F (Wright’s inbreeding coefficient) index were surveyed. The contribution of each analyzed breed to genetic diversity of the whole dataset was assessed using MolKin3.0; global genetic diversity and allelic richness contributions were evaluated. All the investigated loci were polymorphic; 209 alleles were identified . The average number of alleles per locus was 3.62, and the effective number of alleles was 2.27. The Ne resulted lower in all breeds due to the presence of low-frequency alleles that can be easily lost by genetic drift, thus reducing the genetic variability of the breeds, and increasing their risk of extinction. The global molecular kinship was 27%, the average breed molecular kinship was 53%, and the mean inbreeding rate 43%, with a self-coancestry of 78%. Wright’s statistical analysis showed a 41% excess of homozygous due to breed genetic differences (34%) and to inbreeding within the breed (9%). Genetic variability analysis showed that 11 breeds were in endangered status. The contribution to Italian poultry genetic diversity, estimated as global genetic diversity, and ranged from 30.2 to 98.5%. In conclusion, the investigated breeds maintain a unique genetic pattern and play an important role in global Italian poultry biodiversity, providing a remarkable contribution to genetic variability.The preservation of genetic variability of autochthonous poultry breeds is crucial in global biodiversity. A recent report revealed small breed size and potential risk of extinction of all native Italian poultry breeds; therefore, a correct assessment of their genetic diversity is necessary for a suitable management of their preservation. In this work, we provided an overview of the contribution to poultry biodiversity of some Italian autochthonous breeds reared in conservation centers devoted to local biodiversity preservation. The level of genetic diversity, molecular kinship, inbreeding, contribution to overall genetic diversity, and rate of extinction of each breed were analyzed with a set of 14 microsatellite loci in 17 autochthonous chicken breeds. To evaluate genetic variability, total number (Na), and effective number (Ne) of alleles, observed (Ho) and expected (He) heterozygosity, and  In the last decade, many developed countries recorded a raising interest toward local breeds and traditional products ; the mosMathematical software, such as GenAlEx v6.501  and MolK1F (Wright’s inbreeding coefficient) were surveyed. The collected information could be useful for planning new safeguarding strategies.In this work, we aimed to obtain an overview of the contribution of 17 Italian chicken breeds included in the Registry of Indigenous Poultry Breeds and in the project TuBAvI ‘‘Conservation of Biodiversity in Italian Poultry Breeds’’ 1 to ItaliBlood samples were collected in compliance with the European rules [Council Regulation (EC) No. 1/2005 and Council Regulation (EC) No. 1099/2009]. Blood samples were collected during routine health controls by the public veterinary service.A total of 645 samples were collected from 17 different autochthonous chicken breeds , reared in conservation centers devoted to local genetic preservation . All bre® tubes containing EDTA as an anticoagulant; a blood aliquot was immediately frozen at −20°C pending DNA analysis. DNA was extracted with the NucleoSpin® Blood QuickPure kit .All blood samples (about 2 mL) were collected from ulnar veins and stored in VacutainerThe experimental protocol was approved by the Bioethical Committee of the University of Turin (protocol no. 451944).2 nomenclature including nine DNA reference samples. An error assay was performed by replicating the genotyping on a randomly chosen 10% of individual samples (The genetic characterization was done using a panel of 14 microsatellites  chosen f samples .F [Wright’s inbreeding coefficient: 1 − (Ho/He)] was calculated as deviation from expected heterozygosity. The global tests across populations and loci using Guo and Thompson 1992 were performed in GenePOP  as nH/nL where nH is the number of heterozygous loci, and nL is the total number of tested loci. Graphical output of Hind value distribution, global, and for each breed, was performed in GeneAlex to evaluate the quantity and distribution of inbreeding. Indeed, the inbreeding of each breed was evaluated as Ho and statistical analysis was performed: mean, median, standard deviation, standard error (SE), and maximum and minimum values for breed.Fi) was estimated by the formula Fi = 2si − 1 where Si (self-coancestry) is the molecular coancestry of an individual i with itself; it is related to the individual homozygosity. Mean self-coancestry for whole dataset  and that in each breed (IB = breed inbreeding) were estimated.In addition, the coefficient of inbreeding of an individual (KIW), and in breed dataset, breed individual kinship (KIB). Global kinship (KG) is the average of all KIW, whereas within-breed kinship (KBW) and between-breed kinship (KBB) are simply computed averaging the corresponding values for all the within-breed (KIB) or between-breeds pairs of individuals. Additionally, 100 bootstrapping adjusted for 50 sampling size and weighted for PIC was applied. The molecular kinship within breed was also estimated as proportion shared allele (PSA).The kinship was estimated as molecular coancestry coefficient between individuals included itself : for eacThe genetic diversity has been defined in terms of molecular coancestry distances . Global F statistics were used to evaluate the genetic diversity: FIS (heterozygote deficiency within breed), FST (heterozygote deficiency due to breed subdivision), and FIT  were obtained as in MolKin v.3. The bootstrapping method adjusting for sampling to avoid bias in estimates because of unequal sample sizes and weighted for PIC information was applied (Wright’s (1978) applied .W (within-breed) and GDB (between-breeds) were percentage of lost diversity. The contribution of each breed was evaluated by the formula: GDT = GDW + GDB; GDT is the total contribution to GD expressed as GD loss percentage respect to GB. The contribution to GD of each analyzed breed was quantified as 1 − GDT; a pie chart was produced. The allelic richness contribution was estimated as Np and plotted for each breed. A positive GDT value of a breed means that in the remaining dataset the GD increases; consequently, the breed with a positive GDT would not be the first choice for a conservation program.The contribution of each analyzed breed to global genetic diversity and breed extinction impact was assessed using MolKin3.0 following For the assessment of the risk of extinction of studied breeds, more genetic variability indices have been taken into account to quantify the inbreeding depression and genetic drift: He and Ho, as estimation of genetic variability and inbreeding, respectively, together with the number of alleles (Na), allelic richness (Ne), and the number of alleles with frequency of less than 5% as an estimate of the risk of total variability reduction. Graphical output of allelic pattern was performed in GeneAlex.Four indices of variability were selected and combined to define an extinction risk index (ERI).F index estimates the excess of homozygous, IB means inbreeding, KBW, PSA gives the proportion of diversity between individuals, and O = 1 - Ho, homozygosity. ERIs range between 0 and 5. The mean point 2.5 has been arbitrarily chosen as the reference point to define when a breed should be considered at risk of extinction.where, FIT is 0.40 ± 0.2, and FST is 0.34 ± 0.2. The frequency of null allele estimated by FreeNA was reported in FST values , but the mean Ne is 4.35 (1.85–12.78). In eight loci, it is possible to observe a significant excess of homozygosity. The mean T values  estimateFis index underlines a slight but significant excess of homozygous in most breeds even if the mean value is 0.09 ± 0.02 and ranged between 0.21, in Polverara and −0.04 in Livorno Nera; only in 2 breeds the Fis resulted to have a significant negative value: Bianca di Saluzzo (−0.03 ± 0.02) and Bionda Piemontese (−0.01 ± 0.02). Bianca di Saluzzo had the highest Na value, whereas Livorno Bianca had the lowest: Na 1.86 ± 0.14, Ne 1.37 ± 0.10, and Ho 0.17 ± 0.04. Among the 14 loci, the number of loci that deviated from HW equilibrium at the breed level ranged from 2 (Livorno Bianca and Romagnola) to 10 (Bionda Piemontese) with an average of 4.88 (p < 0.05).Overall, breed level indices of genetic diversity  varied across breeds  accordinIB value confirmed a low (0.65) self-coancestry in Bionda Piemontese and Bianca di Saluzzo and a high self-coancestry (0.91) in Livorno. Mean IB resulted to be 0.78 , indicating that they are immediate relatives. The KG (0.27) and mean KBB (0.25) showed the differentiation and isolation between the breeds. KBW ranged between 0.78 ± 0.01 (Livorno Bianca) and 0.31 ± 0.008 (Bionda Piemontese); KBB ranged between 0.20 (Ermellinata), and 0.30 (Livorno Bianca and Romagnola).Mean KBW, mKBB, and PSA for each breed were plotted in KBW, PSA, and KBB, thus highlighting the close kinship between the individuals, and it looks like KBB is not significantly different among breeds . On the contrary the Ermellinata shows the lower KBB. The Millefiori resulted to have the lowest value of PSA (0.33); this means that individuals share on average only 33% of genetic variability, even if this was not confirmed by the value KBW corrected for sample size.FIT, FST, and FIS) per locus are shown in FIT) was significantly high (p < 0.001). Fixation index of subpopulation in relation to the total population (FST) per locus ranged from 0.023 at LEI192 to 0.043 at MCW0016 locus, with a mean of 0.34 ± 0.02 (p < 0.001). This indicates that about 35% of the total genetic variation in Italian chicken breed is explained by between-breed differences. The average inbreeding coefficient of individuals within the breeds, measured as FIS value, was 0.09 ± 0.008 (p < 0.001). The contribution of each analyzed breed to global biodiversity was plotted in B loss with an increase of GBW . Except for Pepoi and Robusta Maculata that did not show any private alleles, all the other breeds contributed to global allelic richness, with 94 private alleles; this is a rather high number that confirms the results obtained by The fixation indices  to 0.03% (Siciliana). Breeds that showed a negative impact on the GDW were Bionda Piemontese, Bianca di Saluzzo, Valdarnese, Ancona, Millefiori, Mugellese, Polverara, Romagnola, and Padovana; therefore, they should be considered as important sources of genetic variability. On the other hand, Robusta Maculata, Livorno Bianca, Robusta Lionata, Ermellinata, Siciliana, Mericanel della Brianza, Pepoi, and Livorno Nera showed a negative impact on the global biodiversity related to breeds peculiarity.The analysis of breed impact on global genetic diversity is described in The analysis of risk extinction for Italian breeds pointed out that 65% of Italian breed showed an ERI value of 2.5 . LivornoThe autochthonous chicken breeds biodiversity preservation is a topic of rising importance in recent time; indeed, some investigations were