The consideration of the descendance is indispensable in the preservation of endangered animal breeds.The authors compared mitochondrial DNA (mtDNA) control region sequence in three descendant breeds of the extinct Zaupel sheep, firstly. Their investigation was carried out in order to prove the common origin of Waldschaf (Austria), Bovec sheep (Slovenia) and Cikta (Hungary).A total of 118 biological samples were taken from non‐related representatives of the three breeds between 2015 and 2017. A newly designed primer pair was also used to amplify the segment (1180 bp) to be tested.Fs statistic  based on haplotype frequencies demonstrated a moderate foreign gene flow. As a novel observation haplogroups C and D appeared in Cikta and Bovec sheep, respectively. The Tajima D‐test value in the entire study population was −0.914 (p > 0.10), meaning that the separation of the three descendant breeds did not cause genetic drift, these are collectively in genetic equilibrium.The total number of haplotypes in the whole study population was 49. The majority of which fell into haplogroup B. The significant negative value of the Fu's The genetic information confirmed the common origin of the breeds known from the breed history. This manuscript deals with significant questions of the common root of Zaupel sheep's descendant breeds and investigates these breeds based on mtDNA control region using a novel approach called founder sampling method. Genetic information confirmed the origin of the breeds known from the breed history separation of the three descendant breeds did not cause genetic drift, these are collectively in genetic equilibrium. At the same time, pedigree analysis that determines sampling leads to a more realistic diversity of results. This circular genome is mainly used in evolutionary research, in the phylogenetic study of mammalian species and populations by maternal origin , there are several major lineages , of which group B is found mainly in European breeds .Haplogroups D and E are the least frequent and have only been identified in samples from Turkey and the Caucasus  stated the Karayaka breed from Northern Anatolia cannot be categorized as a genetically homogeneous population, but even has four different haplogroups .Our hypothesis was that, with modern genetic knowledge, we would be able to show common roots and connections in breeds considered to be of common origin. If this is true, then statistical processing of the data does not justify a significant genetic difference between descendant breeds of Zaupel. In this sense, we surveyed the diversity of maternal genetic background in the following three breeds: Waldschaf, Bovec sheep and Cikta based on the sequence order of the mutable CR of the mitochondrion. With our results we wanted to prove genetically the common origin of the related varieties known from their history, and on the other hand to create a basis for the further maintenance of the genetic characteristics of the representatives in the Zaupel breed group.2To avoid the random sampling ancestries provided information. Biological samples were taken from non‐closely related individuals in Waldschaf  and Bovec sheep  breeds using three‐generation pedigrees. In Bovec sheep, blood samples were collected from potential breeding rams included in the regular prion genotyping program, determined by order of the Ministry of Agriculture, Forestry and Food of the Republic of Slovenia. All rams were offspring of different ewes. In Cikta sheep , the so‐called ‘founder sampling’ was used , 2 μL primer (10μM), 1 μL BSA (20 mg/mL), 0.4 μL Taq polymerase (5 U/μL) (ThermoFisher Scientific) and 10 ng DNA template and PCR grade water to volume. Two primer pairs were used to amplify the segment to be tested: newly designed primers (Primer Designer 4.0 software) for the beginning of the region, and with a second pair as described by Hiendleder et al. (DNA was isolated using the GenElute Blood Genomic DNA Kit (Sigma‐Aldrich) according to the manufacturer's instructions. The 25 μL PCR reaction mixture prepared for each sample contained 2.5 μL dNTP (10 mM), 2.5 μL 10× PCR buffer, 1.5 μL MgClr et al. . First, BigDye® Terminator version 3.1 Cycle Sequencing Kit (ThermoFisher Scientific) was used for sequencing reaction in the manner recommended by the manufacturer. For sequence detection, an ABI Prism 3130XL Genetic Analyzer (Applied Biosystems) was applied, according to manufacturer's guidelines. Sequence data were analysed using Sequencing Analysis Software 5.1 (Applied Biosystems) and aligned by Sequencher™ 4.1.2 software (Gene Codes Corp).The length of aligned and trimmed CR sequences was 1180 bp long and corresponded to positions 15,437‐16,616 on the reference sequence  was 131. For the latter, 13 single (singleton site positions: 140 286 312 462 499 632 696 913 962 1093 1145 1148 1183) and 118 parsimony mutations occurring in more individuals were found.Table The total number of haplotypes in the whole study population was 49.The haplogroup and haplotype distribution of the studied breeds is shown in Figure k) and average nucleotide diversity (π). The average nucleotide diversity (π) is the number of different nucleotides at a given base site of two randomly selected mtDNAs, which in turn determines the genetic diversity of the stock. It can be seen that the Cikta is characterized by the greatest diversity, with the highest number of average nucleotide differences (21.251) and average nucleotide diversity (18.02 * 10−3). This breed is followed by Waldschaf and then Bovec sheep with the least diversity.The comparison of related breeds is reported in Table k) and the average nucleotide diversity (π) in comparison between breeds. In the comparison of Waldschaf and Bovec sheep, the average number of nucleotide differences was 18.773. Here, the corrected number of base substitutions calculated according to Jukes and Cantor (Dxy (JC)) was 16.77 * 10−3 with a standard deviation (SD) of 2.86 * 10−3. The average number of nucleotide differences between Waldschaf and Cikta was 20.806, while the corrected number of base substitutions (Dxy (JC)) according to Jukes and Cantor was 17.84 * 10−3 with a SD of 2.37 * 10−3. In the third comparison (Bovec sheep and Cikta), the average number of nucleotide differences was 21,383, the Jukes and Cantor (Dxy (JC)) value with SD of 19.18 * 10−3 and 2.36 * 10−3, respectively.Table k and π.The ratio of shared mutations between breeds occurred in inverse proportion to the values of k) was 20.880 and the average nucleotide diversity (π) was 17.71 * 10−3. The Tajima D‐test value was −0.914, not statistically significant (p > 0.10). A significant positive value would be an indicator of genetic narrowing (bottle neck effect) or disintegration into subpopulations of a given population. Meanwhile, a significant negative value would be an indicator of selection that intends to get rid of an undesirable gene or, suitably, demographic expansion.According to the Tajima test performed in the entire study population, the average number of pairwise nucleotide differences (p > 0.10) and 0.562 (p > 0.10), respectively. Furthermore, the Fu and Li's test for haplotype diversity (Hd) gave 0.973 with a SD 5 * 10−3. In contrast, the Fu's Fs statistic resulted in a significant value of −3.296 (p = 0.013).The Fu and Li's D* and F* tests performed on the entire pool yielded the non‐significant values of 1.217 , 98 variable base sites were found in the CR region by Annus et al. , of whic−3) than that of all breeds currently studied, and the Tajima D‐values of both breeds were statistically proven to be in the minus range (Gyimesi Racka −1.734 and Turcana −1.814) in the evaluation of Kusza et al. . Rather, the present study confirms that Waldschaf is also likely to be members of haplogroup B.Wassmuth et al. (2002) found that all individuals of Steinschaf (including Bovec sheep) belong to haplogroup B. However, in the Waldschaf, haplogroup A was found in more than half of the animals. It shows that not all Zaupel sheep descendants can be traced back to the same domesticated ancestor  and the values of the Fu and Li's D* and F* tests . According to these values there is a limited difference in number of polymorphic sites and the mean number of nucleotide pairwise differences among the breeds. These results indicate that the community of Zaupel descendants studied is in a genetic equilibrium position, deviation from this does not endanger it, and there is no force supporting population demographic expansion.The complete identity between the breeds is supported by the value of the Tajima D‐test , which demonstrates foreign gene flow after a spatial expansion. The value of Fs statistic can be considered as low in our case, since in the clusters of other similar sheep research, it was decreased to a greater extent within the negative range with a much lower probability of error, for example, −7.48  prevailed in all breeds  with 81, 62 and 80%, respectively. This was followed by the frequency of haplogroup A . A 9% incidence of haplogroup C indicates an even more complex maternal background of that breed. This means that ewes have entered Hungary in the past from the closer and farther areas, either from the Asia Minor , confirmed that these indeed come from a common ancestor, the Zaupel.Our working hypothesis, which was based on the breed histories, was verified by genetic analyses and their statistical testing. The high proportion of haplotype B and the overlapping mutations found in the three breeds, supplemented by the Tajima D‐, Fu and Li's D* and F* tests giving non‐significant values  is an indicator of population expansion (genetic hitchhiking), which demonstrates foreign gene flow after a spatial expansion.The Fu's In addition to the haplogroups B and A typical of Europe, we were also able to detect the presence of haplogroups C and D in Central Europe.András Gáspárdy: Conceptualization; funding acquisition; methodology; supervision; writing‐original draft; writing‐review & editing. Beate Berger: Conceptualization; data curation; investigation. Jelka Zabavnik‐Piano: Conceptualization; formal analysis; funding acquisition; writing‐original draft. Endre Kovács: Formal analysis; investigation; resources. Kata Annus: Formal analysis; investigation; methodology; project administration. Petra Zenke: Data curation; formal analysis; investigation; project administration; software; validation. László Sáfár: Data curation; investigation; software. Ákos Maróti‐Agóts: Data curation; methodology; software; writing‐original draftAndrás Gáspárdy conceived and supervised the investigation and had substantial inputs into the completion of manuscript with co‐supervision of Beate Berger and Jelka Zabavnik‐Piano. László Sáfár made an effort to process pedigree data. Endre Kovács and Kata Annus conducted the sample taking. Petra Zenke dealt the DNA purification and sequencing with, and Ákos Maróti‐Agóts had substantial role in statistical data analysis and created a first draft of the article.The authors declare no conflict of interest.https://publons.com/publon/10.1002/vms3.585The peer review history for this article is available at


H5-subtype highly pathogenic (HP) avian influenza viruses (AIVs) cause high mortality in domestic birds and sporadic infections in humans with a frequently fatal outcome, while H5N1 viruses have pandemic potential. Due to veterinary and public health significance, these HPAIVs, as well as low pathogenicity (LP) H5-subtype AIVs having a propensity to mutate into HP viruses, are under epidemiologic surveillance and must be reported to the World Organization for Animal Health (OIE). Our previous work provided a unique panel of 6 different monoclonal antibodies (mAbs) against H5 hemagglutinin (HA), which meets the demand for high-specificity tools for monitoring AIV infection and vaccination in poultry. In this study, we selected one of these mAbs to develop an epitope-blocking (EB) ELISA for detecting H5 subtype-specific antibodies in chicken sera (H5 EB-ELISA).In the H5 EB-ELISA, H5 HA protein produced in a baculovirus-expression vector system was employed as a coating antigen, and the G-7-27-18 mAb was employed as a blocking antibody. The performance characteristics of the assay were evaluated by testing 358 sera from nonimmunized chickens and chickens immunized with AIVs of the H1–H16 subtypes or recombinant H5 HA antigen to obtain the reference and experimental antisera, respectively. The samples were classified as anti-H5 HA positive or negative based on the results of the hemagglutination inhibition (HI) assay, the gold standard in subtype-specific serodiagnosis.The H5 EB-ELISA correctly discriminated between the anti-H5 HA negative sera, including those against the non-H5 subtype AIVs, and sera positive for antibodies against the various-origin H5 HAs. Preliminary validation showed 100% analytical and 97.6% diagnostic specificities of the assay and 98.0% and 99.1% diagnostic sensitivities when applied to detect the anti-H5 HA antibodies in the reference and experimental antisera, respectively.The H5 EB-ELISA performed well in terms of diagnostic estimates. Thus, further