Adegenet 1.3-1 is a new R package that provides tools for analyzing genome-wide SNP data. It uses a bit-level coding scheme and parallelized computation to handle large datasets efficiently on standard personal computers. The package includes a new class called genlight, which compresses SNP data significantly by representing each biallelic SNP with a single bit. This allows for efficient storage and processing of large genomic datasets. The package also supports parallel computing, utilizing multi-core processors to reduce computational time. Genlight objects can be created from various data sources, including PLINK and .snp files, and can handle any degree of ploidy. The package includes functions for principal component analysis (PCA) and discriminant analysis of principal components (DAPC), which are used to identify structuring alleles from genomic data. The example demonstrates how DAPC can effectively separate two groups of individuals and identify structuring SNPs. Adegenet 1.3-1 provides efficient data representation and parallel computation as viable alternatives to increasing raw computing power, enabling the analysis of genome-wide SNP data on standard computers. The package is available from CRAN and includes a manual, tutorials, and a dedicated forum for support.Adegenet 1.3-1 is a new R package that provides tools for analyzing genome-wide SNP data. It uses a bit-level coding scheme and parallelized computation to handle large datasets efficiently on standard personal computers. The package includes a new class called genlight, which compresses SNP data significantly by representing each biallelic SNP with a single bit. This allows for efficient storage and processing of large genomic datasets. The package also supports parallel computing, utilizing multi-core processors to reduce computational time. Genlight objects can be created from various data sources, including PLINK and .snp files, and can handle any degree of ploidy. The package includes functions for principal component analysis (PCA) and discriminant analysis of principal components (DAPC), which are used to identify structuring alleles from genomic data. The example demonstrates how DAPC can effectively separate two groups of individuals and identify structuring SNPs. Adegenet 1.3-1 provides efficient data representation and parallel computation as viable alternatives to increasing raw computing power, enabling the analysis of genome-wide SNP data on standard computers. The package is available from CRAN and includes a manual, tutorials, and a dedicated forum for support.