The aim was to ascertain the genetic and geographical structure of the Kyrgyz mountain merino (KMM). We analyzed DNA samples of 109 Kyrgyz mountain merino specimens, bred in three state breeding factories (STB), including “Orgochor” in the Issykul Province, “Katta-Taldyk” in the Osh Province and STb named after Luschikhin in the Talas Province. We identified 126 alleles in 12 microsatellite markers . There were 6 to 16 alleles in each locus . We identified 67 rare alleles , which made up 53.2 % of all alleles found. The greatest number of rare alleles was found in STR-markers of CSRD247, INRA023, INRA005, INRA006, MAF214 and OarFCB20. For each group, there were individual differences in the distribution of allele frequencies across all the STR loci studied. The most significant of them were as follows: with regard to the McM042 locus, allele 87 was major in the TALAS and OSH groups , whereas allele 95 was major in the ISSYK- KUL group (36.2 %); allele 154 was major in all groups with regard to the INRA172 locus, but it was 1.25 times less prevalent in the ISSYK-KUL and 1.66 times less prevalent in the OSH groups compared to TALAS , whereas alleles 156 and 158 were found only in the ISSYK-KUL group. Considering the ETH152 locus, 186 allele prevalence in the TALAS group was 51.1 %, but allele 190 was also markedly prevalent in the ISSYK-KUL and OSH groups, 34.5 and 34.3 %, respectively. The genetic division of the studied groups of KMM (with K from 3 to 10) was homogeneous – the contribution of each subcluster was equivalent. The AMOVA analysis revealed that the groups are located equidistantly. To conclude, the genetic diversity of the Kyrgyz mountain merino in three state breeding factories of the Kyrgyz Republic was high and comparable with each other. The sheep breeds of Kyrgyz mountain merino (KMM) arecommonin all regions of the Kyrgyz Republic, which differin natural and climatic conditions. In order to improve thebreeding and productive qualities of KMM sheep, intra-breed types of sheep were created .The Kyrgyz mountain merino was created in 1990–2006on the basis of the Kyrgyz fine-wool breed using sheep ofthe Australian merino breed and approved in 2006. Thegenetic structure of the breed includes 5 factory types and24 factory lines. KMM sheep wool has high technologicalproperties and has attributed to the highest quality categoriesof merino fine wool. The sheep are also known for high meatpropertiesEach breed or animal type shows some heterogeneity inmorphological, productive and technological qualities. Microsatelliteloci  can be used to solvebreeding tasks related to the determination of breed affiliationor breed type .To assess the condition and preserve the features of theKMM gene pool, genogeographic studies are needed. Thepreservation and further improvement of the breed should becontrolled by the genetic dynamics studies both in the breedas a whole and in the main breeding farms engaged in KMMbreeding. We have previously shown that local breeds of farmanimals  are characterizedwith a high genetic diversity, but local differentiation is alsopresent, and the differences are significant for a number ofhigh-altitude experimental zones . In thisregard, studies of similar structure are needed.The information obtained during the molecular geneticanalysis will complement the morphometric characteristicsof breeding rams, repair rams and ewes, which will allowbreedersto develop new and modify existing selection algorithmsand schemes to maintain the inbreed KMM geneticdiversity, as well as preserve the genetic identity of this breed.In the future, they plan a number of measures to improve thebreeding qualities of KMM breed sheep.Thus, the purpose of this study was to conduct a genogeographicstudy of the Kyrgyz mountain merino sheep breed.The biological material for molecular genetic research wasthe blood samples of Kyrgyz mountain merino (KMM)sheep obtained from an adult population of 109 animals bredin three state breeding plants (SBF), including 29 animalsfrom SBF “Orgochor”  (ISSYK-KUL sample), 35 animals fromSBF “Katta-Taldyk”  (OSH sample) and 45 animals from SBF named