The challenge of detecting epistasis (G×G Interactions): Genetic Analysis Workshop 16

An, Ping; Mukherjee, Odity; Chanda, Pritam; Li, Yao; Engelman, Corinne D.; Huang, Chien-Hua; Tian, Zheng; Kovac, Ilija; Dubé, Marie‐Pierre; Liang, Xueying; Jia, Lee; Andrade, Mariza de; Culverhouse, Robert; Malzahn, Doerthe; Manning, Alisa K.; Clarke, Geraldine M.; Jung, Jeesun; Province, Michael A.

doi:10.1002/gepi.20474

Cited by 18 publications

(16 citation statements)

References 33 publications

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“…Of many existing methods for detecting interactions in genetic datasets [An et al., ; Cordell, , ], inference on a product term in the context of a generalized linear model appears to be the most commonly employed. Although product terms can be used to represent four types of epistasis in a model that includes additive and dominance terms [Cheverud, ], here we focus on the simpler model

y = β_{0} + β_{1} x + β_{2} z + β_{3} x z + ɛ

, where x and z indicate single‐nucleotide polymorphism (SNP) dosages.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Interpreting Type I Error Rates from a Product‐Term Model of Interaction Applied to Quantitative Traits

Rao

Province

2015

Genet. Epidemiol.

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Adequate control of type I error rates will be necessary in the increasing genome‐wide search for interactive effects on complex traits. After observing unexpected variability in type I error rates from SNP‐by‐genome interaction scans, we sought to characterize this variability and test the ability of heteroskedasticity‐consistent standard errors to correct it. We performed 81 SNP‐by‐genome interaction scans using a product‐term model on quantitative traits in a sample of 1,053 unrelated European Americans from the NHLBI Family Heart Study, and additional scans on five simulated datasets. We found that the interaction‐term genomic inflation factor (lambda) showed inflation and deflation that varied with sample size and allele frequency; that similar lambda variation occurred in the absence of population substructure; and that lambda was strongly related to heteroskedasticity but not to minor non‐normality of phenotypes. Heteroskedasticity‐consistent standard errors narrowed the range of lambda, with HC3 outperforming HC0, but in individual scans tended to create new P‐value outliers related to sparse two‐locus genotype classes. We explain the lambda variation as a result of non‐independence of test statistics coupled with stochastic biases in test statistics due to a failure of the test to reach asymptotic properties. We propose that one way to interpret lambda is by comparison to an empirical distribution generated from data simulated under the null hypothesis and without population substructure. We further conclude that the interaction‐term lambda should not be used to adjust test statistics and that heteroskedasticity‐consistent standard errors come with limitations that may outweigh their benefits in this setting.

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y = β_{0} + β_{1} x + β_{2} z + β_{3} x z + ɛ

, where x and z indicate single‐nucleotide polymorphism (SNP) dosages.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Interpreting Type I Error Rates from a Product‐Term Model of Interaction Applied to Quantitative Traits

Rao

Province

2015

Genet. Epidemiol.

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“…In this way, we reached a balance between the complexity of the represented phenomena and simplicity in the definition of the model. Moreover, the best strategy to identify even simple interactions as single G×G and G×E with binary environmental variables it is still debated (for an example of the debate, see the report on the 2009 Genetic Analysis Workshop [11,12,17]). For this reason, we believe that a set of simulated populations in which all features are known provides an important tool for the identification of the best strategy to identify and study G×G and G×E.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, studying the complex interactions among risk factors is a daunting task that requires large data sets and specific research designs. Furthermore, the best statistical method for the identification of G×G and G×E in case-control data sets [11,12] is still widely debated. The performance of statistical methods that are used for the identification of G×G and G×E are typically influenced by many factors: sample size, number of involved factors, type of interaction, model of inheritance, allelic frequencies, distributions of the environmental factors, and relative strength of different factors affecting disease risk.…”

Section: Introductionmentioning

confidence: 99%

Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2

et al. 2012

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BackgroundThe analysis of complex diseases is an important problem in human genetics. Because multifactoriality is expected to play a pivotal role, many studies are currently focused on collecting information on the genetic and environmental factors that potentially influence these diseases. However, there is still a lack of efficient and thoroughly tested statistical models that can be used to identify implicated features and their interactions. Simulations using large biologically realistic data sets with known gene-gene and gene-environment interactions that influence the risk of a complex disease are a convenient and useful way to assess the performance of statistical methods.ResultsThe Gene-Environment iNteraction Simulator 2 (GENS2) simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions. GENS2 is based on data with realistic patterns of linkage disequilibrium, and imposes no limitations either on the number of individuals to be simulated or on number of non-predisposing genetic/environmental factors to be considered. The GENS2 tool is able to simulate gene-environment and gene-gene interactions. To make the Simulator more intuitive, the input parameters are expressed as standard epidemiological quantities. GENS2 is written in Python language and takes advantage of operators and modules provided by the simuPOP simulation environment. It can be used through a graphical or a command-line interface and is freely available from http://sourceforge.net/projects/gensim. The software is released under the GNU General Public License version 3.0.ConclusionsData produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for the identification of gene-gene and gene-environment interactions.

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“…Other themes addressed by multiple contributions included methods for imputation, population stratification, subgroup analyses, gene-gene (G×G) interactions, and the use of biomarkers. Some of these issues are common, with several other groups addressing methodological problems in GWAS data for other kinds of endpoints (see especially the reports in this volume from Group 4 for haplotype-based analysis [Hauser, 2009], Group 9 on G×G interactions [An et al, 2009], and Group 13 on population stratification [Hinrichs et al, 2009]). We begin, however, with a brief discussion of a fundamental issue raised by two contributions concerning what parameter should be used to describe the associations with a given locus and will conclude with a brief summary of some of the substantive results.…”

Section: Introductionmentioning

confidence: 99%

Genome‐wide association studies for discrete traits

Thomas

2009

Genetic Epidemiology

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Genome-wide association studies of discrete traits generally use simple methods of analysis based on chi-square tests for contingency tables or logistic regression, at least for an initial scan of the entire genome. Nevertheless, more power might be obtained by using various methods that analyze multiple markers in combination. Methods based on sliding windows, wavelets, Bayesian shrinkage, or penalized likelihood methods, among others, were explored by various participants of Genetic Analysis Workshop 16 Group 1 to combine information across multiple markers within a region, while others used Bayesian variable selection methods for genome-wide multivariate analyses of all markers simultaneously. Imputation can be used to fill in missing markers on individual subjects within a study or in a meta-analysis of studies using different panels. Although multiple imputation theoretically should give more robust tests of association, one participant contribution found little difference between results of single and multiple imputation. Careful control of population stratification is essential, and two contributions found that previously reported associations with two genes disappeared after more precise control. Other issues considered by this group included subgroup analysis, gene-gene interactions, and the use of biomarkers.

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The challenge of detecting epistasis (G×G Interactions): Genetic Analysis Workshop 16

Cited by 18 publications

References 33 publications

A Framework for Interpreting Type I Error Rates from a Product‐Term Model of Interaction Applied to Quantitative Traits

A Framework for Interpreting Type I Error Rates from a Product‐Term Model of Interaction Applied to Quantitative Traits

Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2

Genome‐wide association studies for discrete traits

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