Mus musculus domesticus, M. m. musculus, and M. m. castaneus) to trace their origin and evolutionary fate. We find that ancient house-keeping genes are significantly more likely to generate retrocopies than younger genes and that the propensity to generate a retrocopy depends on its level of expression in the germline. Although most retrocopies are detrimental and quickly purged, we focus here on the subset that appears to be neutral or even adaptive. We show that retrocopies from X-chromosomal parental genes have a higher likelihood to reach elevated frequencies in the populations, confirming the notion of adaptive effects for “out-of-X” retrogenes. Also, retrocopies in intergenic regions are more likely to reach higher population frequencies than those in introns of genes, implying a more detrimental effect when they land within transcribed regions. For a small subset of retrocopies, we find signatures of positive selection, indicating they were involved in a recent adaptation process. We show that the population-specific distribution pattern of retrocopies is phylogenetically informative and can be used to infer population history with a better resolution than with SNP markers.Although the contribution of retrogenes to the evolution of genes and genomes has long been recognized, the evolutionary patterns of very recently derived retrocopies that are still polymorphic within natural populations have not been much studied so far. We use here a set of 2,025 such retrocopies in nine house mouse populations from three subspecies ( Gene duplication is a major mechanism to generate adaptive evolutionary novelties in organisms . Gene reThe accumulating population genomic sequencing data allowed to study polymorphic gene retrocopies  at individual genome level within natural populations. Through analyzing the genomic sequencing data set of natural samples from the Mus musculus) represents a unique model system for the study of evolutionary dynamics of recently originated gene retrocopies. The inclusion of the genomic sequencing data from out-group species allows the detection of newly arrived species-specific gene retrocopies, whereas comparable population data of out-groups  is not available in human studies. Using this unique genomic data set, we identified previously more than 2,000 house mouse lineage-specific gene retrocopies, and showed that the primary rate of gene retroposition in house mouse natural populations is orders of magnitude higher than the previously estimated long-term rate . These retroCNVs are derived from 1,372 unique parental genes  forming the low-frequency group, retroCNVs present in 2–5 individuals forming the intermediate-frequency group (N = 552) and present in ≥6 individuals forming the high-frequency group .To compare these two possibilities, we have categorized retroCNVs into three distinct groups A, with sP value = 0.02, Fisher’s exact test). This pattern suggests that positive selection may play a role on the retention and increase in frequency of some retroCNVs derived from the X chromosome (see Discussion).First, we studied the chromosomal location of parental genes for retroCNVs in the three classes, because it has been suggested that retroCNVs from the X chromosome should have a higher likelihood to have an advantageous effect , presumaNext, we explored the insertion site pattern of retroCNVs from the three frequency groups, regarding the genomic context of their placement B. As expP value = 0.008, Fisher’s exact test). This suggests that they are enriched in regions where they do not disrupt other genes, that is, they have a higher likelihood to be neutral or even advantageous. Besides, we observed that intron regions are depleted for high-frequency retroCNVs , which might be ascribed to the avoidance of retrocopy insertions to disrupt mRNA splicing process  in all representative populations of a focal subspecies  online, D value among nucleotide sequences near the retroCNV insertion site for retroCNV carriers in comparison to background distribution (r2) between SNPs near the retroCNV insertion site for retroCNV carriers in comparison with background distribution  for all three statistics.Three types of population genetic signals have been reported as associated signatures of positive selection acting on retroCNVs with increased frequency in populations: 1) reduction of average nucleotide diversity (Pi) among nucleotide sequences near the retroCNV insertion site  for retroCNV carriers in comparison with non-carriers ; 2) reduribution ; 3) elevribution . Herein,ribution , which wAmong 18 retroCNVs eligible for such tests  online, M. m. musculus populations, where the SNP data would suggest a more basic position of the CZ population and a sister group relationship between the KA and AF populations, but with only a poor resolution. The retroCNV data, on the other hand, resolve this clearly and place the AF population more basic, which is also in line with previous conclusions of AF being ancestral and CZ being derived . Each retroCNV was assigned a population differentiation index (PDI), to indicate its power to differentiate populations. After applying a false discovery rate (FDR) threshold of 0.05, we detected in total 1,800 retroCNVs  that could differentiate house mouse natural populations  in mammalian genomes . It was Our data confirm this assumption, as we did not find evidence for a strong initial bias in the generation of new gene retrocopies by parental genes from the X chromosome. However, there is a tendency among the ones occurring at higher frequency to have derived from the X chromosome, supporting the notion that more positively selected ones are among them.r2 = 1) to these retroCNVs. This finding further supports these retroCNV as the direct target of positive selection. Still, it should be noted one cannot completely rule out the possibility from the effects of partial linkage of these variants or other undetected polymorphisms. Hence, we only interpret these findings as suggestive evidence to show the adaptive signatures of these retroCNVs. All three of the parental genes for these gene retrocopies are located on autosomes. They code for a transcription factor interacting protein (Atf7ip2), an enzyme (Apobec2) and a vesicle protein (Vta1), none of which was so far implicated in specific evolutionary processes.The action of positive selection on some retrocopies is further supported by identifying direct positive selection signatures for three subspecies-specific high-frequency retroCNVs, making them candidates for a particularly strong adaptive process in the respective populations. The agreement between the elevated LD in the flanking regions and the high frequency of these retroCNVs suggests that these retroCNVs are under direct positive selection . MoreoveOur results also show that the evolutionary fate of gene retrocopies is somewhat associated with the genomic context of their placement. The retroCNVs landing in the intergenic regions have a higher probability to increase their frequencies in populations, whereas those ones occurring in the genic regions are restricted to lower frequency. Even though a low fraction of the latter ones may evolve adaptive function such as generating chimeric retrogenes through landing in the exon regions of existing genes , the majGiven that the patterns of retroCNVs in natural populations are shaped by a combination of purifying selection  and posiIndeed, we found that gene retrocopies could be efficient markers to infer population relationships, and to resolve the phylogenetic tree of house mouse natural populations, even with better performance than the commonly used SNP variants. We further found that most of the retroCNVs could differentiate house mouse natural populations. Overall, we conclude that the retroCNVs per se can be applied as effective genomic markers to trace the divergence and differentiation history of natural populations.M.musculus), whereas absent in both outgroup species (M.spretus and M.spicilegus). Following the “gold standard” for calling novel retrocopies , we onlsertions ), corresWe obtained the data set of house mouse lineage-specific SNP variants generated in the same study , for whiWe characterized the features of retroCNV parental genes from three different aspects: 1) gene expression pattern across multiple tissues; 2) GO functional term enrichment; and 3) gene age distribution pattern.M.musculus C57BL/6 inbred line generated by the lab of Michael Snyder in Stanford . This data set contains strand-specific transcriptomic sequencing data for 13 tissues dissected from 10-week-old mice  and all protein-coding genes with multiple exons , we performed a bootstrapping analysis for the latter gene group, by randomly sampling the same number of genes as that in the former group for 100 times. The average fractions of genes with expression and standard errors were then computed for all protein-coding genes with multiple exons based on the random sampling data sets. Additionally, we also compared the expression levels between retroCNV parental genes that were retroposed once only and those ones retroposed recurringly . We utilized Wilcoxon rank-sum tests to compute the statistical significances on the differences of the fraction of genes with expression (FPKM > 0) and gene expression levels.First, we downloaded the raw Illumina paired-end RNA-Seq fastq data for Stanford , from thStanford   . Only sihttp://gentree.ioz.ac.cn/download.php; last accessed December 28, 2021)   and  , respect8, 2021) . The fraN = 977), intermediate-frequency group , and high-frequency group .All these 2,025 retroCNVs were classified into three roughly similar-sized groups based on their frequencies in all sampled house mouse individuals: low-frequency group  on the excess within each frequency group was calculated via Fisher’s exact tests. Additionally, we also calculated the statistical significances on the excess of retroCNVs generated from X chromosome between pairs of frequency groups by using Fisher’s exact tests.We computed the fraction of excess of observed retroCNVs generated from X chromosome and autosomes for each frequency group. The expected number of generated retroCNVs from each chromosome was calculated based on the number of protein-coding genes (with ≥2 exons) in the focal chromosome. The fraction of excess of observed retroCNVs for each frequency group was computed as the deviation between observation value and expectation value divided by the value of expectation within the focal frequency group, and the statistical significance  along the 5 kb up/downstream flanking region for retroCNV carriers  for each statistic was computed by using Wilcoxon rank-sum test, through checking how likely to get the same or stronger positive selection signatures  from these “pseudo” retroCNV insertions as that from true retroCNV insertion. As a result, we found three retroCNVs of positive selection signals with statistical significance  for all three statistics. For visualization, we calculated and plotted the average nucleotide diversity levels within the 50 kb up/downstream (in a sliding window size of 10 kb and a step size of 5 kb) of the insertion site of each retroCNV for carriers and non-carriers separately  the carriers ; 3) avercarriers . Then wecarriers  online. parately  online.r2 = 1). We did not find such SNPs or CNVs for any of these three positively selected retroCNVs.Additionally, with the SNPs and CNVs called from the same set of individual genomes , we searhttps://cran.r-project.org/web/packages/ggfortify/index.html; last accessed December 28, 2021). Due to the close relationships among individuals from natural population in M. m. domesticus and M. m. musculus, we ran additional PCAs for these two subspecies separately. 3) Calculation of the distance between pairwise individual comparison on retroCNV landscape, on the basis of the fraction of different retroCNVs of two individuals to be compared. The heat map plotting of the distance measurement was conducted by using python package “matplotlib” v3.0.1 .We explored the association between retroCNV pattern and population differentiation by conducting three lines of analyses as following: 1) Calculation and comparison on the fractions of shared retroCNVs between all possible pairwise combinations from all individuals, individuals from different subspecies, from different populations but same subspecies, and from the same populations. 2) PCA on the individual retroCNV landscape with R package “ggfortify” v0.4.8 , P is the frequency of a given retroCNV in the individuals from all populations, and iP is the frequency in the ith population of the same retroCNV. Equivalent to the relative population size, iF is calculated as the number of individuals of the ith population (iN) divided by the total number of individuals from all populations. The significance of PDI for each retroCNV was tested by a randomization test in which we randomly shuffled individual labels for 1,000 times, whereas keeping the same population sizes. The P value was calculated by comparing the observed PDI for each retroCNV against the PDI values from the above randomized null distribution. In total 1,800 retroCNVs after multiple testing corrections (FDR ≤ 0.05) were taken as population differentiation markers (Here,  markers  online.Molecular Biology and Evolution online.msab360_Supplementary_DataClick here for additional data file.


