Dataset Information

Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer.

ABSTRACT:

Objectives

We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data.

Methods

The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data.

Results

Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested.

Conclusion

The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.

SUBMITTER: Ma J

PROVIDER: S-EPMC3534768 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer.

Ma Jianzhong J Xiao Feifei F Xiong Momiao M Andrew Angeline S AS Brenner Hermann H Duell Eric J EJ Haugen Aage A Hoggart Clive C Hung Rayjean J RJ Lazarus Philip P Liu Changlu C Matsuo Keitaro K Mayordomo Jose Ignacio JI Schwartz Ann G AG Staratschek-Jox Andrea A Wichmann Erich E Yang Ping P Amos Christopher I CI

Human heredity 20120809 4

<h4>Objectives</h4>We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data.<h4>Methods</h4>The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as f ...[more]

PMID: 22889990

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Project description:Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.

Dataset Information

Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer.

Objectives

Methods

Results

Conclusion

Publications

Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer.

Similar Datasets

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