identifying common genetic variants underlying chronic non-communicable diseases, but have proved to be more difficult for acute infectious diseases that represent a substantial portion of the global disease burden and are most prevalent in tropical regions. This is partly due to the practical difficulties of establishing large sample collections and reliable phenotypic datasets in resource-constrained settings, but also theoretical and methodological challenges associated with the study of pathogenic diseases in populations with high levels of genetic diversity and population structure1,2,3. The Malaria Genomic Epidemiology Network (MalariaGEN) was established in 2005 to overcome these obstacles with standardized protocols, common phenotypic definitions, agreed policies for equitable data sharing and local capacity building for genetic data analysis, enabling large collaborative studies across different countries where malaria is endemic4.
Here we extend previous work by using data collected from 11 countries to perform a comprehensive GWAS of human resistance to severe malaria (SM)
Identifying common genetic variants underlying chronic non-communicable diseases, but has proved to be more difficult for acute infectious diseases that represent a substantial portion of the global disease burden and are most prevalent in tropical regions. This is partly due to the practical difficulties of establishing large sample collections and reliable phenotypic datasets in resource-constrained settings, but also theoretical and methodological challenges associated with the study of pathogenic diseases in populations with high levels of genetic diversity and population structure1,2,3. The Malaria Genomic Epidemiology Network (MalariaGEN) was established in 2005 to overcome these obstacles with standardized protocols, common phenotypic definitions, agreed policies for equitable data sharing, and local capacity building for genetic data analysis, enabling large collaborative studies across different countries where malaria is endemic4.
Here we extend previous work by using data collected from 11 countries to perform a comprehensive GWAS of human resistance to severe malaria (SM).
