Genetic Epidemiology Foundations

University of Colorado System via Coursera

Go to Course: https://www.coursera.org/learn/genetic-epidemiology

Introduction

### Course Review and Recommendation: Genetic Epidemiology Foundations If you're interested in the intersection of genetics and epidemiology, the **Genetic Epidemiology Foundations** course offered through Coursera is a commendable option that merits your attention. Presented by the University of Colorado Denver in collaboration with the Vanderbilt Genetics Institute and the International Genetic Epidemiology Society, this course aims to arm learners with a solid foundation for conducting statistical analyses of genetic association study data. Whether you are a student, researcher, or a professional in the healthcare or biological sciences fields, this course provides critical insights that could enhance your understanding of genetic epidemiology. #### Course Overview The course begins with a comprehensive introduction to genetic epidemiology, tracing its evolution from historical perspectives to the integration of modern genomic technologies. Each module covers specific topics that build upon one another, ensuring that all levels of understanding are catered for, from foundational concepts to advanced methodologies. ##### 1. **What is Genetic Epidemiology? Historical Perspective and Introduction** Taught by Dr. Nancy Cox from Vanderbilt University Medical Center, this module sets the stage for the entire course. Dr. Cox delves deep into the origins of genetic epidemiology, discussing liability and threshold models and the genetic regulation of gene expression. Ideal for students unfamiliar with this field, it contextualizes the subject matter and paves the way for more in-depth learning. ##### 2. **Introduction to Population Genetics: Models and Assumptions** Dr. Bruce Weir of the University of Washington leads this module, introducing the foundational principles that underpin genetic data analysis. You'll explore key concepts such as the Hardy-Weinberg principle, genetic drift, population structure, inbreeding, and linkage disequilibrium. Mastery of these topics is essential for understanding later modules, making it a crucial building block of your education. ##### 3. **Population Structure and Genetic Association Studies** This module, taught by Dr. Todd Edwards, expands upon population genetics by analyzing how genetic variations arise among populations. You'll learn about association studies in structured populations and the application of admixture methods for association mapping, thus equipping you to consider the complexities when assessing genetic data across diverse groups. ##### 4. **Basic Quality Control in Genetic Data: Data Structure** Quality control is a vital yet often overlooked aspect of genetic analysis. In this module, Dr. Gonçalo Abecasis emphasizes various strategies for identifying and managing quality issues in high-throughput genotype data. You will also grasp the importance of genotype imputation, which will augment your analytical capabilities. ##### 5. **Population-Based Association Studies** Led by Dr. Celia Greenwood from McGill University, this module discusses how population-based association studies serve as crucial tools in mapping genes associated with complex traits. You will learn about measures of association, model adjustment strategies, and reproducibility via meta-analysis efforts. ##### 6. **Family-Based Designs** Finally, Dr. Joan Bailey Wilson educates you about the utilization of family-based genetic data. This segment explores methods used to establish genetic links to traits and discusses linkage and association methods to pinpoint genetic underpinnings of complex traits. #### Why You Should Take This Course 1. **Expert Instructors**: With leaders in their respective fields teaching the course, you gain insights from some of the most respected professionals in genetic epidemiology. 2. **Comprehensive Coverage**: The well-structured syllabus ensures comprehensive coverage of both theoretical and practical aspects of genetic analysis, preparing you for real-world research and applications. 3. **Flexible Learning**: Coursera offers the benefit of learning at your own pace, allowing you to manage your studies alongside other commitments seamlessly. 4. **Career Advancement**: The knowledge gained from this course can enhance your qualifications in fields like public health, genetics, and biomedical research, making you a more attractive candidate to employers. 5. **Community Impact**: Understanding genetic epidemiology not only furthers personal knowledge but also enables you to contribute meaningfully to public health and genetic research initiatives. #### Conclusion The **Genetic Epidemiology Foundations** course represents an exceptional opportunity for anyone interested in genetic research and epidemiological methods. Its thorough curriculum, taught by experts, positions learners for success in a rapidly evolving field. If you’re looking to deepen your understanding of how genetics can inform public health and disease prevention strategies, I wholeheartedly recommend enrolling in this course on Coursera. Your journey into the world of genetic epidemiology begins here – don't miss out!

Syllabus

What is Genetic Epidemiology? Historical Perspective and Introduction

Taught by Dr. Nancy Cox, Vanderbilt University Medical Center. 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

Taught by Dr. Bruce Weir, University of Washington. 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

Taught by Dr. Todd Edwards, Vanderbilt University Medical Center. 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

Taught by Dr. Gonçalo Abecasis, University of Michigan and Regeneron, Inc. 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.

Population-Based Association Studies

Taught by Dr. Celia Greenwood, McGill University. Population based association studies have played an important role in mapping genes and genomic regions for complex traits by detecting association between alleles and a trait. In this module, you will learn basic measures of association, common modeling strategies, how to adjust for multiple testing and why, how to evaluate association results, and how to increase reproducibility of study results, including the use of meta-analysis and genetic imputation.

Family-Based Designs

Taught by Dr. Joan Bailey Wilson, National Human Genome Research Institute, National Institutes of Health. In this module, you will learn about the various ways in which family-based collections of genetic data are utilized in Genetic Epidemiology. This includes methods that provide support that a genetic component to a trait exists as well as to identify modes of inheritance consistent with a set of data. In addition, linkage methods, which identify large regions of the genome, and association methods, which identify a smaller set of variants, are covered to understand genetic factors affecting a trait.