Multi-locus interactions predict risk for post-PTCA restenosis: an approach to the genetic analysis of common complex disease

Zee, Robert Y.L.; Hoh, Josephine; Cheng, Suzanne; Reynolds, Rebecca; Grow, Michael; Silbergleit, Arkadiy; Walker, Karen; Steiner, Lori; Zangenberg, G.; Fernández‐Ortíz, Antonio; Macaya, Carlos; Holguín, Emilio Pintor; Fernández-Cruz, A; Ott, Jürg; Lindpainter, Klaus

doi:10.1038/sj.tpj.6500101

Cited by 58 publications

(44 citation statements)

References 27 publications

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“…This increase is tied to the growing number of analytical tools to detect and characterize this phenomenon. For example, Zee et al (51) used set association analysis to identify a combination of seven SNPs in seven genes that are significantly associated with coronary artery restenosis following angioplasty in a cohort of 342 cases and 437 controls. The multifactor dimensionality reduction approach (MDR) has identified significant evidence of epistasis in sporadic breast cancer, (38) essential hypertension, (52) atrial fibrillation (53) and type II diabetes.…”

Section: Statistical Epistasismentioning

confidence: 99%

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

2005

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Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations? Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast and digital organisms will be a useful place to start.

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Section: Statistical Epistasismentioning

confidence: 99%

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

2005

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“…This method is embedded in a regression framework and optimization obtained using simulated annealing algorithm (Box 1). In a simulated GAW12 dataset [113] , logic regression successfully identified an interaction between quantitative trait loci (QTL) 5 and the sequence of gene 2 that influenced the phenotype.…”

Section: Examples Of Statistical Methods For the Detection Of Epmentioning

confidence: 99%

“…The statistical proper- ties of this method have been evaluated by simulation and the method was found to perform better than marker-tomarker approaches, detected more disease loci, and resulted in a more powerful test than did FDR and Bonferroni procedures [137] . In application to restenosis data, the method identified significant additive and interactive effects between tumor necrosis factor type 1 (TNFR1) and apolipoprotein CIII (APOC3), monocyte differentiation antigen (CD14) and MDM2 and a three-way interaction between CD14, MDM2 and cystathionine beta-synthase (CBS) [113] . Attempts to minimize the effects of data sparsity by replacing unobserved trait values in specific genotypes as missing values and then replace them with imputed data were adapted in logic regression; but such undertaking is bound to increase computation burden.…”

Section: (Iii) Controlling Problems Of Multiple Testingmentioning

confidence: 99%

Detection of Gene × Gene Interactions in Genome-Wide Association Studies of Human Population Data

Musani¹,

Shriner²,

Liu³

et al. 2007

Hum Hered

155

134

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Empirical evidence supporting the commonality of gene × gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop methods to handle this important subject. Nonparametric methods have generated intense interest because of their capacity to handle high-dimensional data. Genome-wide association analysis of large-scale SNP data is challenging mathematically and computationally. In this paper, we describe major issues and questions arising from this challenge, along with methodological implications. Data reduction and pattern recognition methods seem to be the new frontiers in efforts to detect gene × gene interactions comprehensively. Currently, there is no single method that is recognized as the ‘best’ for detecting, characterizing, and interpreting gene × gene interactions. Instead, a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene × gene interactions in human data.

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“…14 In addition, LR can be used as the second-stage variable selection in testing for gene -gene or gene-environment interactions for a two-stage analysis that incorporates a bootstrap procedure as the first stage of selection. 17,18 However, the limitations of variable selection in LRs for testing SNP -SNP interactions have been discussed widely. The primary limitation of LR is that poor model parameter estimates may be generated because some genotype combinations have low frequency or zero responses especially when a large number of SNPs and high order of interactions are considered.…”

Section: Introductionmentioning

confidence: 99%

Variable selection in logistic regression for detecting SNP–SNP interactions: the rheumatoid arthritis example

et al. 2008

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Many complex disease traits are observed to be associated with single nucleotide polymorphism (SNP) interactions. In testing small-scale SNP -SNP interactions, variable selection procedures in logistic regressions are commonly used. The empirical evidence of variable selection for testing interactions in logistic regressions is limited. This simulation study was designed to compare nine variable selection procedures in logistic regressions for testing SNP -SNP interactions. Data on 10 SNPs were simulated for 400 and 1000 subjects (case/control ratio ¼ 1). The simulated model included one main effect and two 2-way interactions. The variable selection procedures included automatic selection (stepwise, forward and backward), common 2-step selection, AIC-and SC-based selection. The hierarchical rule effect, in which all main effects and lower order terms of the highest-order interaction term are included in the model regardless of their statistical significance, was also examined. We found that the stepwise variable selection without the hierarchical rule, which had reasonably high authentic (true positive) proportion and low noise (false positive) proportion, is a better method compared to other variable selection procedures. For testing interactions, the hierarchical rule effect was obvious. The procedure without the hierarchical rule requires fewer terms in testing interactions, so it can accommodate more SNPs than the procedure with the hierarchical rule. For testing interactions, the procedures without the hierarchical rule had higher authentic proportion and lower noise proportion compared with ones with the hierarchical rule. These variable selection procedures were also applied and compared in a rheumatoid arthritis study.

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Multi-locus interactions predict risk for post-PTCA restenosis: an approach to the genetic analysis of common complex disease

Cited by 58 publications

References 27 publications

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

Detection of Gene × Gene Interactions in Genome-Wide Association Studies of Human Population Data

Variable selection in logistic regression for detecting SNP–SNP interactions: the rheumatoid arthritis example

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