Courses
Medical population genetics and GWAS for complex diseases 19th April - 24th April, 2015
Submitted by Dr Verena Ras -
Module:
N/A
Material type:
Exercises, Slides, Tutorial
Course Level:
Description:
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.
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.
Organiser:
H3ABioNet, the African Institute for Mathematical Sciences (AIMS), CPGR node of H3ABioNet: Prof. Nicola Mulder, Dr. Emile Chimusa, Dr. Judit Kumuthini, Dr. Gaston Kuzamunu
Lecturer/ Author:
Prof. Scott Hazelhurst, Dr. Noah Zaitlen, Dr. Bogdan Pasaniuc, Dr. Bjarni Vilhjalmsson, Shaun Aron
Year created:
2015
Year of latest Update:
2014
Classification:
Omics >> Genomics
Experimental Design and Studies >> GWAS study
Informatics >> Bioinformatics
Playlist(s):
N/A
Keyword(s):
Genetics, Genomics, Nucleic acid analysis, Biostatistics, GWAS