Linkage Disequilibrium Mapping Using Cladistic Analysis
Author Information
Author(s): Caroline Durrant, Andrew P. Morris
Primary Institution: Wellcome Trust Centre for Human Genetics, University of Oxford
Hypothesis
Can inferred haplotypes from phase-unknown genotypes improve the power of disease locus detection compared to direct analysis of unphased genotypes?
Conclusion
Inferred haplotypes provide more power for detecting disease loci than unphased multilocus genotypes.
Supporting Evidence
- The power of haplotype analysis is generally greater than that of genotype analysis.
- False-positive error rates were consistent with a 5% significance threshold.
- The study demonstrated that analyzing inferred haplotypes outperforms direct analysis of unphased genotypes.
Takeaway
This study shows that guessing the order of genes can help find disease markers better than just looking at the genes themselves.
Methodology
The study used cladistic analysis and the expectation-maximization algorithm to infer haplotypes from unphased genotypes and compared the power of both methods using a simulated dataset.
Potential Biases
The assumption that inferred haplotypes are correct can introduce bias in the analysis.
Limitations
The inferred phase assignment may be incorrect, leading to biased estimates of linkage disequilibrium and inflated false-positive rates.
Statistical Information
P-Value
p<0.05
Confidence Interval
95% CI
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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