We analyzed two quantitative traits (Q1 and Q2) provided in the 'Common Disease' data set with the aim of detecting both genetic and environmental determinants. We used linear regression for screening measured variables, maximum likelihood segregation and linkage analyses for detecting and localizing unmeasured genes, and Gibbs sampling for joint segregation and linkage analyses with estimation of gene-environment interaction and polygenic effects. For both Q1 and Q2, we successfully detected the unmeasured codominant major gene (MG) that was tightly linked to candidate gene C2. We also detected all of the measured variables used in generating Q1 (age, Q3, candidate gene C5) and Q2 (EF). Although our final models differed slightly from the true data generation models, our multifaceted analytic approach was successful in characterizing the determinants of Q1 and Q2.