title: Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction.
abstract: 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.
authors.affiliation: Systems and Modeling Unit, Montefiore Institute, University of Liege, Liege, Belgium.
authors.forename: Jestinah M
authors.initials: JM
authors.lastname: Mahachie John
authors.forename: Tom
authors.initials: T
authors.lastname: Cattaert
authors.forename: François Van
authors.initials: FV
authors.lastname: Lishout
authors.forename: Elena S
authors.initials: ES
authors.lastname: Gusareva
authors.forename: Kristel Van
authors.initials: KV
authors.lastname: Steen
mesh_terms: D004843:Epistasis, Genetic; D005189:False Positive Reactions; D005799:Genes, Dominant / Q000235:genetics; D056726:Genetic Association Studies; D006801:Humans; D008957:Models, Genetic; D058011:Multifactor Dimensionality Reduction / Q000379:methods*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D019655:Quantitative Trait, Heritable