geneHapR
geneHapR performs identification, statistical analysis, and visualization of gene haplotypes to support functional gene dissection and marker-assisted selection.
Key Features:
- Integration of genotype, genomic annotation, and phenotypic variation: Integrates genotype data with genomic annotation and phenotypic variation to link genetic variants to phenotypes and evolutionary relationships.
- Variants visualization and network construction: Provides variants visualization and network construction to represent haplotype relationships and variant patterns.
- Linkage disequilibrium analysis: Implements linkage disequilibrium block analysis to identify non-random associations of alleles across loci.
- Geographical distribution visualization: Visualizes geographical distribution of haplotypes to assess population structure and spatial evolutionary patterns.
- Phenotypic comparison among haplotypes: Performs phenotypic comparisons among haplotypes to associate haplotypes with observable traits and identify superior alleles.
Scientific Applications:
- Marker-Assisted Selection: Clarifies functional-gene haplotypes to aid selection of superior alleles in crop and livestock breeding programs.
- Genetic Research: Enables dissection of gene function and genotype–phenotype relationships through comprehensive haplotype analysis.
- Population Genetics: Supports study of haplotype distribution and evolution across geographical regions to inform population structure and migration analyses.
Methodology:
Integration of genotype, genomic annotation, and phenotypic variation data; statistical assessment of linkage disequilibrium blocks; and visualization of variants, haplotype networks, and geographical haplotype distributions.
Topics
Details
- License:
- GPL-3.0
- Cost:
- Free of charge
- Tool Type:
- library
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- R
- Added:
- 1/2/2024
- Last Updated:
- 11/24/2024
Operations
Publications
Zhang R, Jia G, Diao X. geneHapR: an R package for gene haplotypic statistics and visualization. BMC Bioinformatics. 2023;24(1). doi:10.1186/s12859-023-05318-9. PMID:37189023. PMCID:PMC10186671.
PMID: 37189023
PMCID: PMC10186671
Funding: - National Natural Science Foundation of China: 32241042, 31871630
- China Agricultural Research System: CARS06-14.5-A04
- Fundamental Research Funds of CAAS: 1610092016116, Y2017JC15