Spatio-temporal analyses reveal infectious disease-driven selection in a free-ranging ungulate

Infectious diseases play an important role in wildlife population dynamics by altering individual fitness, but detecting disease-driven natural selection in free-ranging populations is difficult due to complex disease–host relationships. Chronic wasting disease (CWD) is a fatal infectious prion disease in cervids for which mutations in a single gene have been mechanistically linked to disease outcomes, providing a rare opportunity to study disease-driven selection in wildlife. In Wyoming, USA, CWD has gradually spread across mule deer (Odocoileus hemionus) populations, producing natural variation in disease history to evaluate selection pressure. We used spatial variation and a novel temporal comparison to investigate the relationship between CWD and a mutation at codon 225 of the mule deer prion protein gene that slows disease progression. We found that individuals with the ‘slow’ 225F allele were less likely to test positive for CWD, and the 225F allele was more common in herds exposed to CWD longer. We also found that in the past 2 decades, the 225F allele frequency increased more in herds with higher CWD prevalence. This study expanded on previous research by analysing spatio-temporal patterns of individual and herd-based disease data to present multiple lines of evidence for disease-driven selection in free-ranging wildlife.


Review form: Reviewer 2
Is the manuscript scientifically sound in its present form? Yes

Are the interpretations and conclusions justified by the results? Yes
Is the language acceptable? Yes

Do you have any ethical concerns with this paper? No
Have you any concerns about statistical analyses in this paper? No

Recommendation? Accept with minor revision (please list in comments)
Comments to the Author(s) I enjoyed reading this manuscript on the impacts of CWD on mule deer genetics in Wyoming. The authors found that the 225F allele in mule deer is more common in areas with more CWD, increasing over time in those areas, and 225F individuals are less likely to be CWD positive. These results are in-line with expectations based on other studies, but the manuscript provides a wealth of new data to the important issue. I support the publication of this manuscript and have only a few minor suggestions for how the authors might further improve the manuscript.
Prediction 1: 1. Many readers will wonder why age is not included in the model. I may have missed it, but if age is available, then this would be an important covariate. If it is not available, then just state that it was not available. 2. Some may consider it a little odd to have the prevalence as a predictor of individual disease status. An slight alternative here, would be to remove herd prevalence, but add herd ID as a random effect. If some herds are more poorly sampled than others, than this structure would more appropriately account for that issue. 3. The authors could consider post-hoc an 225*herd (or 225*herdprevalence) interaction term. The idea here being that at low prevalence there may be limited impact of the 225 allele, but that impact increases with herd prevalence. 4. The authors should state or discuss their proposed mechanism for prediction 1. I may have missed it, but one mechanism is the longer lag time between exposure and becoming test positive. An alternative (but less likely) is that the exposure rate differs. A third alternative is that 225F's may survive longer with CWD and thus be at higher prevalence than others. The last one is not supported by the data, but going through all the alternatives seems helpful in the intro or discussion. Prediction 2 & 3. It is a little unclear here what models were run and their exact structure. I suggest writing the best model out explicitly or including a table of all the options that were run. In predictions 2 & 3 we have genotype as the dependent variable and CWD as a predictor, whereas Prediction 1 was the reverse direction. In terms of mechanisms, the focus here is on how CWD may alter genotype frequency, so having genotype as the dependent variable makes sense. Given the mechanism of a lag before being test positive I can see why the authors did the reverse for #1, but I think an explanation would help the reader, and maybe a re-ordering such that Prediction 1 gets dropped to the 3rd position.
I thought the discussion around the potential costs of being 225FF and integrating natural selection into population models was really good.
Decision letter (RSOS-210802.R0) We hope you are keeping well at this difficult and unusual time. We continue to value your support of the journal in these challenging circumstances. If Royal Society Open Science can assist you at all, please don't hesitate to let us know at the email address below.

Dear Dr Ernest
On behalf of the Editors, we are pleased to inform you that your Manuscript RSOS-210802 "Spatiotemporal analyses reveal infectious disease-driven selection in a free-ranging ungulate" has been accepted for publication in Royal Society Open Science subject to minor revision in accordance with the referees' reports. Please find the referees' comments along with any feedback from the Editors below my signature.
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We would be happy to see a revised version that takes these comments into account.

