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  • 1
    In: Human Heredity, S. Karger AG, Vol. 57, No. 1 ( 2004), p. 28-38
    Abstract: While hypertension is a complex disease with a well-documented genetic component, genetic studies often fail to replicate findings. One possibility for such inconsistency is that the underlying genetics of hypertension is not based on single genes of major effect, but on interactions among genes. To test this hypothesis, we studied both single locus and multilocus effects, using a case-control design of subjects from Ghana. Thirteen polymorphisms in eight candidate genes were studied. Each candidate gene has been shown to play a physiological role in blood pressure regulation and affects one of four pathways that modulate blood pressure: vasoconstriction (angiotensinogen, angiotensin converting enzyme – ACE, angiotensin II receptor), nitric oxide (NO) dependent and NO independent vasodilation pathways and sodium balance (G protein-coupled receptor kinase, GRK4). We evaluated single site allelic and genotypic associations, multilocus genotype equilibrium and multilocus genotype associations, using multifactor dimensionality reduction (MDR). For MDR, we performed systematic reanalysis of the data to address the role of various physiological pathways. We found no significant single site associations, but the hypertensive class deviated significantly from genotype equilibrium in more than 25% of all multilocus comparisons (2,162 of 8,178), whereas the normotensive class rarely did (11 of 8,178). The MDR analysis identified a two-locus model including ACE and GRK4 that successfully predicted blood pressure phenotype 70.5% of the time. Thus, our data indicate epistatic interactions play a major role in hypertension susceptibility. Our data also support a model where multiple pathways need to be affected in order to predispose to hypertension.
    Type of Medium: Online Resource
    ISSN: 0001-5652 , 1423-0062
    RVK:
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2004
    detail.hit.zdb_id: 1482710-4
    SSG: 12
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  • 2
    In: Human Heredity, S. Karger AG, Vol. 65, No. 2 ( 2008), p. 105-118
    Abstract: 〈 i 〉 Objectives: 〈 /i 〉 A number of common non-synonymous single nucleotide polymorphisms (SNPs) in DNA repair genes have been reported to modify bladder cancer risk. These include: 〈 i 〉 APE1- 〈 /i 〉 Asn148Gln, 〈 i 〉 XRCC1- 〈 /i 〉 Arg399Gln and 〈 i 〉 XRCC1- 〈 /i 〉 Arg194Trp in the BER pathway, 〈 i 〉 XPD- 〈 /i 〉 Gln751Lys in the NER pathway and 〈 i 〉 XRCC3- 〈 /i 〉 Thr241Met in the DSB repair pathway. 〈 i 〉 Methods: 〈 /i 〉 To examine the independent and interacting effects of these SNPs in a large study group, we analyzed these genotypes in 1,029 cases and 1,281 controls enrolled in two case-control studies of incident bladder cancer, one conducted in New Hampshire, USA and the other in Turin, Italy. 〈 i 〉 Results: 〈 /i 〉 The odds ratio among current smokers with the variant 〈 i 〉 XRCC3- 〈 /i 〉 241 (TT) genotype was 1.7 (95% CI 1.0–2.7) compared to wild-type. We evaluated gene-environment and gene-gene interactions using four analytic approaches: logistic regression, Multifactor Dimensionality Reduction (MDR), hierarchical interaction graphs, classification and regression trees (CART), and logic regression analyses. All five methods supported a gene-gene interaction between 〈 i 〉 XRCC1- 〈 /i 〉 399/ 〈 i 〉 XRCC3- 〈 /i 〉 241 (p = 0.001) (adjusted OR for 〈 i 〉 XRCC1- 〈 /i 〉 399 GG, 〈 i 〉 XRCC3- 〈 /i 〉 241 TT vs. wild-type 2.0 (95% CI 1.4–3.0)). Three methods predicted an interaction between 〈 i 〉 XRCC1- 〈 /i 〉 399/ 〈 i 〉 XPD- 〈 /i 〉 751 (p = 0.008) (adjusted OR for 〈 i 〉 XRCC1- 〈 /i 〉 399 GA or AA, 〈 i 〉 XRCC3- 〈 /i 〉 241 AA vs. wild-type 1.4 (95% CI 1.1–2.0)). 〈 i 〉 Conclusions: 〈 /i 〉 These results support the hypothesis that common polymorphisms in DNA repair genes modify bladder cancer risk and highlight the value of using multiple complementary analytic approaches to identify multi-factor interactions.
