Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer

Ritchie, Marylyn D.; Hahn, Lance W.; Roodi, Nady; Bailey, Lisa; Dupont, William D.; Parl, Fritz F.; Moore, Jason H.

doi:10.1086/321276

Cited by 1,740 publications

(1,686 citation statements)

References 29 publications

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“…For testing the epistatic interaction between pairs of SNPs, multifactor dimensionality reduction (MDR) was applied (Moore, 2004; Ritchie et al., 2001). This methodology can reveal high‐order interactions among genes collaborating with respect to a given phenotype, with the aim of determining multilocus genotype combinations associated with high or low risk of disease.…”

Section: Methodsmentioning

confidence: 99%

The genetic component of human longevity: New insights from the analysis of pathway‐based SNP‐SNP interactions

et al. 2018

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SummaryIn human longevity studies, single nucleotide polymorphism (SNP) analysis identified a large number of genetic variants with small effects, yet not easily replicable in different populations. New insights may come from the combined analysis of different SNPs, especially when grouped by metabolic pathway. We applied this approach to study the joint effect on longevity of SNPs belonging to three candidate pathways, the insulin/insulin‐like growth factor signalling (IIS), DNA repair and pro/antioxidant. We analysed data from 1,058 tagging SNPs in 140 genes, collected in 1825 subjects (1,089 unrelated nonagenarians from the Danish 1905 Birth Cohort Study and 736 Danish controls aged 46–55 years) for evaluating synergic interactions by SNPsyn. Synergies were further tested by the multidimensional reduction (MDR) approach, both intra‐ and interpathways. The best combinations (FDR<0.0001) resulted those encompassing IGF1R‐rs12437963 and PTPN1‐rs6067484, TP53‐rs2078486 and ERCC2‐rs50871, TXNRD1‐rs17202060 and TP53‐rs2078486, the latter two supporting a central role of TP53 in mediating the concerted activation of the DNA repair and pro‐antioxidant pathways in human longevity. Results were consistently replicated with both approaches, as well as a significant effect on longevity was found for the GHSR gene, which also interacts with partners belonging to both IIS and DNA repair pathways (PAPPA,PTPN1,PARK7, MRE11A). The combination GHSR‐MREA11, positively associated with longevity by MDR, was further found influencing longitudinal survival in nonagenarian females (p = .026). Results here presented highlight the validity of SNP‐SNP interactions analyses for investigating the genetics of human longevity, confirming previously identified markers but also pointing to novel genes as central nodes of additional networks involved in human longevity.

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Section: Methodsmentioning

confidence: 99%

The genetic component of human longevity: New insights from the analysis of pathway‐based SNP‐SNP interactions

et al. 2018

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“…The evaluation of gene-gene interactions was also performed using the open-source MDR software package 53,54 (v.1.1.0; available at: http://www.epistasis.org). The MDR constructive induction algorithm provides an exhaustive search of all possible combinations of two to four variables.…”

Section: Multivariate Analysismentioning

confidence: 99%

Gene–gene interaction among cytokine polymorphisms influence susceptibility to aggressive periodontitis

et al. 2011

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Aggressive periodontitis (AgP) is a multifactorial disease. The distinctive aspect of periodontitis is that this disease must deal with a large number of genes interacting with one another and forming complex networks. Thus, it is reasonable to expect that gene-gene interaction may have a crucial role. Therefore, we carried out a pilot case-control study to identify the association of candidate epistatic interactions between genetic risk factors and susceptibility to AgP, by using both conventional parametric analyses and a higher order interactions model, based on the nonparametric Multifactor Dimensionality Reduction algorithm. We analyzed 122 AgP patients and 246 appropriate periodontally healthy individuals, and genotyped 28 polymorphisms, located within 14 candidate genes, chosen among the principal genetic variants pointed out from literature and having a role in inflammation and immunity. Our analyses provided significant evidence for gene-gene interactions in the development of AgP, in particular, present results: (a) indicate a possible role of two new polymorphisms, within SEPS1 and TNFRSF1B genes, in determining host individual susceptibility to AgP; (b) confirm the potential association between of IL-6 and Fc g-receptor polymorphisms and the disease; (c) exclude an essential contribution of IL-1 cluster gene polymorphisms to AgP in our Caucasian-Italian population.

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“…For both the family-based and the case-control datasets, the multifactor dimensionality reduction (MDR) method was used to detect gene-gene interactions Moore JH, 2007;Moore JH et al, 2006;Ritchie MD et al, 2001). MDR is a nonparametric and genetic model-free method that uses constructive induction to change the representation space of the data to make nonadditive interactions easier to detect using classification methods such as naïve Bayes or logistic regression.…”

Section: Resultsmentioning

confidence: 99%

Confronting complexity in late‐onset Alzheimer disease: application of two‐stage analysis approach addressing heterogeneity and epistasis

Thornton‐Wells

Moore

Martin

et al. 2007

Genetic Epidemiology

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Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene-gene interactions in the second stage. We applied this two-stage analysis strategy to late-onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian Classification found statistically significant clusterings for independent family-based and case-control datasets, which used the same five markers in LRRTM3 as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene-gene interactions, markers in three genes-PLAU, ACE and CDC2-were found to be associated with late-onset Alzheimer disease in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes.

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Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer

Cited by 1,740 publications

References 29 publications

The genetic component of human longevity: New insights from the analysis of pathway‐based SNP‐SNP interactions

The genetic component of human longevity: New insights from the analysis of pathway‐based SNP‐SNP interactions

Gene–gene interaction among cytokine polymorphisms influence susceptibility to aggressive periodontitis

Confronting complexity in late‐onset Alzheimer disease: application of two‐stage analysis approach addressing heterogeneity and epistasis

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