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Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs) is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS) however involves difficulty in setting overall p-value(More)
Recently, Wu and colleagues [1] proposed two novel statistics for genome-wide interaction analysis using case/control or case-only data. In computer simulations, their proposed case/control statistic outperformed competing approaches, including the fast-epistasis option in PLINK and logistic regression analysis under the correct model; however, reasons for(More)
In gene-gene interaction analysis using single nucleotide polymorphism (SNP) data, empty cells arise in the genotype contingency table more frequently than in single SNP association studies. Empty cells lead to unidentifiable regression coefficients in regression model fitting. It is unclear whether the degrees of freedom (d.f.) for testing interactions are(More)
The impact of cryptic relatedness (CR) on genomic association studies is well studied and known to inflate false-positive rates as reported by several groups. In contrast, conventional epidemiological studies for environmental risks, the confounding effect of CR is still uninvestigated. In this study, we investigated the confounding effect of unadjusted CR(More)
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