optimization and validation work using a larger set of chicken sera and receiver operating characteristic (ROC) analysis are warranted. Moreover, the present assay provides a valuable basis for developing multispecies screening tests for birds or diagnostic tests for humans.The online version contains supplementary material available at 10.1186/s12985-021-01564-6. Orthomyxoviridae family, which consists of seven genera .The optical density (OD) was measured at 450 nm  were evaluated by calculating the percentage of samples negative in this test among the sera lacking HI activity against H5-subtype AIVs Table . Data obDiagnostic sensitivity (Dse) of the H5 EB-ELISA was evaluated by calculating the percentage of samples positive in the test among the antisera with HI titers against H5-subtype AIVs of 1:8 or greater Table . Of noteThe high specificity and sensitivity of the H5 EB-ELISA, as indicated by values of Asp, Dsp and Dse Table , were acThe H5-subtype HPAIVs, such as the H5N1, H5N2, H5N6, and H5N8 viruses, are of both veterinary and public health concern worldwide. Infection with these viruses leads to multiorgan disease and death in domestic birds . For theConsistent with the characteristics of the blocking mAb , all of E. coli [The H5 EB-ELISA enabled not only specific but also sensitive detection of H5 subtype-specific antibodies, especially when applied to examine sera from chickens immunized using the H5 HA protein . Table S2. Reference antisera against LPAIVs of the H5 subtype (x-OvO Ltd.). Table S3. Reference antisera against LPAIVs of the non-H5 subtypes (x-OvO Ltd.). Table S4. Experimental antisera against HA from H5N1 HPAIV (IBA). Table S5. Antigens and antisera (x-OvO Ltd.) used in the HI tests performed at IBA. Table S6. Homology of H5 HA antigens against rH5-mammalian, an immunogen used in mAb production. Table S7. Homology of H5 HA antigens against rH5-BEVS, the coating antigen in the EB-ELISA.Additional file 2. Verification of mAb activity in the hemagglutination inhibition assay. Table S1. Antigens and antisera used in testing the mAbs for HI activity. Table S2. Commercial mAbs against H5 HA used as controls in the HI assays. Table S3. Results of the HI assay with H5N2 LPAIV. Table S4. Results of the HI assay with H5N3 LPAIV.Additional file 3. Development and optimization of H5 EB-ELISA. Fig. S1. The ELISA titration curves of G-7-27-18 mAb against rH5-BEVS coated on the well surfaces with varied hydrophilicity. Titration curves were denoted according to the plate type. Fig. S2. The ELISA titration curves of G-7-27-18 mAb against rH5-BEVS under preliminary assay conditions. rH5-BEVS was coated at (A) 1.0 μg/mL or (B) 0.5 μg/mL on MediSorp plates. Titration curves were denoted according to the coating concentration of rH5-BEVS in μg per mL (1 μg or 0.5 μg) and then the time of plate incubation with G-7-27-18 mAb (60 min or 30 min), anti-mouse antibodies (60 min or 30 min) at the indicated dilution  and TMB (15 min or 10 min). Fig. S3. The ELISA titration curves of G-7-27-18 mAb against rH5-BEVS under assay conditions optimized in step 1. Titration curves were denoted according to the coating concentration of rH5-BEVS in μg per mL (0.5 μg) and then the time of plate incubation with G-7-27-18 mAb (60 min or 30 min), anti-mouse antibodies (60 min) at the indicated dilutions  and TMB (15 min). Fig. S4. The ELISA titration curves of G-7-27-18 mAb against rH5-BEVS under assay conditions optimized in step 2. Titration curves were denoted according to the coating concentration of rH5-BEVS in μg per mL (0.5 μg) and then the time of plate incubation with G-7-27-18 mAb (60 min or 30 min), anti-mouse antibodies (60 min) at the indicated dilutions  and TMB (15 min). Fig. S5. The ELISA titration curves of G-7-27-18 mAb against rH5-BEVS under assay conditions optimized in step 3. Titration curves were denoted according to the coating concentration of rH5-BEVS in μg per mL (0.5 μg) and then the time of plate incubation with G-7-27-18 mAb (60 min), anti-mouse antibodies (60 min) at the indicated dilutions  and TMB (15 min).Additional file 4. Sensitivity of H5 EB-ELISA relative to the commercial FluAC H5 test. Table S1. Detection of H5 subtype-specific antibodies in the reference antisera. Table S2. Detection of H5 subtype-specific antibodies in the experimental antisera.


Conservation of local horse breeds as part of animal genetic resources is of national and global importance. Monitoring of local breeds is often fragmentary, i.e., it involves analysis of pedigrees, phenotype, and genetic structure. Using the Croatian Posavina horse as an example, we analyzed the status of the population with regard to available pedigree information, phenotype measures of stallions and mares, and genetic structure based on microsatellites. The generation interval is about eight years, indicating relatively early involvement of animals in reproduction for economic use of the breed. The depth of the pedigree is relatively modest due to a relatively short period of systematic breeding work (two decades). The number of active sire-lines and mare-lines is favorable and forms a good basis for the preservation of the breed. Regarding conformation, the Croatian Posavina horse kept the recognizability of the small-sized horse breed in the coldblooded type, in which there are clear traces of the earlier controlled introduction of the Arabian and other breeds. Its genetic diversity component has been preserved. The above results are a guide for further implementation of effective programs for the conservation of endangered local horse breeds.The Croatian Posavina horse (CPH) is native Croatian breed under a conservation program and under various programs of economic use . The aim of this study was to analyze the status of the CPH population through an analysis of their pedigree , phenotype , and genetic structure . The average generation interval was 8.20 years, and the number of complete generations was 1.66. The effective number of founders and ancestors was 138 and 107, respectively, with a ratio of 1.29, and the genetic conservation index was 4.46. As for the morphometric characteristics, the average withers height of the stallions was 142.79 cm, the chest circumference was 194.28 cm, and the cannon bone circumference was 22.34. In mares, the withers height, chest, and cannon bone circumference were lower . Genetic microsatellite analysis of the 29 sire-lines showed high genetic diversity, expressed as the mean allele number (7.7), allele richness (4.0), and expected heterozygosity (0.740). There was no evidence of high inbreeding or a genetic bottleneck. The genetic and phenotypic data indicate that the CPH is an important and diverse reservoir of genetic diversity and can be conserved because of its special characteristics (adaptability). Horse breeds are undoubtedly an important part of the mosaic of national, regional, and global genetic resources. In the 19th and 20th centuries, local horse breeds, especially of the coldblooded type, were subjected to extinction pressure due to the loss of their function in agriculture. During the 20th century, three local horse breeds, which represented the largest group of ponies , were lost in Croatia. Of the total number of breeds of mammalian species used for agriculture in the world, horse breeds account for 14.96% [One such breed is the Croatian Posavina Horse (CPH), whose primary genetic base was the archaic horse of areas of the Sava River Basin see . For cenPreserving breeds is a prerequisite for maintaining genetic diversity and relationships within and between populations . If the Therefore, the main objective of this study was a complete evaluation of the status and structure of the breed, associated trends and risks—with special emphasis on pedigree—and its phenotype characteristics and genetic diversity.The Studbook data of the registered CPH up to 2020 were analyzed. The genealogical information was traced back to the founder animals, some of which were born in 1975. The created database consists of the pedigree information for 28,483 horses. For each animal in the database, information such as the UELN, name of the individual, date of birth, sex, and pedigree information were collected. The reference populations consist of those animals that are potentially contributing to the next generation. In this study, animals with both parents known were chosen as the first reference population (REF 01) and active (living) horses of both sexes were selected as the second reference population (REF 02).Pedigree analysis was conducted using the ENDOG v4.8 software . SeveralF), mean average relatedness coefficient (AR), effective population size (Ne)), probability of gene origin (effective number of founders (ef), effective number of ancestors (af), and effective number of founder herds (hf)), and genetic contributions of the founders and ancestors (genetic conservation index (GCI) and generation interval (GI)) were assessed. The ratio between the effective number of founders (ef) and effective number ancestors (af) as an indicator of a population bottleneck were calculated according to Boichard et al. [GI) were calculated as the average age of the parents at the birth of the progeny subsequently used for reproduction, as well as the average age of the parents of all foals [t-tests. The average relatedness coefficients and individual inbreeding coefficients were calculated based on Gutiérrez and Goyache [Parameters related to inbreeding . The current breeding program  prescribHair root samples were collected from the mane of 292 stallions recorded in the CPH Studbook. These stallions originate from all 29 active sire-lines and the number of stallions per line varied from 1 to 57. Isolation of DNA and genotyping were performed in an authorized laboratory. A total of fifteen microsatellite markers  were used, as recommended by the International Society for Animal Genetics (ISAG) for paternity confirmation.AN) and effective (AEN) number of alleles, unbiased estimates for observed (OH) and expected (EH) heterozygosity, Shannon’s Information index (I), and migration rate (Nm) were calculated using GenAlEx version 6.5 [PIC) of each locus and null allele frequency ((null)F) were quantified using Cervus software version 3.0.7. [HWE) using the Markov chain method  and to test the linkage disequilibrium (LD), to determine the extent of distortion from independent segregation of loci, the GenePop 4.3 package was used [ISF) according to Weir and Cockerham [FCA) as implemented by Genetix 4.05 [Allele frequencies, average , while in the third generation the completeness dropped to 80.76% compared to the REF 01 population . Beyond the seventh parenteral generation the completeness was close to zero.p < 0.01, p < 0.01).The estimates of the average age of the parents at the birth of their offspring were generally higher in the maternal pathway  compared to the paternal pathway (ef) and ancestors (af) was 138 and 107, respectively  in the total population was 0.85%, ranging from a minimum of 0.11%  to a maximum of 0.90% (2009) (AR) coefficient in the whole population was 1.08%. The effective population size calculated via individual increase in inbreeding is 170.89 and via regression on equivalent generations is 240.12.The average inbreeding (% (2009) . In the % (2009) . The meaGCI) was 4.46 ± 2.42 and ranged from 0.79 to 16.70. The mean value of the GCI in REF 01 was lower compared to the REF 02 population (4.86 ± 2.26 vs. 5.83 ± 2.46). The GCI calculated for sex was 4.32 ± 2.45 in females and 4.62 ± 2.38 in males.The mean value of the genetic conservation index , which consisted from 29 sire-lines and 152 mare-lines . Mean co-ancestry within sire-lines was 0.056 and the Nei distance between the sire-lines was 0.048. The parameters of the F statistic related to the sire-lines were ISF = −0.049, STF = 0.049, and ITF = 0.002. The mean co-ancestry within mare-lines was 0.135 and the Nei distance between the mare-lines was 0.127. The parameters of the F statistic related to the mare-lines were ISF = −0.144, STF = 0.1281, and ITF = 0.002.The Wright p < 0.01).Regarding the basic morphometric measurements, it was found that the average WH of the stallions was 142.79 ± 3.41 cm, while the mares were 3.08 cm lower (139.71 ± 2.95 cm). The average CC of the stallions was 194.28 ± 7.63 cm, while the average CC of the mares was 3.98 cm lower (190.30 ± 7.29 cm). The average CB of the stallions was 22.34 ± 0.99 cm and is 1.40 cm higher than that of the mares (20.94 ± 0.96 cm). The differences in body measurements between the stallions and mares were significant  required for their suitability for genetic studies, ranging from 0.630 (HMS7) to 0.822 (ASB17), while only one locus (HTG6) was reasonably informative (PIC = 0.301). The mean value for the PIC across all loci was 0.697. The estimated null allele frequency ((null)F) varied slightly between negative (−0.040 AHT4) and positive (0.024 HTG7) values, which were close to zero and assuming the absence of a null allele. Therefore, all 14 microsatellites were retained for the genetic analysis. However, it is possible that the null allele frequency