Whole Genome Sequence Analysis Pipeline
This module will provide an introduction to analyzing genotype data generated from whole genome sequencing (WGS). It will focus on extensions of standard GWAS analyses (e.g. rare-variant association tests) and “post-GWAS” follow-up analyses (e.g. conditional analysis, fine-mapping), and how WGS may improve results or be best utilized for these analyses; methods that incorporate variant annotation information will be highlighted.
Methods and examples will be informed by the instructors’ experience in large human genetics consortia (e.g. TOPMed), and, therefore, will focus on analyzing human data, but may be applicable/extendable to other organisms. A basic introduction to cloud computing will be provided, and students will perform hands-on exercises on a genomic analysis cloud platform.
Learning Objectives: After attending this module, participants will be able to:
- Understand how to perform association analyses for rare variants measured in WGS data using aggregate tests
- Access variant annotation resources and understand how to incorporate annotation information into analyses to improve power and inform results
- Understand the theory of, and how and when to perform, various “post-GWAS” follow-up analyses
- Leverage multi-ancestry WGS data.
- Appreciate the utility of existing genomic analysis cloud platforms and get hands-on experience with cloud computing on one of these platforms.
Course Dates
- Wed June 12, 1:30 p.m. – 5:00 p.m. EST
- Thu June 13, 8:30 a.m. – 5:00 p.m. EST
- Fri June 14, 8:30 a.m. – 5:00 p.m. EST
Instructors
- Laura Raffield
- Matt Conomos
Suggested Course Pairings
Statistical Analysis and Quantitative Genetics Streams
- Module 7: Quantitative Genetics
- Module 11: Mixed Models in Quantitative Genetics
- Module 13: Multivariate Analysis
- Module 15: Advanced Quantitative Genetics
Course Materials
Visit the Box here
About the Instructors
Laura Raffield is an Assistant Professor in the Department of Genetics at the University of North Carolina at Chapel Hill. Her group uses multi-omics approaches to understand inherited and environmental risk factors for human cardiometabolic diseases, Alzheimer’s disease and related dementias, as well as the architecture of related quantitative traits. Learn more about Laura’s work here.
Matt Conomos is a Senior Research Scientist at the University of Washington Genetic Analysis Center in Seattle. He has been involved in developing methods for GWAS in admixed populations and developed GENESIS software for mixed model analysis of diverse large cohort data. Access Matt’s publications here.