already devoted to this aim not only in Italy , but allDifferent parameters were investigated with the aim of recovering a real and reliable picture of genetic variation, and risk status of 17 autochthonous chicken breeds. All the animals investigated in this survey are reared in conservation centers aimed to the preservation of these local genetic resources .KBW = 0.53) similar to the results obtained by Generally, the within-breed genetic diversity of the studied chicken breeds is low  was significantly high (p < 0.001). The fixation index of breed in relation to the total population (FST) with mean 0.35 ± 0.004 (p < 0.001) indicates that about 35% of the total genetic variation in autochthonous Italian chicken breeds is explained by between-breed differences, in comparison to 22% observed by FST = 0.41 in autochthonous breeds from Veneto region. The Livorno Bianca and Romagnola showed higher KBB; on the contrary, the Ermellinata showed the lower KBB. In molecular inbreeding, the breed analysis distribution highlighted a heterogeneous distribution of Hind, with a remarkable right-hand shift in Bionda Piemontese, Bianca di Saluzzo, and Valdarnese. Bianca di Saluzzo (98.5) and Bionda Piemontese (95.3) provided the greatest contribution to global biodiversity and showed the highest variability values, although the percentage of alleles with a frequency lower than 5% was high, so the genetic drift could reduce the variability of these two breeds very quickly. A non-significant F negative value observed in Bionda Piemontese and Bianca di Saluzzo could be related to the fact that in the last years these breeds were submitted to targeted mating schemes with the aim of preserving individual variability, and thus, the inbreeding effect is negligible  but also by low heterozygosity estimates (0.69–0.17), and high within-breed inbreeding (0.67–0.31). The Ne was low in all breeds (<3.57) due to the presence of low-frequency alleles, 20% of which with frequency < 5% that can be easily lost by genetic drift, thus reducing the genetic variability of the breeds, and increasing their risk of extinction. Given the fact that the mean Ho and gligible . The molgligible ; the ValIn addition, despite the fact of presenting a high number of alleles, also in Robusta, Pepoi, and Polverara, the genetic management requires a particular attention due to the high inbreeding coefficient and within-breed kinship. Likewise, Siciliana shows a very low variability and a high kinship; therefore, mating schemes with other Siciliana genetic lines are required. In The Robusta Maculata and Ermellinata as well resulted to have high ERI  and low variability value with few possibilities to increase their individual variability. Given this fact, their conservation status could be worrying as already seen in other local breeds .The Millefiori had the lowest value of PSA (0.33); this means that individuals share on average only the 33% of genetic variability, even if this result was not confirmed by the kinship value corrected for sample size. This could be justified by an effect resulting from the different weight given to the sharing of alleles in the calculation of the MolKin respect to the PSA and the reduced number of sampled subjects. That could be better understood through further studies about breed structure and genetic management of mating.W were Bionda Piemontese, Bianca di Saluzzo, Valdarnese, Ancona, Millefiori, Mugellese, Polverara, and Padovana; therefore, they should be considered as important sources of genetic variability. On the other hand, Robusta Maculata, Livorno Bianca, Robusta Lionata, Ermellinata, Siciliana, Mericanel della Brianza, Pepoi, and Livorno Nera showed a negative impact on the global biodiversity related to breeds peculiarity. This finding confirms the results obtained by W), whereas in a long-term strategy, the allelic richness and breed differentiation are crucial. Summarizing the obtained results, Italian autochthonous breeds provide an important contribution to poultry genetic variability due to their unique genetic pattern, compared to autochthonous breeds of other countries  can be found below: The animal study was reviewed and approved by Bioethical Committee of the University of Turin (prot. no. 451944).DS, SS, and AS: conceptualization, methodology, and original draft preparation. MM, NI, AB, CC, and FC: samples collection. SM, CB, SD, and MP: sample analysis. DS and SS: data analyses. EL, FP, MC, and AC: writing – review, and editing. AS, PS, and MC: supervision. AS and SC: project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pelobates syriacus) would change among successive years in accordance with the short-term changes in environmental conditions. Surprisingly, the effect of short-term climate fluctuations on genetic composition have rarely been addressed in the literature, and to our knowledge the effect of annual climatic fluctuations have not been considered meaningful. Our findings show that climatic variation among successive years, primarily the amount of rainfall and rainy days, can significantly alter both microsatellite allelic composition and diversity. We suggest that environmental (i.e. fluctuating) selection is differential across the globe, and that its intensity is expected to be greatest in regions where short-term climatic conditions are least stable.In addition to variations on the spatial scale, short- and long-term temporal variations, too, can impose intense selection on the overall genetic diversity and composition of a population. We hypothesized that the allelic composition in populations of the eastern spadefoot toad ( There are only a few studies that have examined the changes in genetic characteristics over short-term periods; and most of these were conducted on aquatic species of economic importance in order to determine genetic stability under harvesting pressure . Repeated sampling over short-term periods can reveal correlations between demographic and genetic changes10; can help to detect higher genetic variation and rare genotypes that mostly appear in a single year11; and contribute to monitoring the progress of conservation management practices12. Consequently, there is growing attention to the study of short-term temporal genetic variation in populations of wild animals.Most population genetic studies have focused on the spatial scale, based on samples collected at a single point in time and on the assumption that the observed genetic structure remains stable over short-term periods. When temporal genetic diversity is addressed, it has been mostly tested on long-term periods spread over decades, using preserved historical samples from museums  in Israel. The eastern spadefoot toad is a fossorial species, spending the hot and dry periods in burrows in the soil. Its spawning sites comprise stagnant temporary water bodies, rivers or lakes, and large permanent pools18. In Israel, this species is known to breed only in ephemeral pools and the adults are philopatric, breeding in the same vernal pool in consecutive years19. The distribution range of P. syriacus extends to southern Romania in the north, Greece in the west, Iran in the east, and Israel in the south18. In Israel, it occurs from the Golan Heights and Galilee in the north, through the central coastal plain, which includes its present southernmost breeding site—Robert's pool near Ashdod17. Spadefoot toad species display extreme variation in larval period duration, due in part to evolution of thyroid hormone (TH) physiology20. Eastern spadefoot toad tadpoles exhibit plasticity in the timing of metamorphosis21, and environmental factors have been shown to affect different traits in the tadpoles, allowing rapid development in drying ephemeral ponds22. At each pool spawning is usually happens within two to three weeks, during nights of peak heavy rain19. Metamorphosis at each rainpool is a synchronous event; the tadpoles metamorph within a period of 1 week19. In the coastal plains, on years with a short hydroperiod, tadpoles can already metamorph after 2.5 months. On years with a long hydroperiod, metamorphosis was observed only at 5–6 months. In the northern pools, where hydroperiods are even longer, tadpoles can stay in the water up to 8 months19. Metamorphs were observed up to 1 km away from their natal rainpool shortly after leaving the water. Adults are philopatric, breeding in the same rainpool in consecutive years, and were not observed further than 200 m from the water edge19. From a capture-recapture study in several rainpools, the estimated population size for each pool ranged 22–26 breeding adults19. Out of 50 adults that were collected from two pools in 1983–85, only three individuals were of age 2 years or less.In the present work, we extended the study by Munwes et al.23). We hypothesized that the genetic make-up of the tadpole population  would change among successive years in accordance with changes in environmental conditions . Consequently, in habitats in which environmental conditions vary significantly from year to year each generational cohort can potentially possess a different composition of genotypes or alleles , and each time sampled 15–20 tadpoles. Most pools are small (diameter < 50 m) and shallow (depth < 1 m), enabling extensive sampling. No metamorphs were observed during any of our sampling sessions; thus, all our sampling has been done before any tadpoles metamorphed and left the pool. To maximize the chance of sampling tadpoles from different spawns , we intensively sampled from around each pool. Captured tadpoles were measured (snout to tail tip). Newly-hatched tadpoles (length ≤ 3 cm) were collected whole and tail-tip samples were taken from larger tadpoles. All samples were stored in 96% alcohol and kept frozen until analysis.28. Genomic DNA samples from P. syriacus were sent to the Evolutionary Genetics Core Facility (EGCF) at Cornell University for genomic library construction via Illumina MiSeq sequencing. The EGCF provided an msatcommander29 output file with thousands of potential microsatellite primer pairs. Further details on the selection process of microsatellites for this analysis are provided in Table We developed novel microsatellite loci Table , a set o17 investigated the effect of elevation, distance to the Mediterranean Sea, vegetation type, and soil type on genetic diversity. They found that a combination of hydroperiod, annual rain, and altitude; or a combination of hydroperiod, soil type, and vegetation, best explained the variance in mtDNA among sites (Fst) and accounted for about 17% of the variance. These variables are relevant when testing for spatial genetic patterns. However, when testing for short-term temporal patterns, variables such as elevation, soil type, and vegetation cover do not change much from year to year. Consequently, we did not to include these variables in the analysis. We chose to focus on four annual environmental variables of high relevance for spadefoot toad breeding behavior and tadpole survival: the mean daily maximum temperature in January (MJT), the annual rainfall (AR), the number of days with more than 0.1 mm rain (RD), and