Best regards, J Mergeay
Reviewer comments to Author: Reviewer: 1 Comments to the Author(s) This is a study of how variation in CWD susceptibility linked to the PRNP gene affect selection. Selection of PRNP genotypes relative to CWD status has been done previously a few times, but it is important to replicate such studies given their importance. The paper is very clear and well done. My comments are fairly minor apart from one statistical issue.
A main weakness of the study is that spatial structure was not taken into account in statistical analysis (as in many other studies). It is basically three regions driving these relationships. You should consider adding "area" as a random term in the models of individual data (e.g. LME4 package), and even better, some spatial correlation structure to account for spatial autocorrelation (more technically challenging).
Line 211. If you compare frequencies, why turn to t-test when you correctly use logistic regression elsewhere? (use e.g. the cbind command) Minor comments Line 312 onwards. What is the CWD prevalence in bordering Colorado hunting areas? I am thinking of the paper by Miller et al. 2020 in J Wildl Dis "Hunting pressure modulates..". A different management system for bucks in the areas? Just curious! Anything about the age composition in the herds? This can also affect CWD prevalence and also possible transmission rates.
Introduction first paragraph. Why focus on genomics, when you end up studying a single gene, PRNP?
Line 80. You should add that it was "in 2018" list of emerging issues.
Line 119 and table 1: Was CWD prevalence estimated separately for males and females? (as you state line 180, males are more often infected) How was age taken into account?
Line 124. As you correctly state, detection issues are important with CWD. Any information about sample sizes in the different herds? -could affect this timing variable.
Line 134. Could these genetics affect the PRNP composition in the first place? Should be possible to include in analysis of your 3rd prediction.
Line 265-268. This was not clear. "Our model based on herd prevalence…" -> be explicit on what model?
Line 280 "though they did not consider…" They did not explicitly consider, they did discuss this. Suggest to delete last part of sentence.
Line 326. Add reference after "This study" -it is not your study.
Conclusion is a bit generic.
Reviewer: 2 Comments to the Author(s) I enjoyed reading this manuscript on the impacts of CWD on mule deer genetics in Wyoming. The authors found that the 225F allele in mule deer is more common in areas with more CWD, increasing over time in those areas, and 225F individuals are less likely to be CWD positive. These results are in-line with expectations based on other studies, but the manuscript provides a wealth of new data to the important issue. I support the publication of this manuscript and have only a few minor suggestions for how the authors might further improve the manuscript.
Prediction 1: 1. Many readers will wonder why age is not included in the model. I may have missed it, but if age is available, then this would be an important covariate. If it is not available, then just state that it was not available. 2. Some may consider it a little odd to have the prevalence as a predictor of individual disease status. An slight alternative here, would be to remove herd prevalence, but add herd ID as a random effect. If some herds are more poorly sampled than others, than this structure would more appropriately account for that issue. 3. The authors could consider post-hoc an 225*herd (or 225*herdprevalence) interaction term. The idea here being that at low prevalence there may be limited impact of the 225 allele, but that impact increases with herd prevalence. 4. The authors should state or discuss their proposed mechanism for prediction 1. I may have missed it, but one mechanism is the longer lag time between exposure and becoming test positive. An alternative (but less likely) is that the exposure rate differs. A third alternative is that 225F's may survive longer with CWD and thus be at higher prevalence than others. The last one is not supported by the data, but going through all the alternatives seems helpful in the intro or discussion.
Prediction 2 & 3. It is a little unclear here what models were run and their exact structure. I suggest writing the best model out explicitly or including a table of all the options that were run. In predictions 2 & 3 we have genotype as the dependent variable and CWD as a predictor, whereas Prediction 1 was the reverse direction. In terms of mechanisms, the focus here is on how CWD may alter genotype frequency, so having genotype as the dependent variable makes sense. Given the mechanism of a lag before being test positive I can see why the authors did the reverse for #1, but I think an explanation would help the reader, and maybe a re-ordering such that Prediction 1 gets dropped to the 3rd position.
I thought the discussion around the potential costs of being 225FF and integrating natural selection into population models was really good.

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Decision letter (RSOS-210802.R1)
We hope you are keeping well at this difficult and unusual time. We continue to value your support of the journal in these challenging circumstances. If Royal Society Open Science can assist you at all, please don't hesitate to let us know at the email address below.
Dear Dr Ernest, I am pleased to inform you that your manuscript entitled "Spatiotemporal analyses reveal infectious disease-driven selection in a free-ranging ungulate" is now accepted for publication in Royal Society Open Science.
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On behalf of the Editors of Royal Society Open Science, thank you for your support of the journal and we look forward to your continued contributions to Royal Society Open Science. To Dr. Jeremy Sanders, Editor-in-Chief, Royal Society Open Science: Please find a revision of our manuscript entitled, 'Spatiotemporal analyses reveal infectious disease-driven selection in a free-ranging ungulate,' by Melanie E. F. LaCava, Jennifer L. Malmberg, William H. Edwards, Laura N.L. Johnson, Samantha E. Allen, and Holly B. Ernest, for publication as a research article in Royal Society Open Science.
In the original manuscript, now revised, we investigated natural selection in wild mule deer in relation to chronic wasting disease (CWD), which has gradually spread across Wyoming, USA, creating natural variation in disease history for our study. We characterized the relationship between CWD and a mutation in the mule deer prion protein gene that slows disease progression using 1,156 deer sampled across multiple ecosystems. We used both individual-and herd-based analyses and employed a novel temporal comparison to provide multiple lines of evidence for disease-driven selection in free-ranging mule deer. Our study provides new information to incorporate natural selection into population models to better predict future disease dynamics.
The manuscript received favorable and constructive reviews that guided our revision and resulted in an improved manuscript. Important revisions to the manuscript include the following: 1. We introduced an additional model to our first prediction to address potential spatial autocorrelation 2. We improved our explanation of the logic driving each of our three predictions 3. We clarified descriptions of our data and definitions of our models We made additional minor revisions based on recommendations from reviewers. Below you will find documentation of all changes we made to the manuscript and we have noted point-by-point, with reference to content and line numbers, how we addressed each of the reviewers' comments specifically. We have included in our submission electronic supplementary materials and a link to a Dryad Digital Repository containing data and code to replicate our analyses in full. The enclosed work is not under consideration for publication in another journal. All authors have read and agreed to the submission of this version of the manuscript. The authors have no conflicts of interest to declare.
Thank you for the opportunity to revise this manuscript for publication, Holly Ernest, DVM, PhD (corresponding author) & Melanie LaCava, PhD (first author)

