In addition, we calculate the Bayes factor related to each identified QTL, to test coincident linkage versus pleiotropy.
Previous genetic studies focusing on this gene gave conflicting results, potentially because of the differences in methodology (linkage versus association studies), and the different loci analyzed (the DRD2 gene having many polymorphisms).
However, it was not possible to make a distinction between close linkage versus pleiotropic effects.
The Close Linkage versus Pleiotropism (CLIP) test that we propose here is based on this consideration.
About 6% of the cases had been uninsured as the primary payer after the linkage versus 7% before the linkage.
We performed the same simulation design using the non-parametric bootstrap test (Lebreton et al., 1998) adapted for association analysis to test linkage versus pleiotropy.
In this article, we propose the Close Linkage versus Pleiotropism (CLIP) test, a fast, simple and powerful method to distinguish between these two situations.
Because studies based on data linkage to medical records have a lower chance of misclassification bias, we conducted subgroup meta-analyses stratified by source of information (data linkage versus self-report) and found no substantial differences in the results.
When significant heterogeneity or publication bias was found, we performed subgroup analyses by study design (case-control study versus cohort study), and the source of information (data linkage versus self-report) to assess the impact on between-study variations (heterogeneity).
Some of these methods focus on testing the null hypothesis that a particular variant explains none of the linkage versus the alternative hypothesis that it can explain some or all of the observed linkage in the region [ Horikawa et al., 2000; Dupuis and van Eerdewegh, 2003; Li et al., 2004; Chen et al., 2005; Houwing-Duistermaat et al., 2005].
