Abstract
In genome wide association studies (GWAS), family-based studies tend to have less power to detect genetic associations than population-based studies, such as case-control studies. This can be an issue when testing if genes in a family-based GWAS have a direct effect on the phenotype of interest over and above their possible indirect effect through a secondary phenotype. When multiple SNPs are tested for a direct effect in the family-based study, a screening step can be used to minimize the burden of multiple comparisons in the causal analysis. We propose a 2-stage screening step that can be incorporated into the family-based association test (FBAT) approach similar to the conditional mean model approach in the Van Steen-algorithm (Van Steen et al., 2005). Simulations demonstrate that the type 1 error is preserved and this method is advantageous when multiple markers are tested. This method is illustrated by an application to the Framingham Heart Study.
Highlights
Some of the recently published genome-wide association studies identified the same genetic locus as a disease susceptibility locus for different complex diseases (Amos et al, 2008; Thorgeirsson et al, 2008)
In order to increase power to detect this direct genetic effect, we propose a 2-stage testing strategy to minimize the burden of multiple comparisons in the causal analysis (Van Steen et al, 2005; Murphy et al, 2008; Won et al, 2009)
Throughout, in contrast to other mediation analysis techniques, we will allow for the possibility that some of these confounding variables are themselves affected by the studied marker, as illustrated via the edge from X to L in the causal diagram (VanderWeele et al, 2012)
Summary
Some of the recently published genome-wide association studies identified the same genetic locus as a disease susceptibility locus for different complex diseases (Amos et al, 2008; Thorgeirsson et al, 2008). Because the magnitude of the indirect effect depends on how strongly these secondary phenotypes affect the primary phenotype, these methods consider adjustment for confounding of the relationship between these phenotypes by measured extraneous factors Some of these methods quantify both the direct and indirect genetic effects, but assume that none of these extraneous confounding factors is influenced by the considered marker (VanderWeele et al, 2012). Regardless of the considered framework, all available methods only test one gene at a time and need to be corrected for multiple comparisons. This concern over multiple comparisons becomes an issue in family-based genome wide association studies (GWAS). We show that the type-1 error is preserved and the method is shown to be advantageous when multiple SNPs are tested for a direct effect on the phenotype of interest
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