Advanced Analysis of Genome-wide Association Studies [ESP74]

Detailed information about this course:

Description

Faculty: Prof. Fernando Rivadeneira, MD PhD, Carolina Medina Gomez, PhD, Katerina Trajanoska, 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 correcting for structured data (SAIGE and REGENIE); GWAS Meta-analysis and QC (EasyQC; METAL). Post-GWAS functional follow-up procedures will include downstream analysis (Conditional analyses, FINEMAP and PRS). 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.

Objectives

• This course aims to train participants in the (advance) 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