ArticleGenetic epidemiology2010
Identifying genetic interactions in genome-wide data using Bayesian networks.
Article in Genetic epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed, 60 citations in OpenAlex.
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- A BAYESIAN GRAPHICAL MODEL FOR GENOME-WIDE ASSOCIATION STUDIES (GWAS).The annals of applied statistics · 2016Article
- Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences.BMC bioinformatics · 2016Article
- Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.BioData mining · 2016Article
- A gene-based information gain method for detecting gene-gene interactions in case-control studies.European journal of human genetics : EJHG · 2015Article
- Interaction of Wnt pathway related variants with type 2 diabetes in a Chinese Han population.PeerJ · 2015Article
- A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.Journal of biomedical informatics · 2014Article
- ATHENA: the analysis tool for heritable and environmental network associations.Bioinformatics (Oxford, England) · 2014Article
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3 authors at 1 institution in 1 country.
Funding
Abstract
It is believed that interactions among genes (epistasis) may play an important role in susceptibility to common diseases (Moore and Williams [2002]. Ann Med 34:88-95; Ritchie et al. [2001]. Am J Hum Genet 69:138-147). To study the underlying genetic variants of diseases, genome-wide association studies (GWAS) that simultaneously assay several hundreds of thousands of SNPs are being increasingly used. Often, the data from these studies are analyzed with single-locus methods (Lambert et al. [2009]. Nat Genet 41:1094-1099; Reiman et al. [2007]. Neuron 54:713-720). However, epistatic interactions may not be easily detected with single-locus methods (Marchini et al. [2005]. Nat Genet 37:413-417). As a result, both parametric and nonparametric multi-locus methods have been developed to detect such interactions (Heidema et al. [2006]. BMC Genet 7:23). However, efficiently analyzing epistasis using high-dimensional genome-wide data remains a crucial challenge. We develop a method based on Bayesian networks and the minimum description length principle for detecting epistatic interactions. We compare its ability to detect gene-gene interactions and its efficiency to that of the combinatorial method multifactor dimensionality reduction (MDR) using 28,000 simulated data sets generated from 70 different genetic models We further apply the method to over 300,000 SNPs obtained from a GWAS involving late onset Alzheimer's disease (LOAD). Our method outperforms MDR and we substantiate previous results indicating that the GAB2 gene is associated with LOAD. To our knowledge, this is the first successful model-based epistatic analysis using a high-dimensional genome-wide data set.
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