We report two methods for linkage disequilibrium mapping that involve incorporation of covariates through parametric modeling to utilize combined case-parent trios and unrelated case and/or control data. The proposed two combined methods were used to map the disease locus of hypertension in the angiotensin-converting enzyme (ACE) gene with incorporation of ACE activity. The efficiencies in estimating the disease locus increased by 351- and 100-fold in the hybrid study with respect to the two proposed methods when compared to the estimates from the trios study; and they changed by 1.4- and 0.4-fold, respectively, when compared to the case-control study. Efficiency of disease locus estimates was greatly improved in both simulations and hypertension studies based on the hybrid data, compared to case-parent trio studies only. These newly developed methods preserve the advantages of the previous methods, including flexible modeling and assessment of gene-gene and gene-covariate effects, while providing more power by using all the data combined. The computing program for analysis using the separate and hybrid data sets is freely available on the author's website.
