Haplotype-sharing analysis using Mantel statistics for combined genetic effects
Author Information
Author(s): Beckmann Lars, Fischer Christine, Obreiter Markus, Rabes Michael, Chang-Claude Jenny
Primary Institution: German Cancer Research Center DKFZ, Heidelberg, Germany
Hypothesis
Can Mantel statistics improve the power of haplotype sharing analysis for gene mapping in complex diseases?
Conclusion
The study found that Mantel statistics might be more powerful than alternative tests for gene mapping.
Supporting Evidence
- The Mantel statistic M0(x) identified major gene D2 on chromosome 3 with significant results.
- The permutation test yielded a globally significant p-value of 0.03 in the large sample D.
- The Mantel statistic M1(x) revealed significant statistical interaction between the genes analyzed.
Takeaway
The researchers used a new method to find genes that might cause diseases by looking at how similar people's genes are to their traits.
Methodology
The study applied Mantel statistics to analyze genetic and phenotypic similarity in case-control studies.
Potential Biases
The analysis did not adjust p-values for multiple comparisons.
Limitations
The method may not be robust to population stratification.
Participant Demographics
Participants were drawn from the Danacaa population, focusing on individuals with phenotype P1.
Statistical Information
P-Value
0.014
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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