1. Alfred Hubbard, BS, UNC Charlotte 2. Cheikh C. Dieng, PhD, Drexel University 3. Elizabeth Hemming-Schroeder, PhD, Colorado State University 4. Daniel Janies, PhD, UNC Charlotte 5. Eugenia Lo, PhD, Drexel University
Poster Not on Display
Genomic epidemiology has proven to be an effective method of measuring transmission in vector-borne diseases, allowing estimation of the magnitude of transmission through metrics of genetic diversity and the passage of pathogens between regions using genetic relatedness. However, some diseases, such as malaria, can have highly complex transmission patterns, and conventional genetic markers offer limited power to detect such patterns without the costly sequencing of many genetic loci. Additionally, malaria infections, especially those caused by Plasmodium falciparum, are frequently polyclonal, meaning that multiple parasite strains are present within an infected person. This complicates sequencing and analysis of genetic data, but also presents an opportunity to utilize within-host diversity as an additional metric of transmission. In this study, we use a panel of microhaplotype markers and amplicon deep sequencing to tackle both of these issues, measuring genetic diversity and relatedness in a powerful, cost-effective manner that allows precise measurement of within-host diversity. With data from more than 400 samples spanning eight countries in East, Central, and West Africa, we compare trends in genetic diversity, particularly within-host diversity, across a broad range of geographic regions to expand our knowledge of these complex transmission environments. By estimating relatedness between samples based on identity-by-descent, we also describe patterns of genetic connectivity between these regions, bringing a new level of precision to our understanding of malaria transmission patterns in sub-Saharan Africa.
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