polygene
polygene implements a generalized AMOVA framework and analytical methods for population genetic analysis of polyploid organisms, estimating allele frequencies from allelic phenotypes using genotypic frequencies under double-reduction and accommodating multilocus genotypic and allelic phenotypic data when allele dosage is unknown.
Key Features:
- Generalized AMOVA Framework: Extends AMOVA to support polyploid data with flexible hierarchical classifications beyond the typical two to four levels.
- Handling Polyploidy: Estimates allele frequencies from allelic phenotypes using genotypic frequencies under double-reduction to accommodate polyploid organisms.
- Multilocus Genotypic and Allelic Phenotypic Data: Handles multilocus datasets and accommodates allelic phenotypic information when allele dosage is unknown.
- Comprehensive Population Genetic Analyses: Performs genetic diversity analysis, phenotypic or genotypic distribution assessments, linkage disequilibrium and genetic differentiation tests, genetic distance calculations, principal coordinate and hierarchical clustering analyses, estimation of individual inbreeding coefficients, heterozygosity indices, and pairwise relatedness, and conducts population assignment and parentage analysis.
- Bayesian Clustering: Implements Bayesian clustering methods to infer population structure.
Scientific Applications:
- Molecular Ecology: Analyzing genetic structure and diversity in polyploid species.
- Population Genetics: Estimating population differentiation, relatedness, and inbreeding in polyploid taxa.
- Biodiversity and Evolutionary Biology: Studying biodiversity patterns and evolutionary processes in polyploid organisms.
- Conservation Genetics: Assessing genetic variation and structure in polyploid populations to inform conservation strategies.
- Breeding Programs Involving Polyploid Organisms: Supporting breeding programs through parentage analysis, population assignment, and assessment of genetic diversity in polyploid contexts.
Methodology:
Simulates datasets and employs empirical datasets to validate performance.
Topics
Details
- License:
- GPL-3.0
- Added:
- 1/18/2021
- Last Updated:
- 1/24/2021
Operations
Publications
HUANG K, WANG T, DUNN DW, ZHANG P, SUN H, LI B. A generalized framework for AMOVA with multiple hierarchies and ploidies. Integrative Zoology. 2020;16(1):33-52. doi:10.1111/1749-4877.12460. PMID:32648364.
PMID: 32648364
Funding: - National Natural Science Foundation of China: 31572278, 31730104, 31770411, 31770425