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- Statistical Genetics of Complex Diseases
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1. Population genetics considerations
- Prof. Nan Laird
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2. Gene-by-environment interaction: study designs and analytical methods
- Prof. L. Adrienne Cupples
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3. Analysis of SNP haplotypes
- Prof. Lyle Palmer
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4. Statistical issues in epidemiological studies of gene-environment interaction
- Dr. Peter Kraft
- Prof. Donna Spiegelman
- Molecular Epidemiology of Pulmonary Diseases
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5. Molecular epidemiology of lung cancer
- Prof. David Christiani
- Autoimmune Diffuse Connective Tissue Diseases
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7. Environmental scleroderma: the evidence for putative triggers
- Prof. Maureen Mayes
- Drug-Induced Disease
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8. Drug-induced lupus
- Prof. Evelyn Hess
- Archived Lectures *These may not cover the latest advances in the field
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9. Biomarkers of exposure
- Prof. Thomas Smith
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10. GIS and spatial statistics
- Prof. Louise Ryan
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11. Gene-by-environment interactions in COPD
- Prof. Edwin Silverman
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14. Indians, twins and scleroderma: genetic and genomic studies
- Prof. Frank Arnett
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15. Gene by environment interaction in asthma
- Prof. Scott Weiss
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16. Molecular epidemiology of lung cancer
- Prof. David Christiani
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18. Infectious environmental factors and systemic lupus erythematosus
- Prof. Judith James
Printable Handouts
Navigable Slide Index
- Introduction
- Sir Archibald Garrod
- JBS Haldane
- Examples of diseases
- Metabolism of Dietary Folate
- Conceptual and analytic challenges
- Outline - Designs
- Study designs
- Outline - simple pairwise analyses
- Definition - NOTA BENE
- Example: risk of disease and lof odds of disease
- Different scale gives different in interaction (1)
- Different scale gives different in interaction (2)
- The standard "test for interaction"
- Example: all relationships should be known
- Screening for stratum-specific effects
- Gene associated with risk in any subgroup
- Exposure associated with risk in any genotype
- Example: DNA repair gene XRCC1
- Example: test for genetic effect
- Beyond binary G and E
- Outline - power considerations
- Power
- Programs for power calculations
- Parameters to specify
- Ge_trend example
- Ge_trend example: results
- Ge_trend example: results under minimum power
- Ge_trend example: results under maximum power
- Sample size is necessary to detect genetic effect
- Test for "interaction"
- Screening for genetic effect
- Outline - more sophisticated analyses
- Beyond one gene-one environmental factor
- Ordinal coding
- Ordinal coding - multiple loci
- Ordinal coding - three parameter model
- Hierarchical Models
- Example [Aragaki et al. 1997]
- Machine learning methods
- Relation between training- and test-set error
- Caveat Emptor
- Final Thoughts
- Software
- References (1)
- References (2)
- Acknowledgements
Topics Covered
- Gene-environment interaction in the development of human traits, including disease
- Conceptual and analytical challenges
- Joint analysis of data on both genetic and environmental factors from epidemiologic studies as a way to increase power to detect a polymorphism (or an exposure) that is associated with disease risk
- Study designs
- Statistical modeling issues in the analysis of data
- Sample size and power calculations for a range of designs
- Specification of parameters
- Ordinal coding
- Hierarchical models
- The analysis of highdimensional "pathway" data (multiple related genes and environmental exposures)
Talk Citation
Kraft, P. and Spiegelman, D. (2007, October 1). Statistical issues in epidemiological studies of gene-environment interaction [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 3, 2024, from https://doi.org/10.69645/RUNA1690.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Peter Kraft has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
- Prof. Donna Spiegelman has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
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