H3ABioNet

Pan African Bioinformatics Network for H3Africa

Genomic Epidemiology in Africa 21-26 June 2015

Venue: Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Durban, South Africa

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. Applicants should be researchers or clinicians engaged in relevant research in Africa.

Instructors
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
Tommy Carstensen: Wellcome Trust Sanger Institute, UK

Cost and Deadlines
This course is free to attend and bursaries for travel and accommodation are available.
The deadline for applications is 13 March 2015.

Programme

  • 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.

Full details can be found at: www.wellcome.ac.uk/overseascourses