Functional genome wide association study in susceptibility and resistance of malaria

More than century, malaria is qualified as a mortal infectious disease, worldwide causing high morbidity and mortality. The World Health Organization (WHO) has shown that, Distribution of Malaria in Africa takes a major part, it”s accounting for 95% (about 229 million) and 67% (about 274000) of reported cases and death respectively. One of solutions for reducing this threat is to find drugs or to develop vaccines which can resist and adapt to populations. Unfortunately, despite several efforts, malaria parasites are still developing resistance to the frontline antimalarials. The functional analysis based on fgwas result have shown that 5 genes (ATP2B4, ABO, HBD, HBB, CBLB) are highly associated to malaria across these 3 studies populations (Gambia, Malawi and Kenya) and 10 candidate novel genes, including high number of mutations in the gene C4orf19 which will constitute one of the future major studies. Also, the project has shown the best prediction based on the best threshold estimated of each population. The results have shown that the prediction rate is very low and may fail to distinguish the cases from the controls.

The first analysis is directed to the Genome Wide Association Study (GWAS) of three study populations (Kenya, Malawi and Gambia) using the Emmax tool to identify the genetic variants associated with severe malaria. Then conducted GWAS based meta-analysis was conducted on the summary statistics from the three studies using Metasoft and Metal. Further, the study implemented Functional GWAS (FGWAS) to re-weight the GWAS meta-analysis using functional genomic information software (fgwas-tool). Using results from fgwas-tool, Biological interpretation using Functional Mapping (FUMA) tool was performed. Significant SNPs were mapped to the genes to the genes, and elucidated their functions and their associated cell types. Then a pathway analysis and enrichment analysis of the genes was performed using Genemania and Enrichr. Additionally, a polygenic risk score for individuals in each study population was calculated using PRSice, and evaluated the level of risk exposure for each individual based on the best predictive threshold. Finally, the rare variants from each study were filtered, and SKAT analysis was done to aggregate the effect of the rare variants

Results: The study identified 29 significant SNPs (14 replicates and 15 novels) reweighted from FGWAS based on GWAS Meta-Analysis. The SNPs mapped to 15 genes (HBB, HBD, ATP2B4, ABO, CBLB, EYA2, HERPUDI, IQCJ, MPP7, NAVI, NUP210, SAMD5 , TCERG1L ,TMEM229B, C4orf19) at gene level. Five of these genes (HBB, HBD, ATP2B4, ABO, CBLB) had been reported by different studies to be associated with malaria. In the PRS analysis the best prediction has been shown based on the best threshold estimated of each population. It was found that the best-fit prediction PRS for Gambia is 0.00443458 at PT = 0.00165005, for Kenya is 8.4666e-158 at PT= 1 and for Malawi is 1.5151e-55 at PT = 1 predict the risk of an infectious disease like severe malaria. However, the prediction rate is very low and may fail to distinguish the cases from the controls.

Project details

Combination of Interventions

Feb 2022

Kenya
Malawi
The Gambia