Medical population genetics and GWAS for complex diseases 2015 -20220207T065640Z-001.zip (470.93 MB)
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Medical population genetics and GWAS for complex diseases 19th April - 24th April 2015 Training Material Archive

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posted on 04.03.2022, 13:25 by Scott Hazelhurst, Noah Zaitlen, Bogdan Pasaniuc, Bjarni Vilhjálmsson, Shaun Aron

This is an archive containing training materials for the H3ABioNet course: “Medical population genetics and GWAS for complex diseases 19th April - 24th April, 2015”.


Brief description of the course:

The workshop was split into two tracks:

  1. An introductory track that will cover fundamental assumptions, showcase recent successes and discuss limitations of current GWAS approaches in the field of complex diseases, particularly in the case of African populations with known low linkage disequilibrium (LD). It will also provide a ‘hands-on’ experience of data analysis and a stage for shaping the next generation of GWAS scientists/researchers.

  2. An advanced track which will draw upon the audience’s interdisciplinary expertise in mathematics, simulation studies, statistics and machine learning to overcome present challenges and identify the most promising avenues of future research for effective phenotype–genotype association.

Course trainers/authors:

Prof. Scott Hazelhurst, Dr. Noah Zaitlen, Dr. Bogdan Pasaniuc, Dr. Bjarni Vilhjalmsson and Shaun Aron


Course sponsor/organisers:

Prof. Nicola Mulder, Dr. Emile Chimusa, Dr. Judit Kumuthini and Dr. Gaston Kuzamunu


Course level: Intermediate


The archive contains the following items:

  • Trainer/creator file containing the names of the person/s responsible for organising and delivering the course

  • Course information file - which contains the original information about the course and includes a course schedule

  • Lecture materials which include lecture slides, tutorials and datasets

This upload was performed by: Verena Ras | H3ABioNet Training and Outreach Coordinator

Funding

H3ABioNet: informatics solutions for H3Africa

National Human Genome Research Institute

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History

Department/Unit

Computational Biology, H3ABioNet