MOTIVATION: In the context of genome-wide association studies (GWAS), there is a 
variety of statistical techniques in order to conduct the analysis, but, in most 
cases, the underlying genetic model is usually unknown. Under these 
circumstances, the classical Cochran-Armitage trend test (CATT) is suboptimal. 
Robust procedures that maximize the power and preserve the nominal type I error 
rate are preferable. Moreover, performing a meta-analysis using robust 
procedures is of great interest and has never been addressed in the past. The 
primary goal of this work is to implement several robust methods for analysis 
and meta-analysis in the statistical package Stata and subsequently to make the 
software available to the scientific community.
RESULTS: The CATT under a recessive, additive and dominant model of inheritance 
as well as robust methods based on the Maximum Efficiency Robust Test 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 using 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.
AVAILABILITY AND IMPLEMENTATION: A Stata program and a web-server are freely 
available for academic users at http://www.compgen.org/tools/GWAR.
CONTACT: pbagos@compgen.org.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.