SISG HEALTH & EVOLUTION MODULE HE4

This module serves as an introduction to molecular evolution, and should be considered in tandem with Statistical Genetics (Module HE2) which covers the foundations of population genetics. HE4 contains a mix of evolutionary concepts, mathematical models, application of statistical tests to real-world datasets, and exposure to advanced tools and resources that are available online. We will highlight how the field of molecular evolution has grown due to the availability new types of data and advances in computing power.

In this module we will introduce multiple methods to generate phylogenies of genes and species. We also will discuss the importance the neutral theory and explore the relevance of demographic history to the rates and patterns of molecular evolution. Additional topics include the identification and interpretation of signatures of natural selection, evolutionary conservation scores, and the concept of a molecular clock. Finally, we will discuss genome evolution and the use of ancient DNA in evolutionary inferences. One goal of this module is to help attendees avoid “rookie mistakes” when interpreting evolutionary features of genomic datasets.

Learning Objectives: After attending this module, participants will be able to: 

1. Understand and apply current methods in molecular evolution, including ancestral state reconstruction and building phylogenies of genes and species from molecular data.  

2. Describe Kimura’s neutral theory of evolution and apply tests of neutrality to real-world datasets.  

3. Determine and interpret rates of evolution of different genomic loci and genetic elements to identify functional parts of the genome and those evolving quickly due to natural selection.

4. Interpret the effects of demographic history on evolution, with a focus on how the relative importance of genetic drift and natural selection is modulated by effective population size.  

5. Locate available ancient DNA datasets, interpret studies based on ancient DNA, and identify both the benefits and potential limitations of this type of genomic data.

6. Interpret and apply multiple measures of evolutionary conservation in the context of medical genetics.

Raquel Assis is Associate Professor and Associate Dean in the College of Engineering and Computer Science at Florida Atlantic University. Her group is interested in the role of structural variation in phenotypic evolution as well as health and disease and is developing machine learning approaches to detecting novel associations. Learn more about Raquel’s work here.

Joe Lachance is an Associate Professor in the School of Biological Sciences at Georgia Tech. His research team uses population and evolutionary genetic principles to understand cancer risk in African ancestry populations, study adaptive processes associated with archaic introgression, and derive polygenic risk assessments across populations. Learn more about Joe’s work here.