The Meaning of Interaction

Wang, Xuefeng; Elston, Robert C.; Zhu, Xiaofeng

doi:10.1159/000321967

Cited by 127 publications

(120 citation statements)

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“…Finally, we point out that an interaction detected via statistical models is different from the biological interaction (Phillips 1998;Cordell 2002;Wang et al 2010). The lack of direct correspondence between statistical and biological interactions makes it difficult to make strong inferences concerning biological mechanisms based on interaction terms from a statistical model (Ueki and Cordell 2012).…”

Section: Phenotypic Variability and Implications In Complex Traits Anmentioning

confidence: 87%

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

et al. 2014

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The expression of a gene can vary across individuals in the general population, as well as between monozygotic twins. This variable expression is assumed to be due to the influence of both genetic and nongenetic factors. Yet little evidence supporting this assumption has been obtained from empirical data. In this study, we used expression data from a large twin cohort to investigate the influences of genetic and nongenetic factors on variable gene expression. We focused on a set of expression variability QTL (evQTL)-i.e., genetic loci associated with the variance, as opposed to the mean, of gene expression. We identified evQTL for 99, 56, and 79 genes in lymphoblastoid cell lines, skin, and fat, respectively. The differences in gene expression, measured by the relative mean difference (RMD), tended to be larger between pairs of dizygotic (DZ) twins than between pairs of monozygotic (MZ) twins, showing that genetic background influenced the expression variability. Furthermore, a more profound RMD was observed between pairs of MZ twins whose genotypes were associated with greater expression variability than the RMD found between pairs of MZ twins whose genotypes were associated with smaller expression variability. This suggests that nongenetic (e.g., environmental) factors contribute to the variable expression. Lastly, we demonstrated that the formation of evQTL is likely due to partial linkages between eQTL SNPs that are additively associated with the mean of gene expression; in most cases, no epistatic effect is involved. Our findings have implications for understanding divergent sources of gene expression variability.V ARIATION and variability are central concepts in biology (Hallgrímsson and Hall 2005). Although often used interchangeably in the scientific literature, the two are not synonymous. Variation refers to the differences among individuals, whereas variability refers to the potential of a population to vary (Wagner 1995;Wagner and Altenberg 1996). In many cases, greater phenotypic variability (e.g., transcriptional noise) is disadvantageous (Kemkemer et al. 2002;Bahar et al. 2006;Wang and Zhang 2011) unless it gives rise to greater organismal plasticity-first at the level of an individual organism and eventually at the population level. Genetic factors resulting in more variable phenotypes become favored when they enable a population to more effectively respond to environmental changes (Hill and Zhang 2004;Kaern et al. 2005;Acar et al. 2008;Zhang et al. 2009). Thus, understanding to what extent and in what ways genotypes influence phenotypic variability is of fundamental importance.Much effort has been focused on identifying genetic loci such as expression quantitative trait loci, or eQTL (Stranger et al. 2005(Stranger et al. , 2007Choy et al. 2008;Montgomery et al. 2010;Pickrell et al. 2010;Montgomery and Dermitzakis 2011), that affect the average value of a phenotype, while ignoring those that affect the variance of a phenotype. However, there is increasing evidence across species for genet...

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Section: Phenotypic Variability and Implications In Complex Traits Anmentioning

confidence: 87%

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

et al. 2014

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“…Modern statistical modeling techniques are required for discovering statistical interactions of multiple genetic variants in the context of potential complicating factors, such as multiple genetic markers, linkage disequilibrium (LD), and environmental influences. In addition, because of the smallness of (marginal) main-effects (Wang, Elston & Zhu, 2010), predictive variables are often associated with only a small increased risk of disease, indicating that each has only a small individual predictive value. In statistical terms, we would hypothesize that sets of weakly predictive variables can be jointly predictive.…”

Section: Introductionmentioning

confidence: 99%

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

Dazard

Ishwaran

Mehlotra

et al. 2018

Statistical Applications in Genetics and Molecular Biology

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Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes.

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“…Third, the calculated interaction model is hard to explain. A previous study has reported that this might result from biological interactions between the variants or their products [9].…”

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confidence: 97%

The Association Between Epoxide Hydrolase Genetic Variant and Effectiveness of Nicotine Replacement Therapy in a Han Chinese Population

et al. 2016

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Nicotine is a psychoactive alkaloid that is thought to play a key role in addiction to commercial tobacco products [1] and cotinine is its primary metabolite [2]. Pharmacological treatment, such as nicotine replacement therapy (NRT), is a valid solution to this problem. Tobacco smoke contains many carcinogens such as nitrosamines [i.e., nicotinederived nitrosamine ketone (NNK) and N-nitrosonornicotine (NNN)], and nicotine undergoes chemical conversion into NNK and NNN during the processes of curing and smoking [3]. Microsomal epoxide hydrolase (EPHX) is responsible for the metabolism of polycyclic aromatic hydrocarbons and the detoxification of pro-carcinogens in the process of smoking [4]. Therefore, in this study we aimed to investigate whether the genetic polymorphisms of EPHX, rs1051740 and rs2234922, are associated with the metabolic processing of cotinine in nicotine abstinence and craving.Two hundred and eleven eligible participants took part in this study, and were randomly divided into active or placebo treatment groups in a double-blind manner (see Supplementary Materials). There were no significant demographic differences between the sublingual nicotine tablet and placebo groups except for age (Table S1). The distributions of the genotypes of the two loci did not significantly differ between the two groups. The genotypes of the participants deviated from Hardy-Weinberg equilibrium for both polymorphisms (P \ 0.01 for rs1051740 and P = 0.01 for rs2234922).One-way ANOVA was used between the genotypes with the 2 loci, and age was a covariate (rs1051740 P \ 0.05 and rs2234922 P [ 0.05) (Table S2). There was an interaction between age and genotype (F = 3.90, df = 2, P = 0.02) for rs1051740. The interaction of time, grouping (placebo/NRT), and genotype was significantly associated with the difference between the urinary cotinine concentration at the end of the 2nd month and baseline (F = 5.20, P = 0.01); the TT genotype was significantly associated with the interaction of time and grouping (F = 14.12, P \ 0.01) for rs1051740. In rs1051740, the interaction of time, grouping, and genotype was significantly associated with the difference between the urinary cotinine concentration at the end of the 3rd month and baseline (F = 4.33, P = 0.01); and the TT genotype was significantly associated with the interaction of time and grouping (F = 28.23, Fan Wang and Yanlong Liu have contributed equally to this work. Electronic supplementary materialThe online version of this article

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The Meaning of Interaction

Cited by 127 publications

References 46 publications

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

The Association Between Epoxide Hydrolase Genetic Variant and Effectiveness of Nicotine Replacement Therapy in a Han Chinese Population

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