Abstract
BackgroundInteractions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted.ResultsWe report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs.ConclusionThis comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list.
Highlights
Interactions among genetic loci are believed to play an important role in disease risk
logistic regression (LR), using only main effect terms, serves as a baseline method to compare against all the interaction-detection methods, i.e., to see whether they give any advantage over pure “main effect” methods when the goal is to detect the subset of single nucleotide polymorphisms (SNPs) that either individually, or via interactions, are predictive of the phenotype
The data retains the basic patterns of linkage disequilibrium, missing data, and allele frequencies observed in the original genome scan data
Summary
Interactions among genetic loci are believed to play an important role in disease risk. While there are multiple causes for missing some well-known genetic risk factors or disease heritability (including e.g., rare variants not genotyped in a GWAS study), a frequently cited reason is that most common diseases have complex mechanisms, involving multi-locus gene-gene and gene-environment interactions [5,28,29,30,31]. For detecting interacting loci in high dimensional GWAS data with sufficient power and computational feasibility, some pioneering work, with promising results, has been reported, encompassing: i) real GWAS study papers, as cited above; ii) interaction detection methodology [32,33,34,35,36,37,38,39,40,41,42,43,44]; iii) theoretical papers that characterize the principle problem (interaction detection) and its challenges [30,45,46,47]; iv) review and methods comparison papers [29,31,47,48,49,50,51]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
