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

A score for Bayesian genome screening.

E Warwick Daw1, Ellen M Wijsman, Elizabeth A Thompson

  • 1Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195-4322, USA.

Genetic Epidemiology
|March 26, 2003
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

Case of complete response to immunotherapy in MMR-deficient prostate cancer associated with NK-like and CD4<sup>+</sup>CD8<sup>+</sup> T cells.

Cell reports. Medicine·2026
Same author

Variant Prioritization by Pedigree-Based Haplotyping.

Genetic epidemiology·2026
Same author

Estradiol enhances influenza vaccine responses through B cell metabolic reprogramming in female mice.

mBio·2026
Same author

Multiple-testing corrections in case-control studies using identity-by-descent segments.

American journal of human genetics·2026
Same author

A common 19 bp APOE enhancer deletion is protective against Alzheimer's disease in African Americans.

Nature communications·2026
Same author

Mechanisms of sex differences in acute and long COVID sequelae in mice.

bioRxiv : the preprint server for biology·2025
Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026
Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026
Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026
Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026
Same journal

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
See all related articles

Bayesian Monte Carlo Markov chain (MCMC) methods help analyze complex genetic traits. A new score, the log of the posterior placement probability ratio (LOP), improves quantitative trait loci (QTL) detection and significance assessment.

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Bayesian Monte Carlo Markov chain (MCMC) techniques are valuable for analyzing complex genetic traits.
  • Existing methods like Loki, while effective for quantitative trait loci (QTL) localization, present challenges in result interpretation and significance assessment.

Purpose of the Study:

  • To introduce a novel statistical score, the log of the posterior placement probability ratio (LOP), for enhanced oligogenic QTL detection and localization.
  • To address the difficulties in interpreting and assessing the significance of results from MCMC-based QTL analyses.

Main Methods:

  • Developed the LOP score, calculated as the log of the ratio of posterior probabilities of linkage to a real chromosome versus a pseudochromosome.
  • Estimated the LOP using simultaneous MCMC on both real and pseudochromosomes to handle exact calculation difficulties.

Related Experiment Videos

  • Empirically investigated the distributional properties of the LOP under varying conditions (presence/absence of trait genes).

    • Main Results:

    • The LOP score demonstrates utility in assessing oligogenic QTL detection and localization.
    • The LOP is robust to trait model misspecification, unlike traditional lod scores.
    • The LOP successfully detects linkage for loci with small effects, outperforming lod scores in such scenarios.

    Conclusions:

    • The LOP provides a more reliable method for assessing the significance of QTL detection compared to existing approaches.
    • Empirical distributions derived from simulations allow for robust significance assessment of the LOP in the absence of linkage.