Genetic Data Analysis

Synopsis

This website contains a series of lectures on genetic data analysis, taught by David Gerard, at his research group meetings during the 2021–2022 academic year.

Topics include

• The first three chapters from Weir (1996), covering frequency estimation, Hardy-Weinberg testing, and LD estimation and testing from a statistical point of view.

• Chapters 1, 2, and 5 from Gillespie (2004), covering Hardy-Weinberg, genetic drift, mutation, and non-random mating.

• An introduction to the EM algorithm, with an application from H. Li (2011).

• An introduction to Bayesian inference.

• A discussion of N. Li and Stephens (2003).

I am placing these lecture notes under a CC BY-NC 4.0 licence, so you can use them for non-commercial purposes as long as you provide attribution.

Lectures

• Genetic Data Analysis II Lectures:
  • Chapter 1
    • Genetic data.
    • Statistical/genetic sampling.
    • Chi-squared test.
  • Chapter 2
    • Multinomial distribution.
    • Delta method.
    • Maximum likelihood estimation.
  • Chapter 3, Part 1
    • Hardy-Weinberg equilibrium.
  • Chapter 3, Part 1
    • Linkage disequilibrium.
• The EM algorithm
  • General EM Algorithm
    • Required Reading: Chapter 9 from Bishop (2006).
  • EM of H. Li (2011)
    • Required Reading: H. Li (2011).
  • Example implementation of EM algorithm
• Basic Bayes
• Population Genetics: A Concise Guide Lectures:
  • Chapter 1
    • Alleles.
    • Hardy-Weinberg.
  • Chapter 2, Part 1
    • Sections 2.1 and 2.2.
    • Genetic drift.
    • Decay of heterozygosity.
  • Chapter 2, Part 2
    • Section 2.3.
    • Mutation and drift.
  • Chapter 2, Part 3
    • Section 2.4.
    • Molecular evolution.
  • Chapter 2, Part 4
    • Section 2.5.
    • The neutral theory.
  • Chapter 2, Part 5
    • Section 2.6.
    • The coalescent.
  • Chapter 2, Part 6
    • Sections 2.7 and 2.8.
    • Effective population size and generalizability of Wright-Fisher.
  • Chapter 5
    • Sections 5.1, 5.2, and 5.5.
    • Inbreeding and subdivision.
• N. Li and Stephens (2003)

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation.

References

Bishop, Christopher. 2006. Pattern Recognition and Machine Learning. Springer. https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/.

Gillespie, J. H. 2004. Population Genetics: A Concise Guide. Population Genetics. Johns Hopkins University Press. https://books.google.com/books?id=KAcAfiyHpcoC.

Li, Heng. 2011. “A Statistical Framework for SNP Calling, Mutation Discovery, Association Mapping and Population Genetical Parameter Estimation from Sequencing Data.” Bioinformatics 27 (21): 2987. https://doi.org/10.1093/bioinformatics/btr509.

Li, Na, and Matthew Stephens. 2003. “Modeling Linkage Disequilibrium and Identifying Recombination Hotspots Using Single-Nucleotide Polymorphism Data.” Genetics 165 (4): 2213–33. https://doi.org/10.1093/genetics/165.4.2213.

Weir, B. S. 1996. Genetic Data Analysis II: Methods for Discrete Population Genetic Data. Sinauer Series. Sinauer. https://books.google.com/books?id=e9QPAQAAMAAJ.

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.