Advanced Analysis of Genome-wide Association Studies [ESP74]

Detailed information about this course:

Description

Faculty: Prof. Fernando Rivadeneira, MD PhD, Carolina Medina Gomez, PhD and others

Genome-wide association studies (GWAS) constitute a powerful approach to investigate the genetic basis of complex traits and disorders. The course consists of lectures providing a conceptual framework on crucial aspects of quality control, genotype imputation, methods to detect and correct for stratification, meta-analysis and genomic annotation of GWAS signals. An overview of the most frequently used statistical tools will be accompanied by instructive hands-on computer exercises on the principles of analysis of quantitative traits and disease outcomes. State of the art procedures for running GWAS will be taught, including: Quality Control / QC (PLINK2); GWAS analysis including mixed models (rvtests and SAGE); Meta-analysis QC (EasyQC); and GWAS Meta-analysis (METAL). Post-GWAS functional follow-up procedures will include downstream analysis (FINEMAP and FUMA). While theoretical background is provided on all topics, this is by definition a "hands-on" practical course, meaning you will spend most of the day performing genetic analyses. The course format will allow interactive break-out discussion sessions on theoretical and practical aspects of running GWAS, together with expert-advice procurement on diverse components of collaborative research within networks and consortia.

Participants of this course should be familiar running Linux commands and with running scripts and packages in R-programming language. These skills are taught in the NIHES courses: EL016 (GE14) "Linux for Scientists" and EL019 (GE03) "Genome-wide Association Studies"; and participants are encouraged to follow these courses in advance.

Note for participants of the 2021 NIHES GE03 edition! This edition of GE03 was an advanced version, so content overlap with this ESP74 will be present. Please inquire before registering. There is no overlap for participants of the 2022 edition of the course (now EL019).

Objectives

This course aims to train participants in the principles of GWAS, addressing aspects of study design, data analysis, extending to the interpretation and follow-up of results.

After completing the course, participants will be able to understand the principles of GWAS; perform genome-wide association analysis using state-of-the-art software tools; interpret GWAS results; and integrate them in a genomic context by means of web-based bioinformatics resources.

Participant profile

Clinical researchers, clinical epidemiologists, molecular biologists, bioinformaticians and biostatisticians aspiring to run analyses of genome-wide association studies.

Assessment

Attendance