Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our 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 signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways

AquaSens: exploring the use of 16S rRNA next-generation sequencing to determine bacterial composition of various water matrices

Access to clean water, one of the United Nation’s Sustainable Development Goals, is challenged by an increase in the presence of emerging microbial and other contaminants due to urbanization, among other factors. Traditionally, the presence of indicator microorganisms is determined using culturing methods. However, these classical methods cannot be used to determine the identities of ‘unknown’ bacteria and is limited to isolating the culturable state of microorganisms. Thus with culturing, the identities of many bacteria, particularly novel or non-culturable, may remain unknown. The use of a DNA-based method, 16S rRNA next-generation sequencing (NGS), can assist with determining the identities of bacterial populations in a water sample. The objective of this 16S rRNA NGS study was to investigate the bacterial community composition and diversity in a range of water sources. Water samples comprising of potable, surface, ground, marine, aquaculture, rain, wetland and swimming bath water matrices were subjected to 16S rRNA NGS using the Illumina 16S rRNA Metagenomics analysis pipeline. Operational taxonomic units were analysed and the identities of bacterial genera determined. In this study, genera of Acinetobacter, Mycobacterium, Pseudomonas, Legionella, Burkholderia, Yersinia, Staphylococcus and Vibrio were spread across the water matrices. Alpha (within sample) and beta (between samples) diversities for each bacterial community within the tested samples were also determined