Abstract
Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies.
Highlights
The rapid progress of sequencing technology, in both accuracy and cost, has enabled comprehensive Genome-Wide Association Studies (GWAS) which have identified many genetic contributions to complex phenotypes in humans and continues to be productive
In this paper we show that small minor allele frequencies (MAFs) affect the current interaction measures sharply and, as a result, the downstream interaction search is strongly biased towards genetic markers with higher MAFs
One should be aware that the majority of pairs of strain was genotyped at markers (SNPs) has no effect on the phenotype, and they can contribute to noise that will have an impact on the results provided by Interaction Distance (ID)
Summary
The rapid progress of sequencing technology, in both accuracy and cost, has enabled comprehensive Genome-Wide Association Studies (GWAS) which have identified many genetic contributions to complex phenotypes in humans (see www.genome.gov) and continues to be productive. Complex, non-additive genetic interactions are very common and are potentially critical in determining phenotypes [2,3,4,5]. GWAS and similar studies, including QTL analyses, use statistical methods based on correlation or likelihood and are aimed primarily at detecting single locus effects on a phenotype. These statistical methods usually assume additive models of multigene effects, representing a compound effect of multiple genes on a phenotype as a sum of the effect of each individual gene [7,8]
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