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Princples of Genetic Epidemiology 3

East Baltimore
3rd term
3 credits
Academic Year:
2013 - 2014
Instruction Method:
Class Times:
  • Tu Th,  9:00 - 10:20am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructors:
Alison Klein

140.621-622 or 140.651-652; (2 courses in biostatistics and the first 2 courses in Genetic Epidemiology 340.721 & 340.722)


Brings together the principles of linkage, association and sequence analysis introduced in the first two terms and builds up the students’ skills in applying and interpreting methods for such studies. Introduces advanced analytical methods in genetic epidemiology and illustrates their application using current software tools. Adds depth to the students’ understanding by critiquing example papers from the recent literature, and students develop and design a research project incorporating these methods. Some material is offered as recorded lectures to free up class time for interactive discussion of analytical issues and examples from the literature.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Explain how family data can be used to test for genetic control of a disease or phenotype
  2. Estimate familial correlations for quantitative phenotypes from family data
  3. Use currently available software to check for structural errors in family data, estimate allele frequencies, check for Mendelian inconsistencies and describe familial aggregation of both qualitative and quantitative phenotypes
  4. Explain how models of inheritance are fit to family data and interpret published articles on segregation analysis of complex phenotypes
  5. Explain what linkage analysis means, and the relationship between meiotic recombination, crossing over, genetic distance and mapping functions
  6. Interpret published articles on parametric or model based linkage analysis for both qualitative and quantitative phenotypes
  7. Use currently available software to estimate recombination fraction from informative multiplex families and test for linkage between a single marker and a disease phenotype using maximum likelihood methods
  8. Use currently available software to estimate the map position of an unobserved trait locus and a fixed framework map of multiple markers to map genes
  9. Explain how variance components models can be used to identify quantitative trait loci (QTL) that are used to map genes for quantitative phenotypes
  10. Interpret and critically evaluate non-parametric or model free methods for linkage analysis of complex phenotypes
Methods of Assessment:

Computer-based homework assignments and written critiques of relevant articles from the scientific literature.

Instructor Consent:

Consent required for all students

Consent Note:

Advanced level genetics knowledge required

For consent, contact: