Bioinformatics
Simulating association studies
Bioinformatics
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Incorporating domain knowledge into evolutionary computing for discovering gene-gene interaction
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
ATHENA optimization: the effect of initial parameter settings across different genetic models
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Grammatical evolution of neural networks for discovering epistasis among quantitative trait loci
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Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Whole-genome association (WGA) studies are becoming a common tool for the exploration of the genetic components of common disease. The analysis of such large scale data presents unique analytical challenges, including problems of multiple testing, correlated independent variables, and large multivariate model spaces. These issues have prompted the development of novel computational approaches. Thorough, extensive simulation studies are a necessity for methods development work to evaluate the power and validity of novel approaches. Many data simulation packages exist, however, the resulting data is often overly simplistic and does not compare to the complexity of real data; especially with respect to linkage disequilibrium (LD). To overcome this limitation, we have developed genomeSIMLA. GenomeSIMLA is a forward-time population simulation method that can simulate realistic patterns of LD in both family-based and case-control datasets. In this manuscript, we demonstrate how LD patterns of the simulated data change under different population growth curve parameter initialization settings. These results provide guidelines to simulate WGA datasets whose properties resemble the HapMap.