afterM.N. Lushchikhin  (TALAS sample). The sampling sitesare shown in Fig. 1.DNA was isolated by phenol-chloroform extraction . The samples were genotyped using 12microsatellite markers recommended by the InternationalSociety for Animal Genetics (ISAG): McM042, INRA006,McM527, ETH152, CSRD247, OarFCB20, INRA172,INRA063, MAF065, MAF214, INRA005, INRA023, and alsoby the AMEL sex-specific locus.Genotyping was carried out using a set of COrDIS Sheep reagents for multiplex analysisaccording to the manufacturer’s recommendations. To correctlydetermine the genotype in the studied animals (ampliconsize in bp), a sample with a control genotype included in theCOrDIS Sheep kit was used. PCR were analyzed by capillaryhigh-resolution electrophoresis using an automatic geneticanalyzer Applied Biosystems 3500 .GenAIEx v. 6.503 , STRUCTUREv. 2.3.4 , Past v. 4.03  software was used for statistical analysis.GenAIEx v. 6.503 was used to calculated the average numberof alleles per locus (Na), the effective number of alleles(Ne), the levels of expected (He) and observed (Ho) heterozygosityand the FIS coefficient . STRUCTUREv. 2.3.4 allowed to calculate the Q criterion, which attributedeach individual animal to the corresponding cluster . PPHELPER v. 1.0.10 web application  was used for graphical interpretation of the resultsobtained in STRUCTURE v. 2.3.4.We used GenAlEx 6.503 software  toanalyze population genetic parameters, the degree of geneticdifferentiation based on matrices of pairwise FST values,followed by visualization in Past v. 4.03 .The genetic structure of the studied samples of the KMMsheep breed was evaluated using principal component analysis(PCA) via clustering in STRUCTURE v. 2.3.4  using a mixed model . Ten iterationswere completed for each K value. We also determined the optimalnumber of clusters (ΔK) in POPHELPER v. 1.0.10web application, using the method proposed in .All applicable international, national and/or institutionalprinciples for the care and use of animals have been observedThe modern KMM sheep breed demonstrated a high level ofinbreeding genetic variability, when 126 alleles were identifiedin the 12 microsatellite markers studied. The number ofalleles in each locus varied from 6 to 16 (mean 10.500 ± 0.957).Sixty-seven rare alleles were identified, 53.2 % of the total number of identified alleles.The greatest number of rare alleles was found for the STRmarkers CSRD247, INRA023, INRA005, INRA006, MAF214and OarFCB20.In order to analyze KMM inbreeding genetic subdivisionbred in three geographically isolated zones, we computedNa, Ne, Ho, He, I values and the FIS coefficient, shown inTable 1.The mean number of alleles per Na locus varied from 8.000to 8.500 (mean 8.306 ± 2.595), whereas the maximum valuewas noted in the TALAS group from the M.N. LushchikhinSBF. The number of effective Ne alleles was the highest inthe OSH sample from the Katta-Taldyk SBF. Shannon index,reflecting the complexity of the community structure, averaged1.657 ± 0.333 with the highest value in the OSH sample fromthe Katta-Taldyk SBF. The observed heterozygosity Ho as anindicator of the variability (polymorphism) of the populationreflecting the proportion of heterozygous genotypes inthe experiment ranged from 0.693 to 0.764. The expectedheterozygosity of He as an indicator of the proportion ofheterozygous genotypes, expected in the Hardy–Weinbergequilibrium, ranged from 0.730 to 0.770. Maximum valuesof Ho and He were in OSH from the Katta-Taldyk SBF. Themean value of FIS index was the most neutral (0.006) in thisgroup and indicated a balanced prevalence of heterogeneousgenotypes, i. e. the level of related mating of individuals inthe subpopulation was the least significant compared to theremaining two groups. In general, when comparing Na, Ne,Ho, He, I and the FIS coefficient, we found no statisticallysignificant differences between three studied samples as ofthe Student’s t-test.To assess the genetic subdivision of the KMM samplesusing STRUCTURE v. 2.3.4, we computed the Q criterion,which characterized the stratification of each individual animalin the corresponding group. A Q value of 75 % or higherconfirms