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  • 1
    Keywords: Germany ; MODEL ; MODELS ; DISEASE ; GENE ; COMPLEX ; COMPLEXES ; MARKER ; ASSOCIATION ; LINKAGE ; MARKERS ; LINKAGE DISEQUILIBRIUM ; LENGTH ; PHENOTYPE ; transmission/disequilibrium test ; clustering ; RE ; COMPLEX DISEASES ; case control studies ; LOCUS ; ALLELIC ASSOCIATION ; CASE-CONTROL ASSOCIATION ; complex disease ; FOUNDER POPULATIONS ; genetic association studies ; haplotype sharing ; HASEMAN-ELSTON METHOD ; mantel statistics ; P-VALUES ; SCORE TEST ; TESTS ; TRUNCATED PRODUCT
    Abstract: Objective: The potential value of haplotypes has attracted widespread interest in the mapping of complex traits. Haplotype sharing methods take the linkage disequilibrium information between multiple markers into account, and may have good power to detect predisposing genes. We present a new approach based on Mantel statistics for spacetime clustering, which is developed in order to improve the power of haplotype sharing analysis for gene mapping in complex disease. Methods: The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes for case- only and case- control studies. The genetic similarity is measured as the shared length between haplotypes around a putative disease locus. The phenotypic similarity is measured as the mean- corrected cross- product based on the respective phenotypes. We analyzed two tests for statistical significance with respect to type I error: ( 1) assuming asymptotic normality, and ( 2) using a Monte Carlo permutation procedure. The results were compared to the χ(2) test for association based on 3- marker haplotypes. Results: The results of the type I error rates for the Mantel statistics using the permutational procedure yielded pointwise valid tests. The approach based on the as-sumption of asymptotic normality was seriously liberal. Conclusion: Power comparisons showed that the Mantel statistics were better than or equal to the χ(2) test for all simulated disease models. Copyright © 2005 S. Karger AG, Basel
    Type of Publication: Journal article published
    PubMed ID: 15838176
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  • 2
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    Human Heredity 66 (3), 170-179 
    Keywords: ENVIRONMENT ; CANCER ; Germany ; LUNG ; MODEL ; MODELS ; lung cancer ; LUNG-CANCER ; DISEASE ; DISEASES ; EPIDEMIOLOGY ; EXPOSURE ; RISK ; RISKS ; GENE ; GENES ; COMPONENTS ; COMPLEX ; ASSOCIATION ; SUSCEPTIBILITY ; VARIANTS ; IDENTIFICATION ; lifestyle ; NUMBER ; genetics ; CIGARETTE-SMOKING ; etiology ; smoking ; PARAMETERS ; gene-environment interaction ; INDIVIDUALS ; PREVALENCE ; BEHAVIOR ; CONSUMPTION ; heredity ; RELATIVE RISK ; RE ; DEPENDENCE ; methods ; GENOTYPE ; RISK-FACTOR ; EXTENT ; ENVIRONMENTAL-FACTORS ; interactions ; EXCESS ; familial relative risk ; genotype relative risk ; NEWLY MARRIED-COUPLES ; SMOKING-CESSATION
    Abstract: Objectives: Parents share genes and environmental exposures with their offspring. Spouses are often unrelated and their excess of genetic sharing is low, but the similar lifestyles of spouses regarding, for example, tobacco consumption may also result in a familial clustering of disease. This study investigates the influence of gene-environment interactions on the relative risks of disease for the offspring and the spouses of affected individuals. Methods: Theoretical models were developed to explore the dependence of familial relative risks on environmental parameters (exposure frequency, relative risk of disease for exposed versus unexposed individuals, extent of environmental sharing), on genetic parameters (allele frequency, genotype relative risk and mode of inheritance), on the number of descendants in exposed versus unexposed individuals, and on the model of gene-environment interaction. Lung cancer was used as an example of a complex disease with smoking as a known risk factor. Results: The parent-offspring and the spouse-spouse relative risks of disease varied widely in the strength of the environmental and genetic components and their degree of interaction. The familial relative risk of lung cancer decreased with increasing smoking prevalence. The relationship between exposure frequency and relative risk was markedly different when genes and environment had similar effects on disease susceptibility. Conclusions: Estimates of the relative risk of disease for varying exposure frequencies may help to assess the relevance of genetic effects in disease etiology. In particular, a positive association between offspring relative risk and exposure frequency may be indicative of genes interacting with environmental factors of similar effect sizes, with the corresponding implications for gene identification studies. Copyright (C) 2008 S. Karger AG, Basel
    Type of Publication: Journal article published
    PubMed ID: 18493142
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  • 3
    Keywords: LOCI ; GENOME-WIDE ASSOCIATION ; MISSING HERITABILITY
    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.
