Within the context of genome-wide association studies (GWAS) there is a variety of statistical techniques in order to conduct the analysis but for complex diseases, the underlying genetic model is usually unknown. Under these circumstances, the classical Cochran-Armitage trend test is suboptimal and robust procedures that will detect the true underlying model of inheritance and, at the same time perform the analysis maximizing the power and preserving the nominal type I error rate are preferable. Moreover, performing a meta-analysis of candidate studies using robust procedures will be of highly interest and has never been addressed in the past. The primary goal of this work is to implement as many as possible robust methods for analysis and meta-analysis within the statistical package Stata and subsequently to make the software available to the scientific community.The Cochran-Armitage trend test under a recessive, additive and dominant model of inheritance as well as robust methods based on the MERT statistic, the MAX statistic and the MIN2 were implemented in Stata. Concerning MAX and MIN2, we calculated their asymptotic null distributions relying on numerical integration resulting in a great gain in computational time without losing accuracy. All the aforementioned approaches were employed in a fixed or a random effects meta-analysis setting for summary data with weights equal to the reciprocal of the combined cases and controls. Overall, this is the first complete effort to implement procedures for analysis and meta-analysis in GWAS using Stata