the individual’s attribution to its cluster. Fig. 2 graphicallydemonstrates (using the PPHELPER v. 1.0.10 webapplication (http://pophelper.com/)) the results of the analysiscarried out in STRUCTURE v. 2.3.4 (automatic sorting wascarried out based on the attribution of a particular sample toa major cluster).The genetic material of KMM sheep from three geographicallyisolated zones was used in the study . For allsamples within clusters K = (3–10), there is a general uniformityof structure, whereas the contribution of each subclusteris equivalent. A pairwise comparison of the mean values of Qfor three samples at K = 2 using analysis of variance showed nostatistically significant differences. Thus, F = 0.112, p = 0.739was for the pair TALAS/ISSYK-KUL; F = 0.023, p = 0.881,for the pair ISSYK-KUL/OSH; and F = 0.267, p = 0.607 wasfor the pair TALAS/OSH. This may result from the fact thatthe KMM subpopulations studied have common ancestors; however, other factors mayalso have an effect.Based on the analysis of FST genetic distances calculatedusing the AMOVA algorithm for 12 STR markers, a PCR graph was constructed reflecting the mutual similarity/difference ofthe studied samples The information presented in Fig. 2 and 3 allows to concludethat the studied samples of KMM did not differ significantlyfrom each other. However, each sample had features that arosefrom the differences in the allele’s prevalence in the studiedSTR loci, as well as the presence of rare and private (foundonly in one of the studied groups) alleles .Among individuals from the M.N. Lushchikhin SBF (theTALAS sample), rare alleles accounted for 18.9, 12.2 and10.0 %, respectively, for the CSRD247, INRA005 andINRA023 STR markers; among individuals from the OrgochorSBF (the ISSYK-KUL sample), high prevalence of rarealleles was found for STR markers CSRD247 and MAF214,12.1 and 10.3 %, respectively; and among individuals fromthe Katta-Taldyk SBF (OSH sample) – for CSRD247 (15.7 %),MAF214 (15.7 %) and OarFCB20 (14.3 %).In general, we found individual differences in the distributionprofile of allele frequencies across all the studied STRloci for each group. The most significant of those were allele87 in the major state in the McM042 locus  in the TALAS and OSH groups, whereas allele95 was most prevalent (36.2 %) in the group ISSYK-KUL;major allele 154 for the INRA172 locus in all groups, however,in comparison with the TALAS group, its prevalencewas 1.25 (ISSYK-KUL) and 1.66 (OSH) times lower, 55.2and 41.4 %, respectively, and alleles 156 and 158 were foundonly in the ISSYK-KUL group; the prevalence of 186 allele inthe ETH152 locus in the TALAS group was 51.1 %, whereas190 allele was highly prevalent in ISSYK-KUL and OSH,34.5 and 34.3 %, respectively.We also identified specific peculiarities of private alleles.Among the sheep from the M.N. Lushchikhin SBF (TALAS)those were determined with regard to seven loci , including INRA006, McM527, ETH152, CSRD247,INRA063, MAF214 and INRA005; and for the INRA006locus,the 124 allele was detected in 7.8 %. Seven privatealleles in five STR markers were identified for sheep fromthe Orgochor SBF (ISSYK-KUL), the most common beingINRA172  and INRA023 , as opposed to INRA005  for individualsfrom the Katta-Taldyk SBF (OSH).The highest calculated FST values are shown for the lociMcM042, INRA172 and ETH152, although in general the FST values for all loci were not high and did not exceed 0.05( p < 0.001).We also conducted a comparative analysis of Na and Heparameters for KMM and fine-wool sheep breeds bred inKazakhstan , Russia , Pakistan  and Poland  (Table 4).We found that the mean Na in KMM (in the context ofthe STR markers studied in this paper) was the maximum incomparison with other studies. The calculated Ho index alsoturned out to be one of the largest and was comparable withthe values obtained for the breeds Wielkopolskaya (Poland),Olkuska (Poland), Kail (Pakistan) and Kazakh fine-haired(Kazakhstan) . The high rates of KMM genetic diversityare directly related to the multi-stage breeding processes thatthis breed underwent during the late XX– early XXI century.Taken together, the genetic