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Printable Handouts
Navigable Slide Index
- Introduction
- Talk outline
- Assaying genetic variants for association
- tagSNPs reduce redundancy
- Tagging is a simple form of imputation
- Generalized imputation: MMPs
- The IMPUTE method
- Methodology for GW MM association testing
- Data collected in a genome-wide association study
- HapMap and missing genotype data
- Simulated data - before imputation
- Simulated data - after Imputation
- Approach details (1)
- Approach details (2)
- Model illustration
- Methodological development
- Extension to hidden/untyped SNPs
- External validation of imputed genotypes
- Our method is accurate and well-calibrated
- IMPUTE vs. MACH
- IMPUTE performance
- Testing with genotype uncertainty
- SNPTEST
- Imputation in real dataset association testing (1)
- Imputation in real dataset association testing (2)
- Imputation in real dataset association testing (3)
- Imputation in real dataset association testing (4)
- Imputation for meta-analysis
- The "strand issue"
- Software
- Summary
- References
Topics Covered
- Tag SNPs and multi-marker predictors
- The general problem of imputing genotypes for association studies
- The IMPUTE method
- Measuring accuracy of imputation
- Results of using imputation in a real study
- Using imputation for meta-analysis
- The importance of strand alignment when using imputation
Links
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Talk Citation
Marchini, J. (2008, June 4). Imputing genotypes in genome-wide association studies [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 6, 2024, from https://doi.org/10.69645/APNS4067.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Jonathan Marchini has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.