genodive

genodive analyzes population genetic data in diploid and polyploid organisms to estimate genetic differentiation, infer population structure, and relate genetic variation to environmental and spatial variables.

Key Features:

• Data manipulation: Performs data transformations, genotype imputation, and selection/subsetting of individuals, populations, or loci for analysis.
• File format compatibility: Supports 15 different file formats for importing and exporting genetic data.
• Polyploid data handling: Handles polyploid datasets up to octaploidy (2n = 8x) and hexadecaploidy (2n = 16x).
• Statistical tools: Estimates population differentiation statistics (φST, FST, F' ST, GST, G' ST, G'' ST, Dest, RST, ρ) and performs K-means clustering, Hardy–Weinberg equilibrium testing, hybrid index calculation, population and clone assignment, Mantel tests, spatial autocorrelation analysis, and 23 genetic distance calculations.
• Multivariate analyses: Implements principal components analysis and principal coordinates analysis for multivariate exploration of genetic data.
• Environmental data integration: Incorporates non-genetic variables such as environmental data and geographical coordinates into analyses.
• External program integration: Supports running lfmm, structure, instruct, and vegan for complementary analyses.

Scientific Applications:

• Population genetics: Estimating genetic diversity and population differentiation using multiple statistics across diploid and polyploid datasets.
• Population structure and clustering: Inferring genetic structure and assigning individuals to populations or clones with clustering and assignment methods.
• Landscape and environmental genetics: Relating genetic variation to environmental variables and geographic coordinates using Mantel tests and spatial autocorrelation.
• Hybridization and speciation studies: Calculating hybrid indices and assessing patterns relevant to hybridization and speciation.
• Polyploid genomics: Analysing complex polyploid datasets up to 2n = 8x and 2n = 16x for diversity and structure analyses.

Methodology:

Computational methods explicitly include estimation of differentiation statistics (φST, FST, F' ST, GST, G' ST, G'' ST, Dest, RST, ρ), K-means clustering, Hardy–Weinberg equilibrium tests, hybrid index calculation, population and clone assignment, Mantel tests, spatial autocorrelation, 23 genetic distance calculations, principal components analysis, and principal coordinates analysis.