The total length of sequences was used to generate the haplotypes and evaluate their diversities, whereas the hypervariable region  was analyzed to define the maternal variation and the demographic dynamic. A total of 568 segregating sites that generated 192 haplotypes were observed from the entire d‐loop region (1169 bp d‐loop). Phylogenetic analyses using reference haplotypes from the six globally defined goat mtDNA haplogroups showed that all the three Congolese indigenous goat populations studied clustered into the dominant haplogroup A, as revealed by the neighbor‐joining (NJ) tree and median‐joining (MJ) network. Nine haplotypes were shared between the studied goats and goat populations from Pakistan (1 haplotype), Kenya, Ethiopia and Algeria (1 haplotype), Zimbabwe (1 haplotype), Cameroon (3 haplotypes), and Mozambique (3 haplotypes). The population pairwise analysis (STF) indicated a weak differentiation between the Congolese indigenous goat populations. Negative and significant  values for Fu's Fs (−20.418) and Tajima's (−2.189) tests showed the expansion in the history of the three Congolese indigenous goat populations. These results suggest a weak differentiation and a single maternal origin for the studied goats. This information will contribute to the improvement of the management strategies and long‐term conservation of indigenous goats in DRC.This study aimed at assessing haplotype diversity and population dynamics of three Congolese indigenous goat populations that included Kasai goat (KG), small goat (SG), and dwarf goat (DG) of the Democratic Republic of Congo (DRC). The 1169 bp  d‐loop region of three Congolese indigenous goat populations reveals a weak differentiation between them. They were clustered into the haplogroup A with one maternal origin. Demographic expansion was observed in the studied Congolese goat populations.The analysis of mtDNA  The haplogroup A was shown to have a large geographic distribution  were sampled from farmer's flocks from the three AEZs of DRC  and used for the study. Efforts were made to avoid closely related individuals during sampling. The socioeconomic factors associated with goat keeping  according to the manufacturer's protocol. DNA quality control (QC) was done using a spectrophotometer  and DNA integrity was checked on 1% agarose gel electrophoresis.Genomic DNA was extracted from blood samples using the QIAamp2.2d‐loop region was amplified using primers designed  using a three‐step PCR. PCRs were carried out in 25 µl reaction volumes composed of Phusion master mix (2× concentrated solution which included Taq DNA polymerase (0.05 U/µl), reaction buffer, 4 mM MgCl2, and 0.4 mM of each dNTP), 0.2 µM of each primer (F and R), 2% of dimethyl sulfoxide (DMSO), and 40 ng of template DNA. The three‐step PCR involved an initial denaturation at 98°C for 30 s followed by 35 cycles of amplification  and completed by the final extension step at 72°C for 7 min. The PCR products were purified using the QIAquick® PCR purification kit (Qiagen) following the manufacturer's protocol. The reverse primer (R: 5’‐GGTACACTCATCTAGGCATT‐3’) and a pair of GDLS2 primers  were respectively used as external and internal primers to sequence the purified PCR products . In total, 339 sequences were generated from where haplotypes were generated with DnaSP v5 , haplotype diversity (dH), nucleotide diversity (π), and mean of nucleotide differences between haplotypes (K) and their standard deviations (SD) were analyzed for each goat population and across all populations using DnaSP v5  with the neighbor‐joining (NJ) algorithm implemented in MEGA v6.4 with the level of confidence associated with each bifurcation evaluated with 1000 bootstrap replications. To visualize Congolese indigenous goats in the context of the regional and global caprine diversity and obtain further insights into genetic relationships between the haplotypes, a total of 336 published sequences and 22 reference sequences of domestic goats representing the 6 globally defined mtDNA d‐loop haplogroups  defined based on the variation in the first HVI with 481 bp of length size   following 1000 permutations in Arlequin v3.5.2 . Out of the 192 haplotypes, 23 were shared between the Congolese indigenous goat populations . Furthermore, nine haplotypes were shared between the three Congolese indigenous goat populations and the goat populations in Africa. These include one haplotype  shared with goat populations from Ethiopia, Kenya, and Algeria; three haplotypes mostly represented by dwarf goat shared with goat populations from Cameroon; and three and one haplotypes mostly represented by Kasai goat were respectively shared with goat populations from Mozambique and Zimbabwe. Outside Africa, one haplotype was shared with Pakistan goat.From the 339 generated sequences of the mtDNA π = 0.018 ± 0.004) than for small goat (π = 0.013 ± 0.002) and Kasai goat (π = 0.012 ± 0.022) . The average nucleotide diversity was higher for dwarf goat π = 0.08 ± 0.0043.2The first hypervariable HVI, 453bp) sequences both for Congolese indigenous goat populations and the reference sequences representing the six domestic goat haplogroups were aligned using the ClustalW algorithm implemented in MEGA v6.4 and considered for the NJ tree analysis. All the three Congolese indigenous goat populations (100%) were clustered into mtDNA lineage A Figure . An MJ (bp sequen3.3ST) value revealed low genetic differentiation between the three studied Congolese indigenous goat populations with FST = 0.02165; 0.09182; 0.04567 respectively observed between Kasai and dwarf goat, Kasai and small goat, and small and dwarf goat  to variation within populations  or spatial  expansion modal . The variations around the curves were also not significant except for dwarf goats  if considering the demographic expansion modal (Table D (−2.189) and Fu's Fs (−20.418) and significant  values obtained suggest that the three Congolese indigenous goats have expanded demographically in their past.Mismatch distribution patterns were used to assess the population dynamics of the three Congolese indigenous goat populations grouped into the haplogroup A as revealed by the NJ tree and the MJ network. For each population, the expansion modal of mismatch distribution was bimodal and a valid goodness of fit was observed between observed and expected distributions plus their 95% confidence intervals, indicating a demographic expansion signal Figure . For the4d‐loop region of three Congolese indigenous goat populations from eastern and western DRC were analyzed to assess the genetic diversity, the genetic differentiation, and the demographic historical profiles. The results revealed 192 haplotypes from the analyses of 339 mtDNA d‐loop sequences. Average haplotype diversity and nucleotide diversity of 0.987 ± 0.002 and 0.015 ± 0.003 respectively were obtained for the three populations with an average number of nucleotide differences of 14.740. This average haplotype diversity is similar to that of Kenyan goats  observed between Congolese and Cameroonian goats . This result may be attributed to the evolutionary relationship among the studied goat populations. Relatively divergent haplotypes within breeds and geographical locations suggest that the gene flow has occurred on a regional scale during some time in the recent past and the breeds have not been subdivided by long‐term biogeographic barriers  was confirmed between the three studied Congolese indigenous goat populations  Tajima's D (−2.189) and Fu's D (−20.418) values supported population expansion for all the three Congolese indigenous goat populations. These results suggest that the studied goat populations underwent an expansion. The significant departure  observed for the Tajima's D and Fu's Fs for all populations explained mainly an excess of new mutations corresponding to the results of evolutionary forces due to either selective sweeps or population growth.The AMOVA showed that 5.88% represented the genetic variation among populations in Congolese indigenous goat populations compared to 94.12% within populations. The higher within‐population variation than among populations could be associated with the breeding practices. In addition, it can suggest high levels of female‐mediated gene flow  revealed a high level of genetic diversity in the east and west Congolese indigenous goats with a weak genetic differentiation, and a unique maternal origin belonging to haplogroup A. The three indigenous goat populations share a relatively similar genetic background. The demographic expansion was observed in the studied Congolese goat populations. These results represent an abundant resource for selective breeding in the different AEZs of the DRC.This is the first study that investigates the genetic diversity within and between the three Congolese indigenous goat populations from east and west of the Democratic Republic of Congo. The analyses of the mtDNA control region (No conflict of interest was reported by the author(s).Patrick Baenyi Simon: Conceptualization ; Data curation ; Formal analysis (lead); Investigation (lead); Methodology (lead); Software ; Writing – original draft ; Writing – review & editing . Joseph Owino Jungá: Conceptualization ; Formal analysis (supporting); Investigation (supporting); Project administration ; Supervision (lead); Validation ; Writing – review & editing . Getinet Mekuriaw Tarekegn: Conceptualization ; Formal analysis (supporting); Methodology ; Software ; Supervision ; Validation ; Visualization ; Writing – review & editing . Eunice Machuka: Data curation ; Methodology ; Validation ; Writing – review & editing . Christian Keambou Tiambo: Investigation (supporting); Methodology (supporting); Supervision ; Validation ; Writing – review & editing . Dorine Kabange: Data curation (supporting); Methodology (supporting); Writing – review & editing . Katunga Musale M. Dieudinné: Investigation (supporting); Writing – review & editing . Roger Vumilia Kizungu: Investigation (supporting); Validation ; Writing – review & editing . Joel Winyo Ochieng: Formal analysis (supporting); Investigation (supporting); Methodology (supporting); Supervision ; Validation ; Writing – review & editing . Roger Pelle: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Project administration (lead); Resources (lead); Supervision (lead); Validation ; Writing – review & editing .Table S1Click here for additional data file.


