Dissecting the genetic bases of severe malaria resistance using genome-wide and post genomewide study approaches

P. falciparum malaria remains one of the leading public health problems worldwide. The global tally of malaria in 2018 was estimated at 228 million cases and 405, 000 deaths worldwide. African countries disproportionately carry the global burden of malaria accounting for 93% and 94% of cases and deaths, respectively. Even though most infected children recover from P. falciparum malaria, a small subset (~1%) of cases progresses to severe disease and death. Over the last decade, several genome-wide association studies (GWASs) have been conducted in diverse malaria endemic populations to understand the natural host protective immunity against severe malaria that can provide clues for the development of new vaccines and therapeutics. However, beyond identifying association variants, conventional GWAS approaches can”t inform the underpinning biological functions. To bridge this gap, we applied various contemporary statistical genetic analytic approaches to malaria GWAS datasets of diverse malaria endemic populations. First, malaria resistance GWAS datasets of three African populations (N=~11,000) was accessed including Kenya, Gambia and Malawi from European Genome Phenome Archive (EGA) through MalariaGEN consortium standard data accession procedures. The challenges of GWAS approaches was explored in the genetically diverse Africa populations and figured out how various advanced statistical genetic methods can be implemented to address these challenges. Single nucleotide polymorphism (SNP) heritability (h2 g) of malaria resistance was investigated in the Gambian populations and determined appropriate quality (QC) thresholds to accurately estimate the h2 g in our dataset. Second, the study estimated h2 g in the three populations and partitioned the h2 g into chromosomes, allele frequencies and annotations using the genetic relationship-matrix restricted maximum likelihood approaches.

An African specific reference panel was created from African population datasets obtained from 1000 Genomes Project and African Genome Variation Project dataset and computed linkage disequilibrium (LD). Then LD information obtained from these reference panels was used to compute cell-type specific and none cell-type specific enrichments for GWAS-summary statistics meta-analyzed across the three populations. The results showed for the first time that malaria resistance is polygenic trait with h2 g of ~20% and that the causal variants are overrepresented around protein coding regions of the genome. It further showed that the h2 g is disproportionately concentrated on three chromosomes (chr 5, 11 and 20), suggesting cost-effectiveness of targeting these chromosomes in future malaria genomic sequencing studies. Third, this study systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analyzed across eleven populations in malaria endemic regions in Africa, Asia and Oceania. Then positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping and gene-based association analyses was applied to identify candidate severe malaria resistance genes. Network and pathway analyses were performed to investigate their shared biological functions. Rare variant analysis was further applied to raw GWAS datasets of three malaria endemic populations including Kenya, Malawi and Gambia and performed various population genetic structures of the identified genes in the three endemic populations and 20 world-wide ethnics. The functional mapping analysis identified 57 genes located in the known malaria genomic loci while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signaling elements and neuronal systems. Furthermore, the population genetic analysis revealed that the minor allele frequencies (MAF) of the SNPs residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations.

Overall, the results suggest that severe malaria resistance trait is attributed to multiple genes that are enriched in pathways linked to severe malaria pathogenesis. This highlights the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways. In conclusions, this project showed that malaria resistance trait is mainly a polygenic trait which is influenced by genes and pathways linked to blood stage lifecycle of P. falciparum. These findings constitute the foundations for future experimental studies that can potentially lead to translational medicine including development of new vaccines and therapeutics. However, ‘-omics” studies including those implemented in this study, are limited to single datatype analysis and lack adequate power to explain the complexity of molecular processes and usually lead to identification of correlations than causations. Thus, beyond singe locus analysis, the future direction of malaria resistance requires a paradigm shift from single-omics to multi-stage and multi-dimensional integrative multi-omics studies that combines multiple data types from the human host, the parasite, and the environment. The current biotechnological and statistical advances may eventually lead to the feasibility of systems biology studies and revolutionize malaria research.

Project details

Basic Science

Sep 2021

South Africa