Incorporating covariates into multipoint association mapping in the case-parent design.

BACKGROUND/AIMS: To improve the efficiency of disease locus localization in association mapping using case-parent designs and to assess or account for the main covariate effects and gene-covariate interaction effects, while localizing the disease locus.

METHODS

The present study extends a multipoint fine-mapping approach to incorporate covariates into the association mapping of case-parent designs through parametric and non-parametric modeling. This approach is based on the expected preferential-allele-transmission statistics for transmission from either parent to an affected child.

RESULTS

Simulation studies indicate that the efficiency in estimating the disease locus increases considerably when incorporating a covariate associated with the disease. This is especially true when the genetic effect of the disease locus is small. The proposed approach was applied to a young-onset hypertension data sample. The relative efficiency of estimating the locus of young-onset hypertension increases 110-fold after incorporating triglyceride into the association mapping while localizing the disease variant in the lipoprotein lipase gene in the non-parametric model. By incorporating the information of SNP variants into the fine-mapping, the proposed method further assesses the gene-gene interactions between the SNP and the disease locus.

CONCLUSION

With the incorporation of covariates, the proposed method cannot only improve efficiency in estimating disease loci, but can also elucidate the etiology of a complex disease.