is underestimated if the sample size is small; therefore, the number of 292 individuals of the CPH is sufficient for accurate estimation of the genetic parameters [Thirteen loci (93%) had rameters .OH) was 0.730 and expected heterozygosity (EH) averaged 0.731. All loci examined were in Hardy–Weinberg equilibrium  to a maximum of 13 alleles ASB17, . The aveilibrium . The meailibrium .ISF) was calculated based on EH versus OH and ranged from −0.076 (AHT4) to 0.046 (HTG7). The mean inbreeding value of −0.015 was not significant and did not confirm the evidence of homozygote excess in CPH stallions. Two loci of AHT4 showed a significant (p < 0.05) excess in heterozygotes of 7.6% and 5.4% . With the exception of five individuals that were dislocated from the rest of the population, the other individuals were evenly scattered along the two dimensions of the FCA and the lines overlapped with no indication of grouping. The results show no clear separation and indicate that the CPH stallions studied are genetically heterogeneous.An STF value of 0.02. The high number of migrants (Nm) of 12.4 influenced such a low differentiation. In addition, the low genetic variation due to inter-line differences (2%) is in agreement with the above, while differences within individuals accounted for the remaining 98% of the variation. The overall pairwise relatedness was low (−0.008) among the 252 individuals from the 13 sire-lines. This indicates that members were either unrelated or very distantly related. Pairwise relatedness values above 0.50 (expected of a parent/full sibling) were detected once, values from 0.25 to 0.50  were rare (0.25%), and pairwise relatedness with values below 0.25 was in the majority of cases (99.75%).To gain more insight into the relationships within lines from the 29 sire-lines, we left in the analysis those that were represented by five or more genotyped individuals. Thus, of the 29 sire-lines, 13 remained, with a total of 252 individuals (range per line was from 5 to 57). Genetic differentiation among the 13 most representative sire-lines was negligible, with an average p = 0.0158), suggesting that the CPH may have passed through a recent bottleneck. The second method, the mode-shift indicator test, detected normal L-shaped curves, in which the alleles with low frequencies were the most numerous and have a normal distribution [The Wilcoxon test gave a significant result , is a result of the lack of systematic selection for this breed in the past century. Local horse breeds in Croatia were not selectively built up in the 20th century, and at the end of the 20th century, the Studbook was established, which is the basis for the selection and preservation of the breed. Horse breeds that have a long breeding tradition have a bigger pedigree depth and pedigree completeness. For example, Zechner et al.  for the GI: 10.1 years for the Andalusian [GI is a consequence of the breed’s economic use for meat production , with early initiation into reproduction (three to four years) and regular fertility. Reproductive vitality of the population is desirable in local endangered breed conservation programs.Generation intervals are important factors of population management measures . The GI dalusian , 10.65 ydalusian , and 12.dalusian . In the ef value was observed in the Slovak, Polish, and Hungarian Hucul horse  [ef/af) in the CPH population was 1.29, which is more favorable compared to the Austrian Noriker (4.00) [ef/af ratio in the Slovak, Polish, and Hungarian Hucul horse was 1.6, 2.5, and 1.4, respectively [ef/af ratio reveals a disproportionate use of some breeding animals, presumably stallions, resulting in a loss of genetic diversity compared to that expected under random mating conditions [The number of founders in the CPH was 1486 while the effective number of founders was 138 or 9.29%. A lower ctively) ,14,44; Actively) ; Maremmactively) ; Lipizzactively) ; Americactively) ; and Ausctively) . The effctively) . The botr (4.00) ; Americar (4.00) ; Silesiar (4.00) ; and Marr (4.00) . The obsectively ,14,44. Tnditions . AlthougF values are reported in the case of closed Studbooks and sport-oriented horses, e.g., 8.5% in Andalusian horses [F is followed by a low AR (1.08), indicating a reduced representation of each individual in the whole population. This suggests that the CPH population is large in population size, that the stallions used for reproduction have a limited number of services within their own herd (controlled by the breeder), and that the interchange with other herds is frequent so there will be no difficulties trying to avoid mattings between relatives. Horses with the lowest AR, used as stallions and mares, can reduce inbreeding and balance the gene contributions of the founders in the population and thus the genetic variability [The level and trend of the inbreeding coefficient in the population are crucial for maintaining the genetic diversity of the breed. The inbreeding coefficient in the CPH population is 0.85% and is lower than in some other coldblooded horse populations, such as the Norik, Silesian Norik, and Czech-Moravian Belgian, with 2.00%, 4.60%, and 4.00% , and then horses . The obsiability . High vaGCI in the CPH population  was relatively low (4.46) and slightly higher (5.83) in the living REF 02 population. In general, the trend in GCI in the CPH population increased over time. Giontella et al. [GCI of 5.55 in the local Italian Maremmano horse breed. The GCI value of the actual breeding population of the Hucul horse in Hungary was 15.1, increasing from 8.9 (population born before 1990) to 15.3 (population born between 2010 and 2019) [GCI values exhibit a greater balance in the number of founders and it is assumed that it contains the genes transmitted by the founders. From this point of view, animals with a higher GCI value are crucial for breed conservation and the genetic variability they possess.The a et al.  reportednd 2019) . In geneNe = 438.71), all 29 sire-lines and 174 mare-lines should be maintained because, with careful mating management, they offer the possibility of sustainability of the genetic diversity. The size and diversity of the sire-lines and mare-lines should be balanced.It is important to emphasize that if the effective population size is favorable (The basic morphometric measurements of the CPH from this study compared to previous study of Ivanković and Caput  remain lp < 0.01) in body measurements between stallions and mares were expected due to sexual dimorphism.These morphometric measurements are in the group of “A—desirable” breeding standards; therefore, the studied stallions are a good example of representatively chosen lines for further breeding according to the Breeding Program for Croatian Posavina Horse . To be mA characteristic of the CPH that is particularly valued by breeders is its compactness and adaptability to the environment due to a smaller body conformation compared to coldblooded horse breeds. The experience of breeders is that the compact conformation accommodates the lower maintenance requirement of horses during feeding (compared to coldblooded horse breeds), but also leads to a favorable conformation of the carcass and the ratio of tissues in the carcass. The CPH is phenotypically different from the other two autochthonous  Croatian horse breeds (Croatian Coldblood horse and Murinsulaner horse). For example, the CPH is smaller in withers height  compared to the Croatian Coldblood horse . In addiPhenotypic measures, as well as favorable interior qualities  are often neglected in breeding conservation management, although they are a fundamental and immediate tool for the implementation of the desired selection measures, especially by breeders. Indeed, the breeders do not only care for of the genetic purity and herd structure, but they also improve the economic potential of animals through, e.g., meat production. According to a principle of the conservation of genetic resources, “we use it or we lose it”, breeding consolidation should include and weigh all sources of information to find the optimal model of “maximum conservation of genetic potential with maximum income from local breeds”. Maintaining the good interior qualities of the horse that makes it suitable for work in agriculture and agrotourism as well as for other uses  is another approach to CPH conservation.PIC values of the 13 microsatellite loci (93%) used in the study showed that the markers were suitable for genetic evaluation of CPH. The reason for the lower polymorphism of the HTG6 marker was the high frequency of the allele O (f = 0.83) and the high percentage of homozygous individuals (68%) of the total genotyped stallions. Similar results were obtained by Vostrá Vydrová et al. [PIC value of 0.674.The high á et al.  for threrel(%)I [AN = 5.5–6.33) [AN = 5.13–6.47) [AN, OH, and EH) of the CPH reported 10 years ago [AN was lower, OH and EH are close to the results of this study, indicating preserved genetic diversity of the CPH stallions in recent times. Furthermore, the CPH’s genetic diversity for the common loci was higher compared to five draught horse breeds from Germany (EH < 0.705) [EH < 0.680), but similar to the Noriker (EH = 0.714) [OH and EH in Lithuanian Heavy Draught horses , but a lower total (77) and mean number of alleles (6.4) using 16 microsatellites. The breeding range of the CPH has always been over a large geographical area of Posavina  , which w.5–6.33) ; four dr13–6.47) ; and two13–6.47) . The genears ago  were 5.9< 0.705)  and two = 0.714) . On the = 0.714)  reportedvina see , and theISF value for the CPH was less than zero, indicating heterozygote excess, which did not cause a deviation from HWE  [FCA plot (data not shown) further supports this fact showing that only 6% of the total genetic variation is explained by the first two axes, with no differentiation between individuals according to sire-lines. Inter-line differentiation was particularly low (2%) compared to the values reported by Vostrý et al. [IS(13linesF) = −0.093). This is supported by the absence of inbreeding in the stallion-lines according to the pedigree analysis (ISF = −0.049). Within mare-lines there were a high excess of heterozygotes (14.4%), a high Nei’s genetic distance, and differentiation between lines, as a result of the numerous lines present and the lower number of offspring from the maternal line. Third- and higher-order individuals related to each other indicate that the CPH population is largely composed of multi-generational family units, due to the likely broad genetic base.The from HWE . In prevo 0.021) ,51,52. So 0.021)  for threo 0.021)  in conveo 0.021) . The FCAý et al.  for the AN = 7.9) and high genetic variability (EH = 0.731) within the CPH population, strong evidence of a recent population bottleneck was not confirmed. Although the CPH population experienced a demographic decline in population size [In this study, due to the high number of alleles . The results of the study show that the preserved structure of the population and its genetic diversity justify the closure of the Studbook. Profiling of a lighter riding horse within the CPH population is justified after the interest of the breeders has been expressed and the CPH no longer has an endangered status. In addition, this decision should take into account the indicators of their phenotypic and genetic structure and evaluate the economic impact after the change in breeding objectives.Population heterogeneity of the CPH is desirable because it preserves the necessary genetic variability and breeding survival rates in an uncertain future. However, from the perspective of breeding and selection, it is necessary for the lines to differ to some extent while maintaining breed characteristics. Even in the past, two types of CPH horses were mentioned . HoweverA complex study of the pedigree, phenotype, and genetic structure of the breed reveals the need for a comprehensive approach to dynamic population monitoring of the Croatia Posavina horse. Namely, breeders of the Croatian Posavina Horse monitor the horse’s phenotype and primarily select the best individuals for future mattings and, based on that, for breeding, while the breeding association and the scientific institutions involved provide additional guidelines based on the pedigree and genetic analysis of the population. The results of the present study show that the Croatian Posavina horse has valuable amount of genetic variability with no signs of inbreeding due to the large gene pool. There was no evidence of clustering and phenotypic or genetic differentiation in the population of the Croatian Posavina horse. We can also conclude that the Croatian Posavina horse has maintained its phenotypic recognizability, compactness, and has a favorable number of lines of stallions and mares. This breed, with the appropriate breeding management (and economic reaffirmation), can maintain the existing genetic variability and, with the size of the population and structure, leave the category of an endangered breed. With these results, comprehensive information will be obtained for further breeding management and conservation of the breed.


