Generating Samples for Association Studies Based on HapMap Data
Author Information
Author(s): Li Jing, Chen Yixuan
Primary Institution: Case Western Reserve University
Hypothesis
Can a computer program effectively generate large scale genetic and phenotypic variation data based on real data from the HapMap project?
Conclusion
The program gs can effectively generate large scale genetic and phenotypic variation data that can be used for evaluating new developed approaches.
Supporting Evidence
- The program can generate both qualitative and quantitative phenotypes based on user-defined genetic models.
- Simulated data from the program shows high concordance with local LD patterns from original data.
- The program is efficient and can generate large datasets quickly.
Takeaway
The gs program helps scientists create fake genetic data that looks like real data, which they can use to test new methods for studying diseases.
Methodology
The program uses two methods to generate haplotypes/genotypes based on real data: the extension method and the block method.
Potential Biases
Data generated based on HapMap samples cannot reveal the true distribution of rare SNPs.
Limitations
The program requires inputs to be either haplotypes or haplotype structures, which must be inferred from genotype data, and it may not represent the whole population if the inputs are biased.
Participant Demographics
The study used data from the HapMap project and a genome-wide association study of a neurological disease.
Digital Object Identifier (DOI)
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