This course is designed to provide students with the foundation necessary to conduct statistical analysis of genetic association study data. This course includes topics such as quality control in genetic studies, population-based case-control association studies, genome-wide association studies, and foundational concepts in population genetics and the history of genetics research. Examples of concepts and reference literature are provided in this 6-module course.
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Genetic Epidemiology Foundations
Система университетов штата КолорадоОб этом курсе
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Система университетов штата Колорадо
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Программа курса: что вы изучите
What is Genetic Epidemiology? Historical Perspective and Introduction
In this module you will better understand genetic epidemiology from its origins to how modern ‘omics is integrated into genetic epidemiology of complex traits. Coverage includes introduction of liability and threshold models, genetic regulation of gene expression, and transcriptome imputation.
Introduction to Population Genetics: Models and Assumptions
Methods and designs using genetic data are built upon the foundation of population genetics. In this module, you will learn these foundations, including the Hardy Weinberg principle, genetic drift, population structure, inbreeding, and linkage disequilibrium. These principles will be essential to subsequent modules in this course.
Population Structure and Genetic Association Studies
Building from the introduction to population genetics, in this module you will learn processes that lead to genetic differences between populations, methods to characterize these differences, and how to conduct association studies in structured populations. In addition, you will be able to describe how admixture methods can be applied for association mapping.
Basic Quality Control in Genetic Data: Data Structure
Quality control is an important step for high throughput genotype data. In this module, you will learn a range of different approaches to identify and to deal with quality problems at different stages of the analysis. In addition, genotype imputation is introduced to infer genotypes at markers that were not typed in the study samples.
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