Olearia pannosa is a plant species listed as vulnerable in Australia. Two subspecies are currently recognised (O. pannosa subsp. pannosa (silver daisy) and O. pannosa subsp. cardiophylla (velvet daisy)), which have overlapping ranges but distinct leaf shape. Remnant populations face threats from habitat fragmentation and climate change. We analysed range-wide genomic data and leaf shape variation to assess population diversity and divergence and to inform conservation management strategies. We detected three distinct genetic groupings and a likely cryptic species. Samples identified as O. pannosa subsp. cardiophylla from the Flinders Ranges in South Australia were genetically distinct from all other samples and likely form a separate, range-restricted species. Remaining samples formed two genetic clusters, which aligned with leaf shape differences but not fully with current subspecies classifications. Levels of genetic diversity and inbreeding differed between the three genetic groups, suggesting each requires a separate management strategy. Additionally, we tested for associations between genetic and environmental variation and carried out habitat suitability modelling for O. pannosa subsp. pannosa populations. We found mean annual maximum temperature explained a significant proportion of genomic variance. Habitat suitability modelling identified mean summer maximum temperature, precipitation seasonality and mean annual rainfall as constraints on the distribution of O. pannosa subsp. pannosa, highlighting increasing aridity as a threat for populations located near suitability thresholds. Our results suggest maximum temperature is an important agent of selection on O. pannosa subsp. pannosa and should be considered in conservation strategies. We recommend taxonomic revision of O. pannosa and provide conservation management recommendations. Populations of threatened plant species are typically small and fragmented, suffering low genetic diversity associated with increased genetic drift and elevated inbreeding rates ,2,3,4,5.For plants, natural dispersal generally occurs via distribution of seed and pollen. For threatened plant species, however, undirected natural dispersal is likely insufficient to ensure persistence of, and gene flow among, populations . In thesOlearia (Moench) is widespread across Australasia and New Guinea and there are roughly 130 species of Olearia in Australia, making it the country’s largest Asteraceae genus [Olearia pannosa (Hook) is a long-lived understory daisy shrub, endemic to southeastern Australia. It generally occurs in small and fragmented stands in areas that have been converted to agriculture over the last 150 years and is classed as nationally vulnerable [The genus ae genus . Howeverae genus ,32,33,34lnerable . Olearia pannosa has undergone several taxonomic revisions [O. pannosa subsp. pannosa (Cook), which is widespread across southern South Australia and has small, narrow leaves; and O. pannosa subsp. cardiophylla (F. Muell) which has larger, more heart-shaped leaves and is only found in isolated stands in the Flinders Ranges of South Australia (where it overlaps with O. pannosa subsp. pannosa) and in several isolated populations in Victoria  which spreads by producing root suckers [O. pannosa subsp. pannosa (silver daisy bush), and O. pannosa subsp. cardiophylla (velvet daisy bush). O. pannosa subsp. pannosa is listed as vulnerable under the Australian Government’s Environment Protection and Biodiversity Conservation Act 1999 [O. pannosa subsp. cardiophylla is also of conservation concern and has been listed as vulnerable under the Victorian Advisory List of Rare or Threatened Plants in Victoria—2014 [ suckers . The speAct 1999 ; and vulAct 1999 . O. pannria—2014 .Leaf samples were collected from a total of 191 individuals from 25 localities spanning the ranges of both subspecies . Each saAll DNA extraction, library preparation and sequencing were conducted at the Australian Genome Research Facility . The Machery-Nagel Nucleospin Plant II Kit was used to extract DNA prior to undergoing double digest restriction-associated DNA sequencing (ddRADseq) . The resO. pannosa samples and we processed these using the STACKS pipeline [A total of 637,395,304 sequences were generated across 192 pipeline ,41 at AG2 > 0.8) being removed from the dataset. We used VCFtools  to filteLevels of ploidy were assessed by examining the distribution of allele dosage at heterozygous sites. For this, the counts of reads supporting each allele at a heterozygous site were extracted from the VCF file using a custom Perl script, and each individual’s distribution was plotted. True allelic proportions for diploids, triploids and tetraploids at heterozygous sites are 1:1 (0.5), 1:2 or 2:1 (0.33 or 0.66), and 1:3, 2:2, or 3:1 , respectively. No sample deviated from a normal distribution centred on a frequency of 0.5, suggesting that all samples are diploid.F) values were estimated using the G-stats calculator in GENODIVE v. 3.2 [F indicates greater than expected heterozygosity under Hardy-Weinberg equilibrium, and could be indicative of paralogous reads being stacked together in the STACKS pipeline [F .For each SNP across all samples, inbreeding coefficient , we used two different genetic clustering analyses. First, we used the non-model-based Discriminant Analysis of Principal Components (DAPC) of the R package adegenet : genetice StamPP . This wae StamPP . O. pannosa is known to reproduce clonally. We then used PLINK 2 [The dataset was filtered for all first-degree relations (parent-siblings and sibling-siblings) following methods outlined in Dutheil  to avoid PLINK 2  to calcu PLINK 2  to removF), and observed (HO) and expected (HE) heterozygosity in GENODIVE v3.2 [O and HE using R v.4.0.4 [O. pannosa subsp. pannosa and O. pannosa subsp. cardiophylla ) to compare how genetic variation was distributed within individuals and among collection localities between these two genetic groups.For the collection localities in each of the three genetic groups, we separately estimated the inbreeding coefficient  [In order to investigate the environmental influence on leaf shape and the variation that exists between collection localities, leaf traits were identified and analysed. In total 110 individuals were measured from across 22 collection localities . Leaf traits were based on the methods of Lowe and Abbott  and measer 2020)  from phoWe estimated leaf ovality or roundness by dividing the length along leaf mid rib to widest point of the leaf by total leaf length. A value around 0.5 indicates an oval leaf, <0.5 indicates a more heart-shaped leaf, and >0.5 a ‘gingko’ shaped leaf. The ratio of leaf length to leaf width , was also determined. Where possible, five leaves were measured from each voucher specimen, with five individuals sampled from collection locality. A standardised leaf sampling protocol was conducted by sampling the first mature leaf from the base of the specimen, with subsequent leaves sampled working towards the apex. In some circumstances direct leaf measurement was not possible due to folding or damage, in which case width was calculated by measuring distance from leaf edge (at widest point) to leaf midrib and multiplying by two.A principal component analysis (PCA) was performed using the ‘prcomp’ function in R v.4.0.4  on the mO. pannosa subsp. pannosa samples. We focused on this genetic group as they were distributed over a wide environmental gradient and by omitting O. pannosa subsp cardiophylla samples confounding genetic structure can be removed from the analysis. A set of 44 environmental  variables were sourced from stacks of 9-s resolution  rasters [O. pannosa subsp. pannosa using the RASTER analytical package in R [To assess the degree to which the environment explains observed genetic and phenotypic variation, partial redundancy analyses (RDA) were performed for  rasters ,59,60. Eage in R . A subseage in R . They weO. pannosa subsp. pannosa and (b) leaf trait measurements for all samples of O. pannosa subsp. pannosa in order to assess how much of the genetic and phenotypic variation among our collection localities can be explained by spatial  and environmental  factors -like permutation test for RDA with the ‘anova.cca’ function. In each case, we tested whether any of the genetic or phenotypic variance was significantly explained by environmental variables by running an RDA on the models (a) ‘allele frequencies-environmental variables conditioned on spatial variables’ and (b) ‘leaf traits-environmental variables conditioned on spatial variables’. To assess which of the environmental variables provided the most explanatory power in the models, we performed the same stepwise model building described above for the spatial variables on the environmental variables.Redundancy analyses (RDA) were performed on (a) the allele frequencies of all SNPs in collection localities of  factors  using thO. pannosa subsp. pannosa, MaxEnt and Random Forest were used to assess which environmental variables best explain habitat suitability given the species’ current distribution. Occurrence data were sourced from collection locality sampling records, consisting of 105 locations. We modelled current distribution using the BCCVL online tool ; Ref. [For the largest genetic group, 1); Ref.  by stati1); Ref. .Distributions were predicted based on current climate and soil using MaxEnt and Random Forest, which are widely used machine-learning algorithms for mapping habitat suitability ,66. ModeO. pannosa and was supported by all genetic analyses  in Strong genetic structure was identified across the range of analyses . While tanalyses  and CV eanalyses  both indsolation . This waO. pannosa subsp. cardiophylla (Olearia (FR).The southern Flinders Ranges samples identified by the State Herbarium of South Australia as Olearia (FR) specimens collected at Dutchman’s Stern (DUT) and Melrose (MEL)) were mainly identified as O. pannosa subsp. pannosa  on the Eyre Peninsula to Victor Harbor (VIC) on the Fleurieu Peninsula ). From hereon we will call this genetic group O. pannosa subsp. cardiophylla.All other samples ranging from the NEW (Newland Head) collection locality on the Fleurieu Peninsula across to the Victorian collection localities were identified as STF) showed a considerable range of differentiation between collection localities  group (DUT & MEL_1), there was a moderately high level of genetic differentiation (STF = 0.23). Within O. pannosa subsp. pannosa, apart from collection localities on the Eyre Peninsula, genetic differentiation between collection localities was generally low (STF = 0.00–0.13). On the Eyre Peninsula, however, collection localities displayed a greater range of differentiation (STF = 0.05–0.30) reflecting their more isolated locations. There was considerable variation in the levels of genetic differentiation between collection localities in O. pannosa subsp. cardiophylla (STF = 0.12–0.68). Notably, the small and isolated Kangaroo Island collection locality at Kingscote (KIN), had disproportionately higher levels of STF compared to all other collection localities (STF = 0.36–0.68). Collection localities within the Fleurieu Peninsula and southeast SA, show moderate differentiation (STF = 0.17–0.22) but are highly differentiated from the geographically distant and isolated Victorian collection localities (STF = 0.36–0.59). The collection localities within the Victorian region showed moderate genetic differentiation among Anglesea (ANG) and Brisbane Ranges (BR) (STF = 0.20) but high when compared to the most isolated and northern Victorian locality Rushworth (RUS) (STF = 0.46 and 0.42 respectively).Across the whole dataset, our analysis of pairwise genetic differentiation  and only 11.2% between collection localities  was consistently lower than expected heterozygosity  were found in all localities , genetic diversity was low  samples collected at Melrose (MEL_1) had highest inbreeding (F = 0.332) but lower levels of kinship (kinship = −0.388). Within O. pannosa subsp. pannosa, levels of genetic diversity  and observed heterozygosity (HO) increasing the closer collection localities are to the Fleurieu Peninsula in southeast South Australia, align with increased levels of admixture detected in this region  and Keith (KEI) had the highest levels of genetic