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Human Genomic Epidemiology in African Populations (Johannesburg, South Africa)

16 - 21 August 2020
Deadlines (at 23:59 UTC):
  • Application and bursary deadline 14 May 2020
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Learn how to apply statistical and computational approaches for candidate gene and genome-wide association studies

  • Summary

    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.

    Venue: This week-long course will be held at the University of the Witwatersrand, Johannesburg, South Africa.

    Target audience: The course is 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.

  • Programme

    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)
  • Instructors and speakers

    Course instructors

    Ananyo Choudhury

    Ananyo Choudhury
    Sydney Brenner Institutute for Molecular Bioscience, South Africa

    Segun Fatumo

    Segun Fatumo
    MRC/UVRI & LSHTM Uganda Research Unit

    Arthur Gilly

    Arthur Gilly
    Helmholtz-Centre Munich (HMGU)

    Cheikh Loucoubar

    Cheikh Loucoubar
    Institut Pasteur de Dakar, Senegal

    Nicola Mulder

    Nicola Mulder
    University of Cape Town, South Africa

    Michèle Ramsay

    Michèle Ramsay
    Sydney Brenner Institutute for Molecular Bioscience, South Africa

    Nicki Tiffin

    Nicki Tiffin
    University of Cape Town, South Africa

  • Cost and bursaries

    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.

    Bursaries
    A limited number of bursaries are available for each course. These are awarded on merit to cover travel, accommodation and sustenance. The maximum award for travel (economy class) will be £750. If you would like to apply for a bursary, please complete the bursary section of the online application form.

    Bursaries can be applied for as part of the course application form. Applicants will be notified of a bursary award along with their place on the course, usually within one month of the application deadline. The decision of the selection committee is final.

    Please note that both the applicant and sponsor are required to provide a justification for the bursary as part of the application. Priority will be given to applicants from low- and middle-income countries.


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  • How to apply

    Prerequisites

    The course is 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.

    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

    The course will be taught in English.

    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.