Functional Genome Wide Association Study in Susceptibility and Resistance of Malaria - Kabongo, Etienne Ntumba (Division of Human Genetics, 2022)
Background: 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. Objectives: Our aim in this project is to conduct a systematic Meta-analysis and various functional analysis across three study populations in Africa ( Kenya, Malawi and Gambia ). Method and Materials: Our 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. We then conducted GWAS based meta-analysis on the summary statistics from the three studies using Metasoft and Metal. Further, we implemented Functional GWAS (FGWAS) to re-weight the GWAS meta-analysis using functional genomic information software (fgwas-tool). Using results from fgwas-tool, we performed biological interpretation using Functional Mapping (FUMA) tool. We mapped the significant SNPs to the genes, and elucidated their functions and their associated cell types. We then performed pathway analysis and enrichment analysis of the genes using Genemania and Enrichr. Additionally, we performed a polygenic risk score for individuals in each study population using PRSice, and evaluated the level of risk exposure for each individual based on the best predictive threshold. Finally, we filtered the rare variants from each study, and performed SKAT analysis to aggregate the effect of the rare variants Results: We 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 we have shown the best prediction based on the best threshold estimated of each population. We found best-fit prediction best-fit 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. Conclusion: 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, we have 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.