diversity and inbreeding , Kangaroo Island (KIN), and Anglesea (ANG). Within s region . Within O), expected heterozygosity (HE), inbreeding (F), and kinship, revealed significant differences for O. pannosa subsp. pannosa and O. pannosa subsp. cardiophylla  compared to O. pannosa subsp. pannosa  and O. pannosa subsp. cardiophylla was inbreeding coefficient (F) in Olearia (FR)  explained 25.6% of the variance and mainly explained variation in leaf ovality and location of the widest point of the leaf. On this axis, the majority of Olearia (FR) samples were separated from O. pannosa subsp. cardiophylla and O. pannosa subsp. pannosa samples due to their wider, more heart-shaped leaves. For PC1 , our ANOVA analysis found significant differences between O. pannosa subsp. pannosa and Olearia (FR)/O. pannosa subsp. cardiophylla   and mean summer temperature maxima <29 °C . For MaxEnt, precipitation seasonality had the highest variable relative contribution followed by mean maximum temperature of the warmest month. For random forest, mean maximum temperature of the warmest month had the highest variable importance followed by mean annual rainfall.Final models were fitted with two edaphic , two temperature predictors , and three rainfall  predictors . DistribOlearia pannosa, unearthed a complex picture that is not in agreement with the current subspecific classifications for the species. A key finding of our study was the identification of a likely cryptic species Olearia (FR) in the Flinders Ranges which exists only in small, fragmented collection localities. For the two subspecies, O. pannosa subsp. pannosa and O. pannosa subsp. cardiophylla, we identified a strong east/west genetic divide with signatures of admixture between the two subspecies and substructure within them. Genetic diversity was reduced in Olearia (FR) compared to O. pannosa subsp. pannosa, and was substantially lower in the small, isolated localities of O. pannosa subsp. cardiophylla. Our analyses of associations between environmental and genetic and phenotypic variation identified a strong association with maximum temperature. This finding was further supported by habitat suitability modelling, which identified maximum temperature and precipitation as key variables in determining species’ distribution. Our results have implications for the conservation management for O. pannosa, particularly for the newly identified genetic group Olearia (FR) in the Flinders Ranges.Our analysis of genomic and leaf shape variation in the threatened daisy bush, O. pannosa plants found in the southern Flinders Ranges were thought to be the same subspecies as O. pannosa subsp. cardiophylla found in southeastern South Australia and Victoria. However, our analysis reveals that these southern Flinders Ranges plants (Olearia (FR)), sampled at Dutchman’s Stern (DUT) and Melrose (MEL), are not only strikingly distinct genetically from their neighbouring collection localities of O. pannosa subsp. pannosa but also from all other collection localities of O. pannosa subsp. cardiophylla in the southeastern South Australia and Victoria. This is in clear contrast to the current classification based on morphology, where O. pannosa in the Flinders Ranges is considered to be part of the same subspecies as O. pannosa subsp. cardiophylla found in Victoria. The apparent lack of gene flow among Olearia (FR) and neighbouring O. pannosa subsp. pannosa collection localities strongly suggests that there are reproductive barriers present between these two clusters and these collection localities likely represent two distinct species. Notably, both Olearia (FR) collection localities have high genetic differentiation between them, low genetic diversity, and high historical inbreeding. These findings are consistent with results from other recent genomic studies of threatened and endangered species [We found evidence of strong differentiation among collection localities, with clear support for three major genetic groups across all analyses and substructure with them. Previously,  species ,68,69. AO. pannosa subsp. pannosa or O. pannosa subsp. cardiophylla genetic groups, which are split by an east to west genetic divide. These two genetic groups differed significantly in levels of genetic diversity, inbreeding and kinship, with O. pannosa subsp. cardiophylla having lower levels of genetic diversity and inbreeding but higher levels of kinship. Interestingly, a self-compatibility test was undertaken at the Brisbane Ranges locality which found that individuals could self-pollinate and set seed, and inbreeding was flagged as a concern in O. pannosa subsp. cardiophylla [O. pannosa subsp. cardiophylla shows signs of population genetic decline indicated by the low genetic diversity and high kinship, and our findings consistent with the fact that the Victorian populations are now small and demographically isolated. There is also potential for elevated levels of clonality in these collection localities [The remaining samples could be assigned to either the iophylla . Certaincalities ,71, but O. pannosa subsp. pannosa group at Victor Harbor (VIC) to a collection locality with high assignment to the O. pannosa subsp. cardiophylla group at the closely located (~12 km) collection locality at Newland Head (NEW). Interestingly, Newland Head (NEW) had the variation in pairwise kinship between individuals within O. pannosa subsp. cardiophylla. Genetic admixture patterns were reflected in the leaf shape analysis, with intermediate morphologies described at these sites. Distribution records for both subspecies do not align with these findings, likely due to the difficulties with identification in these admixed sites, highlighting the intrinsic difficulty of defining subspecies boundaries. There was further substructure within O. pannosa subsp. cardiophylla, which is represented by several isolated and geographically distant collection localities. Our findings in Victoria are in agreement with the results of Smith, James and Ladiges [We observed genetic admixture on the southern Fleurieu Peninsula and southeastern SA, with a shift from a collection locality with high assignment to the  Ladiges  who alsoO. pannosa as gene flow will be limited, resulting in greater genetic drift. Therefore, the strong differentiation in isolated collection localities is unsurprising. Similar patterns have been found in other threatened species [Rutidosis leptorrhynchoides) found strong genetic differentiation between geographically isolated collection localities [Overall, the genetic structure results reflect the life history traits and disjunct range of  species ,68. For calities .O. pannosa subsp. pannosa genetic cluster had smaller, more oval shaped leaves compared to plants belonging to the Olearia (FR) and O. pannosa subsp. cardiophylla genetic groups, which we found to overlap more in size than shape. Previous taxonomic work on the species has classified subspecies by morphological traits and demonstrated clear differences between the leaf shapes of O. pannosa subsp. pannosa in South Australia and O. pannosa subsp. cardiophylla in Victoria [Olearia (FR) samples  clustered with O. pannosa subsp. cardiophylla samples from Anglesea (ANG), Victoria. Here, we show that whilst the leaf shape of Olearia (FR) is more similar to O. pannosa subsp. cardiophylla than to O. pannosa subsp. pannosa, there are differences between them, with Olearia (FR) having more heart-shaped leaves. Geographic distance between Olearia (FR) and O. pannosa subsp. cardiophylla is extremely large (~315 km between Melrose (MEL) and the closest O. pannosa subsp. cardiophylla locality at Newland Head (NEW)). We also show that genetic divergence is high between Olearia (FR) and all other O. pannosa collection localities, suggesting they may be a distinct species, although reproductive isolation is yet to be tested. Our leaf shape analysis showed that the three main genetic clusters display relatively distinct leaf morphologies. Specimens measured from the Victoria , similarO. pannosa subsp. pannosa and O. pannosa subsp. cardiophylla on the Fleurieu Peninsula and in parts of the Southeast of SA, indicating that gene flow is possible between these groups. If Olearia (FR) and O. pannosa subsp. pannosa were not reproductively isolated, then the strong signal of genetic structure we observe between such geographically close collection localities inhabiting very similar environments is difficult to explain. The advance in sequencing technologies in recent years has led to numerous studies that have unearthed previously unrecognised ‘cryptic species’ [Olearia genus [O. pannosa. For effective threatened species management plans, a firm understanding of likely species or subspecies boundaries is essential, and our results highlight the importance of combining both genetic and morphological results methods for this approach. We observed evidence for admixture among species’ ,77,78,79species’ . This spspecies’  and refeia genus ,31,32,33O. pannosa subsp. pannosa. These analyses identified maximum temperature as a key environmental variable that is a likely selective pressure on this group. Our analyses of genetic and leaf shape variation both identified maximum temperature as explaining a proportion of the measured variation. These findings are consistent with a number of studies that have found genetic signals [O. pannosa subsp. pannosa appears to be closely related to climate. Our climate suitability models indicated specific constraints with respect to mean annual rainfall and summer maximum temperature gradients, with the species occurring within a transition zone between the more mesic and arid zones of the surrounding region [O. pannosa subsp. pannosa sit at the warm/arid thresholds of these key climatic gradients beyond which modelled probability of occurrence drops off markedly. We conducted further analyses to identify the effect of the environment across the genotype, phenotype, and distribution of  signals ,83,84,85 signals ,88,89,90g region . Many coO. pannosa subsp. pannosa to the imminent effects of climate change and should be considered when planning conservation strategies for the species.Taken together these results suggest that the current climatic trends towards higher summer maxima and increasing aridity are likely to have a detrimental impact on many collection localities. However, the few localities in cooler, wetter habitats may, in fact, benefit in the short term from climate change, leading to shifts in distribution and abundance. Combined, these results highlight the potential vulnerability of Olearia pannosa (FR) had low levels of genetic diversity across both collection localities and the collection locality MEL (Melrose) had the highest level of inbreeding across the whole dataset. We also found moderately high genetic differentiation between the two collection localities Dutchman’s Stern (DUT) and Melrose (MEL). Concerningly, as there are only a few known stands of these individuals, all with small census estimates, this suggests an endangered species, or at least a subspecies which contains distinct genetic variation that will be lost if these they are not conserved. An immediate taxonomic review is essential to address these issues. We suggest a multi-level management strategy centred for this group. Firstly, we recommend in-situ threat abatement  and natural recruitment  [Olearia (FR) truly is a distinct, isolated species from O. pannosa and (b) what the functional genomic differences are between these divergent taxa.eedbank) . We alsoeedbank) . This coeedbank) . FurtherO. pannosa subsp. pannosa, there is evidence of some degree of historical gene flow across collection localities and low population substructure. This genetic group had higher levels of genetic diversity compared to O. pannosa subsp. cardiophylla. Observed heterozygosity was consistently lower than expected heterozygosity and was reflected in the significantly higher levels of historical inbreeding in O. pannosa subsp. pannosa than the other two genetic groups. Inbreeding has previously been flagged as a concern for O. pannosa [Within  pannosa  and histAs our results identify maximum temperature as potential agent of selection for this group, stands in more arid regions may experience detrimental effects from climate change. We recommend close