Felis silvestris) have suffered severe anthropogenic population declines in the past, resulting in a strong population bottleneck at the beginning of the 20th century. In Germany, the species has managed to survive its near extinction in small isolated areas and is currently recolonizing former habitats owing to legal protection and concerted conservation efforts. Here, we SNP‐genotyped and mtDNA‐sequenced 56 historical and 650 contemporary samples to assess the impact of massive persecution on genetic diversity, population structure, and hybridization dynamics of wildcats. Spatiotemporal analyses suggest that the presumed postglacial differentiation between two genetically distinct metapopulations in Germany is in fact the result of the anthropogenic bottleneck followed by re‐expansion from few secluded refugia. We found that, despite the bottleneck, populations experienced no severe genetic erosion, nor suffered from elevated inbreeding or showed signs of increased hybridization with domestic cats. Our findings have significant implications for current wildcat conservation strategies, as the data analyses show that the two presently recognized wildcat population clusters should be treated as a single conservation unit. Although current populations appear under no imminent threat from genetic factors, fostering connectivity through the implementation of forest corridors will facilitate the preservation of genetic diversity and promote long‐term viability. The present study documents how museum collections can be used as essential resource for assessing long‐term anthropogenic effects on natural populations, for example, regarding population structure and the delineation of appropriate conservation units, potentially informing todays' species conservation.Like many carnivore species, European wildcats ( European wildcats have suffered severe anthropogenic population declines in the past and are currently recovering their former habitats in Germany. Spatiotemporal genetic analyses based on historical and contemporary wildcat samples show that the presumed postglacial differentiation between metapopulations is in fact of recent anthropogenic origin. These findings have significant implications for current wildcat conservation strategies aiming to reconnect the populations by implementing migration corridors. These samples encompassed mainly tissues from road kills, as well as blood and noninvasively collected hair samples  and samples from extant German wildcat populations (“contemporary”) were used in this study Figure . Historipe Table . Sample n = 26) were excluded in order to obtain reliable population‐level values. Further, 27 historical samples with ambiguous locality within Germany or imprecise sampling year were excluded from analyses after thorough verification of available collection information  and two geographical groups . Temporal periods were delimited based on recorded year of sampling. The height of the bottleneck was assumed as 1930 . Bones (20–170 mg), turbinal bones (20–80 mg), and teeth (7–150 mg) were additionally washed with an aqueous solution containing bleach for 10 min at 900 rpm before the washing steps described above. After washing, turbinal bones were crushed in reinforced 2 ml tubes with one ceramic bead of 6.8 mm diameter  using a TissueLyser mill (Qiagen) in two consecutive steps of 30 sec at 30 Hz. Less fragile bone fragments and teeth were crushed for three consecutive runs of 10 s at 25,000 rpm, followed by 30 s at 25,000 rpm using an IKA® Tube mill 100 with disposable grinding chambers including a stainless steel beater . The consecutive milling steps were alternated with small breaks of 30 s to prevent overheating of the biological materials. After sample preparation, all historical sample materials were extracted using the QIAamp DNA Investigator Kit (Qiagen) following the manufacturers’ protocol for buccal swabs with slight modifications: Lysis was performed with 400 µl ATL buffer, 25 µl proteinase K and 25 µl DTT. After lysis, 1 µl of carrier RNA was added to each lysate. DNA was eluted in 80 µl ATE buffer (Qiagen). Contemporary tissue and blood samples were extracted using the DNeasy Blood & Tissue Kit (Qiagen) and hair samples using the QIAamp DNA Investigator Kit (Qiagen). No animals were harmed or killed for this study, and all samples were collected in compliance with the respective local and national laws.Special precautions were taken for obtaining DNA from historical samples. All laboratory procedures preceding PCR amplifications were conducted in a physically isolated low‐DNA environment, using a laminar flow hood solely designated to the handling of museum samples and including UV‐light filtering. Working equipment was cleaned with bleach between each sample preparation and radiated with UV for ≥40 min. Extraction batches included a maximum of eleven samples of the same material type and negative controls to monitor possible contamination. Skin, dried tissue remains, and footpads were cut into pieces of 20–70 mg and washed twice with 96% ethanol at 900 rpm for 15 min to remove chemicals from the preservation processes, followed by 30 min of drying at 50°C. Subsequently, samples were washed twice in double‐distilled H2.32.3.1FST between domestic and wildcats for detection of recent hybridization (HYB SNPs) and 84 markers with high heterozygosity values for individual identification .All historical and contemporary samples were genotyped for 96 SNPs selected for genetic assessments of European wildcat populations  sequence analysis, extracts of all historical samples were amplified with primers LF4  ≤ 0.85  were excluded from further analyses (genetic diversity and structure), leaving 56 historical and 608 contemporary wildcat individuals.All genotyped individuals were screened for possible hybrids and domestic cats based on the 10 diagnostic HYB SNPs contained in the marker panel  from STRUCTURE, samples with q(i) < 0.8 to any cluster were excluded. Genetic variability and diversity parameters  and analysis of molecular variance (AMOVA) were calculated using GenAlEx v6.502  between temporal and geographical groups were tested for statistical significance using the wilcox.exact function in R  from all subsequent analyses based on mtDNA data  were obtained for 94 of 175 historical samples (53.7%). Acceptable SNP genotypes  were achieved for 76 samples (43.4%), while 90 samples failed to amplify. Genotyping success varied between sample types, with calcified samples  amplifying better on average , largely fitting to the previously described Western and Central metapopulations.Clustering analyses revealed changes in spatial genetic structure within the German wildcat populations through time Figure . When anK = 5 clusters (ΔK = 30) for all samples together  and lowest between samples from the Harz compared to the Hesse Highlands .However, historical samples collected before the 1930’s bottleneck (pre‐BN) did not show any spatial differentiation. Here, the analyzed individuals were mostly assigned to the contemporary Western cluster, even when collected from the Eastern part of the study area Figure . In contr Figure . At thisr Figure . The extWhen including samples from other wildcat populations in Europe . Levels of differentiation were in accordance with our aforementioned findings of an increasing differentiation between Western and Central metapopulations following the bottleneck . Global FST values for temporal groups followed the same trend  to 6% in post‐BN and extant populations Table . GeneralSeveral complementary analyses were conducted to verify the increasing differentiation of metapopulations after the bottleneck and to test for the influence of potential sampling effects on the analyses  for all temporal and geographical groups (Table p < .001) were detected between pre‐BN Central populations (uHE = 0.47 ± 0.01) and temporally subsequent central groups , as well as with the pre‐BN Western group , highest for post‐BN samples  and lowest for extant samples .The comparison of SNP diversity resulted in highly similar heterozygosity values (ps Table . The onlIS Table  were notHO was discernable for the past ~150 years (not significant) .Within the two metapopulations, the analyses of genetic diversity and distance over time did not evince an obvious effect of genetic drift Figure . A sligh) Figure . This tr) Figure b. Here, ) Figure , Centralp < .01), indicating slight changes in allelic frequencies over time in both metapopulations  < 0.85), but could not be explicitly assigned to one of the genealogical classes. In the individuals sampled from the post‐BN period, seven were identified as domestic cats , one as potential F2 hybrid , and two individuals  could not be explicitly assigned to a genealogical class. Within the extant samples, two individuals were classified as domestic cats  and 40 samples showed varying degrees of admixture (q(wc) < 0.85). Results from STRUCTURE were congruent.Several domestic cats and potentially admixed individuals were detected within both the historical and contemporary sample sets Figure . Among t44.1Strong population bottlenecks are one of the main drivers of genetic drift, often resulting in significant loss of genetic diversity. The extent of diversity loss and the associated consequences for population fitness are, however, not only dependent on the severity of the bottleneck but also its timescale expanding Central population   simulations to assess the phylogeographic history of European wildcat populations and estimated a divergence time of ~30,000 years for the Central German metapopulation and other investigated Central European populations  , as the domestic taxa may genetically swamp their wild relatives and lead to genetic extinction of the latter  in this study. One option to test for potential sampling effects lies in subsampling contemporary populations for more balanced sample sizes, as we did here  aim at implementing forest corridors in different areas throughout the country to allow for effective connection of the disjunct wildcat populations ; data curation (lead); formal analysis (lead); investigation ; methodology (lead); validation ; visualization (lead); writing–original draft (lead); writing–review and editing (lead). Berardino Cocchiararo: Methodology (supporting); writing–review and editing (supporting). Sarah Ashley Mueller: Methodology (supporting); writing–review and editing (supporting). Tobias Erik Reiners: Methodology (supporting); software (supporting); writing–review and editing (supporting). Katharina Reinert: Data curation (supporting); formal analysis (supporting); methodology (supporting); software (supporting); writing–review and editing (supporting). Iris Tuchscherer: Data curation (supporting); formal analysis (supporting); methodology (supporting); writing–review and editing (supporting). Axel Janke: Conceptualization ; supervision ; writing–review and editing (supporting). Carsten Nowak: Conceptualization ; funding acquisition (lead); investigation (supporting); project administration ; supervision ; writing–original draft (supporting); writing–review and editing (supporting).Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


