Document Type
Article
Publication Date
6-9-2014
Publication Title
BioData Mining
Abstract
BackgroundThe statistical genetics phenomenon of epistasis is widely acknowledged to confound disease etiology. In order to evaluate strategies for detecting these complex multi-locus disease associations, simulation studies are required. The development of the GAMETES software for the generation of complex genetic models, has provided the means to randomly generate an architecturally diverse population of epistatic models that are both pure and strict, i.e. all n loci, but no fewer, are predictive of phenotype. Previous theoretical work characterizing complex genetic models has yet to examine pure, strict, epistasis which should be the most challenging to detect. This study addresses three goals: (1) Classify and characterize pure, strict, two-locus epistatic models, (2) Investigate the effect of model ‘architecture’ on detection difficulty, and (3) Explore how adjusting GAMETES constraints influences diversity in the generated models.
DOI
10.1186/1756-0381-7-8
Dartmouth Digital Commons Citation
Urbanowicz, Ryan J.; Granizo-Mackenzie, Ambrose L. S.; Kiralis, Jeff; and Moore, Jason H, "A Classification and Characterization of Two-Locus, Pure, Strict, Epistatic Models for Simulation and Detection" (2014). Dartmouth Scholarship. 547.
https://digitalcommons.dartmouth.edu/facoa/547