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
Funding: - National Natural Science Foundation of China: 32241042, 31871630 - China Agricultural Research System: CARS06-14.5-A04 - Fundamental Research Funds of CAAS: 1610092016116, Y2017JC15

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