Sphenostylisstenocarpa), is an underutilized legume of tropical Africa. AYB can boost food and nutritional security in sub-Saharan Africa through its nutrient-rich seeds and tubers. However, inadequate information on germplasm with desirable agro-morphological traits, including insufficient data at the genomic level, has prevented the full exploitation of its food and breeding potentials. Notably, assessing the genetic diversity and population structure in a species is a prerequisite for improvement and eventual successful exploitation. The present study evaluated the population structure and genetic diversity of 169 accessions from the International Institute of Tropical Agriculture (IITA) collection using 26 phenotypic characters and 1789 single nucleotide polymorphism (SNP) markers. The phenotypic traits and SNP markers revealed their usefulness in uniquely distinguishing each AYB accession. The hierarchical cluster of phenotypes grouped accessions into three sub-populations; SNPs analysis also clustered the accessions into three sub-populations. The genetic differentiation (FST) among the three sub-populations was sufficiently high (0.14–0.39) and significant at P = 0.001. The combined analysis revealed three sub-populations; accessions in sub-population 1 were high yielding, members in sub-population 2 showed high polymorphic loci and heterozygosity. This study provides essential information for the breeding and genetic improvement of AYB.African yam bean, AYB ( Sphenostylisstenocarpa Hochst ex. A. Rich. Harms), otherwise known as “AYB,” is a nutritionally rich seed and tuber producing crop of tropical Africa. AYB is a diploid species with a chromosome count of 2n = 222. Its flowers are cleistogamous and mainly exhibit self-pollination (91%)3. AYB genome size is presently unknown. AYB seeds contain sufficient protein, approximately 37%, and about 64% carbohydrates5. The protein content in tubers is about 16%, and its carbohydrate content is roughly 68%6.African yam bean  identification and its application in crop improvement. Diversity Array Technology (DArT) is a good example of high throughput technology characterized by a relatively low cost, high call rates, and high reproducibility. DArT is, most importantly, capable of generating genome-wide SNPs in species with no prior DNA sequence information Edet et al.22 and Barilli et al.23. such as AYB. The DArT approach has proved helpful in genetic studies of several legumes, including; pigeon peas24, common beans25, and soybeans26. Notably, the science of plant breeding centers on identifying and utilizing genetic variation21, tracking potential DNA markers and regions associated with traits of interest28. However, as no single characterization approach is superior to the other29 and no method has been identified to be sufficient for evaluating every aspect of a species30, characterization based on phenotypic traits and genotypic data can be used both independently or to complement one another31.In contrast, genotypic characterization is selectively neutral and not affected by the environment32. Additionally, few studies reported PCR-based markers for genetic evaluation across selected accessions35. A preliminary study using the DArTseq approach in identifying a few SNPs in AYB was recently reported36. However, no report is available on high throughput sequencing data for genetic diversity and population structure analysis in the crop.Therefore, characterizing AYB using a multifaceted approach is a step in the right direction. Previously, authors used phenotypic traits to assess the diversity of about 100 accessions evaluated in Nigeria37. Therefore, considering the above, the objectives of this study were to evaluate the population structure, genetic diversity, and the differentiation between phenotypic and genotypic data of 169 AYB accessions obtained from IITA’s Genbank collection.Similarly, not enough attempt has been made to phenotypically characterize large AYB accessions outside Nigeria and evaluate the genetic diversity and population structure using SNPs. Also, information is lacking on combined analysis at the phenotypic and genotypic levels using a high throughput approach. Moreover, the scanty information presently available at the phenotypic and genotypic level of the crop needs to be complemented with extensive data set, which would increase the probability of finding a genetic correlation between SNPs and phenotypeThe principal component analysis (PCA) revealed the most discriminative phenotypic traits across the 169 accessions. The traits that largely contributed to the observed variation in each PC axis are shown in bold  in the population was 1.61, and Shannon’s information index (I) was 0.59. The population’s expected heterozygosity (He) and observed heterozygosity (Ho) were 0.35 and 0.15, respectively. Across the 1789 SNPs, the minor allele frequency ranged from 0.05 to 0.5 with an average of 0.22, and the major allele frequency ranged from 0.50 to 0.95 with an average of 0.78 , Shannon information index (0.58 ± 0.004), expected heterozygosity (0.35 ± 0.003), observed heterozygosity (0.17), and % polymorphic loci (100%). In addition, sub-population 2 had the highest number (400) of unique alleles  in contrast to alleles in sub-population 1 (0) and sub-population 3 (154); similarly, sub-population 2 showed the highest number of effective alleles (1.64 ± 0.011) among the sub-populations. Sub-population 1 showed low values for all the estimated diversity parameters, being the least diverse; however, sub-population 3 was the most varied, followed by sub-population 2 , whereas TSs-109 and TSs-89 showed the least distance (0.14) in the sub-population. The average distance across the sub-population was 0.28. In addition, accessions in sub-population 3 presented a genetic distance that varied from 0.60 to 0.99 with an average of 0.71. TSs-60 and TSs-82 were the most diverse accessions based on their genetic distance. In contrast, a closer relationship was observed between TSs-166 and TSs-2015–07 than other accessions of the same population. Expected heterozygosity (He) was higher than the observed heterozygosity (Ho) in all the sub-populations viz; sub-population 1 ; sub-population 2  and sub-population 3  an indication of inbreeding.FST among the three sub-populations varied from 0.14 to 0.39 and were significant at P-value (0.001), while the F’ST ranged from 0.12 to 0.28. A high level of differentiation was observed among accessions in sub-population 1 and sub-population 2 (0.39). Additionally, the level of differentiation observed between sub-populations 1 and 3 (0.20) was slightly higher than that observed between sub-populations 2 and 3 (0.18). . The AMOVA performed on the three sub-population identified by STRUCTURE revealed that 13% of the total variation was found among populations, whereas the remaining 87% was found among individuals ; between the dissimilarity matrixes of phenotypic and genotypic data; however, the correlation r = 0.22 observed between genotypic and combined matrixes suggests that the matrix entries are moderately associated. A high positive association r = 0.96 was observed between the combined matrix and phenotypic matrix , days to 50% flowering (117.17–124.33 days), total seed weight (53.06–66.39 g), observed in our study is an indication of the existing diversity in the crop. However, the mean values reported for days to 1st flowering and days to 50% flowering differs from earlier findings; in the phenotypic evaluation of 16 AYB accessions grown in Nigeria, days to 1st flowering was reported to vary from 139.40 to 159.21 days35. Also, Aina et al.17 obtained mean values between 65.00 and 97.00 for days to 50% flowering in 50 accessions characterized in Nigeria. Similarly, Ojuederie et al.32 reported days to 50% flowering as between 97.50 and 115.83 across 40 accessions evaluated in Nigeria. Nevertheless, the differences between our findings and previous studies could be due to variations in environmental conditions and sample size.The significance of PCA in studying the extent and pattern of variations across populations has been documented by authors Sharma et al.35. Accessions including TSs-2015-07, TSs-1, TSs-12, TSs-10, and TSs-109 found in sub-population 1 characterized with reduced days to 50% flowering (117.17) could be choice materials for breeding of early maturity in the crop. Sub-population 1 was likewise associated with high seed yield (66.93 g) and number of seeds per pod (12.18) and could therefore be exploited for improving seed yield in the crop. The selection of such materials for improvement has been recommended as an important improvement strategy for the crop40. Also, non-shattering accessions in sub-population 1 could be useful in breeding for accessions with reduced pod shattering. Same with our findings, TSs-1 and TSs-12 were also identified as non-pod shattering accessions39. Furthermore, improved cultivars could be developed from hybridizing the distantly related accessions (TSs-363 and TSs-446) identified in this study by phenotypic and genotypic analysis (TSs-431 and TSs-47). Past genetic diversity studies in AYB using AFLP, RAPD, ISSR, and SSR markers transferred from cowpea reported considerable diversity in the crop35. Among the three sub-population observed, sub-population 3 was the most genetically diverse of the three sub-populations followed closely by sub-population 2 and then sub-population 1 as indicated by the population’s high expected heterozygosity (He), Shannon information index (I), and percentage polymorphic loci (PIC). Across the three subpopulations, the observed heterozygosity was lower than the expected heterozygosity, which can be attributed to the non-random mating among the individuals of the population suggesting inbreeding. The finding could be explained by the fact that AYB shows a high percentage of self-pollination3. The SNPs dependent approaches, STRUCTURE, hierarchical cluster dendrogram, PCoA, and AMOVA implemented in the present study consistently identified three subpopulations across the 169 AYB accessions. The consistency in the clustering pattern agrees with reports in Camelina41 rice42, and cowpea43. The genetic differentiation among the three sub-populations was significant was significant (P < 0.001) and the fixation index ranged from , indicating a medium to a high amount of genetic differentiation44. Therefore accessions from each sub-population can be crossed and tested for heterosis.In addition, the correlation among 26 traits phenotypic traits in the present study showed