Summary
Background: Genome-wide association studies (GWAS) have been used successfully to identify genetic
loci associated with complex diseases and phenotypes. Often this association takes
the form of several significant signals (such as small p-values) in a univariate analysis
at various markers within a single genetic region. Once confirmed, these associations
lead to the question if a single marker tags the association signal of another, functionally
relevant variant or if the single marker tags a functionally relevant haplo-type.
To deal with this question, methods for family data based on logistic regression,
adaptations of the transmission/disequilibrium test (TDT) or weighted haplotype likelihood
(WHL) methods have been proposed in the literature.
Objectives: Objectives were to examine the effect of parameters such as sample size, inheritance
model, and the effects of linkage disequilibrium (LD) in the region on the ability
of a selection of methods to detect an independent effect from an additional locus.
Methods: All methods tested were applied to simulated genetic data of trios comprising a single
affected offspring and two parents. Results: While regression-based methods have advantages such as model flexibility, potentially
increasing power, the WHL method was more robust against increasing LD in the scenarios
analyzed.
Conclusions: Simulation results suggest that the regression and WHL methods are better able with
regard to statistical power than the adaptation of the TDT analyzed here to detect
genetic effects at an additional locus while controlling for confounding at another
locus.
Keywords
Genetic association analysis - nuclear family - haplotypes - genetic models - linkage
disequilibrium
