Discovering genetic factors that protect people against malaria

People differ in their level of resistance to malaria. Genetic factors play an important role, but the genes responsible are largely unknown. By discovering genes that confer natural resistance to malaria, we hope to gain vital clues about the molecular basis of protective immunity against the disease, and thereby to accelerate the development of an effective malaria vaccine. MalariaGEN investigators are working together on four consortial projects that address different aspects of this scientific problem:

Background information

For a long time it has been known that people who carry a form of genetic variation called sickle haemoglobin (HbS) are protected against malaria. The HbS variant is found in many people of African ancestry, and it provides a classic example of the benefits and costs of evolutionary adaptation, as those who inherit the genetic variant from both parents suffer from a severe blood disorder called sickle cell disease.

Epidemiological data indicate that HbS is only part of the story, and that many more malaria resistance genes remain to be discovered. Previous attempts to do this have given conflicting results, largely because individual studies have been too small to obtain conclusive results. MalariaGEN aims to overcome this roadblock by integrating data from multiple studies performed by different research groups around the world.

The keystone of our scientific strategy is a new research approach called genome-wide association (GWA) analysis. This involves typing millions of genetic variants in people who develop the most severe clinical forms of malaria, and comparing them to control subjects recruited from the same population. Most (but not all) of these studies are based in Africa, as this is where the vast majority of malaria deaths occur.

Large sample sizes are particularly important for GWA analysis, as a disease such as malaria is influenced by many different environmental and genetic factors, each of which accounts for only a small part of a person’s chance of developing disease.  Thus important findings may not emerge until the number of people studied is in the tens of thousands, and this requires meta-analysis of data from different studies at multiple locations.