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 multiple testing correction method for genetic association studies using correlated single nucleotide

Xiaoyi Gao1, Joshua Starmer, Eden R Martin

  • 1Center for Genetic Epidemiology and Statistical Genetics, Miami Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida 33136, USA. xgao@med.miami.edu

Genetic Epidemiology
|February 14, 2008
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

Longitudinal Blood DNA Methylation Changes During Weight-Loss Intervention and Dementia Progression Risk.

Research square·2026
Same author

Genome wide association study meta-analysis of neuropathologic lesions of Alzheimer's disease and related dementias in a multi-site autopsy cohort.

PLoS genetics·2026
Same author

From aging to Alzheimer's disease: concordant brain DNA methylation changes in late life.

Genome medicine·2026
Same author

Unraveling prothionamide solubility enhancement by ionic liquids: from molecular screening to microscopic insights.

Physical chemistry chemical physics : PCCP·2026
Same author

Blood DNA methylation signature of cognitive reserve moderates the association between CSF tau pathology and memory in prodromal Alzheimer's disease.

Alzheimer's & dementia (New York, N. Y.)·2026
Same author

Sex-specific genetic drivers of memory, executive functioning and language in older adults.

Brain : a journal of neurology·2026
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

This study introduces a new, fast, and accurate method for multiple testing correction in genetic association studies. It effectively addresses linkage disequilibrium (LD) in single nucleotide polymorphism (SNP) data, improving upon existing approaches.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Multiple testing correction is critical in genetic association studies with numerous single nucleotide polymorphism (SNP) markers.
  • Linkage disequilibrium (LD) among SNPs complicates traditional correction methods like Bonferroni, often leading to conservative results or missed signals.
  • Existing permutation-based corrections account for LD but are computationally demanding.

Purpose of the Study:

  • To develop a novel, efficient, and accurate multiple testing correction method for SNP association studies.
  • To provide a solution that accounts for LD without excessive computational cost.
  • To improve the control of Type I errors in genetic association analyses.

Main Methods:

  • A new multiple testing correction method was developed for SNP data.

Related Experiment Videos

  • The method's performance was evaluated using simulated and real genetic datasets.
  • Comparisons were made against existing correction methods, including Bonferroni and permutation-based approaches.

    • Main Results:

    • The proposed method demonstrated simplicity, speed, and enhanced accuracy compared to recent methods.
    • Its performance was found to be comparable to computationally intensive permutation-based corrections.
    • The method effectively controls Type I error in the presence of high intermarker LD.

    Conclusions:

    • The novel method offers a computationally efficient and accurate approach for multiple testing adjustment in genetic association studies.
    • It is particularly suitable for datasets with significant linkage disequilibrium among single nucleotide polymorphism markers.
    • This method presents an attractive alternative for whole-genome association studies aiming to control false positives.