Genomic Epidemiology for Malaria: Novel application of geospatial methods, new genomic markers, and population-level insights

Genomic epidemiology is the use of genetic data to characterize and explain disease occurrence and transmission. Application of these methods to malaria has already yielded substantial benefits, such as identification and surveillance of drug resistance genotypes. However, the potential for genomic epidemiology to accelerate progress towards malaria eradication is far from fully realized. This dissertation demonstrates new applications of genomic information to questions that are impossible to address with conventional epidemiological data. First, the value of correlating genetic and environmental distances to understand the drivers of Plasmodium falciparum transmission is showcased with microsatellite data from 44 sites in Western Kenya. Second, the design and validation of a new panel of genetic markers, microhaplotypes with multiple SNPs on each short read, is presented for P. vivax, enabling sensitive, scalable characterization of within-host diversity in multi-strain infections. Finally, a similar panel of microhaplotype markers for P. falciparum is applied to samples from eight countries throughout Africa, yielding insights into continent scale transmission dynamics. The analysis of environmental drivers revealed the Winam Gulf of Lake Victoria as a barrier to malaria transmission, a conclusion that would be impossible to reach rigorously without this novel methodology. The new P. vivax panel yielded quality sequences and detected expected patterns of genetic relatedness, indicating this tool is ready for broad application. The P. falciparum microhaplotype analysis identified subtle patterns of genetic relatedness and surprisingly little relationship between within-host diversity and incidence, highlighting the potential of these markers but also a need for future work on the interpretation of the resulting data. This dissertation expands the scope of questions about malaria epidemiology that can be answered with genomic data and argues that routine application of these methods could accelerate progress towards malaria eradication.