A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis

Abstract

BackgroundThe difficulty in elucidating the genetic basis of complex diseases roots in the many factors that can affect the development of a disease. Some of these genetic effects may interact in complex ways, proving undetectable by current single-locus methodology.ResultsWe have developed an analysis tool called Hypothesis Free Clinical Cloning (HFCC) to search for genome-wide epistasis in a case-control design. HFCC combines a relatively fast computing algorithm for genome-wide epistasis detection, with the flexibility to test a variety of different epistatic models in multi-locus combinations. HFCC has good power to detect multi-locus interactions simulated under a variety of genetic models and noise conditions. Most importantly, HFCC can accomplish exhaustive genome-wide epistasis search with large datasets as demonstrated with a 400,000 SNP set typed on a cohort of Parkinson's disease patients and controls.ConclusionWith the current availability of genetic studies with large numbers of individuals and genetic markers, HFCC can have a great impact in the identification of epistatic effects that escape the standard single-locus association analyses.

Detecting Epistasis with Restricted Response Patterns in Pairs of Biallelic Loci

“…To our knowledge, only Gayán et al (2008) have attempted to apply this approach to genome-wide scans. However, they neither examined the statistical properties in details, nor compared them to the linear decomposition approach.…”

“…−11 is what is required for scanning all pairs of 100,000 markers (Gayán et al, 2008), assuming independence between tests. A cutoff-free comparison between two methods is done by examining the scatter plot of the logtransformed P-values.…”

“…Our interest is in dissecting fundamental differences in model formulations for two-locus problems, regardless of whether they are applied to a priori known pairs of loci or used in genome-wide scans. Issues in genome-wide scans for pairs of loci, such as exhaustive versus stage-wise search strategy (Evans et al, 2006) and multiple testing correction (Gayán et al, 2008;Cordell, 2009), affect all compared methods equally, and therefore will not be addressed. We do, however, consider the power at stringent P-value thresholds required in genome-wide scans.…”

“…Previous power analyses (Ritchie et al, 2003;Marchini et al, 2005;Evans et al, 2006;Chapman & Clayton, 2007;Gayán et al, 2008) were focused on one proposed method under various conditions such as data generation models, sample size, number of loci scanned, effect size, allele frequencies, and genotyping error. Here we focus on comparing the methods when applied to the same simulated data.…”

Comparing Partial Least Square Approaches in a Gene- or Region-Based Association Study for Multiple Quantitative Phenotypes

“…However, a notable issue with GWAS using case-control design, though discussed extensively in the literature, still needs to be addressed (Zhang and Liu 2007;Gayan et al 2008). Such a design is usually furnished through division of a particular quantitative phenotype into case and control groups with a cutoff that may or may not relate to the underlying genetic variation.…”