Part Two: The Study (https://www.mdpi.com/2076-393X/12/1/96)

Abstract

This study evaluates the immunocontraceptive efficacy of Porcine Zona Pellucida (PZP) treatment on the Virginia Range free-roaming horse population, analysing the impacts of PZP on fertility rates over four years (2019-2022). Researchers monitored 2,817 mares, tracking vaccination records and resulting reproductive outcomes. The analysis demonstrated a significant reduction in foaling rates, suggesting a nearly 60% decrease in pregnancies due to the pzp treatment.

However, the study’s methodology faced criticism for lacking rigorous statistical analysis, insufficient control for confounding variables, and reliance on descriptive statistics without inferential modelling. Recommendations for future research emphasise the need for mixed-effects models and survival analysis to assess vaccine efficacy and duration of effect better, enhancing the overall robustness of findings in wild horse population management.

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Critical Scholarly Evaluation

Scientific Rigour and Methodology

• Strengths: Comprehensive field dataset; basic tracking of age, treatment, and reproductive outcomes.
• Major Weaknesses:
  • No statistical testing or modelling to establish causation.
  • No control group or comparison cohort.
  • No adjustment for confounders (e.g., predation, environmental change, mortality).
  • Relied on visual trends rather than hypothesis-driven methods.
• Verdict: Methodologically weak. Fails to meet the baseline standard for quantitative evaluation of treatment efficacy in population science.

 Statistical Soundness

• Strengths: Descriptive statistics are presented clearly.
• Major Weaknesses:
  • No inferential statistics, p-values, confidence intervals, or effect size estimates.
  • Despite rich longitudinal data, no regression, survival analysis, or repeated measures modelling.
  • Assumptions about vaccine efficacy duration were not tested against observed outcomes.
• Verdict: The study’s lack of statistical rigour is a serious flaw, as it makes population-level claims about contraceptive efficacy.

Journal Quality (MDPI Vaccines)

• Concerns:
  • Critics have criticised MDPI for operating with predatory tendencies, including fast-tracked peer review and questionable editorial practices.
  • While Vaccines is indexed and has an impact factor, it is not a top-tier veterinary science, ecology, or public health journal.
  • The article likely passed with minimal statistical peer scrutiny given its analytic weaknesses.
• Verdict: Publishing in MDPI Vaccines undermines the credibility of an already under-analysed dataset.

Overall Scholarly Contribution

• Comment: While the field data is valuable and the observational findings are intuitively aligned with known PZP effects, the analytical execution is too weak to support evidence-based decision-making. It reads more as a program summary than a rigorous scientific study.

Final Assessment

This study would not pass peer review in a journal with strict standards for statistical analysis or epidemiological rigour. It is unsuitable for drawing firm conclusions about efficacy, duration, or policy recommendations without reanalysis using appropriate statistical models.

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Detailed evaluation of Statistical Methodology in the PZP Wild Horse Study

Introduction

The “Immunocontraceptive Efficacy of Native Porcine Zona Pellucida (PZP) Treatment of Nevada’s Virginia Range Free-Roaming Horse Population” study (Vaccines 2024, 12, 96) evaluated PZP fertility control effects through darts on birth rates and demographic patterns of a significant wild horse population. Researchers monitored monthly records about each mare within the population across four years from 2019 through 2022. The researchers measured vaccination records, birth status, and other factors. The goal was to evaluate vaccine effectiveness by measuring annual birthrate changes and population dynamics as PZP treatment reached more horses. Our assessment focuses on the statistical techniques from this research study for their suitability alongside their reliability and bias control mechanisms, as well as their approach to modelling vaccine persistence and longitudinal data analysis to determine if alternative analytical strategies would have produced more substantial findings.

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Summary of Data and Statistical Methods Used

Data Structure: The study examined 2817 female horses across 48 months. Each mare had multiple monthly data points that recorded her study presence. Using different efficacy models, the authors recorded and computed several variables during each mare-month observation, including pre-2019 vaccination status and total pzp vaccination count. The authors monitored key variables, which included mare pregnancy status and conception status, alongside foaling status, age classification, and social group affiliations, and observed granuloma or abscesses at injection sites. The detailed longitudinal data at an individual level allowed researchers to analyse treatment delivery and reproductive results throughout the observation period.

The analysis employed vaccine efficacy scenarios to model the PZP duration of effect. The field longevity of PZP contraceptive effects remained unknown, so researchers established four vaccine duration scenarios that spanned from permanent effects to six-month, twelve-month, and eighteen-month efficacy. The researchers modified cumulative vaccination records for each mare by removing expired vaccinations based on selected time intervals. A mare’s vaccine efficacy period would expire six months after vaccination unless she received additional booster shots. The team calculated average vaccination numbers per mare through time for all specified scenarios. The program started by giving initial primers and boosters before conducting yearly boosters as needed. The average number of active vaccinations per mare reached approximately one by 2022 under the twelve-month efficacy assumption after an initial boost (Vaccines 2024, 12, 96). The study conducted a sensitivity analysis using these scenarios to show how changing vaccine persistence would impact both coverage measures and booster dosage requirements.

The study analysis focused primarily on descriptive analysis. The authors used database queries for basic count and summary statistics while employing R for exploratory data analysis and graphing. The researchers produced graphical representations of essential metrics, which included mean vaccinations per mare and proportion of mares foaling across different time points. The research results presented statistical data through proportion analysis, count methods, and time-series graphical displays while focusing on changes in the four years. They documented that vaccination coverage reached 72.5% of the herd during the fourth year in 2022, while foaling results underwent substantial alterations. The foaling rate calculated from the percentage of birthing mares revealed a consistent downward trend until 2022, when only 10% of mares became pregnant, while the pre-program percentage stood at approximately 33%. The foaling rate declined by approximately 58-60%, and pregnant mare numbers decreased to 10% of the total population, thus proving contraceptive effectiveness. Statistical significance tests or confidence intervals did not support the observed population associations because the results were presented without these statistical measures.

The research study omitted complex statistical model analysis to determine the vaccine effects on various outcomes while managing other influencing variables. The analysis results lack information about p-values, confidence intervals, and formal model coefficients. The authors show patterns (e.g. “foaling rates approximately halved in 2021 and by 2022 were further reduced by ~60%”) that result from higher vaccine coverage. Yet, they do not include logistic regression models to assess the probability of foaling against treatment variables. The entire study population acted as its time-based comparison in the observational and descriptive research methodology. The study presented visual results showing fertility metric reductions as vaccination programs expanded, instead of performing statistical hypothesis tests.

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Appropriateness and Rigour of the Statistical Methods

The study used descriptive statistical analysis to analyse a whole-population field program without an untreated control group. The study effectively linked PZP vaccine distribution with dramatic reductions in foaling statistics. The descriptive analysis method enables effective communication of raw results through examples, such as comparing the annual foaling rate of 1 in 3 mares before the program to 1 in 10 mares by 2022. The analysis gained strength from using monthly data examination and mare age class separation between mature and yearling groups to distinguish between juvenile non-breeders and adult breeders. The selection of descriptive statistics improved their accuracy by using the correct population of mature mares for foaling rate calculations.

The absence of inferential statistical tests or models restricts the analysis from correctly measuring uncertainty or establishing causality. The study implements PZP treatment changes to explain observed outcomes, but does not present any statistical evidence to support this assumption. The authors fail to demonstrate any statistical analysis which would have allowed them to determine how vaccinated mares compare to unvaccinated mares in terms of foaling rates after adjusting for age. The lack of statistical analysis creates uncertainty about certain aspects of the study. The absence of statistical modelling techniques prevents the assessment of year-to-year statistical significance and individual and subgroup response variability. The lack of error measurements (confidence intervals on the 58% foaling reduction or the 10% conception rate) prevents us from determining the extent to which natural variation or unreported variables contributed to the observed changes. The analysis shows associations, but fails to establish statistical causation or provide measures of treatment effect uncertainty.

The study results are population-level descriptions because the authors include all observed subjects in their analysis, freeing them from typical sampling errors. When considering the outcome as a complete census population, hypothesis testing becomes less significant. Scientists often employ modelling to generalise findings even in situations that could be classified as “census”. The study would have benefited from including inferential analysis to confirm that other factors did not influence the observed patterns. The study should have included a statistical model to account for the missing 2019 data and to check if the observed decrease from 2020 to 2022 surpassed the expected patterns by chance or natural trends. Without statistical evidence for “efficacy”, the study depends on assuming no other substantial changes occurred during the program period.

The observational nature of this study requires an explicit evaluation of the assumption that no other significant changes took place during the study period. A more rigorous approach would involve demonstrating that similar declines were not observed in control areas or untreated periods. The descriptive statistical methods offered essential data insights, yet failed to reach the necessary standards for drawing causal conclusions. The initial field report approach was suitable for the study, but it provides opportunities for additional analysis. The following section highlights the challenges related to confounding and bias that emerge from this study design.

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The treatment of confounders, assumptions, and biases in the study.

Researchers took several data handling steps to minimise possible biases in their study. The efficacy analysis was protected from confounding factors by excluding mares that lived in prohibited areas or received treatment in the last year. The analysis excluded horses living south of a highway because these horses missed one year of treatment opportunities, and this bias would distort results if they were included. The authors removed the mares from the Fernley area who participated in the program during the last year because their observation period was insufficient. The exclusion of these regions was appropriate because including primarily untreated areas in the analysis might have weakened the vaccine effect or introduced geographic factors as confounders. The authors removed these horses from the study because the core population received the treatment consistently. The study distinguished immature females from mature females to prevent foaling rates from being confounded by yearling population growth. The researchers implemented age-based stratification to guarantee that foaling rate data measured breeding-capable mare fertility while excluding young non-reproductive animals. Uncontrolled Confounders: Despite those steps, several potential confounders and biases remain unaddressed or only qualitatively acknowledged:

Time Trends and Environmental Factors: The study took place over 4 years, during which other changes could influence foaling rates. For example, forage availability can fluctuate, drought can occur, disease can spread, predation can be a significant problem, and all of these can influence reproduction and foal survival independent of pzp. The authors noted an excessively high foal mortality rate (up to 63% in 2022) due to predator presence. Such mortality doesn’t prevent conception, but could mean some foals were never documented (if predation occurred shortly after birth). No adjustment or sensitivity analysis was done to account for year-specific environmental factors. A more rigorous approach might include year effects in a model or compare to historic data on foaling in that herd under similar ecological conditions.

