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Can long-range microsatellite data be used to predict short-range linkage disequilibrium?
Thomas G Schulze1, Yu-Sheng Chen, Nirmala Akula
1Department of Psychiatry, The University of Chicago, Chicago, IL 60637, USA. schulze@uchicago.edu
Human Molecular Genetics
|May 25, 2002
Summary
Dense microsatellite data can predict short-range linkage disequilibrium (LD). This helps estimate marker density for disease gene discovery using association studies.
Area of Science:
- Genetics
- Genomics
- Population Genetics
Background:
- Linkage disequilibrium (LD) distribution across the genome is complex.
- The relationship between long-range and short-range LD is not well understood.
- Predicting short-range LD is crucial for efficient genetic association studies.
Purpose of the Study:
- To assess if dense microsatellite data can predict short-range LD.
- To model LD decay over distance in specific genomic regions.
- To estimate the density of markers needed for association analyses.
Main Methods:
- Analysis of intermarker LD using dense microsatellite genotyping data from chromosomal regions 18q22 and 10q25-26.
- Modeling the decay of LD over distance on region 18q22.
- Comparison of predictions with single-nucleotide polymorphism (SNP) data.
Main Results:
- LD patterns were highly heterogeneous within and between chromosomal regions.
- A classical LD decay model explained 63% of the LD-distance relationship on 18q22.
- Predictions based on microsatellite data aligned with SNP data, suggesting ~80% of marker pairs show useful LD up to 15 kb.
Conclusions:
- Dense microsatellite data can effectively predict short-range LD.
- Existing dense microsatellite datasets can inform marker density requirements for association studies.
- This approach aids in optimizing the screening of genomic regions for disease alleles.