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Research Project: Mosquito Vector Predictive Modeling in a Changing Climate

Location: Foreign Arthropod Borne Animal Disease Research

Project Number: 3022-32000-025-009-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2021
End Date: Sep 30, 2025

The impact of climate change on mosquito transmitted pathogens requires further investigation to accurately forecast endemic virus transmission and exotic virus introduction. This project will associate genetic changes in the mosquito with environmental characteristics and reported human and animal cases. The mosquito, Culex tarsalis is a confirmed vector of West Nile, Rift Valley fever, and Japanese encephalitis viruses. Identifying the genetic adaptations within the mosquito that impact survival and pathogen transmission (viral epidemiology), will allow for outbreak forecasts and investigations of predictive biology to model which populations of mosquito will undergo range expansion and be responsible for viral outbreaks. Artificial intelligence and machine-learning will be used to combine the environmental, genetic, and case data into a single predictive algorithm to forecast current and future pathogen transmission in a warming climate.

The ARS and the Collaborator have worked to investigate the Culex tarsalis mosquito genome. Past work identified geographically distinct populations of mosquitoes that have limited migration between discrete populations. To further refine the pattern of population structure and uncover alleles potentially linked to local adaptation, we will assemble and annotate a de novo reference genome for C. tarsalis, and subsequently generated Restriction-Site Associated DNA sequencing (RAD-seq) on over 300 individuals collected from 30 different geographic locations. Using the markers we obtain after aligning the RAD-seq reads to the new reference, we will perform an extensive Genome-Wide Association Time-series Study (GWATS) analysis on 11 environmental variables in order to simultaneously identify which climate variables correlated with population differentiation and which alleles were significantly associated with adaptations to those variables. Similar to Genome-Wide Association Studies (GWAS), GWATS uses y-vector input data across a time-series for multiple hypothesis tests by linear model association. Among other things, GWATS can use publicly available environmental variables aggregated from local collection points under the assumption that samples collected from those locations are adapted to their immediate environment. Importantly, GWATS can identify associations that may only be critical at certain times of the year, rather than relying on the annual means of variables such as rainfall or temperature. We will leverage the power of the GWATS method to find associations indicative of local adaptation across 30 representative Cx. tarsalis collection sites. We will then model the future geographic spread of Cx. tarsalis under a range of different climate scenarios to better understand what kind of economic and health risks are likely to be posed by these mosquitoes in the years to come.