Individual SNP or epigenetic marker association analyses can be performed by modeling genotype counts or methylation signature (or change) in linear or logistic models. In some models including genotype data there may be an a priori motivation for collapsing genotype groups to compare two, instead of three, genotype groups. In its simplest form this test, commonly termed the measured genotype analysis (Boerwinkle, Chakraborty, & Sing, 1986), models the influence of genotypic variation at a given locus on variation in the quantitative trait, essentially a one-way ANOVA. The extension to logistic and linear regression for genetic association testing for discrete and continuous outcome variables is common and appropriate. It is typical to code the genotype as a trichotomous indicator (0,1,2). This approach provides a distinct advantage in that it can easily be extended to incorporate measured ancestry covariates and environmental moderators or mediators. When imputed SNPs are used, it is common to replace the genotype predictor with uncertainty-adjusted dosage. The use of linear mixed models (LMM) for genetic association analysis has grown rapidly in recent years to allow for valid tests
