Lower-Order Effects Adjustment in Quantitative Traits Model-Based Multifactor Dimensionality Reduction

Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big
challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic
effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic
variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we
restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2-
way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order
genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant
coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that
empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use
automatic SNP selection procedures, SNP selection based on ‘‘top’’ findings, or SNP selection based on p-value criterion for
interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects
in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately
controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In
conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be
avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality
Reduction and involve using residuals as the new trait. We advocate using ‘‘on-the-fly’’ lower-order effects adjusting when
screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis.