M-regression, false discovery rates and outlier detection with application to genetic association studies

Robust multiple linear regression methods are valuable tools when underlying classical assumptions are not completely fulfilled. In this setting, robust methods ensure that the analysis is not significantly disturbed by any outlying observation. However, knowledge of these observations may be important to assess the underlying mechanisms of the data. Therefore, a robust outlier test is discussed, together with an adequate false discovery rate correction measure, to be used in the context of multiple linear regression with categorical explanatory variables. The methodology focuses on genetic association studies of quantitative traits, though it has much broader applications. The method is also compared to a benchmark rule from the literature and its good performance is validated by a simulation study and a real data example from a candidate gene study.