Search

H3Africa is developing capacity for health-related genomics research in Africa

Babesia microti is tick-borne disease that is an emerging threat to public health due to increasing prevalence and expanding geographic range. Detection and constant surveillance of babesiosis is imperative for predicting pathogen expansion. Leveraging our whole genome sequence (WGS) analyses of B. microti, we developed a single nucleotide polymorphism (SNP)-based high resolution melt (HRM) surveillance tool. We developed our HRM assay using available sequence data and identified 775 SNPs. From these candidate SNPs, we developed a 32-SNP barcode that is robust and differentiates geographically distinct populations; it contains SNPs that are putatively neutral, located in nuclear, mitochondrial, and apicoplastal regions. The assays are reproducible and robust, requiring a small quantity of DNA (limit of detection as low as 10 pg.). We analyzed the performance of our HRM assay using 26 B. microti clinical samples used in our WGS study from babesiosis endemic regions in the United States. We identified a minimal barcode consisting of 25 SNPs that differentiate geographically distinct populations across all clinical samples evaluated (average minor allele frequency > 0.22). Supporting our previous WGS findings, our 25-SNP barcode identified distinct barcode signatures that segregate B. microti into two lineages: Northeast and Midwest, with the Northeast having three subpopulations: Connecticut/Rhode Island, Nantucket, and the R1 reference group. Our 25-SNP HRM barcode provides a robust means genetic marker set that will aid in tracking the increasing incidence and expanding geographic range of B. microti infections. [ABSTRACT FROM AUTHOR]

Background: Complex malaria infections are defined as those containing more than one genetically distinct lineage of Plasmodium parasite. Complexity of infection (COI) is a useful parameter to estimate from patient blood samples because it is associated with clinical outcome, epidemiology and disease transmission rate. This manuscript describes a method for estimating COI using likelihood, called COIL, from a panel of bi-allelic genotyping assays. Methods: COIL assumes that distinct parasite lineages in complex infections are unrelated and that genotyped loci do not exhibit significant linkage disequilibrium. Using the population minor allele frequency (MAF) of the genotyped loci, COIL uses the binomial distribution to estimate the likelihood of a COI level given the prevalence of observed monomorphic or polymorphic genotypes within each sample. Results: COIL reliably estimates COI up to a level of three or five with at least 24 or 96 unlinked genotyped loci, respectively, as determined by in silico simulation and empirical validation. Evaluation of COI levels greater than five in patient samples may require a very large collection of genotype data, making sequencing a more cost-effective approach for evaluating COI under conditions when disease transmission is extremely high. Performance of the method is positively correlated with the MAF of the genotyped loci. COI estimates from existing SNP genotype datasets create a more detailed portrait of disease than analyses based simply on the number of polymorphic genotypes observed within samples. Conclusions: The capacity to reliably estimate COI from a genome-wide panel of SNP genotypes provides a potentially more accurate alternative to methods relying on PCR amplification of a small number of loci for estimating COI. This approach will also increase the number of applications of SNP genotype data, providing additional motivation to employ SNP barcodes for studies of disease epidemiology or control measure efficacy. The COIL program is available for download from GitHub, and users may also upload their SNP genotype data to a web interface for simple and efficient determination of sample COI. [ABSTRACT FROM AUTHOR]