Background: Statistical approaches to evaluate higher order SNP-SNP and SNP-environment interactions are critical in genetic association studies, as susceptibility to complex disease is likely to be related to the interaction of multiple SNPs and environmental factors. Logic regression (Kooperberg et al 2001, Ruczinski et al 2003) is one such approach, where interactions between SNPs and environmental variables are assessed in a regression framework, and interactions become part of the model search space. Trio logic regression extends the logic regression methodology, originally developed for cohort and case-control studies, for studies of trios with affected probands. The approach accounts for the linkage disequilibrium (LD) structure in the genotype data, and accommodates missing genotypes via haplotype-based imputation. We provide functionality via the Bioconductor / R packages trio and LogicReg, as described in the trio vignette. Trio logic regression is easiest run by downloading the file My_own_scoring.f, and following the steps described in the write your own scoring function in logic regression.

Collaborators: Trio logic regression, developed in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, is a collaboration with Qing Li, Holger Schwender, Tom Louis, and Dani Fallin.

Code: The R packages are available from the The Comprehensive R Archive Network as Free Software in source code form under the terms of the GNU General Public License of the Free Software Foundation.