Modeling Linkage Disequilibrium in Genetic Studies
Author Information
Author(s): Albers Cornelis A, Kappen Hilbert J
Primary Institution: Department of Biophysics, Radboud University
Hypothesis
Can different approaches effectively model linkage disequilibrium (LD) between SNP markers in genetic studies?
Conclusion
The first-order Markov model is generally valid for accounting for strong levels of linkage disequilibrium in SNP data.
Supporting Evidence
- The first-order Markov model accounted for most of the strong linkage disequilibrium in the data set.
- The Bayesian and maximum-likelihood implementations of the first-order Markov model showed only small differences.
- The study found that ignoring linkage disequilibrium led to higher scores in linkage analysis.
Takeaway
This study looks at different ways to understand how genetic markers are related to each other, and found that one method works really well for strong relationships.
Methodology
The study compared three approaches: a clustered-markers approach, a first-order Markov model, and an independent SNPs approach using SNP data from affected sib-pairs.
Potential Biases
The assumptions of the first-order Markov model may not hold in all scenarios, particularly with different marker types and densities.
Limitations
The study's findings may not generalize to all populations due to potential population stratification.
Participant Demographics
Affected sib-pair families from the Canadian population.
Want to read the original?
Access the complete publication on the publisher's website