diversity of KMM breed sheepof the three state breeding plants of the Kyrgyz Republic isquite high and comparable to each other. We found it impossibleto single out a group for which a qualitatively different(high or low) genetic diversity would be different comparedthe other two groups.Nevertheless, it cannot be denied that for Kyrgyz mountainmerino sheep from the M.N. Lushchikhin SBF, therewas still a slight shift towards inbreeding processes – FIS == 0.052 ± 0.025 . Inthis regard we assume that the positive shift of these markers(lack of heterozygotes) occurred due to the purposeful selectionof individuals according to the economically valuablecharacteristics of wool, i. e. resulted from the association ofthese STR markers with the loci of quantitative traits QTL.However, such a relationship can only be assessed in furtherstudiesAn indirect confirmation of the inbreeding processes inthis breeding plant may be the presence of six pairs of individualsamong those selected for molecular genetic analysis,which were likely close relatives to each other (within pairs),because there were matching alleles in each of the 12 STRmarkers. In this regard, we propose to have a closer look at theintensity of inbreeding in the future. 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Grapes are one of the most common agricultural crops in the world. Currently, the analysis of genotypes directly at the DNA level is considered to be the most accurate method for studying the plant gene pool. The study of wild vines and ancient varieties in various regions of viticulture is an important direction of research in this field. The purpose of this work was to study the population of wild grapes growing on the territory of the Utrish Nature Reserve on the Black Sea coast of Krasnodar Region. The territory of the reserve is of interest as it is a site of ancient settlements, and the environmental conditions are suitable for the growth of wild grapes. During the survey of the territory, 24 samples of wild grapes were found, which were described according to the main morphological characteristics and analyzed by the molecular genetic method. The found vines were genotyped using 15 DNA markers, including nine commonly used for DNA fingerprinting  and VVIb23, which allows determining hermaphrodite and dioecious vines. Statistical processing of microsatellite loci polymorphism data was carried out using the GenAlEx 6.5 program. The genetic relationships of the studied vines were evaluated using the PAST 2.17c program. The samples were found to be morphologically and genetically polymorphic. The number of alleles identified in the sample varied from 5 to 18 and averaged 8 alleles per locus. Statistical processing of DNA analysis data made it possible to identify two genetically different populations among the wild discovered vines. An assessment of genetic similarity of the found vines with some local varieties of geographically close viticulture regions, rootstocks and representatives of Vitis sylvestris from other territories was made. One of the populations found in the Utrish Nature Reserve is close to a number of V. sylvestris genotypes, the DNA profiles of which are presented in the Vitis International Variety Catalogue. Grapes are one of the most widespread agricultural crops inthe world. The most significant, both economically and sociohistorically,is the species Vitis vinifera L. This species of thegenus Vitis L. (family Vitaceae), originating from Eurasia,supposedlyappeared about 65 million years ago . Currently, two subspecies are distinguished within thespecies, V. vinifera L. subsp. sylvestris (Gmel.) Hegi, whichincludes wild populations, and V. vinifera subsp. sativa (DC.)Hegi (or subsp. vinifera), which includes cultivars.Wild and cultivated vines differ in a number of features,including their reproductive biology: wild grapevines aredioeciousand cross-pollinate, while the cultivated vines aremostly hermaphrodite and self-pollinating. The domesticationof grapes, which occurred about 8 thousand years ago, is closelyassociated with the emergence of winemaking, althoughit is still not known for