Population Structure Changes: The program itself likely altered the population structure (fewer foals born means the age distribution shifts older, and fewer mature mares may be added each year). The authors observed a decline in the absolute number of mature mares over time, which could partly be an outcome of contraception (fewer new females) but also could result from natural mortality or removals. In fact, by the end of 2022, 1,089 of the study mares were classified as “deceased” (natural attrition) and 22 were “removed” by management. If mortality was high (possibly related to drought or predation), the decrease in foaling could partly reflect fewer mares being alive or healthy enough to reproduce, not just contraceptive effects. The analysis did not control for the changing number of mares at risk each year beyond expressing foaling as a percentage. This is primarily acceptable (foaling rate inherently accounts for several mares). Still, if specific subsets of mares were more likely to die or be removed (for example, perhaps untreated mares ranging in risky areas suffered higher predation), that could bias comparisons. The study did not examine whether the mares that remained in the dataset differed from those lost, which is a potential source of bias

Pre-existing Fertility Differences: In any observational study, treated and untreated subjects might differ. Treatment rollout was widespread here, but not every mare was darted immediately. One potential confounder is individual mare fertility or social status. It’s conceivable that the easiest mares to dart (those often near people or water) might have different reproductive rates than those in remote areas (which possibly had lower baseline foaling or higher foal predation). Since treatment wasn’t randomised, any such differences could confound results. The study did not explicitly compare foaling rates in untreated versus treated mares in the same period – a comparison that could have been informative. Instead, all mares in treated areas were analysed as one group, with increasing overall treatment coverage. This means we assume no systematic differences between the first and last mares darted, which might not hold strictly. The authors did not address this possible bias.

Initial Conditions and Lag: The authors acknowledge a key temporal bias: many mares were already pregnant when vaccination started (April 2019, peak foaling season). Consequently, the first foaling season’s data do not reflect the vaccine’s impact (those foals were conceived pre-program), and even the second year included some foals from mares vaccinated mid-pregnancy. They correctly note that a lag in effect was expected until the second year. They handled this by interpreting 2019’s foaling rate as artificially low (due to undercounting) and focusing on drops after 2020. However, they did not formally adjust the analysis to exclude those initial pregnancies or control for whether a mare had already foaled when first treated. A more rigorous analysis could have, for example, excluded foals born in 2019 from the efficacy calculation or started the “clock” for each mare after her last pre-treatment foal to more cleanly measure new conceptions under treatment. The study’s efficacy metrics (58% reduction in foaling) were computed at the population level without such fine-tuning, which could slightly underestimate true efficacy due to that initial lag.T he authors took sufficient measures to eliminate apparent confounding effects (entirely untreated subpopulations) and acknowledged some biases (for example, underestimation of foals at the beginning). However, they did not use statistical controls for confounders in the analysis. The approach assigns all changes in foaling to the treatment, which, although likely true, is not definitively proven without either a control group or a multivariate analysis. The study would have been more methodologically rigorous if the authors had explicitly controlled for year effects, regional differences, or pre-treatment fertility in a model. A before-and-after comparison of the same mares or a subgroup analysis (e.g. horses that remained untreated longer) could have helped isolate the vaccine’s effect. There remains some (albeit small) possibility that factors other than PZP contributed to the observed outcomes.

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Modelling of Vaccine Efficacy and Assumptions about Longevity

One of the strengths of the study’s methodology was its attempt to bracket the unknown duration of pzp’s contraceptive effect by analysing multiple scenarios. The four efficacy-duration scenarios (permanent, eighteen months, twelve months, six months) adjusted cumulative vaccine counts per mare. This sensitivity analysis is commendable in recognising a key assumption – how long a single primer+booster prevents pregnancy – and showing how different assumptions would change the interpretation of how many “effective treatment units” each mare received. For example, by month 48, mares had 3.74 shots on average (no efficacy loss scenario), but under a 12-month efficacy assumption, this translated to maintaining roughly one active shot per mare (since older shots “expired”). The study found that under a 12-month efficacy model, the program reached a steady state of ~1 vaccination per mare per year after the second year, which the authors cite as a “robust recommendation for treatment frequency” (i.e. an annual booster). In practical terms, their data supports the idea that yearly boosters are sufficient to keep fertility low, aligning with the 12-month efficacy assumption.

The scenario-based modelling was helpful but relatively simplistic, and it assumes rather than infers the vaccine’s longevity. The study did not test which assumption was most consistent with the observed foaling data. Ideally, one could try to infer the duration of vaccine effect from the data, for instance, by examining if pregnancies occur around 12+ months after treatment without a booster. The paper did not report an analysis correlating time since vaccination with pregnancy risk. Instead, it effectively sidestepped that by presenting all scenarios. This is conservative (it doesn’t over-claim how long PZP works), but it means the study doesn’t pinpoint vaccine longevity. They lean on the 12-month scenario as a likely case, noting the system “reached stability” at annual boosting, which suggests the authors’ interpretation that ~12 months is close to the actual duration of strong efficacy.

A more rigorous approach could have used time-to-event modelling or regression to estimate vaccine efficacy decay. For example, a survival analysis could treat the “event” as a mare conceiving or foaling, and include a time-dependent covariate for whether the mare is within X months of a vaccination. This would allow an estimate of the hazard of conception returning as months since the last treatment increase. If the hazard jumps after 12 months, that would empirically support a 1-year duration. Alternatively, a logistic regression for each breeding season could include the number of months since last shot as a predictor of pregnancy, to see if efficacy significantly drops off at 12–18 months. The current study did not undertake these analyses. Instead, assuming several fixed durations provided a range of possible outcomes (from best case permanent to worst case 6-month). It showed that even in the worst case, the program still achieved a substantial fertility reduction (because boosters were given frequently enough). This addresses the question “how sensitive are our results to vaccine longevity?” but not “what is the vaccine longevity given our results?”The study determined modelling success by comparing foaling rate reduction and contraceptive achievement. They employed foaling rate as a surrogate measure for conception rate and an indicator of the efficacy’s opposite effect. The foaling rate directly measures pregnancy prevention because mares can foal only once yearly. The researchers directly confirmed that about 1/3 of mares used to become pregnant annually until the program started, but only 1/10 of mares became pregnant thereafter. According to the study data, the conception rate decreased by about 67%, which matches the reported 58–60% decrease in foaling numbers due to some initial underreporting. The vaccine effectiveness model operated on a basic binary system, which tracked whether mares were pregnant or not pregnant without assessing individual vaccine performance or foaling probabilities. Smaller controlled research studies demonstrate that a primer followed by a booster achieves 90% success in preventing foals during one year. The field study avoided publishing such statistics because mares received multiple vaccinations, and no untreated control group existed for comparison. The researchers based their program evaluation on a 58% reduction in foaling as the primary metric.

The study presented vaccine efficacy assumptions with clear transparency through basic methods. The model presented realistic scenarios instead of performing a statistical analysis to determine how the vaccine effectiveness changed over time. Future research should adopt models that analyse the data to match efficacy-decay patterns. The evaluation could use mixed-effects models with random mare effects to examine the relationship between foaling rates and months since vaccination. The authors must calculate “time since last vaccination” to understand how fertility probability evolves post-treatment. The research would deliver data-based results about how long PZP maintains its effectiveness within this population. Such modelling could become more accurate by adding previously established individual vaccine efficacy from prior studies (e.g., two initial doses produce about 90% contraceptive success in the first year ) as prior knowledge or constraints. The study proves that annual booster injections are necessary for operations, but fails to deliver a precise analysis of vaccine duration, which presents an opportunity to expand research.

The data collection structure allows for advanced statistical analysis by implementing longitudinal data structures. The research contains natural longitudinal data because scientists monitored the same mares for four consecutive years. Advanced statistical models, including mixed-effects models, GEE, and time-series methods, can be applied to utilise repeated measures while accounting for individual differences in the data. The study authors omitted employing advanced statistical models in their published work, and this paper evaluates their decision.

The analysis presented a simplified view by combining monthly observations into yearly summaries and skipped explicit modelling of mare-specific patterns, thus treating each monthly observation as an independent event. The reproductive outcomes between consecutive years for the same mare remain correlated because mares treated consistently have higher probabilities of not foaling in 2021 and 2022. Statistical tests performed on this data without repeated measures consideration would result in incorrect uncertainty calculations because 2817×48 mare-month points cannot be treated as entirely independent data points. The authors safely avoided this error by refraining from carrying out statistical tests on these data points. The longitudinal data served to calculate population-level metrics, including monthly foaling rates, which automatically combined information from different mares. The problem of pseudo-replication remains negligible because their analysis focuses on descriptive statistics rather than statistical tests.

Not implementing a mixed-effects model prevented the study from quantifying mare and band-specific variability. Some horses would fail to become pregnant regardless of treatment, while others might achieve pregnancy despite repeated vaccinations, possibly due to vaccine resistance. A mixed-effects logistic regression model that includes mare-specific random intercepts could measure fertility baseline variations between mares. The analysis would determine vaccine effectiveness by considering these individual differences in the data. The analysis would determine intra-mare correlation (how consistent mare status remains) and residual variance levels. The current study presents an aggregate view that might hide significant patterns regarding the birth of a few foals from specific “problem” mares who either missed booster shots or failed to react immunologically. A more complex modelling approach would provide the solution to this question.

Time-series approaches analyse the herd as one unit to study monthly foal counts and their corresponding rates. The monthly data shows clear seasonal patterns because horses reproduce seasonally, with an overall decrease in the number. A time-series model would use ARIMA with seasonal components or state-space models to measure the trend structure and verify its statistical importance against the observed count variability. An interrupted time series analysis would evaluate April 2019 as the beginning of treatment to determine if there were any significant changes to foaling rate slopes or levels post-intervention when compared to pre-treatment periods. The main obstacle in this situation is the limited pre-intervention data from 2019 because they only had several months before vaccination, which were also partially missing. Analysing historical foal counts from 2018 would enable researchers to determine seasonal patterns, which could be compared to the observed changes in 2019–2022. The study authors did not apply this method since they followed the breeding season timing, which stayed normal, while the foaling peak decreased in amplitude. Through seasonal time-series decomposition and seasonal ARIMA models, researchers could have provided more substantial evidence that the peak timing and duration did not shift along with the confirmed decline in foaling rates. The authors use graphs to demonstrate these findings, yet models provide a statistical backing for their claims.

The data structure (many mares, each observed many times) suggests that a generalised linear mixed model (GLMM) is suitable for analysis. For example, one could set up a logistic GLMM where the outcome is whether a mare foals in a given year (or conceives in a given season) with fixed effects for treatment metrics (e.g., number of shots received, or a binary treated vs not in that year) and random effects for mare and perhaps for year or herd area. Such a model could directly estimate the impact of treatment on the odds of foaling. It could answer questions like: How much does each additional vaccine dose reduce the odds of a mare foaling if adequately specified? Or what is the odds ratio of foaling for a treated mare versus an untreated mare? – controlling for other factors. This would transform the largely qualitative efficacy claim into a quantitative one. For instance, other researchers have used logistic regression to estimate contraceptive effects: Roelle et al. (2017) modelled the probability of foaling in treated vs. control groups using logistic regression and reported that treated mares had dramatically lower odds of foaling (with odds ratios and p-values to demonstrate significance). Adopting a similar approach here would allow the authors to state something like “PZP treatment was associated with an X-fold reduction in the odds of foaling (p < 0.001)”, which is far more statistical language. The current study instead uses phrasing like “associated with a 58% reduction in foaling” without statistical inference, so incorporating a GLMM would tighten that causally.

Another advantage of mixed models is handling group-level effects. The Virginia Range is large, and the data included different herd areas and bands (which were recorded ). There may be random effects of band (harem) – for example, differences in stallion behaviour or band terrain could influence foal outcomes. A hierarchical model could include a random effect for band or geographic area, accounting for clustered outcomes. This was not explored; all data were pooled. While these random effects might not dramatically change the main conclusions, their inclusion would improve the precision of estimates and allow checking that results are not driven by, say, one particular sub-area.