significant associations across most of the traits; for instance, days to 1st flowering showed a significant positive correlation with days to 50% flowering (0.67), which is a good indication towards breeding for early maturity. The availability of accessions with less than 9–10 months maturity could encourage the crop's cultivation by farmers. Seed moisture content correlated positively with total seed weight (0.58), showing the importance of trait in assessing seed yield. Positive correlations between seed weight and other characteristics were also reported in earlier studies46. The Mantel test, Mantel47, showed a low correlation between the phenotypic and genotypic distance matrix, similar to findings reported in the diversity analysis of pepper44 and winged yam18. The absence of a strong association between the phenotypic and genotypic data could be because the SNP data are not associated with the phenotypic traits evaluated; it could also be because molecular markers generally detect the non-adaptive types of variation and are not subjected to either/both natural and artificial selection which is attributed to phenotypic traits48. However, due to the inconsistency observed in studies involving phenotypic and genotypic evaluations, authors have recommended combining genotypic and phenotypic data as the best option for the efficiency of diversity assessment50. The evaluation of the grouping of accessions in the three dendrograms  revealed a high pattern of similarity. The accessions grouped in sub-population 3 of the combined dendrogram retained 100% of their membership in sub-population 2 of the phenotypic dendrogram. Also, 83% of the accessions in sub-population 1 of the combined dendrogram clustered together in sub-population 1 of phenotypic dendrogram; however, the remaining 7% grouped in sub-population 3 of the phenotypic dendrogram. Similarly, 86% of the accessions in sub-population 1 of the phenotypic dendrogram remained together in sub-population 3 of the genotypic dendrogram, while 14% of the accessions clustered in sub-population 2 and 3 of the genotypic dendrogram. Furthermore, 86% of the accessions grouped in sub-population 1 of combined dendrogram maintained their membership in sub-population 3 of the genotypic dendrogram. The high correlation between phenotypic and combined dendrogram observed in this research is similar to the findings in winged yam18. However, the level of correlation obtained between the genotypic and combined dendrogram differs from the reported in winged yam18.In the present study, the combined genetic distance generated from phenotypic and genotypic data also indicated three sub-populations. The high cophenetic correlation coefficient ≥ 7.0 observed across the three distance matrixes used in constructing each hierarchical cluster dendrogram shows each dendrogram’s fitness and ruling distortion in the data. Subjectively, the degree of fit is interpreted as: 0.9 ≤ r, very good fit; r < 0.7, very poor fitIn our study, the genetic diversity across the AYB population was confirmed further by the presence of high polymorphic loci of SNP markers associated with each population across the combined analysis. For example, sub-population 2 showed 100% polymorphic loci; more so, high heterozygosity was visible in sub-population 2, indicating high genetic diversity.Conclusively, a sufficient level of genetic diversity was revealed among and within the 169 AYB accessions evaluated with phenotypic descriptors, DArT-SNPs markers, and combined analysis. The correlations observed between traits, including early maturity, seed yield, and main stem pigmentation, are valuable for AYB breeding activities. The polymorphic DArT-SNPs markers likewise showed efficiency in detecting the population structure and genetic diversity; the markers can therefore be explored for use in genome-wide association study (GWAS) and marker-assisted selection (MAS) in AYB. The complementary approach of combining phenotypic and genotypic data can be implemented in selecting divergent parental materials for hybridization, marker-assisted selection (MAS), and genome-wide association study (GWAS).A total of 169 AYB accessions sourced from the GenBank of the International Institute of Tropical Agriculture (IITA) were evaluated for the present study; the passport data of the materials are shown in Supplementary Table 51. The phenotypic traits evaluated, the assessment period and the method are presented in Supplementary Table The 169 accessions were planted over two cropping seasons  at Jimma Agricultural Research Center (JARC), Jimma, Ethiopia. The field evaluation was carried out under regulations guiding field experimentation of JARC. The experimental field sits at 1739 masl, N07°39.962′, and E036°46.74′ and was laid in Alpha lattice design with two replications of ten plants per accession. After sowing, each plant was stalked with a 3 m stick. Each accession was characterized using 26 phenotypic traits ; the traits were selected based on their abilities to comprehensively capture the existing diversity through all the crop’s vital developmental stages to yield attributes. The IITA AYB descriptor list guided the trait selection52. In brief, genomic DNA was digested with two restriction enzymes; Mst1 was used as the rare cutter and pst1 as the frequent cutter. The digested DNA fragments were ligated using a common adapter, and a barcode adapter, the DNA fragments with a combination of common and barcoded adapters were selectively amplified. The PCR products were pooled and purified using a QIAquick PCR purification kit. The purified PCR products were sequenced on Illumina Hiseq 2500 using single reads. After the sequencing, FASTQ files generated by DArT were aligned against the African yam bean draft genome unpublished , and a HapMap file was generated.Two weeks after planting, about 1 g of young, healthy leaves was collected into labeled 1.2 ml cluster tubes. The tubes were immediately capped, placed on an ice bucket, and transferred to the Plant Molecular Laboratory at Jimma University, where they were kept in −80 °C freezer before lyophilization. The lyophilized leaves were shipped to SEQART Africa Laboratory at International Institute of Tropical Agriculture (ILRI), Nairobi, for DNA extraction and genotyping. The genomic DNA was extracted using the NucleoMag Plant kit, and DNA was purified with genomic DNA clean and concentrator. The purified DNA was quantified on 0.8% agarose gel electrophoresis. The DArT genotyping was done using SEQART Africa genotyping protocol53. Analysis of variance (ANOVA) for each quantitative trait across two years was calculated using the PBIB.test function from the Agricolae R statistical package. Tukey’s HSD test was performed to test the significant difference among the means. The ANOVA was performed using54 distance method, while the phylogenetic and evolution (ape) package was used to construct the hierarchical cluster dendrogram using the Ward.D2 option. The goodness-of-fit of the hierarchical dendrogram was estimated using the cophenetic coefficient of correlation. Finally, the correlation among the phenotypic traits  was performed using the GoodmanKruskal package. The ChartCorrelation function from the PerformanceAnalytics package was used for the quantitative traits.The phenotypic data were analyzed with the R statistical package (Version 4.1.1)55 for further filtering. The filtering was performed on sites retaining SNPs with a maximum of 20% missing values and a minimum and maximum allele frequency of 0.05 and 0.95, respectively. The filtered data generated 1789 SNPs, and the major allele and minor allele frequency were generated for the 1789 SNPs. The pairwise dissimilarity matrix, Identity-by-state (IBS) matrix, was calculated among individuals using PLINK software56. The IBS matrix was inputed into R software version 4.1.153, and the ape package was used to construct a hierarchical cluster dendrogram based on Ward.D2 option. The effect of outliers in the pairwise matrix was minimized by using the cophenetic coefficient of correlation analysis implemented in R to estimate the goodness-of-fit of the hierarchical cluster dendrogram.A total of 7930 SNPs were generated from the DArTseq. The HapMap file was loaded into TASSEL software 5.2.7357. First, the parameter set was inputed as follows; length of run, 30,000, and number of Markov chain Carlo (MCMC) after burning 30,000. Secondly, the “Admixture model” option of the “Ancestry Model” was selected; the admixture model is known to detect historical population admixture and estimate the number of natural genetic clusters. Next, the possible sub-population was estimated with a K-value analysis of k1 to k10; for each simulation, k was independently repeated five times. Finally, the STRUCTURE HARVESTER58 was implemented, and Evanno’s Delta K59 option was used to estimate the appropriate K value to describe the likely sub-population in the data set.The population structure analysis of the 169 AYB accessions was performed using STRUCTURE software version 2.34 (Jul 2012)61 was used in calculating basic diversity parameters, including the number of private alleles, the number of effective alleles (Ne), Shannon information index (I), observed heterozygosity (Ho), expected heterozygosity (He), and fixation index (F) and % polymorphic loci across the 169 accessions and each sub-population. The clustering pattern of accessions was validated using principal component analysis (PCoA) implemented in GenAlex. The pairwise population differentiation statistics (FST), standardized (F’ST), and Shannon index of the observed populations were generated using analysis of molecular variance (AMOVA) implemented in GenAlex.GenAlex software version 6.501The IBS distance matrix generated from the genotypic evaluation and the Gower distant matrix generated from the phenotypic evaluation were loaded into R. The R package Dendextend was used to generate a combined genetic distance by summing the phenotype distance matrix and genotype distance matrix. The combined distance matrix was used to construct a hierarchical cluster dendrogram based on the Ward.D2 method. The cophenetic coefficient of correlation was used to measure the accuracy of the hierarchical cluster dendrogram.47 with 9999 permutations. Similarly, the clusters generated from the combined dendrogram were inputed as variables for ANOVA. Finally, the significance of the cluster means was ascertained through Tukey’s HSD Post-Hoc test.Furthermore, the dendrograms generated from the phenotypic, genotypic and combined evaluation were compared against each other using the R package Dendextend. The significance between the phenotypic matrix and the genotypic matrix, phenotypic matrix versus the combined matrix, and genotypic matrix versus the combined matrix was estimated using the Monte-Carlo option of the Mantel testSupplementary Information.


