Background#R##N#Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects.
['Ryan J. Urbanowicz', 'Jeff Kiralis', 'Nicholas A. Sinnott-Armstrong', 'Tamra Heberling', 'Jonathan M. Fisher', 'Jason H. Moore']
GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.