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Related Experiment Video

Updated: Jul 5, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Handling linkage disequilibrium in linkage analysis using dense single-nucleotide polymorphisms.

Kelly Cho¹, Qiong Yang, Josée Dupuis

¹Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Talbot E 4th Floor, Boston, Massachusetts 02118, USA. kellycho@bu.edu

BMC Proceedings

|May 10, 2008

Summary

Linkage disequilibrium (LD) can bias genetic linkage analysis, especially without parental genotypes. Properly accounting for LD in linkage analysis, particularly with methods like MERLIN-LD, reduces false positives and improves accuracy.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

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Last Updated: Jul 5, 2026

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Traditional multipoint linkage analysis assumes linkage equilibrium, which is often violated by dense markers.
Violation of linkage equilibrium can introduce bias, leading to false positives in genetic linkage analysis, particularly when parental genotypes are absent.

Purpose of the Study:

To evaluate and compare three methods for handling linkage disequilibrium (LD) in both qualitative and quantitative trait linkage analyses.
To assess the impact of LD on linkage analysis using simulated rheumatoid arthritis data, focusing on scenarios with and without parental genotypes.

Main Methods:

Comparison of a simple algorithm, SNPLINK for marker selection, and MERLIN-LD for modeling LD via marker clusters.
Analysis of simulated rheumatoid arthritis data from Genetic Analysis Workshop 15, examining affected sib-pair and quantitative trait locus linkage analyses.

Main Results:

LOD score inflation was observed in affected sib-pair analysis without parental genotypes when ignoring LD, but not in quantitative trait locus analysis.
Methods accounting for LD, especially MERLIN-LD, significantly reduced LOD score inflation in affected sib-pair analysis.
SNPLINK performed better using the D' measure for marker selection compared to the r2 measure and outperformed the simple algorithm.

Conclusions:

Properly handling linkage disequilibrium is crucial for accurate genetic linkage analysis, especially with dense markers and unavailable parental genotypes.
MERLIN-LD demonstrated the most effective reduction in LOD score inflation by modeling LD through marker clusters.