Perca fluviatilis for which divergences in KTA had already been shown.Considering wild inter-populational phenotypic differentiation can facilitate domestication and subsequent production of new species. However, comparing all populations across a species range to identify those exhibiting suitable key traits for aquaculture  expressions is not feasible. Therefore, proxies highlighting inter-populational divergences in KTA are needed. The use of such proxies would allow to identify, prior to bioassays, the wild population pairs which are likely to present differentiations in KTA expressions in aquaculture conditions. Here, we assessed the relevance of three alternative proxies: (i) genetic distance, (ii) habitat divergence, and (iii) geographic/hydrologic distances. We performed this evaluation on seven allopatric populations of We showed differences in the correlation degree between the alternative proxy-based and KTA-based distance matrices, with the genetic proxy being correlated to the highest number of KTA. However, no proxy was correlated to all inter-populational divergences in KTA.For future domestication trials, we suggest using a multi-proxy assessment along with a prioritisation strategy to identify population pairs which are of interest for further evaluation in bioassays.The online version contains supplementary material available at 10.1186/s12862-021-01937-z. Bombus terrestris), which has been domesticated to act as pollinator in greenhouses, displays phenotypic differentiations between allopatric wild populations for traits impacting its ability to be industrially produced and to pollinate valuable crops ).ys (e.g. ). Secondys (e.g. ) do not ys e.g. , 59), al, alP. flys , geographic barriers (i.e. geographic isolation), ecological barriers, or behavioural specificity . However, our results highlight that not all KTA are correlated with the genetic distance proxy. Moreover, some traits which are not correlated with the genetic distance proxy are correlated with another proxy. To explain the absence of correlation of some KTA with the genetic distance proxy, it could be hypothesised that populations could have diverged recently, implying that few divergences in neutral markers had time to appear , 61.Because there is no proxy-based distance correlated with all KTA-based distances, highlighting populations that likely display KTA expression divergences should be based on the use of several proxies. Genetic distance and habitat divergence proxies are theoretically complementary to maximise the detection of such populations. On the one hand, the genetic distance proxy is relevant to highlight divergence by distance or populations with divergent demographic histories while the habitat divergence proxy can reflect local adaptations which are not  visible in neutral markers. On the other hand, populations occurring in similar habitats can display phenotypic divergences, including in KTA. Thus, the use of these two proxies allows taking into consideration both gene flow disruption/limitation and local adaptation to specific selective pressures. However, these proxies can be sometimes misleading due to their intrinsic limitations (see before), particularly the habitat divergence proxy . One way to mitigate their limitations relies on the combination of these two proxies with the hydrologic distance proxy . The hydrologic distance proxy could be correlated with both phenotypic differentiation by distance and local adaptations between distant populations. Moreover, the hydrologic distance proxy is correlated in our study with two KTA which are not correlated neither with genetic distance or habitat divergence proxies. Even though its calculation might be time and system memory-consuming, it requires no specific information except for geographic coordinates which makes it simple and useable in all scenarios. Here, we promote the use of a multi-proxy approach to pinpoint a population or population group to be assessed for KTA expression divergences. Even if some proxies appear as less efficient, their simple evaluation requiring little information (e.g. geographic distance from GPS coordinates) makes their use valuable to increase chances of finding out divergences in KTA. Another potential alternative would rely on ranking the KTA since not all KTA have the same importance to stakeholders. This strategy would consist of choosing the proxy which is correlated to the most important KTA . HoweveRegarding the multi-proxy strategy, it is likely that the different proxies drive to divergent conclusions regarding which populations should be considered. This implies integrating all proxies in the same decision framework to highlight population pairs that should be considered in common garden experiments. Recommending for such experiments any population pair that is somewhat divergent for one or more proxies would fail to limit the number of populations involved in costly bioassays. Therefore, prioritisation procedures should be developed.We recommend evaluating population/population group pairs according to both their degree of divergences in each proxy and the degree of congruence in divergence of all proxies. We applied here a prioritisation procedure on our dataset to validate this procedure.Firstly, in this example, depending on the proxy considered, the conclusion regarding which population pairs are the most interesting would differ Table . For insOur results show differences in the correlation degree between the alternative proxy-based and key trait-based distance matrices. Although we show that the genetic proxy is the most correlated to phenotypic key traits, no proxy was correlated to all inter-populational divergences in key traits. Therefore, we argue that the use of a multi-proxy approach is the most efficient and a population prioritisation strategy is proposed to optimize it.P. fluviatilis populations of interest have been highlighted in previous studies  was increased in the same proportions. After 25 dph, larvae were only fed with dry feed. Tanks were cleaned daily after first-feeding and dead larvae were removed and counted. Oxygen and temperature were checked daily while nitrite and ammonium concentrations and pH were monitored three times a week. At the end of the experiment, larvae left in each tank were counted and sorted according to the presence/absence of swim bladder inflation  until the end of weaning, at 26 dph. For each population, after hatching, larvae from the different egg ribbons were mixed and transferred to three green internal-wall cylindro-conical tanks (three replicates per population) at a density of 50 larvae/L at our experimental platform of aquaculture . Temperature was gradually raised to 20 °C (1 °C rise per day). Photoperiod was 12L:12D and light intensity stayed constant during the lighting period at 400 lx at the water surface (with simulation of dawn and dusk for 30 min). Larvae were hand-fed from 3 days post-hatching, seven times a day, during the illuminated period, every 1h30, with newly hatched  used in ) and ske used in .−1 where Li and Lf are respectively the initial and final length and ∆T the length of experiment. Final growth heterogeneity was calculated in the following way: CVLf/CVLi where CV is the coefficient of variation (100*standard deviation/mean) and Li and Lf the initial and final length, respectively. Swim bladder inflation rate was calculated in the following way: 100*(SB + /Nf) with SB + the number of larvae with swim bladder and Nf the final number of larvae. Deformity rate was evaluated using the following formula: 100*(Nm/Nf) with Nm the number of deformed larvae  and Nf the final number of larvae. Aggressiveness, including enucleation which is a specific aggressive behaviour in P. fluviatilis [Survival rate was calculated using the following formula: Nf*100/(Ni − Ns), in which Nf is the final number of larvae counted at the end of experiment, Ni the initial number of individuals, and Ns the number of larvae sampled along the experiment . In order to evaluate growth traits, 30 larvae per population  were sampled the first and last days of the experiment. After sampling, larvae were euthanized with an overdose of MS-222 and preserved in formalin 4%. Individuals were measured for total length in ImageJ  , and Ndviatilis , 72).The protocol to evaluate inter-individual distances  and actiIn the present study, we did not use any phenotypic proxy because morphological and/or behavioural information was not available for all wild perch populations. Moreover, we did not estimate such a proxy on larvae reared in the bioassays because these phenotypic traits can be strongly influenced by the environment (e.g. ). TherefP. fluviatilis origin of each sequence was verified using BLAST [P. fluviatilis studies; [ST distance [The genetic assessment of the seven populations was performed on 10 individuals per population collected randomly at the end of the experiment. Larvae were stored in 99% ethanol at − 20 °C until analyses. Samples were sent to Genoscreen  for DNA isolation, marker amplification, and Sanger sequencing. Three mitochondrial regions were studied  following BLAST . Sequencng BLAST ). Translng BLAST . A tandestudies; ) and mutdistance  in SPADSdistance .https://www.worldclim.org/data/bioclim.html) as well as annual mean solar radiation (kJ m−2 day−1), annual mean wind speed (m s−1), and annual mean water vapor pressure (kPa) from WorldClim Version2 to assess sampling place environments. We considered these abiotic environmental parameters (at the sampling location) recorded between 1970 and 2000 at a resolution of 10 arc‐minutes [2), catchment area (km2), average and maximum depths (m), and altitude (m). All variables were standardised to zero mean and unit variance due to their different scales of measurement . A prin “vegan”  and “fac “vegan” ) using twww.naturalearthdata.com) databases, and jointly rasterised on a geo-referenced grid with a resolution of 0.5 arcmin. The algorithm implemented in Circuitscape computes pairwise electric resistances by treating a raster as a grid of electric resistance or conductance. In the present case, we used Circuitscape to compute hydrologic distances measured as electric resistance between locations when treating the rivers raster as a grid of electric conductance. One of the advantages of the Circuitscape algorithm is that it considers the contribution of several potential pathways to compute pairwise distances (resistances) between locations. Specifically, we assigned a value of 1 to terrestrial raster cells that were not crossed by a river or occupied by a lake and a value of 1 + k for terrestrial raster cells crossed by a river or occupied by a lake, the parameter k thus defining to what extent raster cells crossed by a river are more conductible (i.e. facilitate movement) than raster cells that are not crossed by a river. To explore the impact of k on the outcome of our analyses, we tested three different values for k: k = 10, 100, and 1000. These three different values led to highly similar results and did not alter our conclusions (results not shown). Therefore, we only report results based on hydrologic distances computed with k = 100.Geographic distance matrix was set through the calculation of the great-circle distances (i.e. shortest distance over the earth’s surface) according to the “Vincenty” (ellipsoid) method  implemenIn order to create a trait-by-trait distance matrix, we first checked the statistical differentiation  of phenotypic traits between populations: we tested normality of distribution and homogeneity of variances using a Shapiro–Wilk test and a Levene test, respectively (R package “lawstat” ). When ahttps://figshare.com/s/1067e47637c5572caaf7). We performed multivariate analyses using multi-regression on distance matrices (1000 permutations) coupled with commonality analyses (MRDM-CA) using R packages “ecodist” [ST genetic distance vs KTA-based distance matrix, (ii) habitat divergence vs KTA-based distance matrix, (iii) geographic distance vs KTA-based distance matrix, and (iv) hydrologic distance vs KTA-based distance matrix.A trait-by-trait distance matrix was created using the Euclidean method . All distance matrices are available in Figshare . Ihttps://figshare.com/s/1067e47637c5572caaf7). Second, the average distance value among replicates was calculated for each proxy/KTA of each population pair. Third, the sum of the three proxy-distance values was calculated per population pair. Similarly, the sum of all KTA-distance values was also calculated per population pair. Relevant ranking of population pairs according to the sum of KTA or proxy values must take into account that pairs with similar sum values should be regarded as equivalent and no prioritisation among them cannot be proposed (i.e. indifference between pairs). Therefore, we chose to use a k-means clustering procedure to determine indifference groups of population pairs. It aims at partitioning population pairs into k clusters in which each population belongs to the cluster with the nearest centroid. For each sum variable (i.e. sum of KTA distances and sum of proxy distances), the optimal number of clusters (k) was determined using the Elbow method . Then, Additional file 1: Figure S1. Principal component analysis biplot representing environmental variables (in blue) and populations (dots in grey) using the first two axes. Populations: BAL: Balaton, VAL : Valkea-Müstajärvi, ISO: Iso-Valkjärvi, KIE: Kierzlinskie, GEN: Geneva, BOU: Bourget, HOH: Hohen Sprenzer. BIO1 to BIO19 correspond to the bioclimatic variables from Worldclim. Vap, wind, and srad correspond to water vapor pressure, wind speed, and solar radiation, respectively.Additional file 2: Figure S2. Barplots representing results obtained for all key traits studied . Different letters indicate significant differences between populations  using post-hoc tests.Additional file 3: Table S1. Correlation results between proxy-based  and KTA-based distance matrices with r the Mantel correlation value and its associated p-value  are indicated in bold).