monitoring, particularly of the more northern and arid localities. Apart from collection localities on the Eyre Peninsula, there is evidence of gene flow and higher levels of genetic diversity compared to other genetic groups. For these stands, we recommend a management approach which maintains connectivity and gene flow among localities. However, the potential future effect of aridification on this genetic group should be considered in future management strategies, and a climate-adjusted seed sourcing strategy  could beO. pannosa subsp. cardiophylla had the lowest levels of both historical inbreeding and genetic diversity, yet the highest levels of kinship. We also found that three of the four sites which had lowest genetic diversity and inbreeding in this group were the only collection sites that had to have highly related individuals removed across the whole dataset. There was considerable substructure in this genetic group, particularly between sites in Southeast SA and Victoria, and between sites within Victoria. It is likely that the large range disjunction of this group is driving genetic drift and genetic divergence. To develop an appropriate conservation management plan for O. pannosa subsp. cardiophylla, there are several knowledge gaps which still need to be addressed, particularly around the genetic divergence we observed between O. pannosa subsp. cardiophylla and the two other genetic groups, and the consequences that the high levels of substructure we found within this genetic group may have on mixing seed between localities . We suggest the establishment of a common garden trial which explores the effects on fitness and genetic diversity of genetic mixing between localities within this genetic group and amongst the three genetic groups. Until this information is known, in a scenario with high structure but low genetic diversity and inbreeding, Ottewell, Bickerton, Byrne, and Lowe [We found that and Lowe  suggestsand Lowe .Olearia pannosa revealed a likely cryptic species (Olearia (FR)) and identified the need to redefine the geographic intergrade between existing subspecific boundaries (O. pannosa subsp. cardiophylla and O. pannosa subsp. pannosa). Levels of genetic diversity and inbreeding differed between the three genetic groups, suggesting each requires a separate management strategy which we define below.Olearia (FR)–We suggest a management strategy centred around in-situ threat abatement and disturbance to encourage seedbank recovery, the recovery of genetic diversity through a translocation plan to encourage gene flow between the two isolated stands, and the development of a seed production orchard to supply the seed.O. pannosa subsp. pannosa—The main priority is to facilitate gene flow between existing stands or to increase connectivity between them, especially in an arid-to-mesic direction since it appears that maximum temperature is an important agent of selection.O. pannosa subsp. cardiophylla—It is likely that the large range disjunction of this group is driving genetic divergence. To develop an appropriate conservation management plan, several knowledge gaps still need to be addressed, particularly to assess the potential for outbreeding depression. Until then, we recommend priority is given to in-situ recovery of genetic diversity.In summary, combining range-wide genomic and leaf shape analysis of the threatened Australian daisy


The model of analysis was a ‘biological’ multivariate mixed model that included additive and dominance SNP effects. The estimates of the additive genotypic variance for the total number born (TNB) were 0.248, 0.282 and 0.546 for the Entrepelado, Retinto and Crossbred populations, respectively. The estimates of the dominance genotypic variances were 0.177, 0.172 and 0.262 for the Entrepelado, Retinto and Crossbred populations. The results for the number born alive (NBA) were similar. The genetic correlations between the purebred and crossbred performance for TNB and NBA—between the brackets—were 0.663 in the Entrepelado and 0.881 in Retinto poplulations. After backsolving to obtain estimates of the SNP effects, the additive genetic variance associated with genomic regions containing 30 SNPs was estimated, and we identified four genomic regions that each explained > 2% of the additive genetic variance in chromosomes (SSC) 6, 8 and 12: one region in SSC6, two regions in SSC8, and one region in SSC12.INGA FOOD S. A., as a Spanish company that produces and commercializes fattened pigs, has produced a hybrid Iberian sow called CASTÚA by crossing the Retinto and Entrepelado varieties. The selection of the parental populations is based on selection criteria calculated from purebred information, under the assumption that the genetic correlation between purebred and crossbred performance is high; however, these correlations can be less than one because of a GxE interaction or the presence of non-additive genetic effects. This study estimated the additive and dominance variances of the purebred and crossbred populations for litter size, and calculated the additive genetic correlations between the purebred and crossbred performances. The dataset consisted of 2030 litters from the Entrepelado population, 1977 litters from the Retinto population, and 1958 litters from the crossbred population. The individuals were genotyped with a GeneSeek Dehesa environment in southwestern Spain. In recent decades, however, many traditional production systems have been substituted with intensive production systems that use crossbreeding with Duroc populations to improve growth and efficiency [The Iberian pig breed is one of the porcine populations that has the highest meat quality . Historificiency . The norficiency  obligateficiency . The INGficiency . In addiTheoretically, the optimal strategy for the selection of purebreds for crossbred performance is Recurrent Reciprocal Selection ; howeverGenomic information facilitates the analysis of crossbreeding data, even if genotyped and phenotyped individuals are not directly related , by the This study estimated the additive and dominance genotypic variances and covariances, which were used to calculate the additive and dominance genetic variances and the genetic correlations between purebred and crossbred performances in the Retinto and Entrepelado populations. In addition, the distribution of the additive genetic variance within the autosomal genome for purebred and crossbred performance was quantified.The phenotypic data included the number of piglets born alive (NBA) and the total number born (TNB) for 306 Entrepelado and 313 Retinto purebred sows, and for 333 crossbred (Entrepelado x Retinto) sows when crossed with Entrepelado, Retinto or Duroc boars .® GPP Porcine 70K HDchip . Filtering excluded genotypes that had a minor allele frequency < 0.05 and an SNP call rate < 0.90 in the overall population. From that, 34,316 SNP markers were used to build the genomic relationship matrices with our own developed software in the R environment [All of the sows were genotyped with the GeneSeekironment . The misy) (TNB and NBA) are explained by the  additive (u) and dominance (v) effects of the SNPs, and a covariate (c) with the average homozygosity (f), the systematic effects (b) order of parity , the sire of service breed  and herd-year-season (122 levels). Phenotypic data were generated in three herds, and herd-year-season effects were defined every 3 months. The sow permanent environmental effects (s) with 2030, 1958 and 1977 levels for the Entrepelado, Retinto and Crossbred populations, and the residuals (e), were as follows:X and T are the corresponding incidence matrices. Following Vitezica et al. [u and v can be described in terms of the vectors of additive (a) and dominance (d) SNP genotypic effects as follows:The model of analysis assumed that the phenotypic values of individuals (Z = (1……zmz) and W = (1…….wmw) are equal to 1, 0, −1 and 0, 1, 0 for SNP genotypes A1A1, A1A2 and A2A2, respectively.The matrices u) and dominance (v) effects isThe covariance across individual genotypic additive  as follows:The variance components were estimated by REML  through software  and, in 1 and A2 at the ith SNP marker and the X = {E,R or ER} population, respectively. The estimates of the contributions to the additive variance in the crossbred population from the Entrepelado  and crossbred (X = ER) populations were obtained by:X = {E,R,ER} population.With these estimates, the heritabilities kth segment of the genome were calculated as follows:n(k) is the number of SNP markers within the kth segment, which was set to 30 after testing several number of the SNP markers . In order to identify the genes within the genomic regions that explained >2.0% of the total genetic variance, we used the biomart tool (www.ensembl.org (accessed on 10 October 2021)).With those, the genetic additive variances  and the crossbred population were all high and positive, and they corresponded to additive genotypic correlations of 0.704  variances were used to calculate the additive and dominance genetic variances in the purebred populations based on expressions (1) to 5) . The est . The esWe used Equations (6) and (7) to calculate the additive variances for crossbred performance in the purebred populations, which were 0.413 (Entrepelado) and 0.293 (Retinto). Therefore, the additive genetic variance in the crossbred population was the average of the two (0.353), which was higher than the additive genetic variances in the purebred populations, which were similar to the results of Vitezica et al.  with regIn addition, the additive genetic correlations between purebred and crossbred performances in the Entrepelado and Retinto populations were calculated based on expressions (12) to (15), which were 0.663 in Entrepelado and 0.881 in Retinto populations. Those correlations were within the range of the estimates summarized by Wientjes and Calus , and sugWe used Equations (16) and (17) to calculate the additive and dominance genotypic effects associated with each of the 34,316 SNP markers, which were used in Equations (18)–(21) to calculate the proportion of the additive genetic variance that was explained by segments of 30 consecutive SNPs . The disThe figure presents the distribution of the additive variance along the autosomal chromosomes in the Entrepelado and Retinto populations, and for the purebred and crossbred performance. Four genomic regions can be highlighted; each explained >2% of the additive genetic variance in at least one of the populations. The SNPs at the center of each of the genomic regions that explained the highest amount of additive genetic variance, and the genes in the Sus_Scrofa 11.1. genomic map that were within 1 Mb downstream or upstream, are presented in β-Carotene Oxygenase 1), which encodes an enzyme that catalyzes the breakdown of provitamin A and provides retinoids for embryogenesis [Glycine Cleavage System H) protein plays an important role in embryonic viability [Among those genes, several can be proposed as candidate genes to explain the additive genetic variation. The genomic region surrounding bp 7,597,405 in SSC6 included BCO1  is involved in the neurogenesis [(Fibroblast Growth Factor 5), a member of the fibroblast growth factor family that is involved in several biological processes, including embryonic development, cell growth, and morphogenesis [Two genomic regions were identified in SSC8 around bp 11,585,865 and bp 137,540,516. Among the genes within those regions, PRDM8  gene, which plays a role in spine morphogenesis [Nuclear Speckle Splicing Regulatory Protein 1) development process, and in utero embryonic development [Ankyrin Repeat Domain 1), which is involved in neuron projection development [The genomic region around bp 46,079,417 in SSC12 contains, among others, the GIT1  terms for the biological processes for the proposed candidate genes are presented as (1) The additive genetic variance and the heritabilities were higher in the crossbred than those in the purebred populations, (2) the genetic correlation between purebred and crossbred performances were higher in Retinto than they were in Entrepelado pigs, and (3) the additive genetic variances were heterogeneously distributed throughout the autosomal genome, and four genomic regions in SSC6, SSC8, and SSC12 with several candidate genes were identified.


