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

Bayesian spatial modeling of haplotype associations.

Duncan C Thomas1, Daniel O Stram, David Conti

  • 1University of Southern California, Los Angeles, CA 90089-9011, USA. dthomas@usc.edu

Human Heredity
|November 14, 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

The Multiethnic Cohort: A Resource for the Study of Genetic and Nongenetic Cancer Risk across Populations.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

The association of epigenetic age acceleration with internal smoking dose, risk of lung cancer, and all-cause mortality in cigarette smokers: the Multiethnic Cohort study.

Clinical epigenetics·2026
Same author

A reanalysis of a genome-wide association study on breast cancer in Asian populations using the SG10K_Health reference panel for imputation: a multi-Centre case-control analysis.

Human molecular genetics·2026
Same author

Racial and ethnic differences in the impact of air pollution on the risk of Alzheimer's disease and related dementias in the Multiethnic Cohort Study.

Environment international·2026
Same author

Exposure to Industrial Air Toxics and Risk of Breast Cancer in California: The Multiethnic Cohort Study.

Journal of occupational and environmental medicine·2026
Same author

Interaction of genetic and lifestyle risk scores on colorectal cancer risk across 5 racial and ethnic populations.

Journal of the National Cancer Institute·2026

This review covers methods linking disease risk to single nucleotide polymorphisms (SNPs). It explores haplotype-based models and Bayesian spatial statistics for fine mapping genes and understanding genetic relatedness.

Area of Science:

  • Genetics
  • Statistical genetics
  • Computational biology

Background:

  • Disease risk association studies often involve single nucleotide polymorphisms (SNPs) within specific genomic regions.
  • Case-control designs with unrelated individuals are used to test candidate genes or fine-map unknown genes via linkage disequilibrium (LD).

Purpose of the Study:

  • To review and compare methods for relating disease risk to collections of SNPs.
  • To explore haplotype-based models and alternative empirical modeling approaches using Bayesian spatial statistics.

Main Methods:

  • Comparison of logistic penetrance models based on haplotypes versus unphased multilocus genotypes.
  • Application of Expectation-Maximization (E-M) or Markov chain Monte Carlo (MCMC) for fitting haplotype models.

Related Experiment Videos

  • Discussion of ascertainment correction for case-control studies and review of LD mapping methods (coalescent/ancestral recombination graphs, haplotype sharing).
  • Proposal of empirical modeling using Bayesian spatial statistics (conditional autoregressive, Potts, Voronoi models).

    • Main Results:

    • Haplotype-based models require summation over possible haplotype assignments, adaptable with E-M or MCMC.
    • Bayesian spatial statistics offer alternative empirical modeling approaches due to computational complexity of other methods.
    • These methods have implications for modeling cryptic relatedness, haplotype blocks, and haplotype tagging SNPs.

    Conclusions:

    • The study provides a comprehensive review of statistical methods for genetic association studies.
    • It proposes novel Bayesian spatial modeling techniques for analyzing SNP data and disease risk.
    • The findings contribute to a better understanding of genetic architecture and inform future genetic research, including the HapMap project.