Once the data have been cleaned and deemed ready for use, genotypes can, for all practical purposes, be incorporated into standard analytic frameworks (e.g., regression-based analyses). However, a critical point is that, unlike many variables that are modeled by social scientists, how a genotype is coded infers something about the biological risk (i.e., the underlying genetic model). For example, genetic markers may influence a particular phenotype in a simple linear manner, where each copy of a “risk” allele confers an equivalent cumulative (i.e., additive) effect. In this sense, individuals with two copies of the allele at a given SNP are presumed to have twice the risk of those with single copy, relative to those with 0 copies. Alternately, the influence of genotype might also function in a dominant (one or more copies of the risk allele is sufficient to produce the outcomes, with an equivalent phenotype across those with one or two copies of the risk allele) or recessive (two copies of the risk allele are needed before the phenotype is manifest, with individuals having 0 or 1 copy showing
