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

Physical mapping: integrating computational and molecular genetic data.

S Le Hellard1, C A Semple, S W Morris

  • 1Medical Genetics Section, Molecular Medicine Centre, Western General Hospital, Edinburgh, UK. S.LeHellard@ed.ac.uk

Annals of Human Genetics
|June 28, 2001
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

Evaluation of quantitative polymerase chain reaction for detecting BRCA1 or BRCA2 copy number loss in high-grade serous ovarian cancer.

Scientific reports·2026
Same author

Continental Patterns of Phenotypic Variation Along Replicated Urban Gradients: A Mega-Analysis.

Ecology letters·2025
Same author

Impacts of ecosystem service message framing and dynamic social norms on public support for tropical forest restoration.

Conservation biology : the journal of the Society for Conservation Biology·2024
Same author

Revealing uncertainty in the status of biodiversity change.

Nature·2024
Same author

Conservation in the maelstrom of Covid-19 - a call to action to solve the challenges, exploit opportunities and prepare for the next pandemic.

Animal conservation·2020
Same author

Butterfly richness and abundance along a gradient of imperviousness and the importance of matrix quality.

Ecological applications : a publication of the Ecological Society of America·2020
Same journal

FIGLA Novel Variant c.385-9G>A Affects RNA Splicing in a Minigene Assay.

Annals of human genetics·2026
Same journal

Epigenetic Shifts in MTNR1A, MTNR1B and Fn14 and Their Links to Preeclampsia Risk.

Annals of human genetics·2026
Same journal

Hip Bone Marrow Adiposity as a Risk Factor for Alzheimer's Disease: Insights From Mendelian Randomization Analysis.

Annals of human genetics·2026
Same journal

A Novel Biallelic REL Frameshift Variant p.(Tyr9Ilefs*2) Causing Immunodeficiency-92 With Profound c-Rel Deficiency.

Annals of human genetics·2026
Same journal

Identification of PSMA4 as a Therapeutic Target for Atherosclerosis: A Comprehensive Multiomics Mendelian Randomization Analysis.

Annals of human genetics·2026
Same journal

Genetic Insights Into Hypertension and Breast Cancer Risk in African Women: A Mendelian Randomization and Colocalization Analyses.

Annals of human genetics·2026
See all related articles

Building a high-resolution physical map is essential for identifying disease-related genes. This review details integrating public data with empirical findings to map chromosomal regions, exemplified by Bipolar Affective Disorder linkage at 4p15.3--p16.1.

Area of Science:

  • Genomics
  • Medical Genetics
  • Bioinformatics

Background:

  • Identifying disease-associated chromosomal regions is a critical first step in genetic research.
  • The Human Genome Project has accelerated physical map construction, but data integration remains challenging.
  • Previous studies linked Bipolar Affective Disorder to the 4p15.3--p16.1 chromosomal region.

Purpose of the Study:

  • To describe the process of building a high-resolution physical map for a specific chromosomal region.
  • To demonstrate how to integrate publicly available genomic data with empirical data.
  • To present methods for identifying novel genetic markers and candidate genes within a defined region.

Main Methods:

  • Collecting and integrating public sequence, DNA fingerprint, and genetic marker data.

Related Experiment Videos

  • Utilizing empirical data to refine physical map construction.
  • Employing strategies for identifying new genetic markers and candidate genes.

    • Main Results:

    • A framework for constructing large-scale, high-resolution physical maps was presented.
    • The process of data integration for physical mapping was exemplified using the 4p15.3--p16.1 region.
    • Methods for identifying candidate genes within mapped regions were discussed.

    Conclusions:

    • High-resolution physical mapping is crucial for advancing genetic disease research.
    • Integrating diverse data sources is key to efficient physical map construction.
    • This approach facilitates the identification of candidate genes for disorders like Bipolar Affective Disorder.