Robust association tests

In order to determine a potential association between the variability of a set of genetic markers and a given complex trait, several statistical tests are commonly employed.  Among these, the Fisher’s exact test, the chi-square test and the Cochran-Armitage trend test are the most popular and frequently used in case-control genetic association studies. All these tests and their characteristics are presented in the recent book of Andreas Ziegler ed Inke Koing "A Statistical Approach to Genetic Epidemiology" published in 2010. However, in addition to several other important factors (power, assumptions, etc), a critical aspect in the application of such tests is represented by the coding scheme adopted in the association analysis. Indeed, the effectiveness of these tests (power) depends also on the coding scheme adopted for handle the available genetic information (categorical data). This is especially true for some complex traits for which the effect of the analyzed genetic variants (dominant, recessive, additive, etc) is generally unknown. As a result, to take into account the genetic model uncertainty, some authors adopted the strategy to use in a single association test not a unique, but several coding schemes. Although this kind of approach increases the power do detect possible associations, in parallel it also dramatically increases the number of false-positive results. To take into account the problem of the genetic model uncertainty and also the problem due to multiple comparisons, several “robust” tests have been developed. Among these the most popular was MAX test (or MAX3) originally proposed by Freidlin and co-workers (2002) and subsequently modified and improved by Zang and co-workers. This last version was implemented in the packages SNPassoc and Rassoc of R (Zang et al., 2010). The most recent versions of these tests, RobustSNP and the Robust Mantel-Haenszel Test, allow also adjustment for covariate effects (So and Sham, 2011; Zang and Fung, 2011). Both for the robustness with respect to the adopted genetic models (dominant, additive and recessive), and because they are able to handle genome-wide association (GWA) studies, these tests should represent the standard methodology to adopt in the future case-control genetic association studies.