy vs. tractability dilemma. First proposed by L. Glass [4], piecewise linear (in fact piecewise affine) models appear to be an efficient intermediate between the two previous classes. Thus, the sometimes called ’Glass systems’ have been both used to model real gene networks [3, 17], and studied mathematically. The present paper is more related to this second type of work.
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Study of the interaction with a virtual 3D environment displayed on a smartphone

Study of the interaction with a virtual 3D environment displayed on a smartphone

To overcome the density problem, some techniques use a transparent dynamic selection volume instead of a ray or a point cursor to augment the selection area. Considering a volume selection area augments the chances to find the target inside from the first attempt and does not require a higher selection precision to point the target. A selection volume is defined by a ray and the volume around it. This volume can have different forms : a cylinder in the case of the transparent cylinder technique [Dang et al., 2003] or a cone in the case of the Spotlight technique [Liang and Green, 1993]. Or, the selection volume is defined by a point cursor and the volume around while using the transparent sphere technique [Dang et al., 2003]. These techniques select the target and a set of objects around it. The increase of the selection area minimizes the pointing problem in a dense environment but requests a second refi- nement selection step to point the target from the preselected set of objects. These techniques are accurate ; don’t require accurate movements or multiple adjustments in depth of the input device position to catch the target. They don’t present a hand shake problem and don’t depend on the target size. However, the limitation of these techniques is in the huge number of objects existing in the subset. Consequently, they request a large response time because we have to : choose the target from a long list while using the menu technique, wait for the target to be highlighted while using the circulation technique, wait for the generation of the selection criteria while using the Virtual pointer metaphor [Steinicke et al., 2006], wait for the calculation of the refinement algorithm while using the Spotlight interaction technique [Liang and Green, 1993] or add a navigation phase while using the shadow cone technique [Steed and Parker, 2004].
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Interaction between auditory and visual perceptions on distance estimations in a virtual environment

Interaction between auditory and visual perceptions on distance estimations in a virtual environment

While!some!authors!described!the!ventriloquism!effect!as!a!complete!capture!of!the!auditory! signal!by!the!visual!signal![38,!39,!40],!Alais!and!Burr![41]!have!shown!that!this!effect!can!be!explained! by! a! model! of! optimal! combination! of! visual! and! auditory! spatial! cues,! where! each! modality! is! weighted! by! an! inverse! estimate! of! its! variability.! Our! ability! to! make! use! of! visual! cues! to! localize! stimuli!typically!leads!to!less!variability!than!our!ability!of!using!auditory!cues!only.!So!when!a!conflict! arises! between! these! modalities,! visual! information! tends! to! bias! responses! to! auditory! stimuli.! However! if! visual! stimuli! were! blurred! so! that! they! would! become! harder! to! localize,! vision! could! become!worse!than!audition,!and!the!illusion!would!work!in!reverse,!with!sound!capturing!vision![41].! The! visual&capture! effect!was! more! studied! in! azimut! and! elevation! than! in! distance.! About! distance,!a!classic!experimental!setup!consists!in!placing!several!speakers!at!different!distances!in!a! row! pointing! to! the! subject! (who! can! see! only! the! first! loudspeaker),! in! an! anechoic! or! semi& reverberant!room.!The!results!showed!that!the!subject!reported!that!the!sound!came!from!the!nearest! speaker!(whatever!the!real!active!speaker).!Mershon!et!al.![42]!showed!that!this!«!proximity!image!»! effect! highlighted! by! Gardner! [43]! with! an! anechoïc! chamber! operates! also! in! semi&reverberant! environment.!They!further!indicated!that!the!distance!from!the!sound!source!can!be!overestimated!or! underestimated!depending!on!the!position!of!the!visual!target.!
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Gene-Environment Correlation Linking Aggression and Peer Victimization:
Do Classroom Behavioral Norms Matter?

Gene-Environment Correlation Linking Aggression and Peer Victimization: Do Classroom Behavioral Norms Matter?