    Type of Medium: Online Resource
    ISSN: 0001-5652 , 1423-0062
    RVK:
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2008
    detail.hit.zdb_id: 1482710-4
    SSG: 12
    Location Call Number Limitation Availability
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  • 3
    Online Resource
    Online Resource
    S. Karger AG ; 2001
    In:  Human Heredity Vol. 52, No. 2 ( 2001), p. 113-115
    In: Human Heredity, S. Karger AG, Vol. 52, No. 2 ( 2001), p. 113-115
    Abstract: The influence of epistasis on a quantitative trait can reduce the power of linkage analysis to identify the underlying loci. In the present study, we simulated a complex trait derived from a dynamic one-locus gene expression system with epistasis arising from feedback regulation and tested the power of sib-pair linkage analysis methods for detecting the underlying quantitative trait locus (QTL). Using this simple genetic architecture, we demonstrate that the power of sib-pair linkage analysis can be greatly improved if measures of complex trait dynamics are considered.
    Type of Medium: Online Resource
    ISSN: 0001-5652 , 1423-0062
    RVK:
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2001
    detail.hit.zdb_id: 1482710-4
    SSG: 12
    Location Call Number Limitation Availability
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  • 4
    Online Resource
    Online Resource
    S. Karger AG ; 2007
    In:  Human Heredity Vol. 63, No. 2 ( 2007), p. 120-133
    In: Human Heredity, S. Karger AG, Vol. 63, No. 2 ( 2007), p. 120-133
    Abstract: The workhorse of modern genetic analysis is the parametric linear model. The advantages of the linear modeling framework are many and include a mathematical understanding of the model fitting process and ease of interpretation. However, an important limitation is that linear models make assumptions about the nature of the data being modeled. This assumption may not be realistic for complex biological systems such as disease susceptibility where nonlinearities in the genotype to phenotype mapping relationship that result from epistasis, plastic reaction norms, locus heterogeneity, and phenocopy, for example, are the norm rather than the exception. We have previously developed a flexible modeling approach called symbolic discriminant analysis (SDA) that makes no assumptions about the patterns in the data. Rather, SDA lets the data dictate the size, shape, and complexity of a symbolic discriminant function that could include any set of mathematical functions from a list of candidates supplied by the user. Here, we outline a new five step process for symbolic model discovery that uses genetic programming (GP) for coarse-grained stochastic searching, experimental design for parameter optimization, graphical modeling for generating expert knowledge, and estimation of distribution algorithms for fine-grained stochastic searching. Finally, we introduce function mapping as a new method for interpreting symbolic discriminant functions. We show that function mapping when combined with measures of interaction information facilitates statistical interpretation by providing a graphical approach to decomposing complex models to highlight synergistic, redundant, and independent effects of polymorphisms and their composite functions. We illustrate this five step SDA modeling process with a real case-control dataset.
    Type of Medium: Online Resource
    ISSN: 0001-5652 , 1423-0062
    RVK:
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2007
    detail.hit.zdb_id: 1482710-4
    SSG: 12
    Location Call Number Limitation Availability
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  • 5
    In: Human Heredity, S. Karger AG, Vol. 70, No. 3 ( 2010), p. 219-225
    Abstract: Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR’s ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire.
    Type of Medium: Online Resource
    ISSN: 0001-5652 , 1423-0062
    RVK:
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2010
    detail.hit.zdb_id: 1482710-4
    SSG: 12
    Location Call Number Limitation Availability
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  • 6
    Online Resource
    Online Resource
    S. Karger AG ; 2003
    In:  Human Heredity Vol. 56, No. 1-3 ( 2003), p. 73-82
    In: Human Heredity, S. Karger AG, Vol. 56, No. 1-3 ( 2003), p. 73-82
    Abstract: There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.
    Type of Medium: Online Resource
    ISSN: 0001-5652 , 1423-0062
    RVK:
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2003
    detail.hit.zdb_id: 1482710-4
    SSG: 12
    Location Call Number Limitation Availability
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