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100-SNP Data

The first three models [1, 2] are models displaying margin effects. Their penetrance functions are shown in the table. In Model 1, the penetrance increases only when both loci have at least one minor allele. In Model 2, the additional minor allele at each locus does not further increase the penetrance. Model 3 assumes that the minor allele in the first locus has marginal effect, when minor alleles in both loci are present; however, the effect is inversed. Other epistasis models are models displaying no margin effects with their population prevalence ranging from 0.01 to 0.64. Their penetrance are directly cited from references [3-5]. Specifically, Model 4 ~ Model 7 are randomly chosen from references [3, 4]; Model 8 is a ZZ model [5]; and Model 9 is an XOR model [3].
 
These data sets are simulated by EpiSIM using above models. For each model, 100 datasets are simulated each containing 2000 cases and 2000 controls genotyped by 100 SNPs. For each dataset, two SNPs are phenotype-associated, and others are phenotype-unassociated, which are set independently with MAFs chosen from [0.05, 0.5] uniformly.
 
These data sets are .mat matrices, where a row represents genotypes of a sample and a column represents an SNP. Genotypes of an SNP are coded as 1, 2, 3, corresponding to homozygous common genotype, e.g., AA, heterozygous genotype, e.g., Aa and aA, and homozygous minor genotype, e.g., aa, respectively. The label of a sample is a binary phenotype being either 1 (case) or 2 (control).
 
 
1.             Zhang Y, Liu JS: Bayesian inference of epistatic interactions in case-control studies. Nat Genet 2007, 39(9):1167-1173.
2.             Tang W, Wu X, Jiang R, Li Y: Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy. PLoS Genet 2009, 5(5):e1000464.
3.             Li W, Reich J: A complete enumeration and classification of two-locus disease models. Hum Hered 2000, 50(6):334-349.
4.             Greene CS, Penrod NM, Williams SM, Moore JH: Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS One 2009, 4(6):e5639.
5.             Frankel WN, Schork NJ: Who's afraid of epistasis? Nat Genet 1996, 14(4):371-373.




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