certain which process preceded theother. The Middle East and the Caucasus are considered to bethe initial centers of domestication of V. vinifera. The earliestevidence of wine production from 7,400–7,000 BC is foundin Iran . Seeds of domesticated grapes,about 8,000 years old, have also been found in Georgia andTurkey. However, Neolithic seed remains found in WesternEurope also suggest grape exploitation during this time, andwild form seed remains have been found at Bronze Age sitesin France .Wild grape populations are currently represented by wildvines of V. vinifera cultivars and a wild subspecies. The studyof wild grapevines in the ancient regions of grape cultivationhas been actively conducted in recent years at the moleculargenetic level .The study of the local gene pools of various viticulture regions(including native varieties and wild specimens) at theDNA level makes it possible to more fully assess the geneticdiversity of varieties and vines, to identify closer and moredistant genotypes. Wild exemplars of agricultural crops arealso significant for breeding as unique sources of genetic variability.The territory of the Utrish Nature Reserve on the Black Seacoast of the Krasnodar Territory is of interest for this kind ofresearch, since it is a site of ancient settlements, and the ecologicalconditions of the territory are suitable for the growthof wild vines .Expeditionary research to search for wild grapevines, study theecological conditions of their habitats and morpho-biologicalfeatures was carried out for three years (2019–2021) on theterritory of the Utrish Nature Reserve (Krasnodar Territory).The reserve is located in the northwestern part of the BlackSea coast of the Western Caucasus, on the Abrau Peninsula.The climate is sub-Mediterranean, moderately warm.For molecular genetic analysis, 24 samples of wild-growinggrapes were selected. DNA samples were isolated from shootapical parts of vine plants by a method based on the use ofCTAB (cytyltrimethylammonium bromide) .Genotyping was performed at 15 microsatellite loci, 9 ofwhich are standard for DNA fingerprinting of grape varieties . DNA markers linked to grape pathogens resistance lociwere also included in the study – downy mildew resistance:UDV305, UDV737 (Rpv3) and GF09-46 (Rpv10) and powderymildew resistance: ScORGF15-02 (Ren3), CenGen6 (Ren9). All found specimens werealso analyzed with the VVIb23 marker, which makes it possibleto detect hermaphrodite and dioecious samples .Polymerase chain reaction (PCR) was carried out using anEppendorf MasterCycler nexus GX2 device (Germany) accordingto the following scheme: 5 minutes at +95 °С ; 35 cycles: 10 seconds at +95 °С (denaturation),primer annealing for 30 seconds at +55 °С for VVS2, VVMD5,VVMD7, VVMD27, UDV305, UDV737, CenGen6, VVIb23,at +58 °С for VrZAG62, VrZAG79, ScORGF15-02, at +60 °Сfor VVMD25, VVMD28, VVMD32, GF09-46, 30 seconds at+72 °C (elongation); the final cycle was 15 minutes at +72 °С.PCR mixture with a total volume of 20 μl contained: 50 ng ofgenomic DNA, 1.5 units of Taq polymerase, 1x buffer for Taqpolymerase with ammonium sulfate and magnesium, 2 mMMgCl2, 0.2 mM of each dNTP (deoxynucleotide triphosphates) and 200 μM of each of the primers. The results were visualized by capillaryelectrophoresisusing a Nanofor 05 genetic analyzer  and a special built-in software package.The following varieties were used as reference genotypes:Pinot noir ,Saperavi Severniy (GF09-46), Villard blanc , Regent , KishmishVatkana (VVIb23), since the allelic compositions in the DNAof these varieties for the analyzed loci are known.Statistical processing of microsatellite loci polymorphismdata was carried out using the GenAlEx 6.5 program . The genetic relationships of the studiedgrapevines were assessed using the PAST 2.17c programusing the method of principal coordinates (PCoA) .In order to study the genetic similarity of the studied wildgrowingsamples with the indigenous gene pool of grapes,the work included the sampling of DNA profiles of cultivarsaccording to nine standard SSR loci from the internationaldatabase Vitis International Variety Catalog (VIVC), whichbelong to the