Macruronus novaezelandiae). This species is significant to Māori people and supports the largest commercial fishery in New Zealand, but uncertainty about its stock structure presents a challenge for management. Here, we apply a comprehensive genomic analysis to shed light on the demographic structure of this species by (1) assembling the genome, (2) generating a catalogue of genome‐wide SNPs to infer the stock structure and (3) identifying regions of the genome under selection. The final genome assembly used short and long reads and is near complete, representing 93.8% of BUSCO genes, and consisting of 566 contigs totalling 501 Mb. Whole‐genome re‐sequencing of 510 hoki sampled from 14 locations around New Zealand and Australia, at a read depth greater than 10×, produced 227,490 filtered SNPs. Analyses of these SNPs were able to resolve the stock structure of hoki into two genetically and geographically distinct clusters, one including the Australian and the other one all New Zealand locations, indicating genetic exchange between these regions is limited. Location differences within New Zealand samples were much more subtle , and while small and significant differences could be detected, they did not conclusively identify additional substructures. Ten putative adaptive SNPs were detected within the New Zealand samples, but these also did not geographically partition the dataset further. Contemporary and historical Ne estimation suggest the current New Zealand population of hoki is large yet declining. Overall, our study provides the first genomic resources for hoki and provides detailed insights into the fine‐scale population genetic structure to inform the management of this species.The assessment of the genetic structuring of biodiversity is crucial for management and conservation. This is particularly critical for widely distributed and highly mobile deep‐water teleosts, such as hoki ( Hoki are widely distributed throughout New Zealand and Australian waters, being found in most abundant quantities in depths of 200–800 m  provided advice on a process to manage the gathering, storage, access and use of genetic data. We assembled a high‐quality de novo genome using a combination of short‐ and long‐read sequencing. To investigate the genomic stock structures, we sampled 12 locations in New Zealand and two locations in Australia  and performed whole genome sequencing at greater than 10× coverage to generate a powerful genome‐wide SNP dataset. This genome‐wide dataset was then used to assess the degrees of genetic diversity between all sampling sites to identify independent clusters, and how these are related to other sampling locations. The dataset was further used to test for population genetic differentiation using both neutral SNPs and putative adaptive SNPs. Our results are compared and discussed in the light of previous studies on hoki, and other teleost species.22.1Gathering genomic data requires strong relationships with indigenous peoples , and the aqueous phase was transferred to a new tube and precipitated with 2 volumes of 100% ethanol at room temperature. After ~5 min, the DNA was collected by centrifugation . The pellet was dissolved in 400 µl TE buffer  and 100 µl 5 M NaCl. RNA was digested by adding 8 µl of RNase A (50 mg/ml) and incubating at room temperature for 5 min. Twenty‐five microlitres of 10% SDS was added, and the sample was incubated in the thermomixer at 37°C at 300 rpm for 15 min. The sample was extracted with an equal volume of chloroform:isoamyl alcohol (24:1 v/v), mixing gently by hand for 5 min, and the phases were separated as described above. The DNA contained in the aqueous phase was precipitated with 0.7 volumes isopropanol, mixed, and incubated at room temperature for 10 min. The DNA was collected by centrifugation . The pellet was washed with 1 ml 70% ethanol and centrifuged again. The almost invisible pellet was dissolved in 75 µl TE buffer by letting it resuspend overnight at 4°C. The quality of the DNA was assessed by absorbance  and gel electrophoresis (average size 40 kbp) and quantified with a high sensitivity fluorescent‐based method.A single adult female hoki from the Cook Strait was sampled in May 2020  to generate DNA for the genome assembly. Five tissues were preserved in RNAlater following the manufacturer's instructions: brain, gills, liver, heart and white muscle. High‐quality DNA was extracted from preserved liver with a CTAB‐based extraction buffer as follows: 100 mg of tissue was homogenized in 1 ml CTAB buffer  with a sterile plastic pestle, on ice. After adding 25 µl Proteinase K (20 mg/ml) and 10 µl 1 M dithiothreitol, the mixture was incubated at 50°C for 18 h in a thermomixer 30 s on, 300 rpm, 30 min off. The sample was extracted once with an equal volume of chloroform:isoamyl alcohol (24:1 v/v), by gently mixing by hand for 5 min. The phases were separated by centrifugation .For the population genomics analysis, individual fish were sampled from commercial fishing vessels in New Zealand and Australian waters between May and September 2020 Figure . In tota2.3High molecular weight DNA was sent in July 2020 to the Australian Genome Research Facility (AGRF) for short‐ and long‐read sequencing. The DNA was quality checked by AGRF upon arrival, and passed quality thresholds. The TrueSeq DNA Nano kit (Illumina) was used for library preparation for the Illumina sequencing, and the Illumina NovaSeq 6000 was used for short‐read sequencing (150 bp PE reads). For long‐read sequencing, the Oxford Nanopore Technologies (ONT) technology was used. Three MinION FLO‐MIN106 flow cells were run in‐house and at AGRF, to verify that DNA could be sequenced with this technology, which produced approximately 3 Gb of sequencing data. Then, ONT libraries were prepared at AGRF using the SQK‐LSK109 kit and run using a PromethION FLO‐PRO002 flow cell, using minKNOW version 4.0.5 and base calling using Guppy 4.0.11, producing 7.57 M reads totalling 19.7 Gb of data (passed reads).Kmer analysis was performed using jellyfish v2.2.10 with kmer size of 21. The ONT reads generated in‐house (MinION) were base‐called using Guppy v4.2.2 with parameters ‘‐‐compress_fastq ‐‐input_path ‐‐save_path ‐‐flowcell FLO‐MIN106 –kit SQK‐LSK109 ‐x “cuda:0”’ and quality‐assessed using pycoqc v2.5.0.21. The long‐read assembly was generated using FLYE v2.8.1 with default parameters based on long reads generated from both in‐house (MinION) and at AGRF (MinION and PromethION). It was then subjected to two rounds of short‐read error‐correction using PILON v1.23 with parameters ‘‐‐genome FLYE.fasta ‐‐frags mapped.bam ‐‐output ‐‐outdir ‐‐changes ‐‐diploid ‐‐fix all ‐‐threads 60 ‐‐flank 5’. Assembly completeness was assessed using BUSCO v5.1.2 with parameters ‘‐l actinopterygii_odb10 ‐o test_long ‐m geno ‐‐augustus ‐‐augustus_species zebrafish ‐c 20 ‐‐long ‐‐out_path busco/’  short reads were generated at Neogen GeneSeek. In total, 510 fish samples were sequenced with a target read depth greater than 10×. A summary of the sequencing data is in Table 2.5FST statistics from ancestral allele frequencies that are estimated using the packages snmf function. Tess3 was implemented using the tess3r package in R v3.6.1 applying the following parameters: method = “projected.ls”, ploidy = 2, max.iteration = 5000, rep = 10, keep = “best”, tolerance = 1e−05. The R package pcadapt v4.3.3 was implemented using default settings and method = “mahalanobis”. The following parameters were used to run LEA's snmf function: genotype, K = 1:14, entropy = T, ploidy = 2, repetitions = 10, tolerance = 1e−05. The three analyses were conducted on two different datasets – the first contained all filtered SNPs and all hoki samples (510 samples); the second contained all filtered SNPs but only New Zealand samples (450 samples). A Q‐value of 0.05 was used as a threshold of statistical significance for all three analyses. Q‐values were calculated using the qvalue R package , observed (Ho) and expected (He) heterozygosity and Tajima's D statistics were calculated for each sampling site and for each of the six datasets using vcftools v0.1.14. Weir and Cockerham's  for each dataset before the DAPC analysis was run using this optimized K value, in addition to an optimized number of principal components (PCs). The snmf function in the R package LEA was used to explore the ancestral admixture , in addition to the following parameter settings: entropy = T, ploidy = 2, repetitions = 10, tolerance = 0.00001. For the DAPC and LEA analyses, individual analyses were run for each of the six datasets. Contemporary effective population size (Ne) was estimated for New Zealand samples using the linkage disequilibrium (LD) method in NeEstimator v2  all SNPs and all hoki samples, (2) all SNPs and New Zealand‐only samples, (3) neutral SNPs and all hoki samples, (4) neutral SNPs and New Zealand‐only samples, (5) adaptive SNPs and all hoki samples, and (6) adaptive SNPs and New Zealand‐only samples. Nucleotide diversity ; however, it was positive for the other (Tasmania A1) site. Pairwise FST was examined between all sampling sites, FST estimates were very low among New Zealand sites, ranging from 0.00027 (p < 0.001) to 0.00164 (p < 0.001) (mean = 0.00067), with pairwise FST between the two Australian sites being higher  , and when the FST genetic distances were examined as a heatmap, clear genetic differentiation between New Zealand and Australian samples was visible  within the dataset  of all hoki samples and all SNPs identified two clusters  within the dataset . Pairwise FST distance among New Zealand sites ranged from 0.00026 (p < 0.001) to 0.00163 (p < 0.001) (mean = 0.00067), and pairwise FST for the two Australian sites was 0.0024 (p < 0.001)  to 0.00158 (p < 0.001) (mean = 0.00062)  Table . Mean ob2) Table .Kmeans clustering, DAPC and LEA analysis results for both the neutral datasets  were very similar to the results of the complete dataset, with two clusters being identified in the dataset containing all sites , and one cluster in the dataset containing only New Zealand sites Figure . No clus3.4.3D ranged from −0.522 to −0.401 for New Zealand sites, and from 1.341 to 1.495 for the two Australian sites. The pairwise FST estimates among New Zealand sites ranged from 0.0000 (p = 0.83) to 0.00237 (p < 0.001) (mean = 0.00079), and pairwise FST between the two Australian sites was 0.0027 (p < 0.001) , and when the FST genetic distances were examined as a heatmap, the genetic differentiation between New Zealand and Australian samples remained very clear. In addition, there appeared to be some genetic differentiation occurring within the New Zealand samples, with FMA3 A3, FMA6 Snares and FMA6 Norgie forming their own subcluster within New Zealand; however, as there was poor support for many of these pairwise FST values, this should be interpreted cautiously  to 0.02358 (p = 0.16) (mean = 0.00517)  were very similar to the results of the complete and neutral datasets, with two clusters being identified in the dataset containing all sites , and one cluster in the dataset containing only New Zealand sites Figure .3.4.4Ne of 133,463.4  (MAF = 0.05). SNeP estimated historical Ne to be 228,681 999 generations ago (2997–7992 years ago), declining to an Ne of 42,816 189 generations ago (567–1512) years ago)  Table .4M. novaezelandiae). Results showed two clear clusters, one grouping both Australian sites in