We have also shown that the presence of heterogeneity strongly influences the estimates of the effect sizes. This result suggests that the effect sizes of marginally significant variants identified in GWAS might be larger if they were instead examined in more homogeneous samples. This is in agreement with previous studies pointing to the importance of accurate and stringent phenotypic definition in GWAS data [11], [12]. Interestingly, van der Sluis et al. [12] employed simulation studies to show that accurate modelling of phenotypic information improved the estimation of the genetic variants effect size. Further, Evangelou et al. [11] found empirical support for importance of phenotypic definition in the analysis of GWAS data in HIV-1 infected subjects. They showed that the observed genetic effects in HIV-1 seroconverters (a more stringent phenotype) were larger than in seroprevalent subjects. Using simulation and real data both studies consistently showed how accurate phenotyping increased the power to detect association signals and to estimate correctly their effect size.
