Testing Gene–Gene Interactions in Genome Wide Association Studies

Hu, Jie; Wang, Xianlong; Wang, Pei

doi:10.1002/gepi.21786

Cited by 22 publications

(26 citation statements)

References 43 publications

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“…Alternatively, other measures can be used, for example, interaction logistic measure

I L :

I L false(X_{1}; X_{2}; Y false) : = γ_{11}^{2} + γ_{12}^{2} + γ_{21}^{2} + γ_{22}^{2},

considered by Hu et al. (), which equals 0 only when

γ_{i j} \equiv 0

. Thus,

I L > 0

is equivalent to existence of logistic interactions.…”

Section: Methods For Detecting “Gene–gene” Interactionsmentioning

confidence: 99%

“…In other words, we verify how much Y influences the dependence between X 1 and X 2 . Property shows that

I I

is loosely related to the popular group of methods based on measuring the difference of interloci associations between cases and controls (Cordell, ; Hu et al., ; Kang, Yue, Cui, & Zhang, ). This important group is represented by the methods that compare the linkage disequilibrium (

L D

) in cases and controls (Yang et al., ), in particular

L D

measure defined in .…”

Section: Methods For Detecting “Gene–gene” Interactionsmentioning

confidence: 99%

“…considered by Hu et al (2014), which equals 0 only when ≡ 0. Thus, > 0 is equivalent to existence of logistic interactions.…”

Section: Logistic Regressionmentioning

confidence: 99%

“…is a representative of a large family of measures that are based on comparing the interloci associations between cases and controls. More examples of -based measures can be found in Hu et al (2014) and Zhao et al (2006). Observe that for independent 1 and 2 , ( 1 ; 2 ; ) = 0 implies ( 1 ; 2 ; ) = 0.…”

Section: Simulation Studymentioning

confidence: 99%

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A deeper look at two concepts of measuring gene–gene interactions: logistic regression and interaction information revisited

Mielniczuk

Teisseyre

2017

Genetic Epidemiology

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Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures.

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“…Alternatively, other measures can be used, for example, interaction logistic measure

I L :

I L false(X_{1}; X_{2}; Y false) : = γ_{11}^{2} + γ_{12}^{2} + γ_{21}^{2} + γ_{22}^{2},

considered by Hu et al. (), which equals 0 only when

γ_{i j} \equiv 0

. Thus,

I L > 0

is equivalent to existence of logistic interactions.…”

Section: Methods For Detecting “Gene–gene” Interactionsmentioning

confidence: 99%

“…In other words, we verify how much Y influences the dependence between X 1 and X 2 . Property shows that

I I

L D

) in cases and controls (Yang et al., ), in particular

L D

measure defined in .…”

Section: Methods For Detecting “Gene–gene” Interactionsmentioning

confidence: 99%

“…considered by Hu et al (2014), which equals 0 only when ≡ 0. Thus, > 0 is equivalent to existence of logistic interactions.…”

Section: Logistic Regressionmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

See 2 more Smart Citations

A deeper look at two concepts of measuring gene–gene interactions: logistic regression and interaction information revisited

Mielniczuk

Teisseyre

2017

Genetic Epidemiology

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show abstract

“…This has been called the missing heritability problem [1]. As a result, interest has shifted toward more complex hypotheses including gene-gene and gene-environment interactions and rarer variants [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In gene-gene interaction studies with millions of SNPs, the number of possible SNP-SNP pairs is about half a trillion.…”

Section: Introductionmentioning

confidence: 99%

On the choice of degrees of freedom for testing gene–gene interactions

Ueki

2014

Statistics in Medicine

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In gene-gene interaction analysis using single nucleotide polymorphism (SNP) data, empty cells arise in the genotype contingency table more frequently than in single SNP association studies. Empty cells lead to unidentifiable regression coefficients in regression model fitting. It is unclear whether the degrees of freedom (d.f.) for testing interactions are reduced for such sparse contingency tables. Boolean Operation based Screening and Testing is an exhaustive gene-gene interaction search method in which a fixed d.f. of four (the most conservative choice) is used in the chi-squared null distribution for the likelihood ratio test for gene-gene interactions under a logistic regression model. In this paper, the choice of d.f. is investigated theoretically by introducing a decomposition of type I error. An adaptive method using the observed d.f. can be less conservative than the fixed d.f. method, thereby enhancing power. In simulated data, type I error rates for the adaptive method were usually better controlled under various scenarios for Gaussian linear regression and logistic regression, including prospective and retrospective sampling designs, as well as for artificial data that mimic actual genome-wide SNPs. When the adaptive method was applied to public datasets generated from simulations, it exhibited an improvement in power over the fixed method.

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Genome‐Wide Analysis of Gene‐Gene and Gene‐Environment Interactions Using Closed‐Form Wald Tests

Demetriou

Gillen

2015

Genetic Epidemiology

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Despite the successful discovery of hundreds of variants for complex human traits using genome-wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene-gene and gene-environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two SNPs in a pair or gene-environment independence. Here we derive closed-form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests, largely considered to be the gold standard tests but generally too computationally demanding for genome-wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the likelihood ratio tests but are much more computationally efficient. Applying the proposed tests to a genome-wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both single nucleotide polymorphisms (SNPs) are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power.

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Testing Gene–Gene Interactions in Genome Wide Association Studies

Cited by 22 publications

References 43 publications

A deeper look at two concepts of measuring gene–gene interactions: logistic regression and interaction information revisited

A deeper look at two concepts of measuring gene–gene interactions: logistic regression and interaction information revisited

On the choice of degrees of freedom for testing gene–gene interactions

Genome‐Wide Analysis of Gene‐Gene and Gene‐Environment Interactions Using Closed‐Form Wald Tests

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