Tasmania, and another one grouping all New Zealand sites. Within New Zealand, genetic differentiation between sampling locations was weak, indicating reproductive mixing between locations and no clear differentiation between different life history stages (adults vs. juveniles) within New Zealand waters.Knowledge of the stock structure of important fisheries species is a key component that informs management plans. Marine fish populations typically consist of large numbers of individuals with high dispersal potential, which can result in high gene flow and weak population structure  with the dataset for only the New Zealand samples, it became evident that the sampling locations within each subcluster were different (FMA3 A3, FMA6 Snares and FMA6 for the whole dataset, and FMA3 A1 and FMA3 J1 for the New Zealand dataset, Figure FST‐based subclusters, and detected only the two clusters differentiating the Australian and New Zealand sampling locations. Together, this evidence indicates that the FST differentiation within New Zealand waters is overall weak and does not carry a strong geographic signal.The high gene flow and low genetic differentiation between populations characteristic of many teleost species (reviewed in Benestan, While panmixia of New Zealand hoki stocks was inferred, outlier scans including the two Australian sites revealed a large number of loci under putative selection. In total, 2797 SNPs could be identified as outliers that were in common across all three analysis methods. This high number of SNPs under putative selection indicates that the wider Australasian hoki population has a high potential for future genetic adaptation in response to changing environmental conditions. In the context of a rapidly changing world, this high standing adaptive potential may provide this species with a resilience buffer to combat future changes. In contrast to the large number of outlier loci in the overall dataset, when only New Zealand locations were analysed, the number of shared outlier loci between sampling locations fell to only 10 SNPs. This finding could indicate that large‐scale environmental selection selects for different locally adapted genome associations in each cluster; however, further analysis is required to better understand this small number of shared outlier SNPs. Our finding of multiple outlier loci, particularly in the complete dataset, is not in line with previous views on the adaptive ability of marine teleosts Waples, . In the Ne indicated a general decline in the population size of hoki, from a historical Ne of 228,681, 999 generations ago, to a Ne of 42,816, approximately 189 generations ago. These estimates have to be taken with caution, however, as estimation of Ne values is always plagued by high uncertainty owing to the need to sample a high number of individuals to produce accurate estimates (Blower et al., Ne, and our sample size is well below this recommend threshold. Indeed for species with large populations, this would mean that several thousands to millions of individuals would have to be sampled (Marandel et al., Ne with smaller confidence intervals (Ruzzante et al., Our population size estimates of In New Zealand, hoki forms the largest fishery by volume of catch (Papa et al., In conclusion, genetic variation at the genome‐wide level is invaluable to identify fish stock structure in fisheries management, and the recent increase in the accessibility and resolution of population genetic data has facilitated the detection of previously unidentified structures, as well as signatures for natural selection in wild populations (Bernatchez, The authors declare no competing interests.Supplementary MaterialClick here for additional data file.


The Inclusion of ethnically diverse populations in genetic research can provide deep insights into specific pathogenic variants that differ across and within populations. Such insights may be useful to guide diagnosis and provide relevant clinical interventions . UnfortuWe accepted only high-quality research work in this research topic, including review manuscripts describing the genetics of diseases in underrepresented populations such as African, Asian, Latin American, and other indigenous populations. Topics could include Genome-wide association studies, Gene-environment interactions, Population genetics, epigenetics, pharmacogenomics, Polygenic/Genetic Risk Scores. The final topic issue has 10 published articles out of 18 submitted manuscripts, covering various genetic studies in African, Asian, Arab, and other previously under-studied populations.Font-Porterias et al. analysed genome-wide array data and exome sequences of 89 healthy Spanish Roma individuals to investigate chronic diseases and traits. The Roma represents the largest trans-national minority ethnic group in Europe, originating from South Asia and receiving extensive gene flow from West Eurasia. The authors found frequency differences between the Roma and other non-Roma populations for disease-causing variants and variants linked to drug response. The work demonstrated the value of merging population genetics and genetic epidemiology methodology.Most of the studies demonstrated that better representation of ethnically and ancestrally diverse populations is crucial for our ability to comprehensively characterise the genetic architecture of complex traits and human disease. For example, Chande et al. analysed the effect of genetic ancestry and ethnicity on observed disease prevalence. They predicted disease risk in Colombia populations by mainly comparing the disease prevalence and socioeconomic indicators of Colombia’s three major ethnic groups; from Mestizo, Afro-Colombian, and the Indigenous group. The study demonstrated a strong link between ethnicity, ancestry, and health outcomes. In their mini review Nagaraj and Toombs discussed “the history of pharmacogenomics and highlight the inequalities that must be addressed to ensure equal access to pharmacogenomic-based healthcare and reviewed the efforts to conduct the pharmacogenomic profiling of chronic diseases among Australian Indigenous populations including the impact of the lack of drug safety-related information on potential ADRs among individuals in these Australian Indigenous communities”.Other papers demonstrated how such differences in clinically relevant genetic variation can impact health inequalities, if populations are not sufficiently represented in genetic research. Soremekun et al. meta-analysed summary statistics from genome-wide association studies of multiple African data, carried out a Bayesian fine-mapping, and assessed the causal relationship between White Blood Cell (WBC) subtypes and Asthma including a two-sample Mendelian Randomization (MR) analysis. This study identified five novel genes not previously reported associated with any WBC subtype. Their MR analysis showed that monocytes count and neutrophils counts are associated with an increased risk of asthma. Singh et al. conducted a systematic review to summarise studies on genetic association studies for blood pressure-related traits in populations with African ancestry. The review highlighted the limited number of available studies, especially in Africa. Nevertheless, across the five blood pressure-related traits, they found that 26 genome-wide significantly associated SNPs have been identified, including 12 associations that have not previously been described in non-African studies.Papers in this special issue actively addressed underrepresentation by investigating the genetics of complex traits in diverse populations. One approach used to address the imbalance is synthesising existing studies to empower more generalizable conclusions. Al-Homedi et al. leveraged several small-scale genome-wide association studies of metabolic syndrome (MetS) performed in Arab populations to investigate the genetic basis of MetS in the population. Their systematic review and meta-analysis identified 36 studies and shows that the most frequently studied genes were FTO and VDR. The study failed to find any unique genetic association between MetS and Arabian populations.Similarly, Dlamini et al. investigated the associations between genetic variation in CYP17A1 and SERPINA6/A1 and circulating glucocorticoid concentrations in black South African adults. The authors found that a variant rs17090691 with G effect allele at SERPINA6/A1 was associated with increased diastolic blood pressure in all adults at p-value of 9.47 × 10–6. However, no association was seen between these genetic variants and glucocorticoids or between any variants in CYP17A1 and metabolic outcomes after adjusting for multiple testing.It may be unsurprising that other studies in this Special Issue focussed on cardiometabolic outcomes, given the immense public health burden of cardiovascular disease. Wang et al., the authors recruited over 1,400 Han Chinese patients with coronary artery disease. They conducted linear regression analyses on the genetic variants of mitochondrial DNA (mtDNA) and lipidomic traits in two independent groups where they found three associations. They also explored the associations between genetic variants in mitochondrial and lipids traits and reported two significant associations. The study provided “insights into the lipidomic context of mtDNA variations and highlighted the importance of studying mitochondrial genetic variants related to lipid species”.In Owalla et al. investigated the effects of stress and the genetic variation within the FK506 binding protein 5 (rs1360780) and glucocorticoid receptor (rs10482605) genes on internalizing disorders (ID) status in a CA-HIV cohort in Uganda. Using logistic regression, the authors assessed the relationships between IDs and recent stress, chronic stress, and the investigated genotypes. They reported no significant association between IDs and rs1360780 and rs10482605 polymorphisms within the FKBP5 and glucocorticoid receptor genes nor observed any gene–environment effect on vulnerability IDs in the Ugandan population, but shows that severe recent stress increases the vulnerability to IDs among CA-HIV. The findings further supported that polymorphisms at genetic loci only contributed minimal to the genetic vulnerability to IDs.Two of the studies focused on mental health genetics in African populations. Genetic research could play an important role to decrease stigma and better understand the causes of this highly prevalent, yet poorly understood global health problem. Kalungi et al. assessed the relationships between acute stress and Internalizing mental disorders (IMDs), and moderation by chronic stress and SNPs in SLC6A4 and TPH2 in Ugandan populations. The authors observed a “statistically significant association between severe acute stress and IMDs. Acute stress was found to interact with 5-HTTLPR-rs25531S-A-S-A haplotype to increase the risk for IMDs among Ugandan HIV+ children and adolescents but no evidence for a combined interaction of acute stress, chronic stress, and 5-HTTLPR-rs25531 on IMDs”.Similarly, Overall, the studies in this Special Issue highlighted the importance of genetic research in diverse global populations. They synthesized existing data to enable conclusions for diverse populations, demonstrate population variation linked to clinically relevant genes, discover novel genetic associations, and investigate the implications for health inequalities.