MAGMA is a novel tool for gene and gene-set analysis of genome-wide association study (GWAS) data. It addresses limitations of existing methods by using a multiple regression model for gene analysis and a regression-based approach for gene-set analysis. The gene analysis incorporates linkage disequilibrium (LD) between markers and detects multi-marker effects, while the gene-set analysis allows for the analysis of continuous gene properties and multiple gene sets. MAGMA is more powerful than other methods in detecting genes and gene sets associated with Crohn's Disease and is significantly faster. It also provides flexibility for future extensions. The tool was evaluated using the Wellcome Trust Case-Control Consortium (WTCCC) Crohn's Disease GWAS data and compared to other gene and gene-set analysis tools such as VEGAS, PLINK, ALIGATOR, INRICH, and MAGENTA. The results showed that MAGMA has greater statistical power and is faster than these methods. The gene-set analysis models are a specific instance of a more general gene-level regression model that can be used for conditional, joint, and interaction analyses of gene sets and other gene properties. MAGMA also provides traditional SNP-wise gene analysis models for when raw genotype data is not available. The tool is distributed as a standalone application with a command-line interface and is available under an open source license. The results of the analysis show that MAGMA is effective in detecting genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. The computational performance of MAGMA is also superior to other methods, making it a valuable tool for gene and gene-set analysis in GWAS studies.MAGMA is a novel tool for gene and gene-set analysis of genome-wide association study (GWAS) data. It addresses limitations of existing methods by using a multiple regression model for gene analysis and a regression-based approach for gene-set analysis. The gene analysis incorporates linkage disequilibrium (LD) between markers and detects multi-marker effects, while the gene-set analysis allows for the analysis of continuous gene properties and multiple gene sets. MAGMA is more powerful than other methods in detecting genes and gene sets associated with Crohn's Disease and is significantly faster. It also provides flexibility for future extensions. The tool was evaluated using the Wellcome Trust Case-Control Consortium (WTCCC) Crohn's Disease GWAS data and compared to other gene and gene-set analysis tools such as VEGAS, PLINK, ALIGATOR, INRICH, and MAGENTA. The results showed that MAGMA has greater statistical power and is faster than these methods. The gene-set analysis models are a specific instance of a more general gene-level regression model that can be used for conditional, joint, and interaction analyses of gene sets and other gene properties. MAGMA also provides traditional SNP-wise gene analysis models for when raw genotype data is not available. The tool is distributed as a standalone application with a command-line interface and is available under an open source license. The results of the analysis show that MAGMA is effective in detecting genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. The computational performance of MAGMA is also superior to other methods, making it a valuable tool for gene and gene-set analysis in GWAS studies.