the hydroperiod length (HP). We selected to use both hydroperiod and rainfall predictors because in our study system heavy rainfall occasionally occurs late in the season . Such short but intense rainstorms can refill pools that were nearly dry, and in which most of the tadpoles have already metamorphed, and extend the hydroperiod by a few more weeks. Thus, rainfall and days of rain may contain more relevant information on tadpole survival compared to hydroperiod duration alone. Climate variables were obtained from the Israel Meteorological Service (IMS) database (https://ims.data.gov.il/), in which the annual rainfall and number of rainy days are measured between August 1st of one year and July 31th of the next year. Accordingly, in this study, we refer to a sampling season as the time between August of a given year and July the year after. The IMS has meteorological stations spread across the country, and the climate data were taken from the closest meteorological station to each vernal pool. In cases where there was no close station to the pool, we used the average of two or three stations in the area . These diversity measures were calculated per year and pool using GeneAlEx (version 6.532). The presence of null allele, stuttering and large allele dropout was tested with Micro-Checker (version 2.2.333). Deviation from Hardy–Weinberg equilibrium (HWE) and evidence of linkage disequilibrium (LD) were tested as implemented in Genepop (version 4.434). Since the number of annual samples varied among vernal pools, we standardized Na values within each pool.We evaluated genetic diversity using three common measures: mean number of alleles per locus (Na), observed heterozygosity (Ho), and Shannon's information evenness  framework in SPSS . In these models, the four environmental parameters  were set as fixed effects. In addition, the number of annual samples per pool was added as a covariate, and the vernal pool identity was set as the random effect. Finally, we standardized the climatic variables and Na values within each pool in order to control for geographical difference among pools.A35) using ARLEQUIN (version 3.536). To test whether the pairwise DA values (between years within pools) significantly differed from those expected by random, we used a randomization test and 10,000 permutations.To test for changes in allele composition among years we applied two approaches. First, we counted the numbers of alleles in each year for each pool, and constructed a contingency table. We used the Chi-Square test to test for significant differences in allele frequencies among years. Since many alleles had a low frequency (< 5), we used a randomization test  to avoid sample size bias. We used correspondence analysis to visualize the differences among years and to associate between alleles and year. Second, to evaluate the temporal component in genetic variation we calculated the corrected Nei’s average number of differences in allele frequency between years within each pool , among years within vernal pools , among individuals within a pool (the same pool and year), and within individuals.37).The correspondence analysis provided the distinction between years according to the allele frequencies. To assess which climate variables have a significant effect on allele frequencies, we used a GEE mixed model with gamma distribution and log as the link function. All four environmental parameters  were standardized within each pool and were set as fixed effects, and the vernal pool identity was set as the random effect. We assessed variable importance (VI) using the Independent Resampled Inputs approach, which evaluates the contribution of each of the factors in a model by random sampling, independently of the model type and fitting methodThis study was conducted with permits from the Israeli Nature and Parks Authority for capturing and sampling populations of the eastern spadefoot toad across Israel . All procedures performed in this study were in accordance with the ethical standards of the Israeli Nature and Parks Authority.During the years 2012–2015, we sampled DNA from seven vernal pools in Israel. Unfortunately, the winter of 2013–2014 was a drought year in Israel with only one significant rainfall event in mid-December 2013. Most of the pools in the coastal plain were completely dry by mid-January 2014, and the pools in the Golan and Galilee also dried up very early in that season, i.e. before the breeding peak. Therefore, no tadpoles were collected from any of those sites during that winter.P. syriacus DNA from seven vernal pools. All eight loci showed high polymorphism, with allele numbers ranging from 19 to 36 (Table P ≥ 0.06) or in any population (P ≥ 0.82). Significant heterozygote deficit was detected for three loci , and in four pools . No allele dropout or stuttering was detected at any locus. The possible presence of a null allele was detected only at locus Psy2.Using a set of novel microsatellite markers Table , we succ36 Table . The meaThe annual mean number of alleles per locus (Na), the Shannon's information evenness (J), and the observed heterozygosity (Ho) for each sampled pool varied substantially Table . The meaWe examined whether the annual variations in Na, J, and Ho could be explained by annual climatic and environmental fluctuations. The annual variations in Na, J, and Ho were associated with the climatic variables, either directly or through the interactions among them Table . ShannonThe number of alleles per locus and observed heterozygosity were significantly affected by all climatic variables through their interactions Table . Both diP = 0.028; GB: P < 0.001; GC: P < 0.001; GN: P < 0.001; GO: P = 0.012; GQ: P < 0.001; LD: P < 0.001; Fig. Overall, alleles frequencies differed significantly among years in every pool we examined  between 2013 and 2015 was also significantly greater than that expected by random (P < 0.0001).The Drs Table . For exaP < 0.0001). The spatial component (i.e. among pools) accounted for 12% of the total genetic variation (P < 0.0001), and an additional 2.3% (P < 0.0001) accounted for the genetic variance among individuals within years. The remaining unexplained variation occurred within individuals . A recent study found temporal genetic instability between two consecutive years in an edge population of the western toad (Anaxyrus boreas16), the temporal component explained 2.4% of genetic variability, similar to the finding in the present study  in the Lake Tahoe basin, and found a significant genetic differentiation between successive years. Since the harsh landscape in that area restricts gene flow, the authors had expected that the small local breeding populations at each site would be relatively inbred and genetically consistent from year to year. They attributed their unexpected findings to high asymmetry in reproductive success among adults in a breeding deme; however, they did not offer an explanation for this variation in reproductive success. Waples and Teel42 showed that annual genetic variation in a population of Pacific salmon (Oncorhynchus tshauytscha), a highly philopatric breeder, fluctuated considerably (up to 36%) in a hatchery. They attributed this result to the relatively few adults which succeed in breeding every year (low effective population size), even though large number of adult salmons reached the hatchery. Similar pattern was shown by Chen et al.43 using a detailed long term (23 years) pedigree of Florida Scrub-Jays (Aphelocoma coerulescens) and SNPs data. They showed that annual allele frequency variations can be explained by change in survival and reproductive success of breeders, and that the contribution of gene flow is small. In the current study, however, if annual variation in allelic frequency was only associated with annual reproductive skew, we would have expected a lack of correlation with annual climate. One alternative explanation to our results is that annual climate conditions affect the breeding behavior of adult toads. Spadefoot toads reach sexual maturity only after 3–4 years44, and can live for 12 years in the wild45. Like many anuran species occupying dry habitats, breeding of P. syriacus is explosive, and takes place in temporary pools of water left by rainfall19. The adults synchronize the emergence from aestivation using the low frequency sound of rain hitting the ground46. Thus, the activity of breeding in spadefoot toads is limited to a few rainy nights19, giving no room for misopportunity, and most likely pushes the philopatric adults living around a pool to participate in breeding every year. Thus, it is less likely that the variation we observed in annual allelic frequency is due to reproductive skew.Savage et al.23) reveals that certain areas on the globe show a high annual instability in rainfall. The largest area of rainfall instability on earth is the Saharo-Sind climatic zone . Other extensive regions of annual instability in rainfall (60–100%) are the Australian deserts, Namib in Africa, south-western North America, the Atacama Desert in South America, and the Mongolian Plateau (Fig. Let us generalize even further. Our hypothesis posited that annual climatic variation would affect allele frequency. Extending this idea could suggest that populations living in regions of higher annual climate fluctuations would be expected to show greater annual variation in allele composition and diversity in their progeny; whereas, in contrast, populations in regions where annual fluctuations in climate are low, would be expected to show low annual variation in allele composition and diversity. One clear exception to this trend is the result of a catastrophic climatic event that can wipe out genetic variation altogether, but such extreme events are usually rare. The extent of annual fluctuations in climate is not evenly or randomly distributed across the globe. Examination of the map of annual fluctuation in rainfall on a global scale Fig. a,23 reve47, but was not evident in a follow-up study in 200848. Repeated sampling in successive years is especially important when sampling the larvae of species in which not every adult breeds every year49, or when populations are exposed to strong seasonal or interannual variation in abiotic conditions48. The temporal genetic variation could become emphasized in small isolated populations due to increased effects of genetic drift and inbreeding12. In the present study, private alleles were identified in most of the years (Table P. syriacus tadpole population in a given pool is not available in any single year. Thus estimates based on samples taken at one point in time might not be valid across time, whereas sampling over several successive years may lead to more accurate estimates of a population's genetic structure.Last, the results of our and previous studies highlight the importance of temporal sampling in population genetics research. Although the temporal variance component accounts for only about 2% of the total genetic variance, it accounts for about 15% of the total explained genetic variance Table . Thus, trs Table . PrivateSupplementary Information.Supplementary Information.


