Stat 367

Statistical Models in Genetics

This course will be cotaught by Ken Lange, a visiting professor from UCLA, and Chiara Sabatti. The course will feature statistical problems in genetics, with an emphasis on association and linkage analysis of qualitative and quantitative traits in human and experimental populations. The class will provide a broad outline of how statistical models help addressing the fundamental questions in genetic research. We will pay special attention to current research topics, illustrating the challenges presented by genomic data obtained via high-throughput technologies.

Students will be expected to have a good background in probability, statistics, mathematical analysis, and linear algebra. Familiarity with a programming language such as C, Fortran, Matlab, R, or Julia is required. Grades will be based on homework. Some lecture material will be drawn from the book Statistical and Mathematical Methods for Genetic Analysis by Professor Lange. Relevant chapters and other reading material will be made available by the instructors.

Depending on time and interest we will cover a selection of the following topics. As we move through the quarter entries will become more precise, reflecting the material actually presented in class, and including references.
  1. Introduction to Genetics, models and data
  2. Principles of Population Genetics.
    • Lecture notes available on Coursework.
  3. Applied population genetics and Measures of Genetic Relationship
  4. Classical Polygenic Model
  5. MM Principle in Parameter Estimation
    • Lecture notes available on Coursework.
  6. SVD and applications to Genotype Imputation
    • Lecture notes available on Coursework.
  7. Recombination models and linkage analysis
  8. Association Testing
  9. Sequence analysis: alignments and word counts.
    • Lecture notes available on Coursework.
  10. Multiple Comparisons in GWAS and genomic studies

CourseWork Web References Books Homework