An introduction to statistics for statistical genetics: models and techniques common in statistical genetics

Published on July 31, 2017   18 min

Other Talks in the Series: Statistical Genetics

0:00
This lecture, An Introduction to Statistics for Statistical Genetics, is the first talk in the statistical genetics series. I'm Dr. Paul O'Reilly, a senior lecturer in statistical genetics, performing research at King's College London. This is Part Two: Models and Techniques Common in Statistical Genetics. In this section of the talk, I will give a basic overview of several models and techniques popular in statistical genetics, with the aim of providing an introductory level understanding of each. If you plan to use any of these approaches, then you will need to obtain further details from other lectures in this series, in statistical textbooks, or online.
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In part two of the talk, I'll introduce a number of statistical models and techniques that are often used in statistical genetics. First, I'll describe Hidden Markov models. Then I'll explain the process of statistical imputation, as well as a method of imputation especially tailored for application to genetic data. Next I'll explain principal component analysis, and then mixed models, and finally, I'll describe shrinkage and regularisation methods. For each of these I'll give the general intuition of the model or technique and explain its relevance to statistical genetics using examples from the field.
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An introduction to statistics for statistical genetics: models and techniques common in statistical genetics

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