Use of Repeated Measures for Precision: The study forgoes some statistical power by not using the longitudinal nature in an inferential model. Each mare’s history provides multiple data points that, if modelled appropriately, could strengthen confidence in the effect. For example, if a mare serves as her control (pre- vs post-treatment), that self-comparison can account for individual fertility level and improve detection of treatment effect. A before-and-after paired analysis could have been done for mares that had known fertility before PZP and after. The authors did not explicitly conduct such a paired analysis. Still, one could imagine using the data in that way (e.g., “Of mares that had foals before treatment, 80% had no foal after treatment” – a statement that would be compelling evidence of efficacy). Instead, they looked at aggregate foaling rates year by year.

The data structure was rich, but the analysis did not exploit it with advanced models. Mixed-effects models or GEE (generalised estimating equations) would account for the repeated measures and provide more robust inference (especially if one wanted to generalise to other herds or future years). Time-series models could better characterise the trend and seasonal patterns, offering formal tests for pattern changes. The absence of these models is a limitation in the study’s methodology, not in terms of their making a mistake, but in terms of missed opportunities for more profound insight. The results as presented are credible, but a reader might wonder if a rigorous model would yield the same conclusions (most likely yes, but it should be demonstrated). Employing such models would enhance confidence that the observed decline in foaling is genuinely due to the vaccinations and not an artefact of unmodeled variability or correlation.

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Optimality of Chosen Methods vs. Alternatives

The descriptive longitudinal approach effectively showed a large-scale effect but failed to deliver causal or precise results. We detail how alternative statistical methods would lead to a more robust or informative analysis:

Applying Generalised Linear Models (GLMS) through logistic or Poisson regression models would enable researchers to incorporate treatment as a predictor variable while performing formal tests of its effect. The research design uses a logistic GLM to analyse annual foaling success (yes/no per mare-year) alongside variables like the number of vaccine shots administered during that year, mare age, and geographic location. The analysis produces an evaluation of vaccine effectiveness per dose administered. A Poisson or negative binomial GLM could model the count of foals per mare (mostly 0 or 1, but could handle a mare having zero foals vs one foal over the period, etc.). Due to the large number of zero values, the logistic approach presents the most straightforward solution. Implementing a GLM analysis would boost the research methodology by providing p-values and confidence intervals for evaluating the main effect of interest. The research methodology enables users to verify interaction effects and non-linear relationships between variables (for example, the decreasing value of applying more than two shots). The original study did not implement these statistical procedures; thus, a GLM would represent a more robust method to establish cause-effect relationships and measure effect sizes.

 Mixed-Effects Models: The discussion highlighted that a GLMM (mixed model) would represent an even better solution because it handles repeated measurements and hierarchical data structure. The most suitable method for handling this data type would be mixed models. It could process time-dependent variables (such as total vaccine injections received by each mare) and include random mare intercepts. The outcome would deliver a vaccine effect measurement that applies to the entire population while providing an uncertainty measurement. A mixed model analysis might demonstrate that controlling for mare variations and yearly effects reveals X% decreased odds of foaling with each additional vaccination, precise confidence intervals, and Y times increased likelihood of foaling among untreated mares. The study would validate the efficacy statements through statistical methods. A mixed model would help analyse the predictors of treatment failures by examining whether the failure rates correlate with missing booster shots or being located in certain areas. The detailed information about the population becomes difficult to access through a basic descriptive summary.

Survival Analysis: Survival analysis or time-to-event methods could determine the duration of fertility suppression caused by treatment. The analysis of time to first foaling can start from the program’s initiation by treating death or removal as censoring events for mares who begin the program. The survival curve for “time to foaling” would extend further to the right (foaling time becomes longer) when pzp proves effective compared to a curve without treatment. The remaining untreated mares who unintentionally did not receive treatment could serve as a survival analysis comparison group. A survival model that includes time-dependent covariates enables researchers to analyse the exact moment when a mare received vaccination to examine the immediate change in her foaling hazard. The method provides exceptional power to evaluate waning vaccine efficacy since researchers can assess whether the foaling hazard elevates after twelve months post-vaccination. The current study lacks survival analysis, which would provide detailed information about the duration of vaccine effectiveness.

This study failed to apply these methods. Using these methods would increase confidence that the pZP vaccine caused the decline (over simply being correlated with it).

Modelling Heterogeneity and Uncertainty: Other methods could also increase the understanding of the heterogeneity of efficacy. For example, perhaps older mares are slightly more or less responsive to the vaccine; a model could examine whether there is an interaction between age and treatment. Or possibly efficacy increases after a mare has had multiple boosters (immune response builds) – a longitudinal model could assess whether fertility rates dropped further for mares that received boosters in consecutive years compared to those with gaps. The descriptive analysis provided suggestions (e.g., one year’s treatment is enough). Still, a model could support that by showing, for example, that mares who missed a year were significantly more likely to foal, thus demonstrating the importance of not exceeding a twelve-month gap. This is important, as we would like to know how much trust we have in, for example, “10% conception rate” rather than 10% ± 5%. With thousands of data points, uncertainty is likely small, but it should be stated.

Was the chosen method optimal? From a purist statistical standpoint, no, the methods were not optimal for inference. They were sufficient for description and probably sufficient to convince readers qualitatively (because the effect is significant), but they do not meet the highest standards of analytical rigour. The optimal methodology would likely be a combination of the above alternatives: perhaps a mixed-effects logistic regression for foaling outcomes (to estimate effect size and control confounders), complemented by a survival analysis for duration of efficacy, and possibly a time-series analysis to confirm no extraneous trend shifts. These methods would give a comprehensive, robust picture: that the vaccine works, how strongly it suppresses fertility, how long it lasts, and that the observed decline is due to the intervention and not other factors.

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Critiques and Recommendations for Improved Rigour

Critique Summary: The study was very effective in showing that a pzp darting program can cause a sharp decline in foal production in a wild horse population, but the statistical analysis was heavily based on observation of trends without much formal modelling. Since there is no inferential statistics, the results, although persuasive, are based on the assumption that no other factors could explain the changes. Potential confounders (environment, mortality, heterogeneous treatment application) were not fully controlled, and the powerful longitudinal nature of the data was underutilised. The approach to vaccine efficacy (using predefined duration scenarios) was informative but did not extract the maximum insight that a data-driven model could provide. In essence, the analysis provided evidence of efficacy but did not provide measurement of efficacy with estimates of precision or tests of significance.

Recommendations: To enhance methodological rigour in this and similar studies, we recommend the following:

Incorporate a Control or Comparison: If an outright control group (untreated horses) is not ethically or logistically feasible, use internal comparisons. This could include untreated periods or regions as quasi-controls (with appropriate caveats), or comparing mares before vs. after they receive treatment (within-subject comparison). Even data from the fringes of the study (e.g., the excluded areas) could be leveraged via causal inference techniques to strengthen the argument that the observed declines are due to PZP and not an overall herd phenomenon. For example, a difference-in-differences analysis using the south-of-Highway-50 horses as a reference group could control for year effects on foaling rates.

Use Generalised Linear Mixed Models: Re-analysing the data with a GLMM would likely be the most informative improvement. A mixed model could solve many of the abovementioned problems: it can control for confounders (including covariates such as year or age), handle the repeated measures (random effects for mares), and estimate the treatment effect with a significance test. It would provide outputs like an odds ratio for vaccination effect, which could be directly compared to other studies or used in meta-analyses. Such a model could also implicitly include the vaccine efficacy duration: e.g., include terms for whether a mare is within 0–6 months post-shot, 6–12 months, etc., to see where fertility increases. We highly recommend that the authors or future researchers perform a mixed model analysis to quantify PZP’s effect on individual fertility risk.

Conduct Survival Analysis for Efficacy Longevity: A focused survival or time-to-foaling analysis should be done to estimate how long the vaccine protects a mare, in addition to the above. Mares should be tracked from their last treatment to see when (if at all) they will produce a foal next. A Kaplan-Meier curve would visually show the proportion of treated mares remaining foal-free over time, and a Cox proportional hazards model could test differences between groups (for example, mares that received boosters versus only primers) or estimate the hazard increase as time since treatment increases. This would test the “6, 12, 18 months vs permanent” assumptions and likely pinpoint a more precise duration (for example, perhaps finding that pregnancy hazard starts rising after 12–16 months). It also naturally handles censoring (mares that die or are removed). The survival analysis results could then be translated into an estimated efficacy period with confidence intervals (for example, “PZP effectively prevented foaling for a median duration of X months in treated mares”).

Address Biases in Data Collection: The study noted incomplete foal documentation in the first year and high foal mortality later. Future analyses should consider adjusting for detection bias, perhaps using auxiliary data like known predator kills or pregnancy observations. If foals are being missed, one might incorporate a correction factor or at least do a sensitivity analysis (e.g., “if X unseen foals existed, would it change conclusions?”). Also, explicitly incorporate initial pregnancy status: a suggestion is to start the analysis of foaling rates from mid-2020 onward (once no mare is still carrying a pre-treatment pregnancy) to isolate the treatment effect. Alternatively, include a covariate for whether a mare foaled in 2019 (meaning she wasn’t prevented that year) when modelling 2020 outcomes, etc. This could control for differences between mares that were initially pregnant vs not.

Use Multi-Variable Models to Adjust for Confounders: Even a straightforward multivariable logistic regression (not necessarily mixed if one does per-year analysis) could include year (or environmental indices) to adjust for annual conditions, age of mare (fertility can decline in very old mares, and very young mares have lower fertility; the study assumed >1 year as equal, but a 2-year-old vs a 15-year-old might differ), and location or band as covariates. By doing so, one can say “controlling for year and age, treated mares had an X% lower probability of foaling.” This increases confidence that the effect isn’t due to those other factors. It appears the authors recorded variables like band and herd area, so using them in a model to account for spatial clustering or stallion effects would be feasible and advisable.

Provide Uncertainty Estimates: Wherever possible, future reports should include confidence intervals or similar measures for key outcomes. For example, “58% reduction” could be accompanied by a 95% confidence interval (even if derived from a model or a data bootstrap). This communicates the statistical certainty. Given the large sample, the intervals might be narrow, but reporting them is good practice. Likewise, the “10% conception rate” could be given as 10% ± some margin. This would formally indicate how much variation in these percentages could occur due to randomness (though here randomness is mostly from which mares were observed or missed, since it’s population-level).

Explore Alternative Outcome Metrics: The study focused on foaling and conception rates. Another complementary metric is population growth rate. By combining foaling rates with mortality rates, one can estimate the annual population growth and see how it has changed. The authors mention zero-population-growth targets and that other studies took years to see a decline. A population projection model (even a simple exponential or matrix model) could be used to estimate the growth rate with and without the observed fertility control. This would translate the findings into a more aggregate outcome (herd growth slowed from x% to y% per year). It’s not purely a statistical method, but rather a modelling exercise that could strengthen the argument that pzp moved the herd from a growing state to a near-stable state. Coupling such a model with uncertainty from the data (via simulation) would further enhance the rigour. The research study employed basic methodologies, which produced easy-to-understand descriptive results, although more complex statistical methods should be used to verify and expand these findings. The analysis would gain strength through applying GLMMS for treatment effect and survival analysis for duration, alongside causal inference for unbiased effect estimation. The research methods would demonstrate the same conclusion regarding pzp darting effectiveness in lowering wild horse birth rates, but with strengthened evidence from statistical significance, controlled comparisons, quantitative effect size, and longevity measurements. The application of rigorous methods is vital because it enhances scientific precision and helps decision-makers rely on exact numbers (e.g., “The vaccine will reduce foaling probability by at least X% for up to Y months at a 95% confidence level”).

