Giter Site home page Giter Site logo

gda's Introduction

Genetic Data Analysis

NSF-2132247 CC BY-NC 4.0

Website URL: https://dcgerard.github.io/gda/

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.

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

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.

gda's People

Contributors

dcgerard avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.