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Quantifying the amount of missing information in genetic association studies
1Departments of Medicine and Statistics, The University of Chicago, Chicago, Illinois 60637, USA. nicolae@galton.uchicago.edu
Genetic Epidemiology
|September 21, 2006
Summary
This study introduces a new framework to measure information in genetic data, crucial for studies with missing genetic phase. This helps quantify linkage disequilibrium and optimize study design for genetic association analyses.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Genetic analyses often face incomplete information, such as unknown phase in haplotype studies.
- Linkage disequilibrium (LD) quantifies information between marker sets, essential for missing data problems.
Purpose of the Study:
- To introduce a framework for measuring association between two variable sets.
- To quantify information in one dataset for estimating parameters in another, relative to complete data.
Main Methods:
- Developed a framework to measure association between sets of variables (e.g., genotypes, haplotypes).
- Interpreted measures as asymptotic ratios of sample sizes for equivalent case-control testing power.
Main Results:
- The framework quantifies multi-locus LD for genotype data on distinct marker sets.
- The measure generalizes the classical r(2) for single markers.
Conclusions:
- The framework provides a robust method for quantifying information in genetic data.
- Applications include optimizing study design, navigating databases like HapMap, and guiding genotyping strategies.
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