Future analyses of this dataset or similar field studies should utilise mixed-effects logistic models to estimate efficacy while accounting for repeated measures, apply survival analysis to determine how long the contraceptive effect lasts per treatment, carefully control for confounding factors either by design or statistical adjustment, and include measures of statistical uncertainty. This study’s excellent large-scale field effort will achieve equal robustness in statistical evidence through proper analysis, thus establishing its findings and guiding best practices for wild horse population management with enhanced accuracy.

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Summary of the Paper

The study investigates the immunocontraceptive efficacy of Porcine Zona Pellucida (pzp) treatment on the free-roaming horse population in the Virginia Range. Over four years (2019-2022), researchers monitored 2,817 mares, tracking their vaccination records and reproductive outcomes. The results indicated a significant reduction in foaling rates, suggesting a nearly 60% decrease in pregnancies attributable to the PZP treatment. However, criticisms arose regarding the study’s methodology, which lacked rigorous statistical analysis and adequate control for confounding variables. Recommendations for future research highlighted the necessity of employing mixed-effects models and survival analysis to improve the robustness of findings related to vaccine efficacy and its duration of effect.

Recommendations for Future Research

1. Engage Statistical Experts: Collaborate with a statistician with experience analysing ecological data, particularly involving wild horse populations. Their expertise can enhance the rigour of the statistical methods used.
2. Local Research Collaboration: Involve researchers who are geographically closer to the Virginia Range horses. This can provide valuable insights into local environmental factors and horse behaviour that may influence reproductive outcomes.
3. Mixed-Effects Models: To analyse the data, use mixed-effects models. This approach can account for individual mare variation and repeated measures, providing a clearer understanding of the treatment effects.
4. Survival Analysis: Conduct survival analysis to accurately assess the duration of PZP contraceptive effects on the mare population. This method can help determine how long the vaccine remains effective post-treatment.
5. Control for Environmental Variables: Incorporate environmental factors such as forage availability and predator presence into the analysis to control for confounding influences on foaling rates.
6. Longitudinal Tracking and Comparison: Implement a longitudinal design that allows for before-and-after comparisons within the same mares, offering more precise insights into the treatment’s effects over time.
7. Community Engagement: To ensure the research aligns with community goals and conservation efforts, foster relationships with local stakeholders and horse management organisations.

By enhancing statistical rigour and incorporating localised expertise, future research can produce more reliable findings that support effective wild horse population management strategies.

Dr. Meredith Hudes-Lowder
Biostatistician
©April 2025

Some background history

White markings in horses serve no biologic purpose, nor are they an adaptation to avoid predation or increase survivorship. On the contrary, white markings on a horse can make it easier for predators to detect them due to the way light interacts with different colors and how animals perceive contrast in their environment. Dark colors, such as black and brown, tend to absorb more light and blend more seamlessly into natural surroundings, whereas white reflects more light, making it more conspicuous, especially in low-light conditions or against darker backgrounds (Caro, 2005). This increased visibility can make a horse with white markings stand out from the rest of the herd, drawing the attention of predators.

Additionally, many predators rely on motion detection and contrast sensitivity rather than color perception when hunting. Studies on animal vision suggest that predators like wolves and big cats have dichromatic vision, meaning they perceive colors differently than humans, primarily distinguishing between blue and yellow wavelengths while having difficulty differentiating reds and greens (Jacobs, 1993). Because white markings create a stark contrast against darker coat colors or natural surroundings, they can enhance the visibility of movement, making a horse’s motion more detectable to a predator’s keen eyesight.

In some environments, particularly in wooded or shadowed areas, a horse with large white patches may stand out more distinctly compared to a uniformly dark-colored horse, which benefits from more effective camouflage (Caro, 2016). This is particularly relevant in species where natural selection has favored solid or muted coat colors in wild equines, such as zebras, which use disruptive coloration to confuse predators (Ruxton, Sherratt, & Speed, 2004).

While white markings may increase visibility in certain conditions, their impact varies based on habitat and lighting. In bright, open environments, such as snowy regions or sandy plains, white markings may offer some blending advantages. However, in forests, grasslands, or dusk and dawn lighting conditions—when many predators are most active—white markings can provide a disadvantage by increasing a horse’s contrast against the environment, making it easier for predators to spot and track them.

Why White Markings are Preferred

Humans selectively bred horses for white facial and leg markings due to a combination of aesthetic, practical, and genetic factors. These markings were often associated with visibility, tradition, and cultural significance, as well as linked to specific genetic patterns.

1. Increased Visibility

One of the primary reasons for selecting horses with white markings, especially on the face and legs, was to enhance their visibility in low-light conditions. White facial blazes and leg stockings made it easier to identify individual horses, particularly in herds or during nighttime activities (Gower, 1999). This was especially beneficial for cavalry, working horses, and herding livestock in dimly lit environments.

2. Aesthetic and Cultural Preferences

Throughout history, different cultures favored distinct coat patterns. In many European and Middle Eastern traditions, white markings were seen as noble and desirable. Medieval knights often preferred horses with white blazes and stockings because they were considered more striking and prestigious (Bennett, 1998). Similarly, in North America, flashy white markings became popular in breeds such as the American Paint Horse and Pinto horses.

3. Association with Temperament

Some studies suggest a correlation between coat color, white markings, and temperament. While anecdotal, many horse breeders believed that certain markings were linked to docility or alertness (Haase et al., 2021). This perception may have led to selective breeding for specific markings as a means of predicting and influencing behavior.

4. Genetic Linkages and Breed Standards

White facial and leg markings are largely controlled by genes affecting pigmentation, particularly the KIT and MITFgenes (Brooks et al., 2007). As breeding programs developed, horses with desirable traits—including markings—were preferentially selected. Some breed registries, such as the American Quarter Horse Association (AQHA), permit or even favor white markings within certain limits, reinforcing their propagation through generations (AQHA, 2023).

5. Cultural and Superstitious Beliefs

Certain markings have been historically linked to superstitions. For example, the “badger face” marking (inverted white blaze) was considered lucky in some cultures, while others believed extensive white on the face (e.g., a bald face) could indicate poor vision or sensitivity to sunlight (Sponenberg, 2009). These beliefs influenced breeding choices in different regions.

Humans bred horses to have white facial and leg markings primarily for their visibility, aesthetic appeal, perceived temperament traits, and genetic linkages. Over time, these preferences became ingrained in breed standards and cultural traditions, leading to the continued propagation of these markings in many modern horse breeds.

White Markings in Equine Ancestry

The absence of white markings in wild horses, such as the Przewalski’s horse, suggests that mustangs in America are not descended from native horses can be supported by several points drawn from the provided references.

1. Lack of White Markings in Wild Horses: Przewalski’s horse, the last truly wild horse species, lacks white markings, indicating that such traits are not inherent to wild equine populations. As stated, “the markings were not present, or were very rare, in the primeval wild horse, as evidenced by their absence in Przewalski’s horse” (Woolf, 1991, p. 3). This suggests that white markings are a trait associated with domestication rather than a natural occurrence in wild equines.
2. Selective Breeding and Aesthetic Preferences: Domestic horses have been selectively bred for various traits, including coat color and markings, primarily for aesthetic purposes. The document mentions that “white markings are assumed to be flashy, highlighting the limb action which is particularly important in shows” (Stachurska & Ussing, 2012, p. 75). This selective breeding has led to the proliferation of white markings in domesticated horse breeds, which are not found in their wild counterparts.
3. Genetic Evidence of Selective Breeding: The presence of white markings in domestic horses is associated with several genetic loci that are not prevalent in wild populations. The genetic basis for white markings is multifactorial and involves complex inheritance patterns, as discussed in multiple studies that highlight the heritability and genetic underpinnings of these traits (Woolf, 1990, p. 250). In contrast, wild horse populations like the Przewalski’s horse show a different genetic makeup, lacking the alleles responsible for white markings.
4. Implications for Mustang Ancestry: If mustangs were directly descended from native horses in the Americas, one would expect to find similar genetic traits, including white markings. However, the absence of such markings in their wild ancestors suggests that mustangs likely descended from domesticated horses, which were selectively bred for these traits in Europe and brought to the Americas by European settlers. The genetic diversity and presence of white markings in mustangs can thus be attributed to the introduction of domestic horses with these traits rather than a lineage from native equines.

In conclusion, the lack of white markings in wild horses like the Przewalski’s horse supports the notion that mustangs, as domesticated descendants, inherited these traits through selective breeding practices focused on aesthetics, rather than through lineage from native American horses. This argument is reinforced by genetic evidence indicating that the selective pressures for coat color and markings are a product of domestication rather than a natural evolution in wild horse populations. There are other reasons we know mustangs are descended from domestic horses, but that a topic we will touch upon later in this post.

Inheritance of White Markings

The white facial and leg markings on horses are described as multifactorial, meaning that the genes are not the only factor that determines their inheritance. It has been found that these markings are controlled by several genetic loci and at least some of the genes involved include MC1R, KIT and MITF. 

1. Multifactorial Inheritance: Some white facial and leg markings are not inherited in a simple Mendelian fashion but rather by a polygenic or multifactorial inheritance system where many genes interact to produce the phenotype. This pattern of inheritance suggests that the frequency of white markings can vary greatly between individuals because of the additive effects of several alleles (Woolf, 1990; Rieder et al., 2008). 

2. Key Genes Involved: Melanocortin 1 Receptor gene (MC1R): Mutation of this gene product causes changes in coat color and plays an important role in the extent of white markings especially in chestnut horses where they have more white markings than other colors (Woolf, 1990; Haase et al., 2013). KIT: The KIT gene is also responsible for the development of the white marking and the size of the area with white marking. Many alleles at this locus have been found to be associated with various forms of white spotting including dominant white (Haase et al., 2013; Rieder et al., 2008). MITF (Microphthalmia-associated Transcription Factor): This gene has been found to be responsible for certain white spotting phenotypes including the splashed white phenotype (Hauswirth et al., 2012). 

3. Genetic Correlation: Studies have shown that heritability for white markings is high, suggesting genetic basis. For instance, heritability estimates for facial markings can be 0.69, and total leg markings can be 0.68, which indicates that genetics is a significant determinant of these traits (Woolf, 1990; Rieder et al., 2008). 

4. Environmental Factors: In addition to genetic influences, environmental factors and stochastic (random) events can also affect the development and manifestation of white markings. This includes the possibility of developmental noise, which are stochastic variations that occur during embryonic development that may result in the variation in the migration and survival of melanoblasts (Mintz, 1974; Woolf, 1995). 

