• Special Designs
• 46 min
  1. Family-based association tests: introduction

  • Dr. Christoph Lange
• 29 min
  2. Family-based association tests: FBAT testing strategies for large-scale association studies

  • Dr. Christoph Lange
• 37 min
  3. Two-stage genome-wide association designs

  • Dr. Andrew Skol
• Special Populations
• 40 min
  4. Admixture mapping

  • Dr. Paul McKeigue
• Whole-Genome Studies: Types of Data
• 34 min
  5. QTL association mapping

  • Prof. Bruce Weir
• 50 min
  6. Adjusting for population structure in genetic association studies

  • Prof. David Balding
• Whole-Genome Studies 2: Methods
• 47 min
  7. Whole-genome association studies: practical advice and considerations

  • Dr. Mike Weale
• Archived Lectures *These may not cover the latest advances in the field
• 49 min
  8. Family-based association tests: FBATs for various data types

  • Dr. Christoph Lange
• 44 min
  9. Choosing between family-based and population-based association designs

  • Dr. Ren-Hua Chung
• 54 min
  10. Use of isolated populations: pitfalls and potential

  • Dr. Leena Peltonen
• 40 min
  11. Information on untyped variation in genome-wide association studies

  • Dr. Dan Nicolae
• 37 min
  12. Detecting multiple associations in genome-wide studies

  • Prof. Frank Dudbridge
• 30 min
  13. Genome scanning by composite likelihood

  • Dr. Andrew Collins
• 41 min
  14. Multistage sampling for genetic studies

  • Prof. Robert Elston
• 39 min
  15. Screening large-scale association study data: exploiting multi-gene interactions with random forests

  • Dr. Kathryn Lunetta
• 43 min
  16. A survey of multi-locus methods for analysis of large SNP datasets

  • Mr. A. Geert Heidema
• 34 min
  17. Sequential analysis methods in genomic scans

  • Prof. Michael Province

Printable Handouts

PDF

Navigable Slide Index

1. Introduction
2. Genetic epidemiology
3. Single-locus methods
4. Multi-locus methods
5. Statistical challenges
6. Outline of presentation
7. Survey of multi-locus methods
8. Logistic regression
9. Strength and weakness of logistic regression
10. Neural networks
11. Methods for using neural networks
12. Neural networks: PDM
13. Strengths and weaknesses of PDM
14. Neural networks: PDM software
15. Neural networks: GPNN
16. Strengths and weaknesses of GPNN
17. Set association approach
18. Strength and weakness of set association
19. Set association approach software
20. Combinatorial methods
21. CPM
22. RPM
23. MDR
24. Steps of MDR method
25. Combinatorial methods' strength and weakness
26. MDR software
27. Random forests
28. Strengths and weaknesses of random forests
29. Random forests software
30. Overview of strengths and weaknesses
31. Multi-locus methods: conclusions
32. Multi-step approach
33. Steps of multi-step approach
34. Prioritization and selection: multi-locus methods
35. Statistical interpretation
36. Dataset of a case-control study
37. Results: set association approach
38. Results: genotype by HDL level
39. Results: allele by HDL level
40. Set association approach: conclusions
41. Results: random forests
42. Graphic view of random forests results
43. Random forests: conclusions
44. Results: MDR
45. Prioritization results using MDR
46. Best 1, 2 and 3-SNP model
47. Validation results using MDR
48. Comparison of methods
49. Conclusions of the example
50. Methods for statistical interpretation
51. Interaction entropy graph
52. General conclusions
53. Acknowledgements

Topics Covered

• Statistical challenges in genetic association studies
• Selection features of multi-locus methods and their strengths and weaknesses
• A multi-step approach to analyze large numbers of SNPs
• Application to a real case-control dataset

Links

Series:

• Genetic Epidemiology II

Categories:

• Genetics & Epigenetics
• Methods

Talk Citation

Publication History

• Published on October 1, 2007
• Archived on February 25, 2021

Financial Disclosures

• Mr. A. Geert Heidema has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.