Trends in Parasitology

Trends in Parasitology

Volume 33, Issue 4, April 2017, Pages 264-275
Journal home page for Trends in Parasitology

Opinion
Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework

https://doi.org/10.1016/j.pt.2016.10.008Get rights and content

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.

Section snippets

Parasites, Genes, and Landscapes

Individual parasite species around the world are distributed across different ecological settings, spanning rural, peri-urban and urban areas. For widely distributed parasitic diseases, ‘patchy’ geographic distribution of cases frequently occurs, where parasite, vector and host-related factors conspire to promote intense local transmission [1]. Understanding how abiotic and biotic environmental features affect the movement of parasites, their hosts and vector species, is critical for disease

What Is Landscape Genetics?

A primary goal of landscape genetics is to understand how landscape features influence observed spatial genetic (neutral or selection-driven) structure [25]. Key concepts in landscape genetics involve correlating genetic data with geographic data through individual-based measurements of dissimilarity. For example, genetic distances (i.e., dissimilarity matrices) can be quantified using individual-based metrics, such as proportion of shared alleles Dps [26] or Rousset's A [27]. In all but the

Landscape Genomics to Study Parasitic Disease

With the exception of recent theoretical work in the context of Lyme disease [21], essentially all landscape genetic studies applied to parasitic diseases to date have considered a single level of transmission, focusing primarily on landscape resistance hypotheses that influence movement processes and thus, gene flow, of principal reservoir hosts. For complex, multispecies disease systems, we find that today's landscape genomic methods warrant a more inclusive, multilevel approach. In

Accuracy in Detection, Precision in Prediction

Spatially explicit models of parasite dispersal (increasingly individual (e.g., [35]) and network-based algorithmic methods (e.g., [36])) have traditionally been fitted and validated against occupancy and abundance data. Genetic structure of the disease agents still rarely replaces these response variables despite several clear advantages for host–vector–parasite systems. Interpretation of occupancy and abundance data is complicated by imperfect detection, and many zoonoses (e.g., Chagas

Conservation Genomics in Reverse

In conservation biology, landscape genomics strives to identify ‘conservation units’, that is, genetically unique subpopulations to be preserved and/or managed distinctly to sustain biodiversity of the whole [52]. In epidemiology, spatial genomics are crucial to identifying operational units that maximize the reach of surveillance and control. Apprised of such epidemiological units and their distribution, insecticidal campaigns (often too indiscriminate to be sustainable in the past [53]), for

Concluding Remarks

Here, we claim a strategic place for host–vector–parasite interactions to join spatially explicit analyses of genetic connectivity. This integration not only allies molecular epidemiology with landscape ecology, but advances both into the realm of ‘landscape community genomics’ [66], only just envisioned to explore previously impenetrable eco-evolutionary causes and consequences of genomic structure. First inroads would be well-timed to seek out the potential of landscape genomics in

Glossary

Cost–distance
the cumulative resistance of intervening landscapes to the movement of individuals (or populations, etc.) between a pair of sites. These ‘distances’ are typically calculated by scoring landscape variables (e.g., elevation) based on (putative) resistance-to-movement, plotting resistance scores into a raster grid (see ‘resistance surface’ below) and adding up grid values along the path(s) of interest.
Genotype-by-environment association (GEA)
a correlation between genetic and

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