Testing Association in Binary Traits with Linkage
Author Information
Author(s): Gudrun Jonasdottir, Juni Palmgren, Keith Humphreys
Primary Institution: Department of Mathematical Statistics, Stockholm University; Department of Medical Epidemiology and Biostatistics, Karolinska Institute
Hypothesis
Can a new gamma random effects model improve the detection of associations in binary traits compared to existing methods?
Conclusion
The gamma random effects model shows promise in detecting associations in binary traits, particularly in regions with known haplotype associations.
Supporting Evidence
- The GRE model was able to detect associations in region D3 with a p-value of around 0.0001.
- In region D2, all methods indicated a marker with borderline significance but did not reach statistical significance.
- The study used simulated data from the Genetic Analysis Workshop 14 to compare methods.
Takeaway
This study looks at how to find links between genes and traits in families, using a new method that works better for certain types of traits.
Methodology
The study compares a new gamma random effects model with FBAT and GEE methods using simulated genetic data.
Limitations
The GRE model requires more computational time and has challenges in handling missing information on transmission.
Participant Demographics
The study focused on the Aipotu population, which consists of nuclear families of varying sizes.
Statistical Information
P-Value
p~0.0001 for region D3, p~0.01 for region D2
Digital Object Identifier (DOI)
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