Dataset Information

Multilocus analysis of atopy in Korean children using multifactor-dimensionality reduction.

ABSTRACT: BACKGROUND: Atopy is considered to be a complex genetic trait and does not follow a simple mendelian pattern of inheritance. It is now well recognised that gene-gene interactions are important in complex genetic disease. AIM: To analyse the influence of gene-gene interactions in the development of atopy. METHODS: A total of 2055 ethnically identical participants aged 10-18 years living in rural areas on Jeju Island, Korea, were randomly recruited. Atopy was defined as a positive skin prick test response to one or more common inhalant allergens. Gene-gene interactions among 12 polymorphic loci were analysed in the seven candidate genes of atopy using the multidimensionality-reduction method. RESULTS: A significant interaction was found between V297I in the gene coding vascular endothelial growth factor receptor 2 (KDR) and -308G-->A in the gene coding tumour necrosis factor (TNF)alpha on the risk of atopy, with a cross-validation consistency of 10 out of 10 and a prediction error of 35.9% (p = 0.001). Conventional logistic regression also revealed significant interactions between KDR and TNF for atopy. Individuals with the variant allele of -308G-->A in TNF (GA or AA) and V297I in KDR (VI or II) had a significantly higher risk of atopy (OR 2.23; 95% CI 1.48 to 3.57). CONCLUSION: KDR and TNF may synergistically influence the development of atopy through gene-gene interaction in Korean children and adolescents.

SUBMITTER: Park HW

PROVIDER: S-EPMC2117169 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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Multilocus analysis of atopy in Korean children using multifactor-dimensionality reduction.

Park Heung-Woo HW Shin Eun-Soon ES Lee Jong-Eun JE Kwon Hyouk-Soo HS Chun Eunyoung E Kim Sun-Sin SS Chang Yoon-Seok YS Kim Yoon-Keun YK Min Kyung-Up KU Kim You-Young YY Cho Sang-Heon SH

Thorax 20061122 3

<h4>Background</h4>Atopy is considered to be a complex genetic trait and does not follow a simple mendelian pattern of inheritance. It is now well recognised that gene-gene interactions are important in complex genetic disease.<h4>Aim</h4>To analyse the influence of gene-gene interactions in the development of atopy.<h4>Methods</h4>A total of 2055 ethnically identical participants aged 10-18 years living in rural areas on Jeju Island, Korea, were randomly recruited. Atopy was defined as a positi ...[more]

PMID: 17121871

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Project description:BackgroundIt is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data.ResultsMDR with 3WS is computationally approximately five times faster than 5-fold cross-validation. The power to find the exact true disease loci without detecting false positive loci is higher with 5-fold cross-validation than with 3WS before pruning. However, the power to find the true disease causing loci in addition to false positive loci is equivalent to the 3WS. With the incorporation of a pruning procedure after the 3WS, the power of the 3WS approach to detect only the exact disease loci is equivalent to that of MDR with cross-validation. In the real data application, the cross-validation and 3WS analyses indicate the same two-locus model.ConclusionsOur results reveal that the performance of the two internal validation methods is equivalent with the use of pruning procedures. The specific pruning procedure should be chosen understanding the trade-off between identifying all relevant genetic effects but including false positives and missing important genetic factors. This implies 3WS may be a powerful and computationally efficient approach to screen for epistatic effects, and could be used to identify candidate interactions in large-scale genetic studies.

Project description:BackgroundRecently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls.MethodsIn this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point.ResultsResults from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations.ConclusionsIn this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests.

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Multilocus analysis of atopy in Korean children using multifactor-dimensionality reduction.

Publications

Multilocus analysis of atopy in Korean children using multifactor-dimensionality reduction.

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