Epistasis Analysis Using Multifactor Dimensionality Reduction

Moore, Jason H.; Andrews, Peter C.

doi:10.1007/978-1-4939-2155-3_16

Cited by 45 publications

(34 citation statements)

References 60 publications

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“…MDR is a nonparametric, genetic model-free machine learning method that collapses high-dimensional genetic data into a single dimension through a process called constructive induction [10]. More information about MDR and its implementation can be found here [11]. We exhaustively evaluated all pairs of SNPs and then assessed statistical significance using a 1000-fold permutation test as described previously [11].…”

Section: Resultsmentioning

confidence: 99%

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Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

et al. 2016

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BackgroundBladder cancer is common disease with a complex etiology that is likely due to many different genetic and environmental factors. The goal of this study was to embrace this complexity using a bioinformatics analysis pipeline designed to use machine learning to measure synergistic interactions between single nucleotide polymorphisms (SNPs) in two genome-wide association studies (GWAS) and then to assess their enrichment within functional groups defined by Gene Ontology. The significance of the results was evaluated using permutation testing and those results that replicated between the two GWAS data sets were reported.ResultsIn the first step of our bioinformatics pipeline, we estimated the pairwise synergistic effects of SNPs on bladder cancer risk in both GWAS data sets using Multifactor Dimensionality Reduction (MDR) machine learning method that is designed specifically for this purpose. Statistical significance was assessed using a 1000-fold permutation test. Each single SNP was assigned a p-value based on its strongest pairwise association. Each SNP was then mapped to one or more genes using a window of 500 kb upstream and downstream from each gene boundary. This window was chosen to capture as many regulatory variants as possible. Using Exploratory Visual Analysis (EVA), we then carried out a gene set enrichment analysis at the gene level to identify those genes with an overabundance of significant SNPs relative to the size of their mapped regions. Each gene was assigned to a biological functional group defined by Gene Ontology (GO). We next used EVA to evaluate the overabundance of significant genes in biological functional groups. Our study yielded one GO category, carboxy-lysase activity (GO:0016831), that was significant in analyses from both GWAS data sets. Interestingly, only the gamma-glutamyl carboxylase (GGCX) gene from this GO group was significant in both the detection and replication data, highlighting the complexity of the pathway-level effects on risk. The GGCX gene is expressed in the bladder, but has not been previously associated with bladder cancer in univariate GWAS. However, there is some experimental evidence that carboxy-lysase activity might play a role in cancer and that genes in this pathway should be explored as drug targets. This study provides a genetic basis for that observation.ConclusionsOur machine learning analysis of genetic associations in two GWAS for bladder cancer identified numerous associations with pairs of SNPs. Gene set enrichment analysis found aggregation of risk-associated SNPs in genes and significant genes in GO functional groups. This study supports a role for decarboxylase protein complexes in bladder cancer susceptibility. Previous research has implicated decarboxylases in bladder cancer etiology; however, the genes that we found to be significant in the detection and replication data are not known to have direct influence on bladder cancer, suggesting some novel hypotheses. This study highlights the need for a complex systems approach to the geneti...

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Section: Resultsmentioning

confidence: 99%

“…More information about MDR and its implementation can be found here [11]. We exhaustively evaluated all pairs of SNPs and then assessed statistical significance using a 1000-fold permutation test as described previously [11]. Each SNP was assigned the p -value of its strongest pairwise association.…”

Section: Resultsmentioning

confidence: 99%

Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

et al. 2016

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“…The Multifactor Dimensionality Reduction (MDR) package software was performed all potential recognition of SNP-SNP interactions and that are fine identified as playing a significant role in insight complicated properties. MDR has the testing balance accuracy (TBA) and cross-validation consistency (CVC) [18].…”

Section: Discussionmentioning

confidence: 99%

Combinations of Interleukin-10 Gene Promoter Polymorphisms with -1082A, -819T, -592A Minor Allele are Associated with Sinonasal Polyposis

Özdaş¹

2018

Hittite J. Sci. Eng.

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Sinonasal polyposis (SP) is a cronic inflammation of the sinonasal mucosa. Chronic sinusitis, odor loss headache and nasal obstruction are among the clinical symptoms of SP with low life quality among the patients. Sinonasal polyposis is often accompanied by allergic rhinitis, allergy, aspirin susceptibility and asthma [1]. SP has been implicated in many immunological pathways and is a localized manifestation of systemic disorders [2,3]. SP cases frequently accompanied by T helper (Th) type 1-, Th2-, or Th17-biased inflammatory processes regulated by increased expression levels of a number of cytokines with pro-and anti-inflammatory activities [4]. However, the underlying etiology of SP is multifactorial. Also the pathogenesis of SP is still unexplained.Interleukin (IL)-10 is a crucial Th2 group anti-inf-

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“…no parameters are estimated) and genetic model-free (i.e. no genetic model is assumed) data mining and machine learning strategy for identifying combinations of genetic and environmental factors that are predictive of a discrete clinical endpoint [15, 16, 19–22]. Unlike most other methods, MDR was designed to detect interactions in the absence of detectable marginal effects and thus complements statistical approaches such as logistic regression and machine learning methods such as random forests and neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…As such, MDR significantly complements other classification methods such as those reviewed by Hastie et al [24]. This method has been evaluated in numerous simulation studies, [19, 25] and a user-friendly open-source MDR software package written in Java is freely available [22, 26]. …”

Section: Introductionmentioning

confidence: 99%

Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases

et al. 2017

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BackgroundLarge-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway.ResultsWe show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer’s disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer’s disease.ConclusionsWe discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.

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Epistasis Analysis Using Multifactor Dimensionality Reduction

Cited by 45 publications

References 60 publications

Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

Combinations of Interleukin-10 Gene Promoter Polymorphisms with -1082A, -819T, -592A Minor Allele are Associated with Sinonasal Polyposis

Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases

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