Mustela eversmanii and M. putorius, in Europe. The population size of steppe polecat is known to continuously shrink, whereas its sister species, the European polecat, is still somehow widespread. In this study, we perform an analysis using microsatellite (SSR) and genomic (SNP) data sets to identify natural hybrids between polecats. Four populations were genotyped for eight polymorphic SSR loci, and thousands of unlinked SNPs were generated using a reduced‐representation sequencing approach, RADseq, to characterize the genetic make‐up of allopatric populations and to identify hybrids in the sympatric area. We applied standard population genetic analyses to characterize the populations based on their SSR allelic frequency. Only a single sample out of 48 sympatric samples showed exact intermediacy that we identified as an F1 hybrid. Additionally, one specimen was indicated in the genomic data sets as backcrossed. Other backcrosses, indicated by SSRs, were not validated by SNPs, which highlights the higher efficacy of the genomic method to identify backcrossed individuals. The low frequency of hybridization suggests that the difference in habitat preference of the two species may act as a barrier to admixture. Therefore, it is apparently unlikely that polecat populations are threatened by significant introgression. The two species showed a clear genetic differentiation using both techniques. We found higher genetic diversity values in the sympatric steppe polecat population than in the other studies on polecat populations. Although M. putorius is a hunted species in most countries, genetic diversity values indicate worse conditions in Europe than in the protected sibling species M. eversmanii. Suspending hunting and providing protected status of the former seems to be reasonable and timely.Introgressive hybridization can pose a serious threat to endangered species which have an overlapping distribution such as in the case of two polecat species,  Native wildcat populations are imposed to genetic erosion via introgressive hybridization with domestic cats (Felis catus) , hybridization with the European polecat  was mentioned among potential threatening factors  in a phylogeographic study , having separated from them approximately 2.13 million years (MY) ago (median time: 1.21 MY)   and some outside of it, such as New Zealand , where its distribution overlaps with M. putorius. A candidate‐gene approach (i.e. the utilization of one or a few highly informative genes) is sometimes suboptimal for understanding of phylogenetic relationships in the Order Carnivora. For example, cytochrome‐oxidase B is one of the most heavily utilized phylogenetic markers phylogenetic reconstruction, but it did not provide significant phylogenetic resolution between M. eversmanii and M. putorius  in the analysed European polecat populations, which is similar to that found in other mustelid species  and M. putorius. They found that hybridization and genetic introgression occurred at low levels and in an asymmetric way, where only pure European polecat males mate with pure European mink females. Furthermore, backcrossing and genetic introgression were detected only from first‐generation (F1) female hybrids of European minks to polecats. They suggested that hybridization and genetic introgression between the two species should be considered a rather uncommon event, although current low population densities of the European mink might change this trend. As no similar study exists for polecats, the study of hybridization between them is therefore timely  and the European hamster (Cricetus cricetus), had a major impact on the population of both polecat species in the 20th century  , to assess the extent of hybridization between M. eversmanii and M. putorius in the Pannonian Basin. We compare our genetic results with allopatric populations of both species. Furthermore, we aim to contribute towards a better understanding of the evolution and conservation of the biological consequences, if any, of hybridization in these mustelids.However, the recovery of 22.1M. putorius and M. eversmanii partially overlaps, the microsatellite data collection focused on three regions. Firstly, we sampled the Pannonian Basin for both species , which is the exclusive distribution area of the subspecies M. eversmanii hungarica. Secondly, we also sampled the allopatric area of both species: M. putorius was collected in Jutland, Denmark (n = 24), allopatric M. eversmanii samples originated from the Ural Mts., from which only four samples were available. Microsatellite data set included 75 specimens altogether . All samples suspected to be of hybrid origin in the microsatellite analysis were included in the genomic sample set. In addition, we aimed to represent the allopatric area with two specimens from each sampled population. Finally, 10 further samples were chosen randomly from the sympatric area . Thus, a total of 22 samples was genotyped using RADseq  was isolated from muscle, ear tip or hair samples and stored individually in 96% ethanol or silica gel. DNA extraction followed the protocol described in detail in Cserkész et al. . In shor2.2We generated genetic information in two steps. First, we performed a traditional microsatellite (SSR) analysis, to facilitate the comparison of our results with previous studies. Additionally, we used a reduced‐representation genomic approach, restriction site associated DNA sequencing (RADseq), to create a set of high‐density markers (single nucleotide polymorphism: SNP) based on a subset of samples, which was selected in the light of the results of the microsatellite analysis.Ursus americanus (G1A)    , 10 µM of each primer (of which the forward was fluorescent labelled by Thermo Fisher Scientific Ltd.) and 1μl gDNA diluted to 10ng/μl. The PCR conditions were as follows: initial 95°C for 3 min, then 40 cycles of 94°C for 15 s, 54°C  or 60°C  for 30 s, 72°C for 30 s and a terminal elongation at 72°C for 14 min in an Mastercycler Gradient PCR Machine (Eppendorf AG). PCR products were capillary electrophoretized on an ABI 3130 Genetic Analyzer (Applied Biosystems) with the use of LIZ‐600 standard. Fragment lengths were determined by the visual inspection of the raw electropherograms by the same person using Peak Scanner v. 1.0 software (Applied Biosystems).2.3HO) and expected (HE) heterozygosities with GenAlEx v.6.5  was calculated using GENETIX v.4.05.2.  with GenAlEx v.6.5.We calculated basic population genetic parameters, such as allelic patterns  and observed (Q) is a widely used method to identify hybrid individuals. In general, there is a trade‐off for the determination of a threshold between type I  and type II  errors  values from 1 to 20. The preferred ancestry model was the admixture model, since it allows each individual to draw some fraction of its genome from each population at any given K. The optimal K value was estimated after the simulation with the online software Structure Selector  from package adegenet v. 2.0.1 Jombart,  in R. DAa priori parental population  DNA quantity by a Qubit v.3 fluorimeter (Thermo Fisher Scientific) using the Qubit dsDNA HS Assay (Thermo Fisher Scientific), and 210 ng dsDNA was used in an original RADseq library preparation   from LEA 2.0 package , while the Russian M. eversmanii (4) and the Danish M. putorius (4.375) populations showed similarly low numbers. In contrast, mean allele numbers were again somewhat higher in the Pannonian M. putorius (5)  for M. eversmanii. All four tested populations had a set of private alleles as well. Ten alleles were exclusive to the Pannonian M. eversmanii population, whereas the Danish M. putorius was the population with the fewest private alleles (2). The frequencies of these private alleles were typically under 0.1. However, the most frequent private allele (Locus G1A – 174 bp) occurs with high frequency (0.45) among Pannonian steppe polecats. Moreover, three other private alleles exceeded 0.25 in this population. Unbiased expected heterozygosity (uHE) was relatively high in all populations (mean ± SD = 0.615 ± 0.04). In general, genetic diversity was higher in M. eversmanii populations than in M. putorius. The genetically least diverse population was the Danish M. putorius  calculated with FreeNA. The differences between the two values were zero or <1.5%. On that basis, we decided to analyse all eight loci using the corrected frequency values wherever possible.The test of HWE indicated that locus ‘Mvis087’ significantly deviated from the equilibrium. We assumed that the presence of null alleles could have been the cause of deviation. Testing that with MicroChecker v.2.2.3 revealed that most probably there are null alleles on locus ‘Mvis087’ in three populations. To estimate the effects of the presence of null alleles, we compared the uHM. eversmanii population suffered a collapse in the past  and more strongly by the stepwise mutation model (SMM)). In contrast, the mode shift test showed a normal L‐shape in all three cases. The Russian M. eversmanii population was not tested due to the low sample size. The inbreeding coefficient (FIS) of all studied populations was significantly positive (i.e. homozygote excess).The test results of the former dramatic population size changes were slightly controversial. The Wilcoxon tests suggest that the Pannonian FST) based on the corrected allele frequencies (ENA method). The calculated FST = 0.283 is smaller than that reported for M. putorius and M. lutreola (FST = 0.531) by Cabria et al. (2012). We also calculated the pairwise FST for each population , whereas the sampled populations of M. putorius were highly similar (FST = 0.077). The genetic difference between the two populations of M. eversmanii (FST = 0.143) was higher, but at the same time smaller than the difference between the two taxa. The corrected Cavalli‐Sforza chord distance‐based tree  from the Pannonian M. putorius population, which occupy an intermediate position between the two species  analysis to reveal the genetic structure of our microsatellite data and identify individuals