Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework. Issue 4 (April 2017)

• Record Type: Journal Article
• Authors: Schwabl, Philipp; Llewellyn, Martin S.; Landguth, Erin L.; Andersson, Björn; Kitron, Uriel; Costales, Jaime A.; Ocaña, Sofía; Grijalva, Mario J.
• Is Part Of: Trends in parasitology. Volume 33:Issue 4(2017:Apr.)
• Page Start: 264
• Page End: 275
• Publication Date: 2017-04
• Subjects: Parasitology -- Periodicals; Parasitology -- Periodicals; Biology -- Periodicals; Parasitology; Biology; Parasitologie -- Périodiques; Online resources; 571.999
• DOI: 10.1016/j.pt.2016.10.008
• Languages: English
• ISSNs: 1471-4922

Abstract

Abstract : Substantial heterogeneity exists in the dispersal, distribution and transmission of parasitic species. Understanding and predicting how such features are governed by the ecological variation of landscape they inhabit is the central goal of spatial epidemiology. Genetic data can further inform functional connectivity among parasite, host and vector populations in a landscape. Gene flow correlates with the spread of epidemiologically relevant phenotypes among parasite and vector populations (e.g., virulence, drug and pesticide resistance), as well as invasion and re-invasion risk where parasite transmission is absent due to current or past intervention measures. However, the formal integration of spatial and genetic data ('landscape genetics') is scarcely ever applied to parasites. Here, we discuss the specific challenges and practical prospects for the use of landscape genetics and genomics to understand the biology and control of parasitic disease and present a practical framework for doing so. Trends: Landscape genomics introduces novel analytical tools to derive the ecological determinants of spatial genetic structure from unprecedented remote sensing and genomic resources. Human risk to parasitic disease relates directly to the distribution and movement of host, vector and parasite genotypes in the environment. Landscape resistance to host and vector movement constrains parasite dispersal and cumulates high ecological sensitivity to the spread of parasitic disease. Nonetheless, landscape genomic tools are rarely engaged to model multidependent parasite dispersal or selection-driven genetic structure. Intuitive adjustments to cost–distance methods, resistance surface simulation modelling and genotype-by-environment association analyses offer to unite epidemiology with landscape genomics for enhanced disease surveillance and control.