The landscape genetics of infectious disease emergence and spread

Abstract

The spread of parasites is inherently a spatial process often embedded in physically complex landscapes. It is therefore not surprising that infectious disease researchers are increasingly taking a landscape genetics perspective to elucidate mechanisms underlying basic ecological processes driving infectious disease dynamics and to understand the linkage between spatially dependent population processes and the geographic distribution of genetic variation within both hosts and parasites. The increasing availability of genetic information on hosts and parasites when coupled to their ecological interactions can lead to insights for predicting patterns of disease emergence, spread and control. Here, we review research progress in this area based on four different motivations for the application of landscape genetics approaches: (i) assessing the spatial organization of genetic variation in parasites as a function of environmental variability, (ii) using host population genetic structure as a means to parameterize ecological dynamics that indirectly influence parasite populations, for example, gene flow and movement pathways across heterogeneous landscapes and the concurrent transport of infectious agents, (iii) elucidating the temporal and spatial scales of disease processes and (iv) reconstructing and understanding infectious disease invasion. Throughout this review, we emphasize that landscape genetic principles are relevant to infection dynamics across a range of scales from within host dynamics to global geographic patterns and that they can also be applied to unconventional 'landscapes' such as heterogeneous contact networks underlying the spread of human and livestock diseases. We conclude by discussing some general considerations and problems for inferring epidemiological processes from genetic data and try to identify possible future directions and applications for this rapidly expanding field.

Figure 1

Chronic wasting disease (CWD) in white-tailed deer as an example of using host population genetics to identify landscape determinants of disease spread. (a) Prevalence of CWD in 15 study areas in Wisconsin and (b) genetic differentiation (FST) of deer host populations in study areas relative to core area of CWD infection, with study areas grouped based on the type of landscape feature separating them from the core-area. Reprinted from Blanchong et al. (2008) with permission.

Figure 2

Schematic depiction of source-sink dynamics characterising the seasonal spread of influenza A from tropical, permanent source populations (probably located in southeast Asia, see Russel et al., 2008) to temperate regions on both hemispheres where the virus is introduced and subsequently goes extinct each season. Reprinted from Rambaut et al. (2008) with permission from Macmillan Publishers Ltd: Nature.

Figure 3

Phylogenetic and spatial analyses of a plague outbreak within a single prairie dog town in Arizona, 2001. (A) Unrooted phylogeny of plague isolates based on maximum-parsimony. (B) Individual genotypes are represented by colored symbols and spatially mapped by using ARCVIEW. Genotypes observed only once are represented by squares and are numbered. More common genotypes are represented by colored circles and defined in the legend. Reprinted from Girard et al. (2004) with permission based on Copyright (2004) National Academy of Sciences, U.S.A.

Figure 4

Distribution and local frequency of five resistance alleles lineages against malarial drugs that arose independently within Plasmodium falciparum populations in sub-Saharan Africa. Resistance alleles whose flanking microsatellite haplotypes did not conform to a defined major lineage are shown in grey. Reprinted from Pearce et al. (2009).

Figure 5

Spread of avian influenza A H5N1 across Asia, May 1997 – May 2005, as inferred from phylogeographic anayses of two viral genes, hemaglutinin (HA) and neuraminidase (NA). Lines between locations represent branches in the Maximum Clade Credibility (MCC) tree along which the relevant location transition occurs. Location circle diameters are proportional to square root of the number of MCC branches maintaining a particular location state at each time-point. The white-green and yellow-magenta color gradients inform the relative age of the transitions for HA and NA respectively (older-recent). Reprinted from Lemey et al. (2009).

Figure 6

Phylogenetic relationships among virus samples collected during a raccoon rabies virus (RRV) epizootic in eastern North America. (A) Portion of Bayesian phylogeny that corresponds to initial infection wave. Estimated locations of internal nodes and the tree root are as shown as black symbols. White star marks the epizootic's documented origin in 1977. (B) Full MCC tree projected onto landscape, including samples collected 4 –14 yrs (squares) and 15 –25 yrs (triangles) after the first case within a county. (C) Annual rate of RRV spread along the US mid- Atlantic relative to elevation and major rivers, 1977 -1999. See Biek et al. (2007) for further details. Reprinted with permission based on Copyright (2007) National Academy of Sciences, U.S.A.