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

Statistical methods in genetics.

Giovanni Montana1

  • 1Department of Mathematics, Imperial College London, London, UK. g.montana@imperial.ac.uk

Briefings in Bioinformatics
|September 12, 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

Life event trajectories and suicidal ideation throughout adolescence: a prospective cohort study from Germany, France, Ireland, and the UK.

The lancet. Psychiatry·2026
Same author

Decoding viewer emotions in video ads.

Scientific reports·2024
Same author

Increasing transparency of computer-aided detection impairs decision-making in visual search.

Psychonomic bulletin & review·2024
Same author

Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case-control study.

Breast cancer research : BCR·2024
Same author

Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications.

Radiology. Artificial intelligence·2023
Same author

Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study.

The Lancet. Digital health·2023
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
See all related articles

This review covers statistical methods for analyzing genotype data to find disease-susceptibility genetic variations. It details techniques from single-marker tests to complex gene-gene interaction detection for association mapping.

Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Numerous statistical methodologies have been developed to analyze genotype data.
  • These methods aim to identify genetic variations linked to disease susceptibility.
  • Population-based association mapping is crucial for understanding genetic contributions to disease.

Purpose of the Study:

  • To provide a concise overview of selected statistical methods for population-based association mapping.
  • To cover a range of techniques from simple single-marker tests to complex multi-marker analyses.
  • To highlight methods for detecting gene-gene interactions.

Main Methods:

  • Review of single-marker association tests.
  • Examination of multi-marker data mining techniques.

Related Experiment Videos

  • Focus on statistical approaches for genetic association studies.

    • Main Results:

    • A curated selection of statistical methodologies for genotype data analysis is presented.
    • The review encompasses methods applicable to both single-locus and multi-locus analyses.
    • Techniques for identifying complex gene-gene interactions are discussed.

    Conclusions:

    • Statistical methods are essential for dissecting the genetic basis of disease susceptibility.
    • A spectrum of statistical tools is available for population-based association mapping.
    • Advanced methods are necessary for detecting intricate gene-gene interactions.