Breast cancer is the leading cancer in American women. Due to the inherent aggressiveness of triple-negative and HER2-enriched breast cancers, it is imperative to identify novel molecular targets for therapeutic intervention. Due to their abnormal activities in metastatic breast cancers, JAK2–STAT3 and TrkA pathways have been individually implicated in aggressive breast tumors. However, their co-activation and signaling interactions have not been thoroughly investigated. Therefore, our study aimed to elucidate the extent of crosstalk between JAK2–STAT3 and TrkA signaling pathways and its impact on breast cancer. Our data revealed a novel interaction between TrkA and STAT3, and that this interaction results in STAT3 phosphorylation and activation by TrkA, leading to enhanced stemness gene expression and stem cell renewal. We further found that the co-activation of JAK2–STAT3 and TrkA pathways is correlated with shorter time to develop overall and organ-specific metastasis, suggesting that this signaling crosstalk underlies the aggressiveness of triple-negative and HER2-enriched breast cancers.SOX2 and MYC. The co-activation of both pathways promotes breast cancer stem cells. Finally, we found that TNBC and HER2-enriched breast cancer with JAK2–STAT3 and TrkA co-activation are positively associated with poor overall metastasis-free and organ-specific metastasis-free survival. Collectively, our study uncovered that TrkA is a novel activating kinase of STAT3, and their co-activation enhances gene transcription and promotes breast cancer stem cells in TNBC and HER2-enriched breast cancer. JAK2–STAT3 and TrkA signaling pathways have been separately implicated in aggressive breast cancers; however, whether they are co-activated or undergo functional interaction has not been thoroughly investigated. Herein we report, for the first time that STAT3 and TrkA are significantly co-overexpressed and co-activated in triple-negative breast cancer (TNBC) and HER2-enriched breast cancer, as shown by immunohistochemical staining and data mining. Through immunofluorescence staining–confocal microscopy and immunoprecipitation–Western blotting, we found that TrkA and STAT3 co-localize and physically interact in the cytoplasm, and the interaction is dependent on STAT3-Y705 phosphorylation. TrkA–STAT3 interaction leads to STAT3 phosphorylation at Y705 by TrkA in breast cancer cells and cell-free kinase assays, indicating that STAT3 is a novel substrate of TrkA. β-NGF-mediated TrkA activation induces TrkA–STAT3 interaction, STAT3 nuclear transport and transcriptional activity, and the expression of STAT3 target genes,  Breast cancer is the most commonly diagnosed cancer in American women and is the second-leading cause of cancer-related deaths among women . Breast Janus kinase 2 (JAK2) is a non-receptor tyrosine kinase that is frequently amplified or aberrantly active in triple-negative and HER2-enriched breast cancers ,6. JAK2 Tropomyosin receptor tyrosine kinase A (TrkA) is a neurotrophic receptor encoded by the NTRK1 gene and is implicated in numerous cancer types, in part, due to its propensity for forming oncogenic NTRK fusion genes that drive the malignant progression of some cancer types . HoweverAlthough previous reports show that the activation of TrkA by its cognate ligand, β-NGF, promotes the phosphorylation of STAT3 on its Y705 and S727 residues and promotes STAT3 transcriptional activity, the co-activation of JAK2–STAT3 and TrkA signaling pathways, and the mechanisms underlying their crosstalk, have never been investigated in any cell or tumor type . In thisTo determine the extent to which JAK2–STAT3 and TrkA signaling pathways are co-activated in breast tumor samples, we performed IHC staining on 33 node-positive breast carcinomas across three major subtypes  to detect phosphorylated TrkA (Y490) and STAT3 (Y705) proteins. Results showed that node-positive triple-negative breast cancer (TNBC) and HER2-positive breast tumors highly co-express p-TrkA and p-STAT3Of the nine TNBC samples, we found that 100% of them have high p-STAT3 and 77.8% of these samples concurrently express high p-TrkA. Similarly, 83% of HER2-positive breast tumors were highly stained for both p-TrkA and p-STAT3. Although over 80% of node-positive luminal samples showed high p-STAT3 levels, only 41% of these samples also highly expressed p-TrkA. These results indicated that concurrently activated JAK2–STAT3 and TrkA signaling is preferentially found in TNBC and HER2-positive breast cancer. We found a 61% (20/33) positivity rate for p-TrkA and p-STAT3 co-overexpression after examining each individual sample. Representative IHC images are shown in NTRK1 fusions [NTRK1 mRNA levels  using a STAT3 activation signature, TrkA activation signature, or a combination of STAT3 and TrkA signatures to examine 1533 breast cancer patient samples from TCGA and GEO databases ,33,34,35 fusions  and founA levels , suggestThe observation of co-overexpression of p-STAT3 and p-TrkA prompted us to examine if the co-expression leads to their physical interaction. Of note, these two proteins have never been reported to physically interact. For this, we conducted immunoprecipitation (IP) followed by Western blot using HEK293 cells transfected with flag-tagged STAT3 (STAT3-WT-Flag) and found an interaction between STAT3 and p-TrkA/total TrkA A. The inTo confirm the novel STAT3–TrkA interaction and the requirement of STAT3-Y705 for the interaction, we conducted immunofluorescent (IF) staining–confocal microscopy on HEK293 cells co-transfected with TrkA and either STAT3-WT-Flag or STAT3-Y705F-Flag plasmids. To induce phosphorylation of both STAT3 and TrkA, transfected cells were stimulated with human epidermal growth factor (hEGF) and human β-nerve growth factor (β-NGF), respectively. Results of these experiments I. Taken Since our data showed that phosphorylated TrkA can interact with STAT3 and the interaction is abolished when STAT3-Y705 is mutated, we hypothesized that TrkA phosphorylates STAT3 at its Y705 residue. While TrkA activation has been observed to promote the phosphorylation of STAT3 on Y705 and S727 residues  and the STAT3 phosphorylation at Y705 leads to STAT3 nuclear translocation and activation of transcriptional activity. Previous studies have shown that STAT3-mediated transcriptional activity can be enhanced by the addition of TrkA ligand β-NGF  or overeTo determine if β-NGF-induced activation of STAT3 is attributed to TrkA kinase activity, we examined TrkA-WT versus TrkA-K538N (dominant-negative kinase-dead mutant)  in theirSOX2   (high/CD24low population (10.3% vs. 23.3%), which supports the signaling crosstalk between JAK2/STAT3 and TrkA pathways  C, with ahibition H, MYC exactivity I. It is RK1 mRNA ; howeverrectinib , which irectinib . To confctively) J. Comparpathways K. Then, pathways L. The adpathways M. To furpathways N, althoupathways O. Taken We have shown that activated STAT3 and activated TrkA can be found in triple-negative and HER2-enriched breast cancers and that the combined JAK2–STAT3/TrkA activation signatures are significantly enriched in these two subtypes . Since tN = 96), we found that high STAT3 activation and high co-activation, but not TrkA activation, were associated with a shortened time to develop brain metastasis (N = 166), we observed that high STAT3 activation, high TrkA activation, and high co-activation, were associated with a high likelihood to develop lung metastasis  Co-activation of TrkA and STAT3 promotes breast cancer stem cells. (6) JAK2–STAT3 and TrkA pathway co-activation is significantly higher in triple-negative and HER2-enriched breast cancers when compared to luminal subtypes of cancers. (7) High JAK2–STAT3 and TrkA co-activation in TNBC and HER2-enriched breast cancers is associated with shorter metastasis-free survival and significantly higher risks for lung, bone, and brain metastasis. Through these significant observations, our study uncovers TrkA-mediated STAT3 phosphorylation as a novel mechanism underlying the crosstalk between the JAK2–STAT3 and TrkA pathways in triple-negative and HER2-enriched breast cancers, and it identifies the novel prognostic value for the pathway co-activation. In this study, we have made the following novel and impactful findings. (1) JAK2–STAT3 and TrkA pathways are significantly co-activated in triple-negative and HER2-positive human breast cancer specimens. (2) STAT3 and TrkA proteins interact directly as observed in cells and MFP xenograft tissues. (3) The STAT3–TrkA interaction is dependent on STAT3 Y705. (4) TrkA phosphorylates STAT3 at Y705 to induce its activation, nuclear import, and transcriptional activation of known STAT3 target genes, While previous reports showed TrkA activation can lead to STAT3 phosphorylation ,28 and tSince STAT3 serves as a converging point of several oncogenic signaling pathways, including JAK1/2, EGFR, HER2, Src , and Trk®) was approved in August 2019 for treatment of solid tumors that bore oncogenic NTRK1 fusions. Larotrectinib (Vitravki®) was approved in 2018 as a tissue-agnostic inhibitor for any solid tumors bearing NTRK1 fusions [®) was approved in 2011 for treatment of myelofibrosis and is currently in Phase 1 and 2 clinical trials for treating breast cancer patients. Baricitinib (Olumiant®) is approved for the treatment of rheumatoid arthritis and is not being evaluated for breast cancer. Fedratinib (Inrebic®) is approved for treating primary and secondary myelofibrosis; however, it has not entered clinical trials for breast cancer patients. Additional JAK1/2 inhibitors, pacritinib, gandotinib, lestaurtinib, and momelotinib, are in clinical trials for myelofibrosis, myeloid neoplasms, or relapsed lymphomas, but they have not yet been investigated clinically in breast cancer patients [Given the observed co-activation of JAK2 and TrkA in triple-negative and HER2-enriched breast cancers, we speculate that co-targeting of JAK2 and TrkA would be a promising new therapeutic modality against breast cancer. Currently, there are two FDA-approved orally active TrkA inhibitors, entrectinib and larotrectinib. Entrectinib . The breast cancer cell lines used in this study included triple-negative MDA-MB-231, MDB-MB-468, and BT20 cells, as well as LumB HER2-amplified SKBR3 cells. Cells were maintained in Corning DMEM  or MEM  supplemented with 10% heat-inactivated fetal bovine serum  and 1% penicillin/streptomycin. Breast cancer tissue microarray (BR10010e) was purchased from US Biomax  and stained for p-TrkA (Y490) and p-STAT3 (Y705) as previously described ,56. IHC pCMV5-TrkA was generated by Raymond Birge  . pLEGFP-Publicly available breast tumor RNA-Seq expression profiles were retrieved from TCGA and GEO  with subtype and/or metastasis-free survival information from 166 triple-negative breast cancer patients and 96 HER2-enriched breast cancer patients. Median centering was used to generate STAT3 or TrkA activation signature comprised of known genes regulated by STAT3  or TrkA For crude protein lysates, adherent cells were washed with cold 1× PBS before lysis with RIPA buffer (ThermoFisher Scientific) or SDS-free RIPA buffer. Lysis buffers were prepared with 1× Halt Protease/Phosphatase inhibitor (ThermoFisher Scientific). Lysates were sonicated and clarified by centrifugation before quantification with Bradford assay . Flag IP experiments were performed as previously described . To IP pWestern blot of IP products was performed as previously described . All ant4 cells/well into Nunc Lab-Tek II 8-well chamber slide (ThermoFisher) and transfected with the indicated plasmids. Transfected cells were starved overnight before stimulation with 100 ng/mL of β-NGF  or human EGF  for 20 min at 37 °C. Cells were fixed with 4% paraformaldehyde