5. Complex Interactions: The interaction between the basic coat color (which is determined by MC1R and Agouti loci) and white markings is also rather complicated. For example, chestnut horses (e/e) are typically more heavily marked than bay (E/ –) and black (a/a; E/ –) horses, which shows the role of other genetic factors (Woolf, 1991). 

The Next Generation

The inheritance of white facial and leg markings in horses is a complex trait controlled by several genetic loci and environmental factors with significant contributions from MC1R, KIT, and MITF genes and their products, which result in various phenotypic outcomes. The heritability of facial and leg markings is fairly high, with the heritability estimates for facial markings being estimated at 0.69 and for limb markings at 0.68, and the overall heritability for both facial and limb scores can be up to 0.77 (Woolf, 1990; Stachurska and Ussing, 2012). These markings are caused by several loci, and the two most important genes have been identified as MC1R and KIT that are involved in pigmentation. The MC1R gene codes for the receptor that regulates type of melanin produced while the KIT gene is associated with white spotting patterns (Rieder et al., 2008; Haase et al.,2013). Furthermore, it has been postulated that the expression of white markings is not only dependent on genetic factors but also on stochastic events that take place during embryonic development that affect melanoblast survival and migration (Woolf, 1990). This complexity suggests that other factors, including environmental factors, may also contribute to the variation in phenotypic expression of white markings in different individuals (Woolf, 1995; Rieder et al., 2008).

White markings can be inherited from parents to offspring. White markings in horses are known to be influenced by genetic factors, especially the mutation of certain alleles at certain loci. For example, a study showed that if both parents have white facial markings, the offspring are likely to have these markings and 61.8% of such offspring will have white markings (Document: Encina et al. – 2024). On the other hand, if both parents are without white markings, then a smaller percentage of the offspring (22.6%) have these markings.

Size matters?

The size of white markings on legs in horses is dependent on genetic and non-genetic factors. Key points include:

1. Genetic Factors: The heritability of white markings is quite high, and the studies have shown that the heritability values are between 0.68 and 0.77 for different parts of the body (Woolf, 1990; Rieder et al., 2008). The MC1R, KIT, and MITF genes have been associated with the frequency of white markings. For instance, the mutation of the recessive allele of the MC1R gene, which is responsible for the chestnut coat color, has been associated with more extensive white markings (Negro et al., 2017; Haase et al., 2013).

2. Interaction of Genes: There are interactions between different genetic loci such as MC1R and KIT, where the genetic control of the quantity of white marking depends on the base coat color of the horse (Patterson Rosa et al., 2022; Cuffe, 2024).

3. Environmental and Stochastic Factors: Other factors, which can be regarded as environmental variables, affecting the intrauterine development of the horse, can also influence the expression of the phenotype of white markings. Stochastic events that affect the survival, movement and mitosis of melanoblasts (the precursor of melanocytes) contribute to the variation in white markings (Woolf, 1995; Stachurska and Ussing, 2012).

4. Sex and Coat Color: It has been found that males have slightly more white markings than females and the base coat color greatly affects the frequency of white markings with chestnut horses having more extensive markings than bay or black horses (Woolf, 1990; Rieder et al., 2008). In summary, the quantity of white in leg markings is controlled by genetic inheritance, particular gene interactions, environmental factors and stochastic events that occur during development.

The Base Coat

Also, different coat colors have different effects on the chances of white markings in offspring. For example, chestnut horses are more likely to have white markings than black horses (Document: Encina et al. – 2024). In general, although genetics is an important factor in the expression of white markings, other factors like environmental conditions and developmental processes also play a role in the expression of these markings. This indicates that the above mentioned genes are involved in the determination of the shape of white markings, such as a star or a blaze.

For example, chestnut horses, which have a specific genotype (e/e), have more extensive white markings than bay or black horses and the amount of white marking is generally higher on these horses than on other horses (Woolf, 1990; Rieder et al., 2008; Haase et al., 2013). Furthermore, the heritability of markings suggests that the extent and perhaps the shape of these markings can be passed down from generation to generation and that certain genetic combinations are more likely to result in more pronounced markings (Woolf, 1990; Rieder et al., 2008). Therefore, even though there are stochastic events that can lead to the final appearance, the genetic predisposition to certain shapes of stars and blazes is indeed inherited. Specific coat patterns are associated with specific facial markings?

Horse coat patterns are accompanied by specific facial markings due to genetic control of pigmentation. Here are some notable associations:

1. Tobiano Pattern: Tobiano horses are homozygous for white and have large patches of white that cross the spine and have face marks that are usually star or stripe like. This is associated with the KIT gene which is involved in white spotting phenotypes (Haase et al., 2013).

2. Sabino Pattern: This pattern has a patchy white marking on the face and other parts of the body and is similar to the leopard complex. The KIT gene is also involved here, that is through mutations that result in different levels of white marking including facial patches (McFadden et al., 2024).

3. Splashed White: Horses with this type of splashed white pattern have a lot of white on the face including up to a full blaze or completely white face. This phenotype is caused by mutations in MITF and PAX3 genes (Hauswirth et al., 2012).

4. Leopard Complex: Horses with this pattern, such as Appaloosa, may have distinctive facial markings with their coat pattern. The gene that causes the leopard spotting is TRPM1 and this can include facial spots (Neves et al., 2017).

5. Frame Overo: This pattern has patchy white patches which do not run across the back and may have facial markings. This is associated with the EDNRB gene which can result in significant white facial markings (Patterson Rosa et al., 2022).

Blue Eyes

The analysis of the coat patterns and facial markings shows that genetics is a crucial determinant of equine coat colour and patterns, and certain genes can lead to specific phenotypic characteristics such as blue eyes. Blue eyes are usually associated with certain white markings in horses. In particular, horses with the splashed white phenotype, which is characterized by significant white markings, have blue eyes. This is explained in the literature on the genetics of white spotting patterns where mutations in genes such as MITF and PAX3 are responsible for both the splashed white markings and blue eyes (Hauswirth et al., 2012; McFadden et al., 2024). Also, in the context of the Lethal White Overo (LWO) syndrome, which is also characterized by white markings, affected horses may have blue eyes as well (Neves et al., 2017).

Cut-outs, reverse colour, negative shapes, superimposed colour…

White marking with ‘cut-outs’, or the base color seen within the blaze, similar to freckles, but on a larger scale are inherited. The body color “cut-outs” in a white blaze on horses, which are the areas of depigmentation, are caused by mutations in genes regulating the migration and differentiation of melanocytes, the pigment producing cells of neural crest origin. Indeed, mutations in genes such as MITF (Microphthalmia-associated transcription factor) and PAX3 (Paired box gene 3) have been linked to splashed white phenotype, which is characterized by extensive white markings including “cut-outs” or unpigmented areas in the coat.

These traits are inherited in a composite fashion. Some white spotting patterns, including splashed white, can be inherited in an autosomal dominant manner; however, the expression can be highly variable among horses with similar pedigree because of the involvement of other genetic factors and possibly environmental factors. The inheritance is not strictly monogenic since many mutations can contribute to the final phenotype and thus result in varying levels of depigmentation from minimal to near complete (Hauswirth et al., 2012, Neves et al., 2017). In summary, the ‘cut-outs’ in a white blaze are the result of particular genetic mutations that affect the development of melanocyte and these traits can be inherited, but the inheritance is not very clear.

Variations in a horse’s health including skin and coat conditions may also affect how much white markings are seen in its life. In summary, the variability of white markings that come and go during the lifetime of a horse can be explained by the genetic, stochastic developmental events, environmental factors, and the health of the horse. Blazes or partial blazes on horses’ faces are forms of white facial marking and their inheritance is quite complex. According to Woolf (1990), the existence and intensity of white markings including blazes can be influenced by several genetic loci and the frequency of these markings may vary among the offspring depending on the genetic contribution of the parents (Woolf, 1990, p. 250-256). However, the patterns of white markings can be complicated and horses with highly marked parents may have more genes that cause white markings and tend to pass on similar markings to their offspring (Woolf, 1990, p. 250-256). Therefore, if a horse has a broken or incomplete blaze, it is possible that this trait can be inherited by their offspring, but the expression of the trait may vary depending on the genes of other factors. In conclusion, although there is a genetic basis for a broken or incomplete blaze, the specific expression in offspring is not fixed and may be influenced by other genes as well as environmental factors.

Clean edge or jagged?

The variation in the edges of facial markings in horses, whether they appear clean or jagged, can be attributed to several factors related to the developmental processes of melanoblasts, which are the precursor cells that develop into melanocytes (the cells responsible for pigment production).

1. Melanoblast Migration and Proliferation: The presence of clean or jagged edges in white markings is influenced by how well melanoblasts migrate and proliferate in the developing tissues. If melanoblasts migrate uniformly and proliferate evenly, the resulting markings tend to have clean edges. Conversely, if there are irregularities or disturbances in the migration and proliferation processes, this can lead to markings with jagged edges (Woolf, 1995).
2. Developmental Noise: The concept of “developmental noise” refers to random fluctuations that occur during the developmental processes. This can lead to variations in how melanoblasts settle in the skin and hair follicles. Such random events can result in differently shaped edges of the markings, with some being more defined (clean edges) and others being less so (jagged edges) (Woolf, 1995; Stachurska and Ussing, 2012).
3. Genetic Control: The genetic makeup of the horse also plays a significant role in how these markings develop. The interaction between multiple genes (a polygenic inheritance) affects the extent and pattern of white markings, which can contribute to differences in edge definitions (Woolf, 1990; Rieder et al., 2008).

Half a Head… Harlequin?

The phenomenon where a blaze covers half of a horse’s face in an almost perfect midline demarkation is probably due to the failure of melanoblasts (the cells that become melanocytes, which produce pigment) to migrate or proliferate properly during embryonic development. This migration and proliferation occurs independently on each side of the embryo to produce symmetrical or asymmetrical pigmentation patterns. If the melanoblasts on one side of the midline survive and proliferate while those on the other side do not, this can result in a clearly demarcated blaze covering one half of the face. This condition represents the concept of ‘developmental noise’, where random events during development can result in alterations in pigmentation. This explanation is in conformity with the fact that such markings are genetically and environmentally determined (Woolf, 1990; Mintz, 1967, 1974).

The Environment

The environment can greatly affect the phenotypic expression of white markings in horses through genetic and epigenetic changes. In particular, the migration and proliferation of melanoblasts, The cells that produce melanin in the skin are referred to as melanocytes and these cells migrate from the neural crest to their destination in the skin and hair follicles during early embryonic development. Specifically, the migration and proliferation of melanoblasts, The cells that produce melanin in the skin are referred to as melanocytes and these cells migrate from the neural crest to their destination in the skin and hair follicles during early embryonic development. However, environmental factors and epigenetic mechanisms can also affect the development and function of the melanocytes and, therefore, the formation of white markings. Stochastic factors that alter gene expression during early development can also influence the phenotype of the markings. Furthermore, epigenetic mechanisms such as DNA methylation, histone modifications, and microRNA activity can influence gene expression without affecting the underlying DNA sequence. The environment can also influence the development of white markings through stochastic (random) events that occur during melanoblast migration and survival. Epigenetic mechanisms such as DNA methylation and histone modifications can also affect gene expression in response to environmental factors. Together, these genetic and epigenetic mechanisms help to determine the pattern of white markings in horses. The environment can also cause white markings through stochastic (random) events that affect the development of melanoblasts and their survival.

