Mapping Genetic Variants for Complex Diseases Using HapMap
Author Information
Author(s): Cui Yuehua, Fu Wenjiang, Sun Kelian, Romero Roberto, Wu Rongling
Primary Institution: Michigan State University
Hypothesis
Can a generalized linear model effectively identify nucleotide variants associated with complex human diseases?
Conclusion
The study presents a novel model that successfully identifies genetic variants associated with complex binary diseases, demonstrating its utility in a case study of large for gestational age neonates.
Supporting Evidence
- The model provides a powerful tool for elucidating the genetic basis of complex binary diseases.
- Simulation studies indicate that the model has reasonable power and type I error rates.
- Significant binary trait nucleotides were detected in association with large for gestational age neonates.
Takeaway
This study helps scientists find specific DNA changes that can cause complex diseases by looking at genetic data from many people.
Methodology
The study uses a generalized linear model and a two-stage estimation procedure based on the expectation-maximization algorithm to analyze genetic data.
Potential Biases
Potential bias may arise from the assumption of known haplotypes and the use of tag SNPs.
Limitations
The model's effectiveness is limited by the availability of complete functional sequence variant information in candidate regions.
Participant Demographics
The study involved 552 unrelated maternal individuals aged 13 to 45 years, with 117 cases and 435 controls.
Statistical Information
P-Value
0.024
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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