with admixed ancestry. Evanno's method suggested K = 2 as the most probable number of groups , the structure software separates the Russian steppe polecat samples from the Pannonian steppe polecat samples. At levels of population complexity higher than K = 4, new clusters caused disturbance into previous and biologically highly meaningful structure, so the individual assignment probability values became uncertain (Q<50%) in each sampled population. This means that those clusters are most probably ‘ghost’ or spurious groups . Two of them, sample #26626  and #26714 , belonged to the M. putorius phenotype group. At K = 2, when the two species were separated, the group identity for the sample #26626 was equally probable (Q = 0.5) for both groups, whereas in sample #26714 the invasive genetic material proportion just exceeded the 10% threshold determined formerly to identify individuals with admixed ancestry. The third putative admixed specimen was sample ‘Cs10’ . Although this individual was clearly identified as M. eversmanii based on phenotypic traits, its SSR genotype resembles Danish M. putorius.Only three individuals showed significant signs of admixture  on each level of probable clustering , sample ‘P01’  was added as a potential third admixed specimen in this analysis. All three individuals were identified as M. putorius based on morphology  reached a minimum value at 2, suggesting s Figure . At K = M. eversmanii. None of the potential backcrossed specimens had hybrid probability exceeding 23%.Finally, we ran assignment test of hybridity using NewHybrids. The analyses with different priors yielded similar results; therefore, we only present the run using the uniform prior Figure . The ana3.2The raw genomic data set consisted of a total of 177,303,172 paired‐end reads of 150 bp length. After demultiplexing and filtering for low‐quality reads, adapter contamination and ambiguous barcodes with a maximum of one mismatch, we retained a total of 149,386,828 (84.2%) reads. The average paired‐end read count across samples was 7,183,240 .Mustela furo. The ref_map pipeline of stacks did not identify any completely unmapped read. Skipping reads with insufficient mapping qualities (6.3%) and excessively soft‐clipped alignments (6.4%) retained 129,398,212 (87.3%) alignments in total . Based on these alignments, 616,542 could be built with a mean coverage of 21× . The mean length of loci was 296.4 bp.All reads could be successfully mapped to the reference genome of The data set contained 164,644 unlinked SNPs (i.e. a single SNP of each polymorphic locus) in total. Filtering for minor allele frequency, missingness and HWE resulted in 10,433 SNPs retained for all downstream analyses. These SNPs were checked for read depth and genotype quality for each individual Figure . The 1stM. eversmanii samples are more distant from each other than the M. putorius samples, and a clear edge separates the samples collected east of the Carpathians from other M. eversmanii specimens. The intermediate space is occupied by two specimens , suggesting that they are the ones with admixed ancestry. Both specimens are situated closer to the species to which they were classified by pelage  were clustered to the hybrid group , whose ancestry proportions exceeded the 10% threshold from both species at K = 2. The #26626 specimen was identified as a hybrid group member with 99% probability. Besides that, the four samples outside the Carpathians  are clustered together.For the admixture analysis and clustering, we used the snmf method from the LEA package. The cross‐validation algorithm of the snmf function suggested e Figure . Once agK = 4 with only a few differences  M. eversmanii samples from the Pannonian region (III) all the M. putorius samples across the whole sampled area and (IV) the two samples from the Pannonian region with admixed ancestry . This time the SMP15 sample from Spain clustered together with other European polecats. The first axis separates the two species. The subspecies of M. eversmanii are mainly separated along the second axis. The hybrid group is in an intermediate position  supported the clusters returned by the snmf method at s Figure . The foun Figure .M. eversmanii with high certainty (probability 83%).Finally, we also ran the assignment of specimens into hybrid categories by NewHybrids based on 200 highly discriminatory SNPs. We found the results based on Jeffreys prior and the uniform prior to be highly concordant, so we only show and interpret results based on the latter Figure . This an44.1M. eversmanii and M. putorius. In this respect, this is the first population genetic study of a European population of M. eversmanii. We expected to detect a serious loss of heterozygosity in the Pannonian M. eversmanii population, as this population has shown a severe decline in the last century  and M. putorius (HE = 0.17); they found worryingly low values for M. nigripes populations in Kansas and South Dakota  may also show a biased value. Similarly to Pertoldi et al. (HE = 0.5), our sample of the Danish European polecat population displayed the lowest value of genetic diversity (uHE = 4.85). Conversely, Møller et al. (HE = 5.83) in the Danish population based on 83 samples and six microsatellite loci. In this perspective, Cabria et al. (HE = 0.357) heterozygosity for M. putorius determined from 114 samples across the whole distribution area from Russia to Turkey and Spain. In the meta‐analysis of Garner et al. (HE≈0.6). In full concordance, heterozygosity values reported in polecat populations  i et al.   in all populations. This can hint at contemporary inbreeding. We detected a former bottleneck event, with equivocal results, only in the Pannonian M. eversmanii population. Similarly, Møller et al.  can be observed as demonstrated by positive inbreeding coefficients . Vähä and Primmer   are fertile, opening the possibility for introgressive hybridization between our studied sympatric populations. As suggested by the model of Boecklen and Howard . However, the genomic approach nested this sample well‐within M. putorius , from the M. eversmanii  as backcrossed. The morphology, as well as the distribution of the ancestry proportion (LEA K = 2), the position on the NeighborNet and the high probability (83%) of being a backcrossed individual in the NewHybrids analysis suggest that specimen #26837 is a result of series of backcrossings with M. eversmanii. In agreement with that, it was collected in a barely forested, mainly agricultural lowland region, where the environmental conditions seem to be much suitable for M. eversmanii. Unfortunately, this specimen was not included in the morphometric analyses made by Cserkész et al.  identified only a single individual of z et al. . AdditioM. putorius sample ‘SMP15’ from Spain, outside the sympatric area, to the admixed group. This surprising result is unlikely to be the sign of recent hybridization; instead, it could be a result of inheriting shared ancestral alleles with M. eversmanii from M. lutreola, since M. putorius and M. lutreola do hybridize in Spain  analysis clustered the Clearly, thousands of SNPs can easily outperform microsatellites , whereas the difference between M. putorius and M. lutreola was 0.531  than the two sampled populations of M. putorius (FST = 0.077). Nevertheless, reaching a solid taxonomic conclusion on the subspecies status of M. eversmanii hungarica would require a phylogeographical approach that is beyond the scope of our current study.We regard the study of Cabria et al. , who wer4.4M. eversmanii has been driven to the brink of extinction in Central‐Eastern Europe. However, this conclusion is not supported by the population genetic results of this study. The apparent alarming rate of decline of M. eversmanii may instead be an indication of the lack of surveys rather than that of the decrease of population sizes. Alternatively, our results may document a rapid population recovery following a precipitous decline of the species. The contrary seems to be the case for M. putorius, which is not a protected species in most countries and listed on Annex V of the Habitats Directive—an Annex that largely concerns animals for which hunting or gathering can still be allowed. Our population genetic results hint at M. putorius populations probably being in worse condition in Europe. Suspending hunting and providing protected status seems to be reasonable and timely, particularly in Western Europe where the polecat's conservation status is unfavourable , and this trend is expected to continue. As a result, in villages the possibility of interactions between the two polecat species can be expected to be more frequent in future. Long‐term monitoring is required to acquire samples to track whether hybridization does increase especially in such situation where ecological conditions are likely to change. A good precedent is given by the track record of hybridization between polecat and ferret in the British Isles  this number can be expected to increase in the future. Further regular genetic monitoring is recommended to see whether introgression will begin to occur.The genetic diversity of the studied steppe polecat population was surprisingly high and higher than any of the European polecat populations studied, although the latter is a hunted game species and not protected. This practise should be revised in the future. The genomic approach, RADseq, clearly outperformed conventional SSRs in detecting admixed individuals, because backcrossed specimens were inconsistently identified across analyses based on SSRs, whereas SNPs indicated the same pattern regardless of the analysis. However, it was possible to identify an F1 hybrid using a smaller number of informative microsatellite loci as this specimen was consistently detected as an F1 hybrid in all analyses irrespective of the molecular marker system. We only observed hybridization in the Pannonian area at a low frequency (around 2%) and at the periphery of the distribution area of None declared.Supplementary MaterialClick here for additional data file.