Environmental factors can also influence gene expression through epigenetic mechanisms such as DNA methylation and histone modifications. These two types of mechanisms acting in concert determine the configuration of white markings in horses. The environment can also lead to white markings through stochastic (random) events that affect the development of melanoblasts and their survival. Epigenetic mechanisms can also be involved in the modulation of gene expression in response to environmental cues through DNA methylation and histone modifications. Both of these types of mechanisms are involved in the control of white markings in horses and they act in concert to determine the final outcome.

The most common markings

The most common marking in horses is the “half-stocking,” which was found to be present on the left back leg in a study of Arabian and Thoroughbred horses,with a prevalence of 16.9% (Kocakaya et al., 2023). The most common facial marking in horses is typically referred to as a “star.” In studies of various horse breeds, including Arabian horses, the presence of a star marking is frequently noted. For example, in the study by Woolf (1990), different types of facial markings were categorized, and the star marking is recognized as one of the common types observed.The prevalence of white facial markings was also mentioned in the Pura Raza Española horses, and it was mentioned that small white markings like stars are common (Encina et al., 2024).In general, depending on the breed, the star marking is one of the most common types of facial markings observed in horses.

Mustangs are not Native (no matter what you read online)

The primary genetic reason that mustangs in America are considered descendants of domestic horses rather than a native species is that their ancestry can be traced back to domesticated horses introduced to the Americas by Europeans, not to the prehistoric native horse species native to North America.

Key Genetic and Evolutionary Factors:

1.         Extinction of Native North American Horses     
Horses were first found in North America about 55 million years ago and evolved into many species. However, at the end of the Pleistocene epoch (~10,000 years ago) these horses went extinct, possibly due to climate change and hunting by early humans.This means that there was a genetic discontinuity – there were no wild horses in North America for a thousand of years.

2.         European Reintroduction of Domesticated Horses
 The modern Equus caballus was reintroduced to the Americas by the Spanish in the 16th century.  These horses were domesticated breeds from Europe and not the wild horses of North America.      Some escaped or were let loose, or were released, and formed feral populations, but their genetic origin is from the domestic stock, and not from the extinct native species.

3.         Genetic Studies Confirm Domestic Origins.  
DNA analysis of mustangs supports their origin from Iberian (Spanish) horse breeds, with later input from other European breeds (e.g. draft horses, Thoroughbreds). Their genetic continuity with the extinct North American horse species is absent.

4.         Legal and Conservation Classification
Mustangs are classified as feral and not wild because they are not genetically close to the prehistoric species and are descended from domesticated horses. The U.S. government has classified them as “wild” under the Wild Free-Roaming Horses and Burros Act of 1971, but from a biological point of view, they are domesticated feral animals and not a native species. In conclusion, mustangs in America are not considered to be a native species because they are descended from domesticated European horses and not the extinct native horses of prehistoric North America.

The following is a list of genetic traits that are present in true wild horses and are lacking in domestic horses: traits associated with domestication syndrome and the loss of genetic variability in regions linked to tameness and morphology. The analysis of the available data.

Key Genetic Differences Between Wild and Domestic Horses

1.         Greater Genetic Diversity in Przewalski’s Horse: The Przewalski’s horse (Equus ferus przewalskii) is considered the only living wild horse, that was never tamed. Genetic analyses show that Przewalski’s horses are genetically closer to their ancestral stock than domestic horses, especially with regard to the regions linked to the immune system and open environments. Librado et al. (2015) reported that Przewalski’s horses have unique haplotypes that are not seen in modern domestic horses, particularly at the mitochondrial DNA level.

2.         Distinct Y-Chromosome Haplotypes:  He noted that domesticated horses are suffering from a relative lack of variety at the Y-chromosome level as compared to true wild horses. Przewalski’s horses have a distinct Y-chromosome lineage that is not seen in domesticated horses (Lippold et al., 2011).

3.         Mutation in the G-Protein-Coupled Receptor (GPR143) Gene: In a study by Gaunitz et al. (2018), it was determined that domesticated horses have been selected at genes associated with behavior, including GPR143, which is linked to visual perception and may have influenced tameness. The true wild horses, including Przewalski’s, do not have these same mutations, suggesting a difference in their sensory perception and behavioral response.

4.         Genetic Selection in Neural and Behavioral Genes: Domesticated horses have been selected for tamer and less aggressive conformation through genes like SORCS1 and NRXN1 that are involved in the development of the nervous system and plasticity of synapses (Schubert et al., 2014).Przewalski’s horses do not have all of these domestication-related genetic changes and are therefore more skittish and less submissive than domestic breeds (Schubert et al., 2014).

5.         Difference in Coat Color Genetics: The ancient wild horses (including Przewalski’s) were mostly of the dun coat color, which is controlled by the TBX3 gene and is camouflage color. Many domestic horses lost the dun dilution allele in the process of selecting for other coat colors, whereas Przewalski’s horses retained this ancestral trait (Ludwig et al., 2009).

Horses, including the Przewalski’s horse, do not have the genetic markers of domestication that are seen in modern horses, especially in the behavioural, neurologic, and coat colour genes. They own higher Y-chromosome diversity, have conserved immune system genes, and have not been selectively bred for tameness and coat color, which makes them different from the domesticated breeds.

References
American Quarter Horse Association (AQHA). (2023). AQHA Official Handbook of Rules and Regulations.

Amarillo, TX.Avila, F., Hughes, S. S., Magdesian, K. G., Penedo, M. C. T., & Bellone, R. R. (2022). Breed Distribution and Allele Frequencies of Base Coat Color, Dilution, and White Patterning Variants across 28 Horse Breeds. Genes, 13(9), 1641. https://doi.org/10.3390/genes13091641

Bellone, R. R. (2010). Pleiotropic effects of pigmentation genes in horses. Animal Genetics, 41(s2), 100–110. https://doi.org/10.1111/j.1365-2052.2010.02116.x

Belousova, N. F., Bass, S. P., Zinoveva, S. A., Kozlov, S. A., & Markin, S. S. (2020). Features of coat color and markings and impact of dun factor on Vyatka horse breed. BIO Web of Conferences, 17, 00202. https://doi.org/10.1051/bioconf/20201700202

Bennett, D. (1998). Conquerors: The Roots of New World Horsemanship. Amigo Publications.

Bianca Haase1,2, Samantha A. Brooks3, Angela Schlumbaum4, Pedro J. Azor1,5, Ernest Bailey3, Ferial Alaeddine6, Meike Mevissen6, Dominik Burger7, Pierre-Andr  ́ e Poncet7, Stefan Rieder8, Tosso Leeb1,2*. (n.d.). Allelic Heterogeneity at the Equine KIT Locus in Dominant White (W) Horses.

Brooks, S. A., Makvandi-Nejad, S., Chu, E., Allen, J. J., Streeter, C., & Sutter, N. B. (2007). “Morphological effects of KIT gene mutations in horses.” PLoS Genetics, 3(11), e99.

Cuffe, C. (n.d.). EUROPEAN MASTER’S IN ANIMAL BREEDING AND GENETICS. Does homozygosity contribute to the asymmetry of common white leg markings in the Arabian horse? (n.d.).

Druml, T., Grilz‐Seger, G., Neuditschko, M., Horna, M., Ricard, A., Pausch, H., & Brem, G. (2018). Novel insights into Sabino1 and splashed white coat color patterns in horses. Animal Genetics, 49(3), 249–253. https://doi.org/10.1111/age.12657

Encina, A., Valera, M., Ligero, M., Rodriguez Sainz De Los Terreros, A., & Sánchez-Guerrero, M. J. (2024). Characterisation of white facial markings in Pura Raza Española horses (a worldwide population genetic study). Italian Journal of Animal Science, 23(1), 929–937. https://doi.org/10.1080/1828051X.2024.2367673

Gaunitz, C., Fages, A., Hanghøj, K., et al. (2018). Ancient genomes revisit the domestication history of       Przewalski’s horses. Science, 360(6384), 111-114. DOI: 10.1126/science.aao3297

Gower, S. (1999). Horse Color Explained: A Breeder’s Perspective. Trafalgar Square Publishing.

Haase, B., Brooks, S. A., Schlumbaum, A., Azor, P. J., Bailey, E., & Burger, D. (2021). “Genetics of white markings in horses.” Animal Genetics, 52(2), 140–154.

Haase, B., Brooks, S. A., Tozaki, T., Burger, D., Poncet, P. ‐A., Rieder, S., Hasegawa, T., Penedo, C., & Leeb, T. (2009). Seven novel KIT mutations in horses with white coat colour phenotypes. Animal Genetics, 40(5), 623–629. https://doi.org/10.1111/j.1365-2052.2009.01893.x

Haase, B., Signer-Hasler, H., Binns, M. M., Obexer-Ruff, G., Hauswirth, R., Bellone, R. R., Burger, D., Rieder, S., Wade, C. M., & Leeb, T. (2013). Accumulating Mutations in Series of Haplotypes at the KIT and MITF Loci Are Major Determinants of White Markings in Franches-Montagnes Horses. PLoS ONE, 8(9), e75071. https://doi.org/10.1371/journal.pone.0075071

Hauswirth, R., Haase, B., Blatter, M., Brooks, S. A., Burger, D., Drögemüller, C., Gerber, V., Henke, D., Janda, J., Jude, R., Magdesian, K. G., Matthews, J. M., Poncet, P.-A., Svansson, V., Tozaki, T., Wilkinson-White, L., Penedo, M. C. T., Rieder, S., & Leeb, T. (2012). Mutations in MITF and PAX3 Cause “Splashed White” and Other White Spotting Phenotypes in Horses. PLoS Genetics, 8(4), e1002653. https://doi.org/10.1371/journal.pgen.1002653

Hauswirth, R., Jude, R., Haase, B., Bellone, R. R., Archer, S., Holl, H., Brooks, S. A., Tozaki, T., Penedo, M. C. T., Rieder, S., & Leeb, T. (2013). Novel variants in the  KIT  and  PAX 3  genes in horses with white‐spotted coat colour phenotypes. Animal Genetics, 44(6), 763–765. https://doi.org/10.1111/age.12057

Hofer, H., & Sigrist, S. J. (2012). Coat colours and mitochondrial lineages of ancient horses to document domestication. https://api.semanticscholar.org/CorpusID:209175621

Identification of a novel missense variant in SLC45A2 associated with dilute snowdrop phenotype in Gypsy horses. (n.d.).

Jacobs, G. H. (1993). The distribution and nature of color vision among the mammals. Biological Reviews, 68(3), 413-471.

