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• W2090165362 abstract "ABSTRACT Single nucleotide polymorphism ( SNP ) high‐dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000‐SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high‐dimensional datasets and determining their probability." @default.
• W2090165362 created "2016-06-24" @default.
• W2090165362 creator A5039264502 @default.
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• W2090165362 date "2015-02-12" @default.
• W2090165362 modified "2023-10-14" @default.
• W2090165362 title "LEAP: Biomarker Inference Through Learning and Evaluating Association Patterns" @default.
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• W2090165362 doi "https://doi.org/10.1002/gepi.21889" @default.
• W2090165362 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4366363" @default.
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• W2090165362 hasPublicationYear "2015" @default.
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