Project #8565
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#8565 : Multi-traits GWAS in Malaria
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Group :
Name of Applicant : Anavaj Sakuntabhai
Date of application : 10-07-2017
Unit : Functional Genetics of Infectious Diseases
Location : Francois Jacob, 5 floor, room 11A
Phone : O144389103
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Project context and summary :

Malaria is a complex disease resulting in more than 700,000 deaths per year, most notably among under 5-year olds in sub–Saharan Africa. Malaria is caused by several Plasmodium spp. parasites of which P. falciparum is responsible for the majority of deaths worldwide. Parasites are inoculated by infectious mosquitoes during their bloodmeal. Subsequent development of the parasite within the liver leads to the blood stage infection, where parasites replicate within red blood cells leading to fever, anemia and cerebral complications. Acquired sterilizing immunity is never attained despite repeated infections, but clinical immunity does develop whereby the individual can harbor parasites for long periods without expressing clinical symptoms – an asymptomatic infection. Evidence for a contribution of host genetic factors to mild clinical malaria and biological phenotypes, such as number of clinical episodes, parasite density, immune responses to P. falciparum antigens has progressed with the development of increasingly sophisticated techniques. Population level differences in susceptibility to malaria have been observed between sympatric ethnic groups1 and, at a finer scale, differential phenotypic expression was observed in monozygotic and dizygotic twins.2 The importance of host genetics has been further demonstrated by segregation studies,2,3 linkage analysis,4-7 and candidate gene approach.8,9 Overall, emphasis has understandably been placed on clinical malaria and very few studies have considered asymptomatic malaria.

Related team publications :
Genetic Determination and Linkage Mapping of Plasmodium falciparum Malaria Related Traits in Senegal. Sakuntabhai et al, 2008
Heritability of the Human Infectious Reservoir of Malaria Parasites. Lawaly et al.,2010
Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. Aschard et al. 2014
Service Delivery
Status : Declined

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