Chronic wasting disease (CWD), a transmissible spongiform encephalopathy of cervids, was first documented nearly 50 years ago in Colorado and Wyoming and has since spread to cervids in 23 states, two Canadian provinces, and the Republic of Korea. The expansion of this disease makes the development of sensitive diagnostic assays and antemortem sampling techniques crucial for the mitigation of its spread; this is especially true in cases of relocation/reintroduction of farmed or free-ranging deer and elk or surveillance studies of private or protected herds, where depopulation is contraindicated. This study sought to evaluate the sensitivity of the real-time quaking-induced conversion (RT-QuIC) assay by using recto-anal mucosa-associated lymphoid tissue (RAMALT) biopsy specimens and nasal brush samples collected antemortem from farmed white-tailed deer ( n = 409). Antemortem findings were then compared to results from ante- and postmortem samples (RAMALT, brainstem, and medial retropharyngeal lymph nodes) evaluated by using the current gold standard in vitro assay, immunohistochemistry (IHC) analysis. We hypothesized that the sensitivity of RT-QuIC would be comparable to IHC analysis in antemortem tissues and would correlate with both the genotype and the stage of clinical disease. Our results showed that RAMALT testing by RT-QuIC assay had the highest sensitivity (69.8%) compared to that of postmortem testing, with a specificity of >93.9%. These data suggest that RT-QuIC, like IHC analysis, is an effective assay for detection of PrP ^CWD in rectal biopsy specimens and other antemortem samples and, with further research to identify more sensitive tissues, bodily fluids, or experimental conditions, has potential for large-scale and rapid automated testing for CWD diagnosis.

In the northeastern United States, chronic wasting disease has recently been detected in white-tailed deer (Odocoileus virginianus) populations, and understanding the relationship between landscape configuration and home range may improve disease surveillance and containment efforts. The objectives of our study were to compare size of home range for deer occupying a continuum of forested landscapes and to investigate relationships between size of home range and measures of landscape configuration. We used a movement-based kernel density estimator to estimate home range at five spatial scales among deer across study areas. We developed 7 linear regression models that used measures of the configuration of the forested landscape to explain size of home range. We observed differences in size of home range between sexes among areas that differed based on landscape configuration. We documented size of home range changed with various metrics that identifying connectivity of forested patches. Generally, size of home range increased with an increasing proportion of homogenous forest. Our results suggest that deer in our region occupy a landscape at hierarchically-nested scales that is controlled by the connectivity of the forested landscape across local or broad geographical regions.

Microsatellite loci have been used extensively over the past two decades to study the genetic characteristics of non-model species. The ease of microsatellite development and ability to adapt markers from related species has led to the proliferation of available markers for many commonly studied species. Because it is often infeasible to genotype individuals across all available loci, researchers generally rely on subsets of markers. Marker choice can bias inferences made using disparate suites of loci. This has been a primary motivation for efforts to identify uniform marker panels. Here, we use the geographic distribution of previous studies to identify microsatellite loci for white-tailed deer (Odocoileus virginianus) with the potential for widespread use, and we evaluate the effectiveness of this panel in a portion of the range where few previous studies have been conducted. The purpose was to consolidate the numerous genetic resources for this species into a manageable panel and to provide a uniform methodology that improves comparisons between past and future studies. We reviewed microsatellite panels from 58 previous or ongoing projects and identified 106 candidate loci. We developed a multiplex protocol and evaluated the efficacy of 17 of the most commonly used loci using 720 DNA samples collected from the Mid-Atlantic region of the United States of America. Amplification errors were detected in six of these loci. The 11 remaining loci were highly polymorphic, exhibited low frequencies of null alleles, and were easy to interpret with the aid of allele binning software. The development of broadly-applicable, core microsatellite panels has the potential to improve repeatability and comparative ability for commonly studied species. The properties of the consolidated 11 microsatellite panel suggest that they are applicable for many common research objectives for white-tailed deer. The geographic distribution of previous studies using these markers provides a greater degree of confidence regarding the robustness to common sources of error related to amplification anomalies, such as null alleles, relative to loci with more limited use. While this does not replace further evaluation of genotyping errors, it does provide a common platform that benefits future research studies.

Genetic variability in the prion protein (Prnp) gene influences host susceptibility to many pathogenic prion diseases. Understanding the distribution of susceptible Prnp variants and determining factors influencing spatial genetic patterns are important components of many chronic wasting disease mitigation strategies. Here, we describe Prnp variability in white-tailed deer (Odocoileus virginianus) from the Mid-Atlantic region of the United States of America, an area with a recent history of infection and low disease incidence. This population is characterized by lower rates of polymorphism and significantly higher frequencies of the more susceptible 96GG genotype compared to previously surveyed populations. The prevalence of the most susceptible genotypes at disease-associated loci did vary among subregions, indicating that populations have innate differences in genotype-dictated susceptibility.

Understanding the geographic extent and connectivity of wildlife populations can provide important insights into the management of disease outbreaks but defining patterns of population structure is difficult for widely distributed species. Landscape genetic analyses are powerful methods for identifying cryptic structure and movement patterns that may be associated with spatial epizootic patterns in such cases. We characterized patterns of population substructure and connectivity using microsatellite genotypes from 2,222 white‐tailed deer (Odocoileus virginianus) in the Mid‐Atlantic region of the United States, a region where chronic wasting disease was first detected in 2009. The goal of this study was to evaluate the juxtaposition between population structure, landscape features that influence gene flow, and current disease management units. Clustering analyses identified four to five subpopulations in this region, the edges of which corresponded to ecophysiographic provinces. Subpopulations were further partitioned into 11 clusters with subtle (F_ST ≤ 0.041), but significant genetic differentiation. Genetic differentiation was lower and migration rates were higher among neighboring genetic clusters, indicating an underlying genetic cline. Genetic discontinuities were associated with topographic barriers, however. Resistance surface modeling indicated that gene flow was diffuse in homogenous landscapes, but the direction and extent of gene flow were influenced by forest cover, traffic volume, and elevational relief in subregions heterogeneous for these landscape features. Chronic wasting disease primarily occurred among genetic clusters within a single subpopulation and along corridors of high landscape connectivity. These results may suggest a possible correlation between population substructure, landscape connectivity, and the occurrence of diseases for widespread species. Considering these factors may be useful in delineating effective management units, although only the largest features produced appreciable differences in subpopulation structure. Disease mitigation strategies implemented at the scale of ecophysiographic provinces are likely to be more effective than those implemented at finer scales.

The U.S. Geological Survey (USGS) investigates chronic wasting disease (CWD) at multiple science centers and cooperative research units across the Nation and supports the management of CWD through science-based strategies. CWD research conducted by USGS scientists has three strategies: (1) to understand the biology, ecology, and causes and distribution of CWD; (2) to assess and predict the spread and persistence of CWD in wildlife and the environment; and (3) to develop tools for early detection, diagnosis, surveillance, and control of CWD.