    Type of Publication: Journal article published
    PubMed ID: 22889990
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  • 4
    Keywords: MODELS ; INFORMATION ; LUNG-CANCER ; GENES ; REGRESSION ; SNPs ; RHEUMATOID-ARTHRITIS ; COMPLEX DISEASES ; SETS
    Abstract: Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms. (c) 2014 S. Karger AG, Basel.
    Type of Publication: Journal article published
    PubMed ID: 24434848
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  • 5
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    Keywords: Germany ; MODELS ; ALGORITHM ; DISEASE ; POPULATION ; RISK ; MARKER ; ASSOCIATION ; LINKAGE ; DESIGN ; genetics ; MARKERS ; LINKAGE DISEQUILIBRIUM ; gene-environment interaction ; case-control studies ; case control study ; case-control study ; REGRESSION ; VARIANT ; interaction ; TESTS ; GENOTYPE DATA ; POWER ; prospective ; LINKAGE PHASE ; UNIT ; INFERENCE ; LOGISTIC-REGRESSION ; indirect association ; Genetic ; EM ALGORITHM ; Expectation maximization algorithm ; Hardy Weinberg equilibrium ; Logistic regression ; MAXIMUM-LIKELIHOOD-ESTIMATION ; Retrospective and prospective likelihood
    Abstract: Objective: We compared four haplotype-based approaches for the analysis of gene-environment interactions when haplotype-phase is ambiguous. The methods employ different versions of the expectation maximization algorithm and differ in the choice of the reference group and in the way the risk of disease is modeled (retrospective versus prospective). Furthermore, the methods are based on distinct assumptions (such as Hardy Weinberg equilibrium). The haplotype-based methods were also compared to single-marker logistic regression (LR). Methods: We simulated case-control scenarios where the risk variant was directly genotyped (direct scenario) or in linkage disequilibrium with the genotyped markers (indirect scenario). Results: The retrospective methods tended to be more powerful for detecting interactions than the prospective methods. In the indirect scenarios, the power of all methods was decreased. However, the power of the retrospectives methods was high in some scenarios and the interactions may only be detectable when using these approaches. Furthermore, we observed that the precision of one prospective method was clearly lower than the precision of the retrospective methods. Conclusion: For the analysis of gene-environment (GxE) interactions in case-control data, the investigated retrospective methods can be an attractive alternative to haplotype-based methods which do not account for the retrospective sampling design. Copyright (C) 2009 S. Karger AG, Basel
    Type of Publication: Journal article published
    PubMed ID: 19622892
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  • 8
    Keywords: CANCER ; MODELS ; DISEASE ; RISK ; RISKS ; GENOME ; ASSOCIATION ; BREAST ; BREAST-CANCER ; MARKERS ; STRATEGIES ; case-control study ; COMPLEX DISEASES ; TRAITS ; LOGISTIC-REGRESSION ; GENOME-WIDE ASSOCIATION ; genetic association ; familial relative risk ; COMMON VARIANTS ; imputation ; MISSING HERITABILITY ; Causal susceptibility variants ; Common-disease common-variant hypothesis
    Abstract: Objectives: Genome-wide association (GWA) studies still rely on the common-disease common-variant hypothesis since the assumption is associated with increased power. In GWA studies, polymorphisms are genotyped and their association with disease is investigated. Most of the identified associations are indirect and reflect a shared inheritance of the genotyped markers and genetically linked causal variants. We have compared six statistics of genetic association regarding their ability to discriminate between markers and causal susceptibility variants, including a probability value (Pval) and a Bayes Factor (BF) based on logistic regression, and the attributable familial relative risk (FRR). Methods: We carried out a simulation-based sensitivity analysis to explore several conceivable scenarios. Theoretical results were illustrated by established causal associations with age-related macular degeneration and by using imputed data based on HapMap for a case-control study of breast cancer. Results: Our data indicate that a representation of genetic association by FRRs and BFs generally facilitates the distinction of causal variants. The FRR showed the best discriminative power under most investigated scenarios, but no single statistic outperformed the others in all situations. For example, rare moderate-to low-penetrance variants (allele frequency: 1%, dominant odds ratio: 〈= 2.0) seem to be best discriminated by BFs. Conclusions: Present results may help to fully utilize the data generated in association studies that take advantage of next generation sequencing and/or multiple imputation based on the 1000 Genomes Project.
    Type of Publication: Journal article published
    PubMed ID: 22025134
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