Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks

Abstract

BackgroundDetecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype.ResultsTo address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and three real disease datasets. Experimental results on simulation data show that our method outperforms some other commonly-used methods in terms of power and sample-efficiency, and is especially suitable for detecting epistatic interactions with weak or no marginal effects. Furthermore, our method is scalable to real disease data.ConclusionsWe propose a Bayesian network-based method, EpiBN, to detect epistatic interactions. In EpiBN, we develop a new scoring function, which can reflect higher-order epistatic interactions by estimating the model complexity from data, and apply a fast Branch-and-Bound algorithm to learn the structure of a two-layer Bayesian network containing only one target node. To make our method scalable to real data, we propose the use of a Markov chain Monte Carlo (MCMC) method to perform the screening process. Applications of the proposed method to some real GWAS (genome-wide association studies) datasets may provide helpful insights into understanding the genetic basis of Age-related Macular Degeneration, late-onset Alzheimer's disease, and autism.

Multiple Quantitative Trait Analysis Using Bayesian Networks

“…First, the difference in the number of SNPs included in the BNs compared to the ENET models can be attributed to the limited ability of BNs to capture small epistatic effects (Han et al 2012). Consider, for instance, a polygenic effect in which two SNPs are jointly associated with a trait but in which each SNP is not significant on its own.…”

“…Their modular nature makes them ideal for analyzing large marker profiles. As far as SNPs are concerned, BNs have been used to investigate linkage disequilibrium (LD; Mourad et al 2011;Morota et al 2012) and epistasis (Han et al 2012) and to determine disease susceptibility for anemia (Sebastiani et al 2005), leukemia (Chang and Mcgeachie, 2011), and hypertension (Malovini et al 2009). The same BN can simultaneously highlight SNPs potentially involved in determining a trait (e.g., for association purposes) and be used for prediction (e.g., for selection purposes): a network capturing the relationship between genotypes and phenotypes can be used to compute the probability that a new individual with a particular genotype will have the phenotype of interest (Lauritzen and Sheehan 2004;Cowell et al 2007).…”

Differences in Metabolites of Different Tongue Coatings in Patients with Chronic Hepatitis B

“…VIP values exceeding 1.0 were selected as potential biomarkers [28]. Furthermore, the retention times and MS/MS behaviors of metabolites with the data from databases of METLINE were compared (http://metlin.Scripps.edu/).…”

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

“…This work had been followed up in Neapolitan et al (2014), Jiang and Neapolitan (2015), and Jiang et al (2015), but remains subject to these and related limitations. Large-scale genetic epidemiology dataset BN analysis was also pursued in Han et al (2012) at the cost of specifying a single target variable. BN Webserver (Ziebarth et al, 2013) is a comprehensive biological BN analysis tool, which, among other things, efficiently deals with hybrid models (heterogeneous variables/data types) in a biological user-friendly manner; unfortunately, its scalability is essentially nonexistent (<20 nodes).…”