for 30 min at ambient temperature. Fixed cells were washed in 1× PBS before blocking (5% goat serum + 0.3% Triton X-100 in 1× PBS) for 1 h, followed by incubation with primary antibody overnight at 4 °C. Cells were incubated with a fluorophore-conjugated secondary antibody  and rabbit anti-phospho-TrkA (Y490) simultaneously, in 1× PBS/0.3% Tween/5% goat serum overnight at 4 °C. Phospho-STAT3 (Y705) was detected with goat anti-mouse Alexa Fluor 488 (Invitrogen) while phospho-TrkA (Y490) was detected using goat anti-rabbit Alexa Fluor 568 (Invitrogen). Nuclei were labeled with DAPI, and slides were mounted with a cover slip (Vector Labs). Slides were imaged using the Olympus FV1200 confocal microscope, and Z-stack images were acquired and processed with FV10-ASW 4.2 viewer software.2, 1 mg/mL BSA, pH 7.5) in the presence of 50 mM ATP . Kinase reactions were terminated by the addition of Laemmli sodium dodecyl sulfate (SDS) buffer and resolved on 8% SDS polyacrylamide gel. For LC/MS analyses, STAT3 bands were excised using sterile blades and submitted for liquid chromatography/mass spectrometry at the Massachusetts Institute of Technology Proteomics Core. For Coomassie staining, gels were fixed (50:40:10/methanol:water:acetic acid) for 1 h, followed by overnight staining with Coomassie R-250  and de-staining until bands of interest were visible. Coomassie-stained gels were imaged using the Bio-Rad ChemiDoc. For IP-kinase assays, HEK293 cells were transfected with flag-tagged wild-type STAT3 or STAT3-Y705F and underwent Flag IP. Flag IP products were subjected to TrkA kinase assay as described and reaction products were analyzed by Western blot. For kinase assay-IP, TrkA cell-free kinase assay was repeated and recombinant active TrkA was immunoprecipitated using anti-TrkA (Y490) antibody, resolved using SDS-PAGE and examined for co-immunoprecipitation of recombinant STAT3 using Western blot. Recombinant human STAT3  was incubated with enzymatically active recombinant human TrkA  or TPM3-TrkA  for 30 min at 30 °C in kinase reaction buffer  or 1% DMSO vehicle overnight at 37 °C and immediately harvested for Western blot analysis.Luciferase assays were performed using a dual-luciferase assay kit  and readouts were collected using the Molecular Devices iD3 plate reader . Cells were co-transfected with a STAT3-responsive luciferase reporter (pGAS-luc), and either vector or TrkA (pCMV5-TrkA) plasmid for 28 h before serum starvation for 16 h. Then, cells were stimulated with 100 ng/mL β-NGF for 4 h before the addition of lysis buffer. Lysates underwent mechanical lysis by scraping followed by trituration with a pipette to ensure complete lysis. Firefly luciferase activity was measured as previously described ,63. ResuTotal RNA was isolated from cells using QIAGEN RNEasy Mini Kit . RT-PCR was performed as previously described using primers listed in high/CD24low cells and mammosphere assays were performed as previously described [Flow cytometry for CD44escribed . For ALDThe current study is the first to illustrate that JAK2–STAT3 and TrkA pathways are co-activated in metastatic triple-negative and HER2-enriched breast cancers. Additionally, we show that TrkA interacts with and phosphorylates STAT3 on its Y705 residue, inducing oncogenic gene transcription to support breast cancer cell stemness, and that loss of Y705 residue abrogates STAT3 interaction with TrkA. These findings suggest that JAK2–STAT3 and TrkA signaling pathways converge at STAT3, supporting the crosstalk between these two signaling cascades. Importantly, we show that JAK2–STAT3/TrkA pathway co-activation is a new prognostic indicator of distant metastasis to various organs, prompting a future study to examine combined inhibition of JAK2–STAT3 and TrkA pathways as a novel and viable combination therapy in metastatic triple-negative and HER2-enriched breast cancers.


Trypanoxyuris multilabiatus, species-specific; and T. minutus, genus-specific) and their host, the mantled howler monkey , based on mitochondrial DNA sequences and microsatellite loci (these only for the host). Given that pinworms and primates have a close co-evolutionary history, covariation in several genetic aspects of their populations is expected.In parasitism arm race processes and red queen dynamics between host and parasites reciprocally mold many aspects of their genetics and evolution. We performed a parallel assessment of population genetics and demography of two species of pinworms with different degrees of host specificity  or more susceptible (for howlers). Finally, a positive correlation was found between pairwise p-distance of host haplotypes and that of their associated pinworm haplotypes.Mitochondrial DNA revealed two genetic clusters (West and East) in both pinworm species and howler monkeys, although population structure and genetic differentiation were stronger in the host, while genetic diversity was higher in pinworms than howler populations. Co-divergence tests showed no congruence between host and parasite phylogenies; nonetheless, a significant correlation was found between both pinworms and T. multilabiatus in comparison with T. minutus, highlighting the role of host-specificity in coevolving processes. Trypanoxyuris minutus exhibits genetic specificity towards the most frequent host haplotype as well as geographic specificity. Results suggest signals of potential local adaptation in pinworms and further support the notion of correlated evolution between pinworms and their primate hosts.The genetic configuration of pinworm populations appears to be molded by their own demography and life history traits in conjunction with the biology and evolutionary history of their hosts, including host genetic variation, social interactions, dispersal and biogeography. Similarity in patterns of genetic structure, differentiation and diversity is higher between howler monkeys and The online version contains supplementary material available at 10.1186/s12862-021-01924-4. Degeeriella regalis) showed congruent genetic structure with the hawk, yielding insights about the host’s recent evolutionary history [Schistosoma mansoni and its definitive rat host [Potamopyrgus antipodarum) and its trematode parasite  [Ecological interactions drive evolutionary change, where each member of the association acts as a natural selective agent to its counterpart. Coevolutionary changes occurring among participants will be more or less evident depending on the strength, frequency and dependency of the interaction. Parasitism constitutes an intimate association in which arm race processes and red queen dynamics between host and parasites reciprocally mold many aspects of their genetics, physiology, morphology, behaviour, and life history traits –3. Genet history . Additio history , while s history . Correlarat host , and betlus sp.) . Furtherlus sp.) .Although evolutionary interactions between host and parasites can be tight enough to yield correlated genetic patterns and even cophylogenetic relationships and cospeciation –16, concStudies about genetic relationships, divergence and coevolutionary patterns between non-human primates and their parasites have evaluated parasite host-specificity and parasite diversification regarding host phylogeny, predominantly with infectious disease agents and some ectoparasites –25. HereAlouatta palliata) are endangered primates, whose arboreal nature and predominant folivorous diet significantly limit their dispersal capability across an unforested matrix [Alouatta palliata is parasitized by two species of pinworms, Trypanoxyuris minutus which is widely dispersed, found in several howler monkey species including A. belzebul, A. caraya, A. guariba, A. pigra, and A. seniculus [T. multilabiatus, which has only been reported in A. palliata [Mantled howler monkeys  for both host and parasites along with microsatellite data only for the host. Given the biology and direct mode of transmission of these parasites, with no vectors or intermediary hosts that could influence parasite genetic configuration other than howler monkeys, the close evolutionary association between pinworms and primates, in conjunction with the likely higher evolutionary potential of the parasite compared to that of their host, we predict: (1) higher genetic diversity and stronger genetic structure in pinworms in comparison with howler groups, given that the parasite’s larger populations sizes and shorter generation times render the effects on genetic patterns of habitat fragmentation and limited host dispersal more quickly detectable in parasites in comparison with the host; (2) a positive association between genetic distances of host and parasites, indicating that genetically similar host populations harbour similar parasite populations, thus implying a dependence of the pinworms gene flow on primate movement; (3) concordant genealogical patterns between host and parasites; and (4) signs of local adaptation in the pinworm species; the last two predictions are associated with both the host-specificity and the coevolutionary hypothesis for pinworms and primates. In order to address this last point, we assessed host–parasite mtDNA haplotype relationships and evaluated how these associations relate to haplotype divergence, parasite infectivity and host susceptibility.We analysed the genetic diversity, genetic structure and demographic history of Structure results revealed two main genetic clusters corresponding to West and East sampling localities in both pinworm species and the howler monkey, although the clustering is stronger in the host , further dividing western localities into two genetic clusters  and T. multilabiatus , while smaller in T. minutus . Pairwise FST differentiation was significant between all regions for howler monkeys and between West and East clusters for T. minutus, whereas T. multilabiatus exhibited no significant differentiation ; all other computed genetic distances showed no correlation   and FST ; D-Jost (r = 0.08 p = 0.42) and Edward distances  were not significant  results supported larger population sizes in parasites compared with those of their host, exhibiting particular demographic histories for each species. The howler monkey showed a gradual population growth and recent population decline. tus Fig. .Fig. 3HaT. minutus and 8 in T. multilabiatus. The host median-joining network differed in several aspects from that of the pinworms. First, a most frequent haplotype, likely ancestral, in the howler monkey network, present across all regions except TUX and CML , suggesting random evolutionary associations between howler monkey and T. minutus haplotypes.Regarding the haplotype evolutionary history, 19 different haplotypes were found in howler monkeys, 59 in CML Fig. a. Insteaies Fig. b. Also, eps Fig. c. The coT. minutus haplotypes along with their frequency are shown in Fig. T. minutus haplotypes (48/59) were associated to single host haplotypes, most of them specific to certain localities, whereas only 11 pinworm haplotypes infected 2 or more host haplotypes, with a mean of 1.3 host haplotypes infected by each pinworm haplotype. Haplotype Hap5 was the most infective, parasitizing five different howler haplotypes. A positive correlation was observed between infectivity and frequency, with the most infective T. minutus haplotypes also being the most frequent , or more susceptible (howler haplotypes parasitized by a larger number of pinworm haplotypes)   in both parasites and their host, genetic structure and genetic differentiation, contrary to our prediction, were stronger in the host than in both pinworms species, in agreement with limited dispersal in the primate as previously documented for this species –42. MazéTrypanoxyuris infection in howler monkeys has