Genetics courses in South Africa
Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Durban, South Africa
Large-scale genetic studies of human populations have become a powerful tool for understanding resistance and susceptibility to disease. There is growing interest among medical researchers in Africa in applying these new methodologies to gain a better understanding of common diseases that affect African populations. This computational course aims to describe the key aspects of human population genetics and genome-wide association studies (GWAS) so that participants will be able to perform analyses of their own research. The programme will cover both theoretical and practical issues of genetic epidemiology via association analysis, illustrating particular concepts with examples from recent studies in type 2 diabetes, sickle cell disease and malaria.
The outline of the course will follow the experimental process: an introduction to human population genetics and its relevance to study design, through data collection and analysis, and on to interpreting and following up results. Particular emphasis will be placed on the use of publically available software and resources (such as PLINK, HapMap, and 1000 genomes) and the benefits of collaborative research. Material will be covered by lectures, computational practicals and break-out discussion sessions.
Population genetics and association studies
Patterns of diversity in natural populations and underlying molecular processes. Linkage disequilibrium and ancestry. Differences between populations and its consequences for GWAS.
Study design and exploiting population cohorts
The GWAS approach and its power to detect genetic effects. Choice of commercially available genotyping products and study individuals. Choice of control individuals. Integrating GWAS into epidemiological and cohort studies.
Data quality and basic association analysis
Genotype calling and quality control. Simple tests for association and performing a genome-wide scan. Interpreting evidence for association and identification of regions of interest.
Controlling for confounding effects
Tools for investigating possible population structure and relatedness within study individuals. Methods for correcting for confounding effects. Comparing data to existing collections.
Follow up analysis
Replicating signals of association. Options for functional studies. Trans-ethnic fine-mapping. Exploiting whole genome sequence information. Imputation, meta-analysis and data sharing.
Kirk Rockett (Wellcome Trust Centre for Human Genetics, Oxford, UK)
Manj Sandhu (Wellcome Trust Sanger Institute, UK)
Gavin Band (Wellcome Trust Centre for Human Genetics, Oxford, UK)
Luke Jostins (Wellcome Trust Centre for Human Genetics, Oxford, UK)
Geraldine Clark (Wellcome Trust Centre for Human Genetics, Oxford, UK)
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