Using a genetically informed design based on 197 Monozygotic and Dizygotic twin pairs assessed in grade 4, this study examined 1) whether, in line with a gene-environment correlation (rGE), a genetic disposition for physical aggression or relational aggression puts children at risk of being victimized by their classmates, and 2) whether this rGE is moderated by classroom injunctive norm salience in regard to physical or relational aggression. Physical aggression and relational aggression, as well as injunctive classroom norm salience in regard to these behaviors, were measured via peer nominations. Peer victimization was measured via self-reports. Multi- Level Mixed modeling revealed that children with a genetic disposition for either aggressive behavior are at higher risk of being victimized by their peers only when classroom norms are unfavourable toward such behaviors. However, when classroom injunctive norms favor aggressive behaviors, a genetic disposition for physical or relational aggression may actually protect children against peer victimization. These results lend further support to the notion that bullying interventions must include the larger peer context instead of a sole focus on victims and bullies.
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A Multi-view and Multi-interaction System for Digital-mock up’s collaborative environment

A Multi-view and Multi-interaction System for Digital-mock up’s collaborative environment

On the other hand, many commercial DMU and BIM plat- forms can integrate design, analysis and manufacture. How- ever, an expert only uses a part of the platform to finish his work. Separate displays, like using single laptop or screen wall that put several separate screens together, display dif- ferent domains of information separately. Expert has to ex- change eyes and body to deal with the information frag- ments. This may reduce the expert’s concentration psycho- logically and increase the possibility of misunderstanding and complex of communication [ ZW14 ]. When attending a project review, in which facial expressions and hand gestures interaction are important to express ideas among each other, experts requires more face-to-face communication. If an ex- pert can be presented only with his own POV in a shared visual space with other experts, he can avoid switching eyes between another expert and himself. This will help the expert to communicate and collaborate [ ABM ∗ 97 ] with others and also to overcome the sense of isolation that happens when
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Demonstration of a unified and flexible coupling environment for nonlinear fluid-structure interaction

Demonstration of a unified and flexible coupling environment for nonlinear fluid-structure interaction

• Interfacing tool for strong coupling of independent solvers • High fidedility models for nonlinear FSI. • Flexible partitioned tool for large range of physics • Validated on typical be[r]

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Gene–environment interactions in the study of asthma in the postgenomewide association studies era

Gene–environment interactions in the study of asthma in the postgenomewide association studies era

Introduction: It is now well established that heritable and environmental factors play a role in asthma pathogenesis. Genetic factors are likely to be involved in the development, the activity and the severity of asthma, and they act primarily through complex mechanisms that involve interactions with environmental factors and with other genes. Gene-environment (GxE) interaction studies aim to explain how the strength and direction of associations between certain genetic variants and asthma may depend on given environmental exposures, and vice versa. So far, most GxE interactions have been identified through hypothesis-driven research involving only few candidate genes (reviewed in [1]). To go further, investigating GxE interactions may help to better understand the role of the genes identified by genome-wide association studies (GWAS) of asthma. For example, variants at chromosome 17q21 that emerged from GWAS have shown particularly strong associations with asthma in children who had had wheezing illnesses or tobacco exposures in early life [2,3**]. Understanding the mechanisms through which genes and environment interact represents one of the major challenges for pulmonary researchers.
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An Interaction Centred Approach to the teaching of Non-technical Skills in a Virtual Environment

An Interaction Centred Approach to the teaching of Non-technical Skills in a Virtual Environment

Because VE’s create a safe learning environment and can provide multiple unique critical situations for the learning of NTS, they can strongly contribute to the successful training of these skills. Our objective is to provide an efficient VE for the training in NTS in different domains by targeting the critical situations which are the more adapted to the specificities of each learner, given a number of constraints such as the duration of a training session (about 1 hour) and the limited number of training sessions for a learner. In the next session, we will first present a number of interaction-based ap- proaches to teaching in VE, then explain why the learning of NTS inside a VE needs to take into account the differences among learners, and why it is necessary to model the learner’s knowledge in order to maximise the efficiency of the learning experience. What is of interest is the kind of coupling that may be achieved between the learner's skills and behaviour and the scenarios played by the virtual environments. As we will discuss in the following paragraphs, this coupling leads to ill-defined issues that are particularly interesting to consider in the VE context.
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