local forms of Dagestan , Don ,Georgia, Crimea ,as well as to rootstock grape cultivars (the greatestcontribution to the genotype of which was made by differentNorth American species) and V. sylvestris genotypes from differentgeographical zones. Bayesian analysis was carried outin the Structure 2.3.4 program using 65 genotypes (41 varietiesfrom the VIVC database and 24 genotypes of the studiedforms) with the following parameters: 500000 Burn-in period,500000 Reps, K=7 .The study was carried out using the instrument park of theCenter for Collective Use of Technological Equipment in thedirection of “Genomic and postgenomic technologies” of theNorth Caucasian Federal Scientific Center for Horticulture,Viticulture, Wine-making.In the process of surveying the territory of the Utrish NatureReserve, 24 samples of wild-growing grapes were found, thedescription of which according to the main morphologicalfeatures is given in Table 1.Polymorphism of morphological traits was noted both betweenpopulations from different sampling sites, and betweenplants within nominal populations, designated by us according to their places of growth on the territory of the reserve (plantswere found in the locations of Atmacheva Shchel (A1–A3),Shirokaya Shchel (Sh1–Sh5), Lobanova Shchel (L1–L6),VodopadnayaShchel (V1–V10) . The ecological and geographicalcharacteristics of the places where grape plants were foundare presented in Table 2.DNA analysis of the found grape samples revealed differentlevels of polymorphism in the studied 15 loci – the numberof identified alleles in the sample varied from 5  to 18 (UDV305) and amounted toan average of 8 alleles per locus (Table 3). The mean observedheterozygosity (Ho = 0.664) was lower than expected(He = 0.712). DNA marker analysis using VVIb23 showed thatall wild vines were dioecious. Among the nine microsatelliteloci, the data on polymorphism of which are used for DNAfingerprinting of grape genotypes, the most polymorphic wasVVS2 , the least – VVMD25,VVMD27  (see Table 3).A similar situation was noted in the study of the diversity ofwild vines in Armenia – the studied sample (77 samples) wasalso the most polymorphic in the locus of VVS2 – 13 types ofalleles, and the least in VVMD25 and VVMD27  . Atthe same time, in the study of Gorislavec S.M. and co-authors(2017), in which Crimean wild-growing vines were studied,the smallest polymorphism  was revealedat the VVS2 locus .When distributing genotypes in the space of the maincoordinates, a group of samples from territory of the VodopadnayaShchel can be distinguished . In general, allsamples from this place are localized separately from othersin the space of the main coordinates, while samples V1, V2,V3, V4, V9, V10 form a separate subgroup. Samples V6 and V7 have very similar genotypes. Among other found samples,the complete coincidence of genotypes at the studied loci wasdetermined in wild vines Sh3 and Sh4.Analysis of samples using DNA markers linked to theresistance genes to downy mildew Rpv3, Rp10 and powderymildew Ren3, Ren9 did not reveal resistance loci in the genotypesof wild vines. The inclusion of these DNA markers inthe study was carried out in order to study the polymorphismof wild grapes at the analyzed loci, as well as a tool for thepossible identification of wild-growing vines of hybrid origin.The resistance determined by Ren3, Ren9 and Rpv3 is inheritedfrom North American grape species , Rpv10 – from V. amurensis.DNA fingerprints of the studied wild-growing vines by9 SSR loci standard for the identification of grape genotypeswere checked for a coincidence in the catalog of grape varieties’DNA profiles of the international database VIVC . No coincidences were found. To analyze the geneticsimilarity, DNA profiles of varieties belonging to geographicallyclose regions of viticulture, where there are local varieties,some of which may have originated from wild grapevinesgrowing earlier in these territories, were included intothe research. For comparison, the identified DNA profiles ofV. sylvestrisgenotypes from different geographical locations presented in VIVCwere also used. The study also included a group of