KOCAKAYA, A., PAKSOY, Y., & ÖZBEYAZ, C. (2023). Color and marking distribution in arabian and thoroughbred horses. Veteriner Hekimler Derneği Dergisi, 94(2), 110–118. https://doi.org/10.33188/vetheder.1225758

Librado, P., Der Sarkissian, C., Ermini, L., et al. (2015). Tracking the origins of Yakutian horses and the genetic basis for their adaptation. Nature Communications, 6, 7256. DOI: 10.1038/ncomms8256

Lippold, S., Matzke, N. J., Reissmann, M., Hofreiter, M. (2011). Whole mitochondrial genome sequencing of domestic horses reveals incorporation of extensive wild horse diversity during domestication. BMC Evolutionary Biology, 11, 328. DOI: 10.1186/1471-2148-11-328

Liu, X., Peng, Y., Zhang, X., Wang, X., Chen, W., Kou, X., Liang, H., Ren, W., Khan, M., & Wang, C. (2024). Coloration in Equine: Overview of Candidate Genes Associated with Coat Color Phenotypes. Animals, 14(12), 1802. https://doi.org/10.3390/ani14121802

Ludwig, A., Pruvost, M., Reissmann, M., et al. (2009). Coat color variation at the beginning of horse domestication. Science, 324(5926), 485. DOI: 10.1126/science.1172750

Maciel, S. V. S. A., De Queiroz, V. H. O., De Oliveira, C. A. A., De Godói, F. N., Pereira, G. L., Curi, R. A., Costa, R. B., & De Camargo, G. M. F. (2020). Genetic heterogeneity of white markings in Quarter Horses. Livestock Science, 232, 103935. https://doi.org/10.1016/j.livsci.2020.103935

Marín Navas, C., Delgado Bermejo, J. V., McLean, A. K., León Jurado, J. M., Torres, A. R. de la B. y R. de, & Navas González, F. J. (2022). One Hundred Years of Coat Colour Influences on Genetic Diversity in the Process of Development of a Composite Horse Breed. Veterinary Sciences, 9(2), 68. https://doi.org/10.3390/vetsci9020068

McFadden, A., Vierra, M., Martin, K., Brooks, S. A., Everts, R. E., & Lafayette, C. (2024). Spotting the Pattern: A Review on White Coat Color in the Domestic Horse. Animals, 14(3), 451. https://doi.org/10.3390/ani14030451

Mongera, A., & Dooley, C. M. (2013). Of white tigers and solute carriers. Pigment Cell & Melanoma Research, 26(6), 787–789. https://doi.org/10.1111/pcmr.12163

Negro, S., Imsland, F., Valera, M., Molina, A., Solé, M., & Andersson, L. (2017). Association analysis of KIT , MITF , and PAX3 variants with white markings in Spanish horses. Animal Genetics, 48(3), 349–352. https://doi.org/10.1111/age.12528

Neves, A. P., Schwengber, E. B., Albrecht, F. F., Isola, J. V., & van der Linden, L. de S. (2017). Beyond Fifty Shades: The Genetics of Horse Colors. In M. Abubakar (Ed.), Trends and Advances in Veterinary Genetics. InTech. https://doi.org/10.5772/65848

Pasternak, M., Krupiński, J., Gurgul, A., & Bugno-Poniewierska, M. (2019). The Genetic Basis of Piebald Coat Colour in Hucul Horses in the Context of White Markings and Crypto-Tobiano as a Breeding Problem in Poland. Annals of Animal Science, 19(4), 955–966. https://doi.org/10.2478/aoas-2019-0036

Pasternak, M., Krupiński, J., Gurgul, A., & Bugno-Poniewierska, M. (2020). Genetic, historical and breeding aspects of the occurrence of the tobiano pattern and white markings in the Polish population of Hucul horses – a review. Journal of Applied Animal Research, 48(1), 21–27. https://doi.org/10.1080/09712119.2020.1715224

Patterson Rosa, L., Martin, K., Vierra, M., Lundquist, E., Foster, G., Brooks, S. A., & Lafayette, C. (2022). A KIT Variant Associated with Increased White Spotting Epistatic to MC1R Genotype in Horses (Equus caballus). Animals, 12(15), 1958. https://doi.org/10.3390/ani12151958

Petersen, J. L., Mickelson, J. R., Rendahl, A. K., Valberg, S. J., Andersson, L. S., Axelsson, J., Bailey, E., Bannasch, D., Binns, M. M., Borges, A. S., Brama, P., Da Câmara Machado, A., Capomaccio, S., Cappelli, K., Cothran, E. G., Distl, O., Fox-Clipsham, L., Graves, K. T., Guérin, G., … McCue, M. E. (2013). Genome-Wide Analysis Reveals Selection for Important Traits in Domestic Horse Breeds. PLoS Genetics, 9(1), e1003211. https://doi.org/10.1371/journal.pgen.1003211

Reissmann, M., Musa, L., Zakizadeh, S., & Ludwig, A. (2016). Distribution of coat-color-associated alleles in the domestic horse population and Przewalski’s horse. Journal of Applied Genetics, 57(4), 519–525. https://doi.org/10.1007/s13353-016-0352-7

Rieder, S., Hagger, C., Obexer-Ruff, G., Leeb, T., & Poncet, P.-A. (2008). Genetic Analysis of White Facial and Leg Markings in the Swiss Franches-Montagnes Horse Breed. Journal of Heredity, 99(2), 130–136. https://doi.org/10.1093/jhered/esm115

Ruxton, G. D., Sherratt, T. N., & Speed, M. P. (2004). Avoiding attack: The evolutionary ecology of crypsis, warning signals, and mimicry. Oxford University Press.

Schubert, M., Jónsson, H., Chang, D., et al. (2014). Prehistoric genomes reveal the genetic foundation and cost of horse domestication. PNAS, 111(52), E5661–E5669. DOI: 10.1073/pnas.1416991111

Sponenberg, D. P. (2009). Equine Color Genetics (3rd ed.). Wiley-Blackwell.

Stachurska, A., & Jansen, P. (2015). Crypto-tobiano horses in Hucul breed. Czech Journal of Animal Science, 60(1), 1–9. https://doi.org/10.17221/7905-CJAS

Stachurska, A., & Ussing, A. (2012). White markings in horses. Medycyna Weterynaryjna, 68, 74–78.

Woolf, C. M. (1990). Multifactorial Inheritance of Common White Markings in the Arabian Horse. Journal of Heredity, 81(4), 250–256. https://doi.org/10.1093/oxfordjournals.jhered.a110987

Woolf, C. M. (1991). Common White Facial Markings in Bay and Chestnut Arabian Horses and Their Hybrids. Journal of Heredity, 82(2), 167–169. https://doi.org/10.1093/oxfordjournals.jhered.a111053

Woolf, C. M. (1995). Influence of Stochastic Events on the Phenotypic Variation of Common White Leg Markings in the Arabian Horse: Implications for Various Genetic Disorders in Humans. Journal of Heredity, 86(2), 129–135. https://doi.org/10.1093/oxfordjournals.jhered.a111542

We hope you enjoyed this Blog Post, email us with any questions or comments.
Dr Meredith Hudes-Lowder & Karen McLain

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Follow our adventures on our Wild Mustang Walkabout™ 2024! We are getting ready for an adventure of a lifetime!

Arizona —- Utah —- NYC

Off on new adventures!

Meredith is in NYC… on East 96th Street between Lexington Avenue and Third Avenue… with a friend

The Genetics behind Chestnuts

All horse color is based on black, red, or a dilution or absence of these colours.

The Melanocortin 1 Receptor (MC1R) and Agouti Signaling Protein (ASIP) are both important genes involved in determining coat color in mammals, including horses.

1. Melanocortin 1 Receptor (MC1R): Red Factor

1. MC1R is a gene that plays a crucial role in determining the distribution of pigments in the coat.
2. The MC1R gene is associated with the production of melanin, the pigment responsible for coloration in hair, skin, and eyes.
3. In horses, the MC1R gene has various alleles that influence coat color. The two main alleles are the “E” (extension) and “e” (non-extension) alleles.
4. Horses with the E allele (dominant) have a black coat, while those with the e allele (recessive) have a chestnut coat. The interactions between these alleles contribute to various coat colors.

2. Agouti Signaling Protein (ASIP):

2. ASIP is another gene involved in coat color regulation, and it acts by regulating the distribution of pigments in individual hairs.
3. The ASIP gene has different alleles that affect the distribution of eumelanin (black pigment) and pheomelanin (red/yellow pigment) in the coat.
4. The interaction between MC1R and ASIP genes determines whether a horse’s coat will have a uniform color or if there will be areas of different pigmentation such as points on a bay horse. Points refer to black located on the lowder legs, mane, tail, and ear rims.

In summary, while MC1R determines the base color of the coat (black or chestnut), ASIP influences the distribution of pigments within individual hairs, leading to variations in coat patterns. The combination of alleles at both MC1R and ASIP loci contributes to the diversity of horse coat colors observed in different breeds. Other genes and modifiers can further influence coat color such as:

• Dilution Genes UC Davis: Champagne, Mushroom Dilution, Pearl, Silver
  Dilution Genes Equus ferus: Cream, Dun
  
• White Spotting Pattern UC Davis: Appaloosa Spotting, Appaloosa Pattern-1, Camarillo White, Dominant White,, Sabino 1, Splashed White,
  White Spotting Pattern Genes Equus ferus: Frame Overo, Gray, Lethal White Overo, Roan, Tobiano

The chestnut coat color in horses is determined by genetic factors. The main gene responsible for chestnut color is the extension (E) gene, specifically the e allele. Horses with at least one copy of the e allele will exhibit chestnut color.

There are two variations of the extension gene: E and e. The E allele is dominant and suppresses the expression of other color genes, resulting in a black coat. The e allele, on the other hand, allows the expression of other color genes, leading to chestnut color.

The possible combinations for chestnut color are:

• ee: Homozygous for the recessive e allele, resulting in a chestnut coat.
• Ee: Heterozygous, with one copy of the dominant E allele and one copy of the recessive e allele. The horse will have a black coat but can pass the chestnut gene to its offspring.
• EE: Homozygous for the dominant E allele, resulting in a black coat.

It’s important to note that chestnut color can vary in shades, from light to dark, and may be influenced by other genetic factors and modifiers. Additionally, there are different variations of the chestnut color, such as sorrel and liver chestnut, which may involve additional genetic elements.

Breeding two chestnut horses together (ee x ee) will always result in a chestnut foal, as both parents carry only the recessive e allele. Breeding two black horses together (EE x EE) will always result in a black foal, as both parents carry only the dominant E allele.

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Chestnut (No mmodifier)

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THE DILUTES

PALOMINO

The Cream Dilution can either be expressed as a single dilution, or a double dilution. Every chromosome has two alleles that represent the way in which each chromosome is inherited and you receive one allele from each parent. Simply put, the chromosomes (usually represented by letters) appear in pairs. To review high school biology, these pairs are generally dominant or recessive. Dominant genes are represented by two capital letters or one capital and one lower-case. The animal appears the same (phenotype) whether they are EE or Ee. The recessive form is represented by two lower-case letters ee.

The cream gene in the single form acts upon chestnut, bay and black by diluting the red colour to cream. The Cream Colour may be light enough to appear almost white to a dark chocolate tan colour. The black is generally unaffected so bay horses horses retain the black points, and mane/tail. Black horses appear somewhat diluted- a mousey chocolate. Horses with a single Cream dilution generally have dark eyes (unless blue from paint patterns) and black skin except where there are white markings (paint markings, facial markings, and leg markings).

