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Environmental Correlation Analysis for Genes Associated with Protection against Malaria
Margaret J Mackinnon, Carolyne Ndila1, Sophie Uyoga1
1Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
Molecular Biology and Evolution
|January 9, 2016
Summary
Environmental correlation analysis (ECA) identified novel genes, CDH13 and HS3ST3B1, associated with malaria resistance by analyzing allele frequencies against historical malaria prevalence. This method proves effective for discovering genes involved in infectious disease resistance.
Area of Science:
- Human genetics
- Infectious disease research
- Population genetics
Background:
- Large-scale genome-wide association studies are ongoing in Africa to find malaria resistance genes.
- Case-control studies are a common method, but an alternative approach is being explored.
Purpose of the Study:
- To evaluate the utility of environmental correlation analysis (ECA) for identifying genes historically protective against fatal malaria.
- To discover novel genes and pathways involved in human malaria resistance.
Main Methods:
- Collected genotype data for 57 candidate malaria resistance loci and 9,756 random single nucleotide polymorphisms (SNPs) from 12,425 newborns.
- Examined allele frequencies for geographic correlations with long-term malaria prevalence data from 84,042 individuals in coastal Kenya.
- Adjusted for population structure and multiple testing.
Main Results:
- None of the 57 candidate SNPs showed significant correlation with malaria transmission intensity.
- Two random SNPs in CDH13 and HS3ST3B1 exhibited highly significant correlations with malaria prevalence (P < 0.01).
- These genes are involved in glycoprotein-mediated cell-cell adhesion, implicated in cerebral malaria, and related pathways.
Conclusions:
- Environmental correlation analysis (ECA) is a valuable tool for discovering novel genes and pathways related to infectious disease resistance.
- CDH13 and HS3ST3B1 are identified as potential novel malaria resistance genes.
- ECA complements traditional methods in identifying genes under historical selection pressure.
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