A testing framework for identifying susceptibility genes in the presence of epistasis

Abstract

An efficient testing strategy called the "focused interaction testing framework" (FITF) was developed to identify susceptibility genes involved in epistatic interactions for case-control studies of candidate genes. In the FITF approach, likelihood-ratio tests are performed in stages that increase in the order of interaction considered. Joint tests of main effects and interactions are performed conditional on significant lower-order effects. A reduction in the number of tests performed is achieved by prescreening gene combinations with a goodness-of-fit chi2 statistic that depends on association among candidate genes in the pooled case-control group. Multiple testing is accounted for by controlling false-discovery rates. Simulation analysis demonstrated that the FITF approach is more powerful than marginal tests of candidate genes. FITF also outperformed multifactor dimensionality reduction when interactions involved additive, dominant, or recessive genes. In an application to asthma case-control data from the Children's Health Study, FITF identified a significant multilocus effect between the nicotinamide adenine dinucleotide (phosphate) reduced:quinone oxidoreductase gene (NQO1), myeloperoxidase gene (MPO), and catalase gene (CAT) (unadjusted P = .00026), three genes that are involved in the oxidative stress pathway. In an independent data set consisting primarily of African American and Asian American children, these three genes also showed a significant association with asthma status (P = .0008).

Figure 1

Histograms for scenarios 1–4. The rank of the observed CSS statistic for the true set of susceptibility genes, gene set {G₁,G₂,G₃}, was recorded for each of the 200 simulations per risk scenario. With 20 candidate genes, there are 1,140 three-gene combinations; thus, there were 1,140 possible ranks. Histograms were constructed for each set of the 200 ranks of gene set {G₁,G₂,G₃} for each risk scenario. The four risk scenarios (following the form of eq. [1]) were as follows: scenario 1, β₁₂₃=log(3); scenario 2, β₁=log(1.5) and β₁₂₃=log(3); scenario 3, β₁=log(1.5), β₂=log(.67), and β₁₂₃=log(3); and scenario 4, β₁=log(1.5), β₂=log(1.5), and β₃=log(1.5). All other nonintercept parameters were set to zero.

Figure 2

Genes identified using CSS cutoffs. Bar height represents the total number of true susceptibility genes identified by the FITF method with the use of a range of CSS cutoff values: CSS2 for the second stage and CSS3 for the third stage.