rootstockvarieties of complex interspecific origin with the largest shareof the genetic contribution of North American grape speciesin order to exclude the presence of rootstock varieties amongthe found wild vines, which are characterized by high adaptabilityto various abiotic and biotic stress factors. The numberof clusters equal to 7 (K=7) was used for Bayesian analysis.The results of the analysis are shown in Figure 2The data obtained during the analysis allowed us to identifycertain patterns of clustering of samples, relative to theirorigin. The fourth and fifth clusters mainly include samples selectedfrom wild grapevines of the Utrish Nature Reserve. Theexceptions are three genotypes of the grape subspecies Vitisvinifera L. subsp. sylvestris (Gmelin) (V. sylvestris) – Khedhayria(Tunisia) (61), Lambrusque Abbadia H (France) (62)and Sylvestris Dirmstein 2 (64), and one local Georgian varietyChkhaveri (45) belonging to cluster 5 with a probability notexceeding 50 %. It is worth noting that the three studied wildgrape vines  were not unambiguously assigned tocluster 4 or 5. In turn, clusters 2, 3 and 7 include all the localvarieties presented in the study: of Dagestan, Don, Georgianand Crimean origin. At the same time, cluster 7 is typicalfor three varieties of Georgian origin – Alexandrouli (40),Mtsvane Kakhuri (41) and Rkatsiteli (42); in other cases, theprobability of its contribution to the genotypes is not significant.With the exception of the three varieties mentionedabove, representatives of the local gene pool are distributedbetween clusters 2 and 3 with varying degrees of confidence.The sixth cluster was formed by a number of genotypes ofrootstock varieties, the minor contribution of this cluster wasalso revealed only in varieties from this sample. Cluster 1includes some rootstock varieties, part of the V. sylvestris genotypesand one Georgian variety Saperavi (43).Based on the above, we can conclude that the samples ofwild grapes of the Utrish Nature Reserve selected during theexpedition are represented by two hypothetical populations(expressed as clusters 4 and 5). There are transitional forms betweenthe two populations. And if the first nominal population(cluster 4) is localized on the Vodopadnaya Shchel territory,then representatives of the second population (cluster 5) arefound at all expeditionary points of sampling of plant material.A genetic relationship was also established between the secondnominal population and some genotypes of V. sylvestris andthe Georgian variety Chkhaveri (45), with varying degrees ofprobability included in cluster 5.Thus, it can be assumed that at least part of the genotypesfound in the Utrish Nature Reserve are close to the genotypesof the subspecies V. sylvestris presented in the VIVC internationaldatabase. Samples from the hypothetical first population are geneticallydifferent from other vines and are predominantly allocatedto cluster 4. An insignificant contribution of cluster 4 wasnoted in the genotypes from the gene pool of Georgian andCrimean local varieties ,and also in two accessions of V. sylvestris (61 – Khedhayria(Tunisia), 65 – Sylvestris Guemuelduer 104-64 (Turkey)).The genetic contribution of American rootstock varieties tothe wild grape population of the Utrish Nature Reserve hasnot been identified.The authors declare no conflict of interest.Chernodubov A.I., Rudenok Y. Phytocenoses biodiversity in “Utrish”Reserve.Lesotehnicheskiy Zhurnal = Forestry Engineering Journal.2015;5(1):120-127. DOI 10.12737/11269. (in Russian)Cunha J., Ibáñez J., Teixeira-Santos M., Brazão J., Fevereiro P., Martínez-Zapater J.M., Eiras-Dias J.E. Genetic relationships among portuguesecultivated and wild Vitis vinifera L. germplasm. Front. PlantSci. 2020;11:127. DOI 10.3389/fpls.2020.00127De Michele R., La Bella F., Gristina A.S., Fontana I., Pacifico D., GarfiG., Motisi A., Crucitti D., Abbate L., Carimi F. Phylogenetic relationshipamong wild and cultivated grapevine in Sicily: a hotspot inthe middle of the Mediterranean basin. Front. Plant Sci. 2019;10:1506. DOI 10.3389/fpls.2019.01506Di Gaspero G., Copetti D., Coleman C., Castellarin S.D., Eibach R.,Kozma P., Lacombe T., Gambetta G., Zvyagin A., Cindrić P., KovácsL., Morgante M., Testolin R. Selective sweep at the Rpv3locus during grapevine breeding for downy mildew resistance.Theor. Appl. Genet. 