been associated to closer partner proximity [While an isolation by distance (IBD) pattern was observed for the pinworms based on mitochondrial data, for the howler monkey it was identified only with nuclear data. The latter can be related to the historical dispersal of howler monkeys, whereas the local genetic differentiation and IBD observed with the genetically more variable microsatellites loci could reflect a contemporary constraint in individual movement. The significant habitat loss and landscape transformation derived from human activities along the primate distribution has most likely limited host dispersal between closer populations. Furthermore, IBD in pinworms implies greater potential for parasite transmission between adjacent howler monkey populations due to spatial proximity that increases the contact rates among host individuals. Indeed, inter-host contact and proximity, animal movement and spatial constrains imposed by a heterogenous landscape, all play a critical role in parasite transmission dynamics in wildlife populations –48. For roximity .Alouatta palliata howler monkeys distribution  suggest higher evolutionary rates in the parasites compared to their host. This is evident in the haplotype genealogies where pinworms showed complex networks formed by many unique haplotypes differing by few mutational steps, compared to the simple and more structured haplotype network in the howler monkey. Even though cophylogenetic patterns between pinworms and their primate hosts have been documented at macroevolutionary scales , 28, 37,t Mexico ), the hiWe aimed to further explore the genetic association between pinworm and howler monkeys by using mtDNA haplotype identity to assign genetic variants to host and parasite individuals, enabling us to describe susceptibility and infectivity traits based on the number of host–parasite connections identified per haplotype. Accordingly, hosts were considered more susceptible if they were parasitized by a greater number (diversity) of pinworm haplotypes, whereas pinworms infectivity was defined by the number of different host haplotypes in which each pinworm was found.T. minutus genetic variant. Conversely, only few pinworm haplotypes were found parasitizing more than one host genetic variant, and most T. minutus were associated to a single howler haplotype, suggesting that T. minutus tends to adapt to one host genetic configuration. Selection can cause parasites to develop genetic specificity towards a particular host genotype, usually the most common, increasing its susceptibility [T. minutus and the howlers’ genetic variants, as well as the higher genetic similarities between pinworm/host haplotypes that share hosts/pinworms haplotypes, supporting that genetic similarity among hosts might be a key factor for pinworm transmission and establishment. Additionally, these genetic associations could be related with the parasite transmission mode, where autoinfection and retroinfection are common mechanisms for pinworm acquisition [Overall, nearly each howler haplotype was parasitized by more than one tibility , 39. Ouruisition , 55. BotT. minutus shows a roughly northwest to southeast gradient where many of the northernmost pinworm haplotypes of Los Tuxtlas are also present in other regions. Comparatively, most of the southeastern haplotypes  are only found in that particular region. The history of dispersion of howler monkeys across southern Mexico helps explain this pattern, which followed a colonization process from south to north [T. minutus suggests that howler monkeys carried most of the pinworm genetic variants as they dispersed towards northern regions. Also, that only a fraction of pinworm haplotypes remained within the already established host populations. Therefore, most recent host populations in the north still harbour a collection of pinworm mitochondrial geographic variation.Our study also reveals that parasite genetic variants are not evenly distributed across geographic regions, and that only some pinworm haplotypes could be considered highly infective . Additionally, host haplotypes unique to a particular geographic region tend to harbour pinworm variants also unique for that region. This geographic specificity  in T. mito north , thus thto north . During T. minutus haplotypes were less infective . Although acquiring genetic variation could favour parasite infectivity, it is suggested that above certain threshold it could also increase host resistance [T. minutus infrapopulations, further suggests a correlated evolution in agreement with a highly host-specific and evolutionary intimate system, as shown by these pinworms and primate.Notably, the relationships between haplotype divergence and parasite infectivity and host susceptibility, jointly with the parasite’s higher gene flow, suggest potential local adaptation in pinworms. Local adaptation, a higher mean fitness of populations in local environments, is linked to the ability of each organism to incorporate new genetic and phenotypic variants that can confer some fitness advantage . Host–pasistance , 60. Flusistance , 20, 61.Parallel studies on host–parasite genetic arrangements, although challenging, are growing attention and interest. Our study, jointly with examples  that encompass systems with different degrees of host specificity and distinct parasite life cycles and life history strategies, contribute to understanding the transmission dynamics, the distribution of resistant and virulence alleles, and the spread of disease. Also enabling a better comprehension of the coevolutionary process and their role in preserving genetic variation, namely the persistence of host and parasite populations. Furthermore, the use of conventional genetic techniques, like in our study, has been of enormous value to uncover relevant information about the evolutionary ecology of hosts and parasites interactions. Incorporating high throughput sequencing techniques and sampling across the whole genome could certainly be of great value in co-structure studies, by yielding detailed information on the microevolutionary changes in pinworms and their primate hosts, fostering a thorough understanding of the genetic, ecological and evolutionary dynamics between hosts and parasites .T. multilabiatus than the host genus-specific T. minutus, highlighting the role of host-specificity in coevolving processes. Our findings show that pinworms are more infective in the genetically similar host, whereas associations of host and parasite genetic variants reveal both genetic specificity towards the most frequent host haplotype and geographic specificity in T. minutus. Moreover, altogether these results suggest signals of local adaptation in the parasite, while the fact that similar howler monkey haplotypes harbour genetically similar T. minutus infrapopulations further supports the notion of correlated evolution between pinworms and their primate hosts.Evolutionary processes of mantled howler monkey populations and their pinworms are indeed tightly linked. Our mtDNA findings show that pinworm gene flow is mediated by host dispersal, while at the same time no codivergence was observed between pinworms and their primate host. The high genetic diversity, high gene flow and large effective population sizes showed by the two pinworm species indicate higher evolutionary rates in the parasites compared to their host. Additionally, genetic structure, differentiation and diversity patterns show higher similarity between howler monkeys and the host species-specific pinworm Alouatta palliata) and their pinworms at ten sampling localities across six geographic regions in southeast Mexico, using non-invasive techniques  were recovered from 58 howler monkey individuals. Pinworm specimens were labelled with host ID to be able to link host and parasite DNA. Host samples and pinworm specimens were stored at − 20 °C until DNA processing.We sampled free-ranging mantled howler monkey groups  and genotyped nine microsatellite markers. Details about host DNA amplification and genotyping (cyt-b and microsatellites loci) procedures are found in Solórzano-García et al. [® reagent  according to the manufacturer’s instructions. A fragment of ~ 800 bp of the mitochondrial cytochrome oxidase subunit 1 gene (COI) was obtained for each recovered pinworm of both species. For details on pinworm DNA amplification procedures see Solórzano-García et al. [https://doi.org/10.5281/zenodo.4538731  from 1 to 6, running 20 replicates per K of 1,000,000 MCMC and 100,000 iterations as burnin under the admixture model. The most probable number of clusters was estimated using the Evanno method [Structure results were visualized using Pophelper v.2.3 in R [We assessed genetic structure in host and parasites with  v.2.3.4 , by testo method ; Structu2.3 in R . Additio2.3 in R  to assesD-Jost [GST [FST with Arlequin. Geographic Euclidian distances between sampling localities were calculated with Raster v.3.3 [RST and estimated in Arlequin. Mantel tests were run with vegan v.2.5 [Because howler monkey movements are expected to be higher among groups located in nearby forest fragments, and dispersing individuals may carry pinworms with them, we tested the isolation by distance hypothesis (IBD) for the host and the two pinworm species with Mantel tests, comparing genetic (mtDNA) and geographic distances. We estimated genetic distances between sampling localities based on D-Jost , Hedrickost [GST , and Edwost [GST  with theost [GST  for hoster v.3.3 . IBD patan v.2.5  and IBD an v.2.5 . Next, iS), haplotype diversity (Hd) and nucleotide diversity (π) were obtained with DnaSP v.5 [Na) and observed (Ho) and expected heterozygosity (He) with Arlequin. The geographic distribution of genetic diversity was explored by mapping the haplotype diversity of both host and parasites, as well as the host expected heterozygosity per sampling locality. We applied the Inverse Distance Weighted interpolation method (IDW) to spatially interpolate genetic diversity values between sampling localities across the distribution of Alouatta palliata in Mexico, using the Quantum Geographic Information System QGIS 3.14.16. The resultant raster map was clipped according to the host distribution polygon [Molecular diversity indices including the number of segregating sites  in BEAST v.1.7.5  for each elegans , and a g elegans , 81. For 5 years , 83. Plo 5 years .Trypanoxyuris minutus haplotypes were built in MrBayes v.3.2.2 [The genealogical relationships between haplotypes were determined by unrooted median joining networks in Network v.5  for each v.3.2.2  and the  v.3.2.2 , includi v.3.2.2 , and the v.3.2.2 .T. minutus given the small sample size we had for T. multilabiatus.Haplotype analyses were run only for T. minutus haplotypes, and (b) those infected by only one pinworm haplotype. Next, we estimated the genetic divergence (p-distance) between howler haplotypes that share pinworm haplotypes and between those infected by unique pinworm haplotypes. The same strategy was followed for the parasite, estimating genetic divergence between co-occurring haplotypes and those found in only one host haplotype. We performed Wilcoxon–Man Whitney tests in R to assess if the haplotypes that shared host/parasites were more like each other than to haplotypes harbouring or infecting distinct host/parasites. We also examined the relationship between susceptibility/infectivity and haplotype frequency and divergence by performing non-parametric correlation tests using the package ggpubr v.0.4 [To further examine howler-pinworm association patterns at the mtDNA level, we first identified (a) the host haplotypes infected by various br v.0.4 . The latAdditional file 1. Additional Tables S1-S5 and Figures S1–S4.


