Malaria represents a co-evolutionary triangle between parasites, vectors, and human hosts. The evolutionary adaptations that impact disease epidemiology leave a signature in the genomes of each member of these triangles. By applying population genomic methods, and generating other data such as transcriptomic and serologic profiles, we can better understand the basic biology and epidemiology of vector-borne diseases, and apply that knowledge to their control.

Whole genome sequencing of parasites and mosquitoes is the ultimate means of understanding malaria from a genomic perspective. While DNA sequencing costs have fallen dramatically over the past 20 years, however, whole genome sequencing remains expensive to perform at a large scale, and technically difficult to perform on some categories of samples. Our lab therefore also develops protocols for multiplexed PCR amplification of informative loci in parasite and vector genomes. Short-read sequencing of the resultant multiplexed amplicons can provide information very cost-effectively for research and public health applications. 

Our laboratory develops new bioinformatic methodologies for interpreting whole genome sequencing data and amplicon sequencing data. Because malaria parasites are eukaryotic organisms that undergo sexual recombination, population and genomic epidemiological analyses must often use methods that are different from those used for non-recombining viral and bacterial datasets. In particular, our laboratory develops and applies methods for interpreting signals of genetic relatedness in malaria parasites and mosquitoes, resulting from recent shared ancestry. Relatedness, or identity-by-descent, can inform transmission, migration, and selection phenomena on a much more recent timescale than mutation and drift, and serve several public health use cases. We work to make our analysis tools broadly available, and are actively developing cloud-based implementations of many tools on the Terra.bio platform.