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Infinium Assay for Large-scale SNP Genotyping Applications
Published on: November 19, 2013
Analysis of Case-Control Association Studies: SNPs, Imputation and Haplotypes
Nilanjan Chatterjee1, Yi-Hau Chen, Sheng Luo
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS. Rockville MD 20852, U.S.A.
Statistical Science : a Review Journal of the Institute of Mathematical Statistics
|June 15, 2010
Summary
Modern genetic epidemiology studies can enhance power using retrospective likelihoods with population genetics models. This approach improves analysis for single nucleotide polymorphisms (SNPs) and haplotype interactions compared to traditional methods.
Area of Science:
- Genetic Epidemiology
- Statistical Genetics
- Bioinformatics
Background:
- Prospective logistic regression is standard for case-control data analysis.
- Retrospective likelihood methods offer increased power in genetic studies by incorporating population genetics assumptions.
Purpose of the Study:
- To review and contrast modern retrospective likelihood methods with classical approaches in genetic epidemiology.
- To present novel methods for analyzing typed and untyped single nucleotide polymorphisms (SNPs) and haplotype effects.
Main Methods:
- Application of retrospective likelihood incorporating Hardy-Weinberg Equilibrium (HWE), gene-gene, and gene-environment independence.
- Development of score-tests and pseudo-likelihoods for novel insights.
- A two-stage method for untyped SNPs involving genotype imputation and retrospective likelihood association testing.
Main Results:
- Demonstrated enhanced power for association tests of typed and untyped SNPs.
- Enabled robust estimation of haplotype effects and gene-environment interactions, even with haplotype-phase ambiguity.
- Illustrated the practical application of these methods using simulated and real data.
Conclusions:
- Retrospective likelihood methods provide a powerful alternative to classical analyses in genetic epidemiology.
- The novel two-stage method offers a flexible and potent approach for handling untyped SNPs.
- These advanced statistical techniques improve the analysis of complex genetic data, including SNP and haplotype associations.
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