STF index, and discriminant analysis of principal components. Fine-scale population stratification due to specific morphological and behavioural traits was assessed by principal component and factorial analyses. In the CWD, a demographic history effect was manifested mainly in a high genome-wide proportion of short homozygous segments corresponding to a historical load of inbreeding derived from founders. The observed proportion of long homozygous segments indicated that the inbreeding events shaped the CWD genome relatively recently compared to other groups. Even if there was a significant increase in genetic similarity among wolf-like breeds, they were genetically separated from each other. Moreover, this study showed that the CWD genome carries private alleles that are not found in either wolves or other dog breeds analysed in this study.This study focused on the genomic differences between the Czechoslovakian wolfdog (CWD) and its ancestors, the Grey wolf (GW) and German Shepherd dog. The Saarloos wolfdog and Belgian Shepherd dog were also included to study the level of GW genetics retained in the genome of domesticated breeds. The dataset consisted of 131 animals and 143,593 single nucleotide polymorphisms (SNPs). The effects of demographic history on the overall genome structure were determined by screening the distribution of the homozygous segments. The genetic variance distributed within and between groups was quantified by genetic distances, the  Canis lupus familiaris) originating from a substantial number of grey wolves (Canis lupus) express different breed-specific phenotypic traits. Even if six dog breeds are generally recognised as the product of grey wolves crossing with domestic dogs, only the Czechoslovakian Wolfdog (CWD) and Saarloos Wolfdog (SWD) are accepted by the Federation Cynologique Internationale (FCI) as wolf hybrid breeds. They were created at independent times from the crossing of the German Shepherd dog (GSD) with grey wolves. These breeds belong to the FCI Group 1, which contains sheepdogs and cattle dogs (except Swiss cattle dogs), including the Belgian Shepherd dog (BSD). Although the BSD is distantly related to the CWD, SWD, and GSD, it has been bred for similar functional purposes.Dogs . In the beginning, the breeding goal was to create a working dog based on the hybridisation of the Carpathian grey wolf (GW) and GSD for military purposes to patrol the country’s borders. Later, the working abilities of the CWD were depressed in the breeding goal (except endurance), focusing strictly on wolf-like morphology and wolf-like coat texture, coat colours, and mask. The CWD is recognised as a temperate, persistent, and trainable dog breed. Thus, the immediate breeding goal of the wolf-like phenotype and dog-like behaviour created, in the CWD genome, a unique set of haplotypes compared to other breeds. On the other hand, the relatively low number of founders resulted in the fact that the CWD genome carries long runs of homozygosity (ROH) islands, leading to much higher inbreeding than the pedigree estimates [The SWD breeding started in the Netherlands in 1932. This breed was accepted by the FCI on a definitive basis in 1981 under Standard No. 311. Similar to the CWD, the SWD was created by crossing a GSD with a wolf originating from the Siberian branch of the European type. The SWD is a strongly built breed whose outer appearance  is reminiscent of a wolf, similar to the CWD. Coat colours range from light to dark shaded black-tipped game-colour (boar and hare), called wolf-grey, and light to dark shaded brown-tipped game-colour. Typical wolf-markings range from light creamy white to white. The reserved and wolf-like manner to avoid unknown situations is typical for the Saarloos wolfdog [The breeding club of GSDs was established in 1899. The GSD was accepted on a definitive basis by the FCI in 1955 (FCI Standard No. 166). The breeding goal is to breed a well-balanced, self-assured, good-natured, instinctive guard, protection, service and herding dog. Its origin traces back to the central and southern German breeds of herding dogs. Compared to CWDs and SWDs, the German Shepherd dog’s standard colour is black with reddish-brown, brown and yellow-to-light grey markings; the white colour is not allowed. The undercoat of GSDs has a light-greyish tone. Thus, different selection footprints in genomic regions concerning coat colour among the CWD, SWD, and GSD can be expected. However, an extensive genetic study mapping the GSD genomic diversity was not performed until now. Only several studies have assessed the level of homozygosity or inbreeding in the GSD genome as a part of autosomal inherited disease research  or populThe BSD, representative of sheepdogs from FCI Group 1 , was officially born between 1891 to 1897. The type and temperament of the BSD were established in 1910. It is recognised as a watchful and active, ready-to-action dog with innate skills of guarding flocks and property, as well as a defence and service dog. A previous study on assessing microsatellite markers of BSDs revealed a decrease in genetic diversity and the presence of bottlenecks without a strong effect on the BSD gene pool [When considering any dog breed’s genetic makeup, repeated mating of close relatives to fix specific traits of breeds can be found. From a genomic perspective, such a mating resulted in offsprings with long ROH segments spread across the genome. Most of them have shared history through close relatives, which led to the segregation of often rare recessive or partially recessive genetic variants producing in the homozygous state a deleterious phenotype . Thus, tTYRP1) polymorphism with coat colour variation patterns in GSDs. Miluchová et al. [MLPH) gene responsible for melanin transport from the skin to the hair follicle in association with slow hair growth (BHFD) or even the fall-down of hair and the presence of alopecia (CDA). In many different species and breeds, the KIT gene was identified as a candidate gene responsible for specific white coat colour patterns [During domestication and the subsequent development of specialised dog breeds, a significant selection pressure acted mainly in genomic regions controlling preferred morphological  and behavioural traits. Previous studies confirmed that the genetic and phenotypic assessment of coat colour mutations could contribute to investigating the origin and dynamics of a functional polymorphism in hybrid wolf populations and developing appropriate guidelines to contrast hybridisation with their domesticated relatives . Dreger á et al.  and Moraá et al.  studied patterns .Except for coat colour variation, several studies infer differences in behaviour among conventional breeds, revealing that herding dogs are more trainable than hunting, sporting, pleasure, or companion (non-sporting) breeds . The inhThis study aimed to quantify genomic differences between the Czechoslovakian Wolfdog and its ancestors, the Grey wolf (Carpathian) and German Shepherd dog. In addition, two other sheepdogs were analysed as representatives of the same FCI section to compare their genome’s composition and study the level of Grey wolf genetics retained mainly in the CWD gene pool. The potential impact of selective breeding for specific traits of interest on their genome composition was estimated concerning polymorphisms in genes related to aerobic trainability of the organism, behaviour and motivation, coat colour, strength, and endurance.The dataset consisted of genotyping information covering the canine autosomal genome of four breeds  and the free-range Grey wolf (GW) as a common ancestor of each of them. Genome-wide data were obtained for a total of 133 animals via high-density canine BeadChip. As the dataset consisted of new and previously published data , the mapα is the proportion of false-positive ROH segments  was then calculated by dividing the sum of the ROH length (kb) in the particular length class by the total length of the autosomal genome (kb) covered by the SNPs. Five inbreeding coefficients for each individual were calculated.The effects of selective breeding in dogs and natural selection in Grey wolves on their genome composition were tested by the screening of ROH with various lengths  to distinguish between genetic information inherited from different ancestor generations. The ROH were defined as genomic segments with a minimum length of 500 kb  as folloo et al. ), ns is  package  separateaD) between animals and the pair-wise Wright’s STF index at the group level using the StAMPP R package [The systematic differences in allele frequencies among tested groups as a consequence of nonrandom mating between animals related to selective breeding for traits of interest were tested by several methodological approaches. The overall degree of population stratification was assessed by calculating Nei’s genetic distances  was used to quantify the variance distributed within and between clusters of genetically related animals. The DAPC analysis was based on the pre-defined groups reflecting the origin of analysed animals. The DAPC analysis allowed for the transformation of the input genotype data using principal component analysis (PCA) to uncorrelated variables describing the target proportion of variance in the dataset. In the subsequent discriminant analysis (DA), the uncorrelated variables were used to maximise inter-group variance estimation and obtain discriminant functions representing a linear combination of the original variables  with the largest between-group variance and the smallest within-group variance. The initial DAPC analysis was set-up to describe the maximum proportion of variance (95%). The subsequent test of a trade-off between the power of discrimination and over-fitting by calculation of the α-score resulted in ten principal components. The final DAPC analysis was conducted on ten principal components (PCs) and four discriminant functions (DFs), representing 42.3% of the total variance conserved in the dataset using the Adegenet v2.1.3 R package .Unsupervised network analysis utilising the Super Paramagnetic Clustering approach  was carrThe proportion of admixture between analysed groups was estimated by the unsupervised Bayesian clustering approach implemented in Structure 2.3.4 . The anaAssuming that the natural and artificial selection acting during breed formation can leave various traces in the animal genome, the genetic differences among evaluated groups were also estimated by analysing the frequency of alleles in specific regions responsible for controlling several phenotypes. The population stratification depending on four categories of phenotypic traits was studied: aerobic trainability of the organism (ATO), behaviour and motivation (BM), coat colour (CC), and strength and endurance (SE). Canis lupus familiaris gene stable IDs were retrieved via over-representation analysis (ORA). The significantly enriched categories were identified based on the Bonferonni method, which is one of the most commonly used multiple test adjustment approaches. The top ten GO