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Navigable Slide Index

1. Introduction
2. Talk outline
3. Assaying genetic variants for association
4. tagSNPs reduce redundancy
5. Tagging is a simple form of imputation
6. Generalized imputation: MMPs
7. The IMPUTE method
8. Methodology for GW MM association testing
9. Data collected in a genome-wide association study
10. HapMap and missing genotype data
11. Simulated data - before imputation
12. Simulated data - after Imputation
13. Approach details (1)
14. Approach details (2)
15. Model illustration
16. Methodological development
17. Extension to hidden/untyped SNPs
18. External validation of imputed genotypes
19. Our method is accurate and well-calibrated
20. IMPUTE vs. MACH
21. IMPUTE performance
22. Testing with genotype uncertainty
23. SNPTEST
24. Imputation in real dataset association testing (1)
25. Imputation in real dataset association testing (2)
26. Imputation in real dataset association testing (3)
27. Imputation in real dataset association testing (4)
28. Imputation for meta-analysis
29. The "strand issue"
30. Software
31. Summary
32. 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

Series:

• Statistical Methods for the Analysis of Genome-Wide Association Studies

Categories:

• Genetics & Epigenetics
• Methods

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 April 15, 2025, from https://doi.org/10.69645/APNS4067.
Export Citation (RIS)

Publication History

• Published on June 4, 2008
• Archived on July 13, 2017

Financial Disclosures

• Prof. Jonathan Marchini has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.