Evaluation of associative classification-based multifactor dimensionality reduction in the presence of noise
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The advancements in genetic epidemiology have focused more on understanding the associations and functional relationships among the genes. Identifying the susceptible genes and their interaction effects over the complex traits remains statistically and computationally challenging. An associative classification-based multifactor dimensionality reduction method (MDRAC) was proposed to improve the identification of multi-locus interacting genes associated with a disease. The method was evaluated for one to six loci by varying heritability, minor allele frequency, case–control ratios, and sample size. The experimental results demonstrated significant improvements in the accuracy over the previous methods. However, the performance of MDRAC in the presence of noise due to genotyping error, missing data, phenocopy, and genetic heterogeneity is unknown. The goal of this study is to evaluate MDRAC for identifying single nucleotide polymorphism interactions in the presence of noise. Several experiments are conducted on simulated datasets and on a published dataset to demonstrate the performance of MDRAC. On average, the results showed improved performance over the previous MDR method in all the models. However, the performance of MDRAC is reduced in the presence of phenocopy and genetic heterogeneity, or their combinations with other sources of noise.
KeywordsEpistasis Multifactor dimensionality reduction Genotyping error Missing data Phenocopy Genetic heterogeneity
We thank John Wallace from the Ritchie Lab, Pennsylvania State University for his expert assistance in simulating the datasets in the presence of common sources of noise. We appreciate the generosity of Dr. Jason Moore and his colleagues at the Dartmouth Medical School in making MDR software tool and java source code available at www.epistasis.org. We also appreciate Dr. Juan R Gonzalez and his colleagues for developing the SNPassoc package available for R environment along with the datasets.
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