Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework

Schwabl, Philipp; Llewellyn, Martin S.; Landguth, Erin L.; Andersson, Björn; Kitron, Uriel; Costales, Jaime A.; S, Ocaña; Grijalva, Mario J.

doi:10.1016/j.pt.2016.10.008

Cited by 31 publications

(33 citation statements)

References 98 publications

(118 reference statements)

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“…These zones can be delineated using parasite population structure as a proxy for 111 transmission zone boundaries: parasites from the same transmission zone are able to interbreed and thus 112 are more closely related genetically than they are to parasites from different transmission zones, with 113 which they are less likely to interbreed and thus are genetically less related. Consequently, the likelihood 114 that parasites originate from the same transmission zone and the likelihood of parasite transmission 115 between two locations (invasion) can be inferred from the degree of genetic relatedness between them, 116 and defined quantitatively by estimating parameters of population structure (Archie et al, 2009;Real and 117 Biek, 2007;Schwabl et al, 2017). 118…”

Section: Abstract 23mentioning

confidence: 99%

Utility of theOnchocerca volvulusmitochondrial genome for delineation of parasite transmission zones

Crawford

Hedtke

Doyle

et al. 2019

Preprint

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23In 2012, the reduction in Onchocerca volvulus infection prevalence through long-term mass ivermectin 24 distribution in African meso-and hyperendemic areas motivated expanding control of onchocerciasis (river 25 blindness) as a public health problem to elimination of parasite transmission. Given the large contiguous 26 hypo-, meso-and hyperendemic areas with an estimated population of 204 million,

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Section: Abstract 23mentioning

confidence: 99%

Utility of theOnchocerca volvulusmitochondrial genome for delineation of parasite transmission zones

Crawford

Hedtke

Doyle

et al. 2019

Preprint

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“…Even fewer multispecies studies have employed landscape genetics methods to study the dynamics of infectious diseases in wildlife systems (Biek & Real, 2010;Hemming-Schroeder et al, 2018;Kozakiewicz et al, 2018). Such comparative landscape genetics frameworks can provide valuable insights into how host-pathogen interactions shape patterns of disease transmission and spread across heterogeneous landscapes (Leo, Gonzalez, Millien, & Cristescu, 2016;Schwabl et al, 2017;Talbot et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparative landscape genetics reveals differential effects of environment on host and pathogen genetic structure in Tasmanian devils (Sarcophilus harrisii) and their transmissible tumour

et al. 2020

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Genetic structure in host species is often used to predict disease spread. However, host and pathogen genetic variation may be incongruent. Understanding landscape factors that have either concordant or divergent influence on host and pathogen genetic structure is crucial for wildlife disease management. Devil facial tumour disease (DFTD) was first observed in 1996 and has spread throughout almost the entire Tasmanian devil geographic range, causing dramatic population declines. Whereas DFTD is predominantly spread via biting among adults, devils typically disperse as juveniles, which experience low DFTD prevalence. Thus, we predicted little association between devil and tumour population structure and that environmental factors influencing gene flow differ between devils and tumours. We employed a comparative landscape genetics framework to test the influence of environmental factors on patterns of isolation by resistance (IBR) and isolation by environment (IBE) in devils and DFTD. Although we found evidence for broad‐scale costructuring between devils and tumours, we found no relationship between host and tumour individual genetic distances. Further, the factors driving the spatial distribution of genetic variation differed for each. Devils exhibited a strong IBR pattern driven by major roads, with no evidence of IBE. By contrast, tumours showed little evidence for IBR and a weak IBE pattern with respect to elevation in one of two tumour clusters we identify herein. Our results warrant caution when inferring pathogen spread using host population genetic structure and suggest that reliance on environmental barriers to host connectivity may be ineffective for managing the spread of wildlife diseases. Our findings demonstrate the utility of comparative landscape genetics for identifying differential factors driving host dispersal and pathogen transmission.

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“…More recently, the field has naturally shifted toward landscape genomics, transformed by advances in NGS, cutting-edge remote sensing, and GIS technologies (Kool et al 2013). Taken together, the incorporation of landscape genomics into wildlife disease biology promises improved prediction of disease risk, transmission, and prevention (Schwabl et al 2017). Three general prospects for the future of integrating landscape ecology with wildlife disease materialized from the horizon scan discussion: 1) inferring host and pathogen gene flow, 2) coordinating technology, data, and expertise, and 3) understanding the effects of rapid environmental change.…”

Section: Integrating Landscape Ecology and Genomicsmentioning

confidence: 99%

The Expectations and Challenges of Wildlife Disease Research in the Era of Genomics: Forecasting with a Horizon Scan-like Exercise

Fitak

Antonides

Baitchman

et al. 2019

Journal of Heredity

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The outbreak and transmission of disease-causing pathogens are contributing to the unprecedented rate of biodiversity decline. Recent advances in genomics have coalesced into powerful tools to monitor, detect, and reconstruct the role of pathogens impacting wildlife populations. Wildlife researchers are thus uniquely positioned to merge ecological and evolutionary studies with genomic technologies to exploit unprecedented "Big Data" tools in disease research; however, many researchers lack the training and expertise required to use these computationally intensive methodologies. To address this disparity, the inaugural "Genomics of Disease in Wildlife" workshop assembled early to mid-career professionals with expertise across scientific disciplines (e.g., genomics, wildlife biology, veterinary sciences, and conservation management) for training in the application of genomic tools to wildlife disease research. A horizon scanning-like exercise, an activity to identify forthcoming trends and challenges, performed by the workshop participants identified and discussed 5 themes considered to be the most pressing to the application of genomics in wildlife disease research: 1) "Improving communication, " 2) "Methodological and analytical advancements, " 3) "Translation into practice, " 4) "Integrating landscape ecology and genomics, " and 5) "Emerging new questions. " Wide-ranging solutions from the horizon scan were international in scope, itemized both deficiencies and strengths in wildlife genomic initiatives, promoted the use of genomic technologies to unite wildlife and human disease research, and advocated best practices for optimal use of genomic tools in wildlife disease projects. The results offer a glimpse of the potential revolution in human and wildlife disease research possible through multi-disciplinary collaborations at local, regional, and global scales.

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Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework

Cited by 31 publications

References 98 publications

Utility of theOnchocerca volvulusmitochondrial genome for delineation of parasite transmission zones

Utility of theOnchocerca volvulusmitochondrial genome for delineation of parasite transmission zones

Comparative landscape genetics reveals differential effects of environment on host and pathogen genetic structure in Tasmanian devils (Sarcophilus harrisii) and their transmissible tumour

The Expectations and Challenges of Wildlife Disease Research in the Era of Genomics: Forecasting with a Horizon Scan-like Exercise

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