Line-by-line responses to reviewer comments:
(Please note that our responses to individual reviewer comments each begin with RESPONSE and are bolded to help distinguish them from the reviews. Line number listed here correspond to the "track changes" version of the manuscript.) Associate Editor Comments to Author (Dr Joachim Mergeay): Dear authors, We have received two promising reviews of your manuscript. Both reviewers provide constructive criticism and suggestions with regards to the statistical analyses (most important is about independence, identified by rev1), most of which are relatively easy to tackle and integrate in the current methodology.
We would be happy to see a revised version that takes these comments into account.
Best regards,

J Mergeay
Reviewer comments to Author: Reviewer: 1 Comments to the Author(s) This is a study of how variation in CWD susceptibility linked to the PRNP gene affect selection. Selection of PRNP genotypes relative to CWD status has been done previously a few times, but it is important to replicate such studies given their importance. The paper is very clear and well done. My comments are fairly minor apart from one statistical issue.
A main weakness of the study is that spatial structure was not taken into account in statistical analysis (as in many other studies). It is basically three regions driving these relationships. You should consider adding "area" as a random term in the models of individual data (e.g. LME4 package), and even better, some spatial correlation structure to account for spatial autocorrelation (more technically challenging).

RESPONSE:
To address the concern about spatial autocorrelation among herds in our study, we ran a model relating individual CWD status to geographic region (CWD+ ~ Region) to compare with our current null model (CWD+ ~ HerdPrev). If region outperformed herd prevalence, this would suggest that individual disease status was based on some unmeasured variable related to these three geographic regions (e.g., neutral genetic structure), rather than due to variation in disease prevalence within the herd. Geographic region performed worse than herd prevalence as a predictor (AIC= 644.5 for CWD+ ~ Region vs. AIC=640.7 for CWD+ ~ HerdPrev), suggesting that herd prevalence represents the observed pattern better than spatial structure. We did not try including geographic region as a random effect in our models because Gelman and Hill 2006 (doi:10.1017/CBO9780511790942) recommend against using random effects with less than five groups, and also the geographic regions are not truly random. We incorporated the additional tested model into our paper in three place: in table 2; in lines 199-203 of the methods section, "Lastly, to address potential spatial autocorrelation among herds in the three geographic regions of the state, we tested a model relating individual CWD status to geographic region. If region outperformed herd prevalence, this would suggest that individual disease status was based on some unmeasured variable related to these three geographic regions (e.g., neutral genetic structure), rather than due to disease prevalence in the herd;" and in lines 280-283 of the results section, "Lastly, our null model relating individual CWD status to herd prevalence outperformed our model relating individual CWD status to geographic region (ΔAIC = 48.7), indicating that the disease-specific variable of herd prevalence predicted individual disease status better than the spatial clumping of herds in our study." Line 211. If you compare frequencies, why turn to t-test when you correctly use logistic regression elsewhere? (use e.g. the cbind command) RESPONSE: The t-test and linear regression serve different purposes for our third prediction. We initially performed a t-test to determine whether the frequency of individuals possessing the 225F allele increased over time. If we had not observed a significant increase in genotype frequency, we likely would not have proceeded with our linear regression to assess the relationship between an increase in genotype frequency and herd CWD prevalence. We do not believe a logistic regression would serve the same purpose as the t-test in this case. We made the purpose of the t-test clearer in our methods section with this updated excerpt on lines 236-244, "We first tested whether the slow 225*F genotype frequency significantly increased over a span of approximately two decades in seven of our focal herds. We compared current 225*F genotype frequencies to previously reported genotype frequencies from mule deer samples collected in 2001-2003 using a paired t-test in the R package stats version 4.0.2. After documenting a significant increase in genotype frequencies over time, we investigated whether herds exposed to higher CWD prevalence rates exhibited greater increases in 225*F genotype frequencies, with the idea that CWD prevalence might represent relative selection pressure imposed by the disease." Minor comments Line 312 onwards. What is the CWD prevalence in bordering Colorado hunting areas? I am thinking of the paper by Miller et al. 2020 in J Wildl Dis "Hunting pressure modulates..". A different management system for bucks in the areas? Just curious! Anything about the age composition in the herds? This can also affect CWD prevalence and also possible transmission rates.