Human Genomic Epidemiology in African Populations (Virtual)

Due to the ongoing situation with Covid-19, this course will be delivered in a virtual format.

In collaboration with the H3Africa Bioinformatics Network (H3Abionet) and the Sydney Brenner Institute for Molecular Bioscience (SBIMB), we are pleased to announce the Human Genomic Epidemiology in African Populations course.

The fast moving field of genomics, and the increasing demand for improving capability and capacity for data analysis and interpretation from African genomic scientists, creates an urgent need for training and continuous skills development. Through continent-wide collaborative research projects and consortia such as the Human Heredity and Health in Africa (H3Africa) initiative, there is a significant increase in African genomics data. However, there is limited analytical capability among African scientists, thereby undermining the potential application of genomic information. African GWAS has both unique opportunities (for fine mapping and assessing transferability) and limitations (population structure and relatively small datasets), requiring nuanced consideration in GWAS analysis. Providing genomic researchers on the African continent with resources and tools to expand capacity for analysing big genome and phenotype data will bridge the skills gap and enable locally tailored opportunities for genomics in health research.

The course will provide participants with ‘hands-on’ practical exercises on the design, data processing, analysis and interpretation of results aimed at understanding the genetic architecture of complex human traits/diseases. Using African datasets, participants will learn how to apply statistical and computational approaches for candidate gene and genome-wide association studies and their meta-analyses. Ethical and legal implications in human genomics data sharing will also be discussed. Participants will have an opportunity to establish links and networks and develop future collaborative projects.

Target audience: The course is open to applicants based in Africa, and  aimed at postgraduate (PhD) students, research assistants, postdocs and researchers from biological, clinical, bioinformatics, statistics, mathematics and computer science backgrounds engaged in, or planning to conduct, genetic and genome-wide association studies in their work.

For this virtual course, we plan to use a combination of video conferencing, streaming, and online and virtual machine (VM) teaching resources to deliver the different elements of the course as interactively as possible.

The hands-on programme will equip participants with skills to analyse and interpret GWAS data with an emphasis on African population studies. The course aims to:

• Provide training on theoretical and practical aspects of genomic epidemiology, candidate gene and genome-wide association studies and their meta-analyses.
• Demonstrate how basic and advanced statistical and computational approaches are applied for analysis of human genotype data in relation to their effects on human traits and diseases.
• Emphasise the genetics of complex traits, based on African datasets (using case studies).
• Provide ‘hands-on’ practical exercises on the design, data processing, analysis and interpretation of results aimed at understanding the genetic architecture of complex human traits/diseases.
• Discuss, and bring awareness to, ethical and legal implications and issues concerning informed consent and data sharing in human genomics and research or health applications.

Learning Outcomes

The course will cover the analysis of genomic datasets in relation to human traits and disease phenotypes. At the end of the course, participants will be able to:

• Describe the general principles, assumptions, ethical considerations and basic techniques used in genetic epidemiology studies.
• Outline the different study designs and appropriate computational approaches.
• Perform the essential steps in the analysis of GWAS data, such as quality control, assessment of population structure and relatedness, genetic association testing using standard approaches and software tools.
• Evaluate and interpret the results of association analysis and visualise them.
• Perform imputation of variants that have not been directly genotyped.
• Perform post-association GWAS analyses including fine-mapping of implicated loci to point to likely causal variants, genes and pathways.
• Conduct meta-analysis of genome-wide association studies.
• Critically evaluate research articles that present results from candidate-gene and genome-wide association studies.

Topics

• Introduction to genetic epidemiology
• Nuances of GWAS : power, population structure, confounders
• Introduction to computational resources and tools
• Introduction to datasets – formats, data manipulation; PLINK
• Genotype QC – phenotype QC and transformation
• Imputation -tools panels, online resources
• Statistical models for genetic association analysis
• Interpretation of results -running iterations
• Meta-analysis and replication
• Assessing- Independence of signals – Conditional analysis
• Narrowing down causal variants- Fine mapping
• Post GWAS analysis (from in silico functional assessment to animal models)
• Advanced methodologies

Seminars

• Opportunities and challenges in African GWAS.
• Practical aspects in sample collection, tracking storage and QC
• Data sharing and Ethical Legal and Social Issues (ELSI)

Course instructors

Ananyo Choudhury
Sydney Brenner Institutute for Molecular Bioscience, South Africa

Segun Fatumo
MRC/UVRI & LSHTM Uganda Research Unit

Arthur Gilly
Helmholtz-Centre Munich (HMGU)

Cheikh Loucoubar
Institut Pasteur de Dakar, Senegal

Nicola Mulder
University of Cape Town, South Africa

Michèle Ramsay
Sydney Brenner Institutute for Molecular Bioscience, South Africa

Nicki Tiffin
University of Cape Town, South Africa

Prerequisites
Target audience: The course is open to applicants based in Africa, and  aimed at postgraduate (PhD) students, research assistants, postdocs and researchers from biological, clinical, bioinformatics, statistics, mathematics and computer science backgrounds engaged in, or planning to conduct, genetic and genome-wide association studies in their work. The course will be taught in English.

Participants should have

• Some background in human genetics and genomics
• A high level of computer competency
• Basic command line skills
• Demonstrated involvement in human genetic/genomic studies, or plan to do so in the near future

Please note that due to the virtual format for this course, participants will require minimum computer specifications and internet access to fully benefit. A guide to these requirements can be found here (PDF).

How to Apply
Please click the Apply button above to begin the online application process. Places are limited and will be awarded on merit. If you have any problems with the online application process, please contact us.

Please note: Applications must be supported by a recommendation from a scientific or clinical sponsor (e.g. supervisor, line manager or head of department). A request for a supporting statement will be sent to your nominated sponsor automatically during the application process. Applicants must ensure that their sponsor provides this supporting statement by the application deadline. Applications without a supporting statement cannot be considered.

Cost
The course is subsidised by Wellcome Genome Campus Advanced Courses and Scientific Conferences and is free to attend for non-commercial applicants. Please contact us for the commercial fee.

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