A better view of Bolder & his brother, Echo (Pryor Mountain, ©equus ferus- wild horse photography)

CREMELLO/PERLINO

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DUNS

The Dun Dilution Factor works on all horse colours. It can appear in black, bay, chestnut, palomino, buckskin, cremello, roan, and even appears in grey horses prior to the horse greying out. Like the Cream Gene, the Dun Dilution lightens the base coat colour, adds primitive markings, but leaves black colour alone. There are so many different shades of dun and the nomenclature varies between horse breeds and regionally. We will describe them related to their base color. Black horses are unique because the resulting dun horses have a specific name called grullo/grulla. Dark bays and blacks can be almost impossible to distinguish.

A black horse with a Dun Dilution turns the horse a lighter shade, with primitive markings, and guard hairs (lighter hairs on the outside of the mane and tail). The head is usually darker. The resulting horses have a slate-blue with less red present although some can have a warm tone. They generally have a very dark head. Without genetic testing, we are only guessing at the actual chromosomes of these horses. .

Primitive markings are named for markings found on ancient horse breeds such as the Przewalski or Tappan horse. They include a continuous line from the withers to the dock (top of the tail). Other markings include wither and/or neck stripes, cobwebbing: faint stripes on the face, zebra stripes on the legs, chest stripes, and darker ear tips. Zebra stripes are the colour of the underlying coat color, so a chestnut horse has darker chestnut stripes and a bay has black zebra stripes. Lighter hair on either side of the mane and each side of the tail, called ‘guard hairs’ may also be present.

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WHITE SPOTTING PATTERNS:

ROANS

(Rock Springs/Pilot Butte, ©equus ferus- wild horse photography)

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PINTOS

References:

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This post is not meant as gratuitous violence, but rather to demonstrate that the free-roaming horses live dangerous, and occasionally deadly lives. No animals perished in this video; the foal Avi was fine in a few days, however, he did not survive the winter. His dam, Madison, is not a maiden mare; she had a foal Juanita Rae in 2019 who survived.

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After the band comes to water. Madison immediately heads to water ahead of her band. Nursing builds a considerable thirst. Her foal, Avi is left alone.

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Avi standing on the berm, is approached by a bachelor stallion. You can see his mother in the lower left of the photo, and Josie in the center.

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Two stallions approach Avi. Generally, when a foal is approached by older, or more dominant horses , foals will “clack”. Clacking/snapping is when a foal (and sometimes older horses) extend their neck and open and close their mouths; the term clacking comes from the sound of teeth striking. This is a subordinate behaviour that tells the dominant horse that they represent no threat. Avi did not do this during any interaction with any strange horses. Also of note is the injury to Avi’s left foreleg and left hind leg. These injuries occurred prior to the bachelor’s attack.

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Avi’s band stallion Eddie, the grey (white) stallion, confronts the curious stallion(s). Avi showed some signs of submission by extending his neck, he did not open and close his mouth audibly.

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A scuffle ensues, and note that Avi is directly behind Eddie.

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The three bachelors leave, and Avi wanders off to the left. This is fairly unusual foal behaviour. Foals tend to stick close to the band members.

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The stallion Shooter begins to cross the waterhole. (Warning: If you don’t wish to see the foal being struck, scroll past the video below. There is no blood.)

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Shooter strikes the foal. In truth, he could have easily killed the foal, but he did not. It is unusual that he deliberately went across the pond to confront the foal.

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The foals walks/limps back to his band, his mother waits.

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You can see the mud from Shooter’s strike on Avi’s chest. In this photo he is just over one month old. He was spotted/named on June 18, 2020 and this photo was taken July 27, 2020.

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Gypsy, an older mare in Eddie’s band comforts Avi.

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The foal Avi was fine a few days later. We saw him running with his band, as well as walking with no sign of lameness. His behaviour was unusual from wandering a considerable distance from his band, as well as not behaving in a subordinate manner when approached by strange stallions.

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Why are bachelors aggressive? As a rule, stallions are more aggressive towards colts over fillies (Górecka-Bruzda, 2023).  This makes sense because colts are future adversaries and competition for bands. A common explanation for infanticide is that the new stallion cannot afford to use his energy to look after another male’s offspring, and some may kill foals when a band is taken over (Gray, 2008).

Avi’s most likely sire is Rendezvous, and his dam Madison changed bands in October of 2019 when Eddie took over some of Rendezvous band. Avi was born in June 2020, which is a reasonably long time from Avi’s conception. This is important because stallion aggression towards foals is inversely proportional to the time spent with their dam. In other words, the time together, called consort time, results in stallions being less aggressive with mares they have spent more time together (Linklater , 1999). Presumably because the chances of the foal being theirs is higher, and so Eddie may have been more willing to risk injury defending Avi against the bachelors (Miller, 1981).

Stallions do kill foals. Bachelors are also known to inflict mortal wounds. Horses have individual personalities, and some or more tolerant than others. Avi did not demonstrate socially appropriate behaviour and showed signs of illness and injury. This alone could pose a risk for all horses by bringing predators close to an injured foal. A similar circumstance occurs in the movie ‘Cloud: Wild Stallion of the Rockies’ when another band stallions kills a foal who could not stand after birth. (https://www.pbs.org/wnet/nature/cloud-wild-stallion-of-the-rockies-introduction/29/).

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Eddie’s Band July, 2020. Sand Wash Basin Colorado
Gypsy: Unknown, older mare
Madison: Bandera x Ohitika, 2016
Ayita: Tecumseh x Flame, 2016
Makwa: Josie x Orgulloso
Josie: Unknown, older mare
Avi: Madison x Rendevous, 2020
Eddie: Lucky x Tuscarora, 2014
Shooter: Flirt x Brave, 2015

References:

Gray, Meeghan. (2008). An infanticide attempt by a free-roaming feral stallion (Equus caballus). Biology letters. 5. 23-5. 10.1098/rsbl.2008.0571.

Górecka-Bruzda A, Jaworska J, Stanley CR. The Social and Reproductive Challenges Faced by Free-Roaming Horse (Equus caballus) Stallions. Animals. 2023; 13(7):1151. https://doi.org/10.3390/ani13071151

Gray, M. E., Cameron, E. Z., Peacock, M. M., Thain, D. S., & Kirchoff, V. S. (2012). Are low infidelity rates in feral horses due to infanticide? Behavioral Ecology and Sociobiology, 66(4), 529–537. http://www.jstor.org/stable/41501750

Linklater WL, Cameron EZ, Minot EO, Stafford KJ. Stallion harassment and the mating system of horses. Anim Behav. 1999 Aug;58(2):295-306. doi: 10.1006/anbe.1999.1155. PMID: 10458881.

Miller, R. (1981). Male aggression, dominance and breeding behavior in Red Desert feral horses. Zeitschrift für Tierpsychologie, 57(3-4), 340–351. https://doi.org/10.1111/j.1439-0310.1981.tb01930.x

Day 2. Our first full day in the Basin., We arrived mid-morning and met up with Cindy of the “Wild Hors eWarrior’ and Kath from “Piceance Mustangs”

Day three of the 2023 Mustang Walkabout™ was our Sixth Annual ‘Meet & Shoot’.

My flight was early, and a lovely car service fetched me at 3:00am. Thankfully, both of my flights were on time, and uneventful. Karen met me at the airport and we began the 4-5 hour drive to Sand Wash Basin.

We made it as far as the huge incline above Steamboat Springs, Colorado but the road was closed. No cars were permitted either direction. We found out a truck driver lost control of his rig (it is a very steep hill), and crashed. The driver did not survive the crash , we were rerouted about two hours to get around the accident. It was very sad, andI have always said, ‘as annoying as traffic may be, I’d rather be in traffic, than the cause of it‘.

We arrived at Craig, Colorado later than expected and so decided to get a hotel room for the night instead of trying to push through. I began my day very early, so it was a nice to relax and rest. The town of Craig is about an hour away from the entrance to Sand Wash Basin,

Another gorgeous hot day in the Basin. We are preparing for a rainy day tomorrow and no solar. We will prepare some snakkies in the solar cooker today to munch on tomorrow. But first, we go to town- Maybell, CO population 74. We get gas, ice and take advantage of the showers ($3). We do shower in the trailer, but there’s something about showering in a real shower that’s positively decadent. (Oh, and real flush toilets!)

We saw Comet Neowise last night It’s getting less bright, but it was amazing to see. We did not get any photos… that we know of since the photos need a lot of post production. Maybe we got lucky.

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Who names horses? Well at Sand Wash, who never finds the new foal gets naming rights. Cindy was kind enough to allow us to name a new foal and since Karen and I have named a few already- we passed that on to another horse lover Sarah Broas. She named the new colt in Bronze’s band ‘Argon’ (as in the noble has) and his dam is Reyne. We got tons of pictures on the good camera, but here is a cell phone picture.

Here are some more photos of horses we saw today.

Today was spent entirely in the Basin. The weather was iffy, so we didn’t go to far from camp. We did see a lot of horses on Lookout Mountain, and a few bands came into Copper Pond.

Karen is a brilliant artist. She wanted to paint a horse from life, and Meteor and his bachelor band obliged us by standing still for an hour. It was uncanny, and almost as if they knew she was painting a portrait. The storm over the Bears Ears also held off as well. The horses: Mr. Kitty (grey), Astro (black), Orion (bay), Coronado (dark/liver chestnut pinto), Remington (chestnut), Meteor (palomino pinto). Our family named Orion when he was a wee colt, 2014.

Finally, I rescued a Prairie Rattlesnake off the road this morning. The weather has been cooler l, and they are sunning on the road to warm up. Under no circumstances should you attempt this yourself; we carry a snake hook and I know how to use it.

We move the snake because they are inadvertently killed because they blend in with the road, or deliberately killed because some people are butt heads. Either way, the little fella is safe off the road.

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The day ended with a rainbow and some beautiful light over the Bears Ears. Good night all!

Today we had the unique opportunity to attend the Moffat County Tourism Association Monthly Board Meeting. At the meeting, Pat Craig of the Wild Horse Refuge, was invited to speak. Pat patiently answered questions and gave a more depth explanation regarding the refuge’s business model. Eventually, the goal is for the public as to be able to see the horses by appointment. There will be no fee, but a donation is appreciated.

He put to rest concerns that the horses might be fed to the predators at the other facility run by the WildAnimal Sanctuary. Pat said the carnivores are fed via donated meat from retail stores such as Walmart. They receive 100,000 lbs of meat weekly. The Refuge horses and property cost approximately 14 million dollars, so why would he spend that sort of money to provide snacks for carnivores?

The Animal Sanctuary is very well equipped and funded. At the Wild Horse Refuge they have:

-Snow cats & D6’s for snow, they are aware of the snowfall in Colorado and are prepared

-Three vets on staff & farrier

-Range specialist surveying the land to ensure the appropriate number of horses. 3,500 of their acreage is BLM land. They are paying a lease for the land.

-Eventually 100-200 horses total

-They pay taxes even as a not-for profit; this help the community

-They buy all supplies from local merchants

-They own fire trucks, fire equipment and have helped communities with this equipment in emergencies

-They have hay, abundant water sources, and generous supplies of fencing and other repair equipment (graders, backhoes, big hoes etc)

We returned to the Basin for the evening and found some old friends at a waterhole. We got some stunning photos with spectacular lighting.

Goodnight friends, from the Basin