significantly over-represented biological processes (p < 0.05) were considered. In the next step, the chromosomal position of selected genes in the dog genome was determined by the ensembldb R package [The genes associated with each phenotype category were first selected by a literature survey . As ther package , and all package . In FA, As shown in previous studies ,44, natuROH16F. In the CWD, the average recent genomic inbreeding at a level of 7.48 ± 3.41% was found. The high standard deviation results from the high ROH length variability observed within this class (average segment length of 21.18 Mbp).ROH16F (4.46%) in GSDs was lower than that in CWDs, mainly due to the large selection basis and worldwide distribution of the breed. The distribution of ROH within length classes in BSDs followed a similar trend as in GSDs. The difference was evident mainly in the lower number of very short ROH segments (0–2 Mbp) observed in BSDs; however, their proportion of the total number of detected ROH was similar (49.77%). GW showed a significantly lower proportion of ROH segments across the analysed length classes than the others, as well as low genomic inbreeding derived from the ROH distribution. The observed proportion of ROH within each class due to natural selection and preservation of population fitness was expected. The highest proportion of the short ROH segments (83.28%) in the GW genome may be related to the fact that those are either responsible for survival or adaptation and are, therefore, inherited in the monomorphic form to next generations. The absence of ROH > 16 Mbp confirms that the mating of close relatives is not common in wild species [Compared to the GSD, the CWD showed a lower genome-wide proportion of short ROH segments and a higher proportion of long segments , confirm species .STF is shown in STF value indicated a higher genetic similarity between CWD and BSD than between CWD and GW. BSD showed, at the same time, a higher genetic similarity to GW compared to the others. The highest genetic differentiation observed between GSD and GW points to the fact that GSD is most distant from its wild ancestor compared to the others, probably as a result of bottleneck events related to the establishment of the breed.The proportion of genetic similarity among tested groups derived from the calculation of STF calculation with growing genetic distances between the analysed dog breeds and GW. The highest differentiation was found between GSD and GW, while CWD showed the highest genetic similarity to GW. Generally, both the STF and Nei’s genetic distances between the shepherd and wolf-like dog groups reflect known information from their genealogy origin. BSD was genetically closest to GSD, logically followed by SWD due to the geographical proximity of the area of breed origin. In the SWD case, Nei’s genetic distances showed a different trend than the STF matrix, probably due to a low number of SWD animals in the analysis. Both methods proved a similar degree of genetic differentiation between CWD and SWD or BSD.The Nei’s genetic distances confirmed that CWD and GSD are genetically close groups as a result of the impact of GSD on the CWD genome. The Nei’s genetic distances followed the trend observed by the aD = 0.20 ± 0.02) or genetic differentiation (STF = 0.33 ± 0.03) indicated a sufficient level of genetic variability and interpopulation diversity among them. This fact also confirmed the matrix of aD values calculated on the intrapopulation levels  that form an important part of breed standards and breeding goals. Even if the analysed breeds were selected for very similar purposes, there are several significant differences in their breeding goals. Both GSD and BSD are recognized as multipurpose working dogs, while in CWD and SWD, wolf-like phenotypes are predominant in selection . In addition, the CWD is the only breed where the endurance is directly included in the breeding goal in the form of the first-degree exam consisting of a 40 km endurance run.RSU1, TTC6, TSHR, and ACSL1) that can potentially affect the organism aerobic trainability through control of the expression of oxidative capacity (VO2Max)  . The genHR genes . In humaadhesion . On the  muscles . As showresponse . In geneTH, COMT, and SEZ6L genes that have, in previous studies, been associated with several traits in humans and dogs. Tyrosine hydroxylase (TH) is involved in synthesising the dopamine precursor, which acts as a precursor of the catecholamines noradrenaline and adrenaline. Except for the brain’s reward system, TH is responsible for various important biological functions in organisms . In CWD, such social behaviour disorders are mainly correlated with maternal instinct .The behaviour and coat colour of dogs belong to the phenotypic traits where the intense selection pressure significantly acted for a long time. In the case of behaviour and motivation, a set of 11 genes was selected to assess the level of genetic diversity distributed within the analysed animals . The GO rganisms , includiformance . The SEZabradors ,27. The D1 genes b, which MLPH, MITF, and AP3B1 genes. The melanophilin gene (MLPH) is a candidate gene responsible for eumelanin dilution. In animals with the black coat colour, a mutation in the MLPH gene leads to the transition from a grey to a blue coat colour, while for a red colour, there is a transition to a cream colour [MITF) is involved in the genetic control of the white-spotted coat colour [AP3B1 gene polymorphism was previously associated with canine cyclic neutropenia, known as Collie syndrome, causing a diluted coat colour [PMEL or SILV) and β-defensin 103 (CBD103) genes. The PMEL gene affects the so-called merle colour, which is characterized by spots of dilute pigment mixed with normal melanin. Blue eyes often occur in individuals who have this gene. The merle coat colour is autosomally inherited with incomplete dominance. Dominant homozygotes are completely white, heterozygotes have a merle coat colour, and recessive homozygotes have a normal colour [CBD103 gene controls pigment type by competitively binding to the MC1R gene and switching responsible pathways. The CBD103 gene in the dominant form leads to the black coat colour [KIT, ASIP, TYR, TYRP1, MC1R, and SLC45A2 genes. In dogs, melanocortin receptor 1 (MC1R) and agouti-signalling protein precursor (ASIP) genes are responsible for the genetic control of melanocytes, producing pheomelanin (red or yellow pigment) or eumelanin (black or brown pigment). The MC1R gene acts epistatically to ASIP and CBD103 genes [MC1R gene-producing pheomelanin results in a uniform black coat colour, whereas the inhibited form is responsible for the red or yellow colour due to the production of pheomelanin [MC1R gene were also linked to the black mask pattern [ASIP, similar to the CBD103 gene, controls pigment type. Gain-of-function mutations of the ASIP gene lead to the dominant inheritance of the yellow colour, while loss-of-function mutations result in recessive inheritance of the black colour [ASIP gene can result in a saddle tan or black-and-tan coat colour as well [TYRP1) encodes an enzyme present in the synthesis of eumelanin and affects the brown coat colour [TYRP1 gene interacts with the MC1R gene. The TYRP1 dominant allele can only be expressed if the individual simultaneously has the EE or Ee genotype of the MC1R gene [TYR) and solute carrier family 45, member 2 (SLC45A2) genes belong to the group of novel identified genes that are often associated with albinism in dogs [The population stratification due to coat colour variation among groups was tested based on the set of SNPs distributed within sequences of 12 protein-coding genes . Table Sm colour . The mict colour . The AP3t colour . The BSDl colour . The CBDt colour . The CWD03 genes . Activatomelanin . Mutatio pattern . The ASIk colour . Mutatio as well . The tyrt colour . The TYRC1R gene . The tyr in dogs . The KIT in dogs . In the AGT, ADRB2, ADRB3, HIF1A, BDKRB2, and MSTN)  genes control energy expenditure and lipolysis and are important for cardiovascular system optimisation during muscle activity. Both genes were associated with elite endurance performance in humans [HIF1) is a transcription factor that regulates gene expression in response to hypoxia that has been associated with athletic performance [2 receptor (BDKRB2) implicated in the increase in skeletal muscle glucose uptake during exercise has been associated with endurance performance [MSTN gene, as a member of the transforming growth factor β family, encodes the myostatin protein. The MSTN controls the total number of muscle fibres by regulating overall myoblast proliferation [ADRB2, ADRB3, and IL6 genes, which play important roles in the regulation of the cardiovascular system, vasodilation, and efficiency of access to fat stores. Interleukin 6 (IL6) modulates the release of different cytokines, such as TNF and IL1Ra. Interleukin 6 plasma concentration is affected by exercise duration, intensity, and the amount of muscle mass involved [Strength and endurance are antagonistic traits, whose expression is encoded by various genes. For this study, a set of 11 genes was selected based on previous research  [. As shnd MSTN) . It was unctions . The β-2n humans ,70,71. Hformance . Variatiformance . The MSTferation . As can involved . Table Sinvolved ,86,87,88Historical selection events have left different traces in the genomes of the analysed groups that have differentiated them from each other. In the Czechoslovakian wolfdog, a strong effect of rigorous selection in the direction of its breeding goal was manifested mainly in a high genome-wide proportion of short ROH segments, corresponding to a historical load of inbreeding inherited from base animals of the source populations. The observed proportion of long ROH segments confirmed that the inbreeding events shaped the Czechoslovakian wolfdog genome relatively recently compared to the other analysed groups. In Grey wolf, a considerably lower genome-wide proportion of ROH segments compared to domesticated breeds was found mainly as a consequence of natural selection effects related to population fitness preservation. The absence of long ROH segments in the Grey wolf genome follows from the fact that the mating of close relatives is not common in wild species. Due to the sufficient level of interpopulation genetic variability, each of the analysed groups formed a separate genetic cluster according to their origin. As expected, the highest proportion of admixture was found in the Czechoslovakian wolfdog genome, where the German Shepherd dog and Grey wolf were recognized as the groups with the highest contribution to its genetic makeup. Even if there was a significant increase in genetic connectedness among wolf-like breeds and their wild ancestor due to the strong selection pressure for the fixation of alleles controlling the so-called wolf-like phenotype, the obtained results showed that the Czechoslovakian wolfdog carries different allelic combinations that are not found in either wolves or other dog breeds analysed in this study.