Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

GeneRecon--a coalescent based tool for fine-scale association mapping.

Thomas Mailund1, Mikkel H Schierup, Christian N S Pedersen

  • 1Bioinformatics Research Center, University of Aarhus Høegh-Guldbergs Gade 10, DK-8000 Arhus C, Denmark. mailund@birc.au.dk

Bioinformatics (Oxford, England)
|April 25, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder.

... International Conference on Learning Representations·2026
Same author

Genetic predictions of eye and hair colour in the Danish population.

Forensic science international. Genetics·2025
Same author

An integrated single-cell atlas of blood immune cells in aging.

npj aging·2024
Same author

Comparative transcriptomic analyses of thymocytes using 10x Genomics and Parse scRNA-seq technologies.

BMC genomics·2024
Same author

An almost infinite sites model.

Theoretical population biology·2024
Same author

Comparing Phylogeographies to Reveal Incompatible Geographical Histories within Genomes.

Molecular biology and evolution·2024
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

GeneRecon is a novel computational tool for fine-scale genetic association mapping. It utilizes a coalescence model and Metropolis-Hastings sampling to pinpoint disease loci from genetic data, offering high configurability.

Area of Science:

  • Genetics
  • Computational Biology
  • Statistical Genetics

Background:

  • Fine-scale association mapping is crucial for identifying disease-associated genetic variants.
  • Existing methods may lack the flexibility to handle diverse genetic data types and complex models.

Purpose of the Study:

  • To introduce GeneRecon, a configurable tool for fine-scale association mapping.
  • To provide a robust method for pinpointing disease locus positions using coalescence modeling.

Main Methods:

  • GeneRecon employs a coalescence model for genetic association analysis.
  • It uses Metropolis-Hastings sampling within the state space of genealogies.
  • The tool accepts case-control data from phased or unphased SNP and microsatellite genotypes.

Related Experiment Videos

Main Results:

  • GeneRecon calculates the posterior distribution of disease locus positions.
  • The tool's input format, search strategy, and sampled statistics are configurable via Guile Scheme.

Conclusions:

  • GeneRecon offers a highly configurable and flexible approach to fine-scale association mapping.
  • The tool facilitates precise identification of disease-related genetic loci.