2012;124(2):227-286. DOI 10.1007/s00122-011-1703-8.Doulati-Baneh H., Mohammadi S., Labra M., De Mattia F., Bruni I.,Mezzasalma V., Abdollahi R. Genetic characterization of some wildgrape populations (Vitis vinifera subsp. sylvestris) of Zagros mountains(Iran) to indentify a conservation strategy. Plant Genet. Resour.2015;13(1):27-35. DOI 10.1017/S1479262114000598Ellstrand N.C., Heredia S.M., Leak-Garcia J.A., Heraty J.M., BurgerJ.C., Li Y., Nohzadeh-Malakshah S., Ridley C.E. Crops gonewild: evolution of weeds and invasive from domesticated ancestors.Evol. Appl. 2010;3(5-6):494-504. DOI 10.1111/j.1752-4571.2010.00140.x.Gorbunov I.V., Lukyanov A.A. Kuban wild-growing grapes and theirmorphological features. Vinogradarstvo i Vinodelie = Viticultureand Winemaking. 2020;49:30-33. (in Russian)Gorbunov I.V., Lukyanov A.A., Bykhalova O.N. Morphological peculiaritiesof the Kuban wild-growing forms of grapes. Plodovodstvoi Vinogradarstvo Yuga Rossii = Fruit Growing and Viticulture ofSouth Russia. 2020;65(5):70-82. DOI 10.30679/2219-5335-2020-5-65-70-82. (in Russian)Gorislavec S.M., Risovannaya V.I., Volkov Y.A., Kolosova A.A., VolodinV.A. Identification and evaluation of wild growing vines on theterritory of Yalta mountain-forest nature reserve using molecularmarkers. “Magarach”. Vinogradarstvo i Vinodelie = “Magarach”.Viticultureand Winemaking. 2017;1:19-21. (in Russian)Hammer Ø., Harper D.A.T., Ryan P.D. PAST: Paleontological statisticssoftware package for education and data analysis. Palaeontol. Electron.2001;4(1):1-9.Kupe M., Ercisli S., Jovanović-Cvetković T., Eyduran S.P., Ayed R.B.Molecular characterization of wild grapes from northeastern partof Turkey. Genetika. 2021;53(1):93-102. DOI 10.2298/GENSR2101093K.Lukšić K., Zdunić G., Hančević K., Mihaljević M.Ž., Mucalo A.,Maul E., Riaz S., Pejić I. Identification of powdery mildew resistancein wild grapevine (Vitis vinifera subsp. sylvestris Gmel Hegi) fromCroatia and Bosnia and Herzegovina. Sci. Rep. 2022;12(1):2128.DOI 10.1038/s41598-022-06037-6.Margaryan K., Maul E., Muradyan Z., Hovhannisyan A., Melyan G.,Aroutiounian R. Evaluation of breeding potential of wild grapeoriginating from Armenia. BIO Web Conf. 2019;15:01006. DOI10.1051/bioconf/20191501006.McGovern P.E. Ancient Wine: The Search for the Origins of Viniculture.Princeton: Princeton Univ. Press, 2004. DOI 10.2307/j.ctvfjd0bk.Merdinoglu D., Butterlin G., Bevilacqua L., Chiquet V., Adam-BlondonA.F., Decroocq S. Development and characterization of a largeset of microsatellite markers in grapevine (Vitis vinifera L.) suitablefor multiplex PCR. Mol. Breed. 2005;15:349-366. DOI 10.1007/s11032-004-7651-0.Peakall R., Smouse P.E. GenAlEx 6.5: genetic analysis in Excel. Populationgenetic software for teaching and research – an update. Bioinformatics.2012;28(19):2537-2539. DOI 10.1093/bioinformatics/bts460.Pritchard J.K., Stephens M., Donnelly P. Inference of population structureusing multilocus genotype data. Genetics. 2000;155(2):945-959. DOI 10.1093/genetics/155.2.945.Riaz S., Boursiquot J.M., Dangl G.S., Lacombe T., Laucou V., TenscherA.C., Walker M.A. Identification of mildew resistance in wildand cultivated Central Asian grape germplasm. BMC Plant Biol.2013;13(1):149. DOI 10.1186/1471-2229-13-149.Rogers S.O., Bendich A.J. Extraction of DNA from milligram amountsof fresh, herbarium and mummified plant tissues. Plant Mol. Biol.1985;5:69-76. DOI 10.1007/BF00020088.Schwander F., Eibach R., Fechter I., Hausmann L., Zyprian E., TöpferR. Rpv10: a new locus from the Asian Vitis gene pool for pyramidingdowny mildew resistance loci in grapevine. Theor. Appl.Genet. 2012;124(1):163-176. DOI 10.1007/s00122-011-1695-4.This P. Microsatellite markers analysis. In: Minutes of the First GrapeGen06 Work-shop, March 22nd and 23rd. INRA, Versailles (France).Paris: INRA, 2007;3-42.This P., Jung A., Boccacci P., Borrego J., Botta R., Costantini L., CrespanM., Dangl G.S., Eisenheld C., Ferreira-Monteiro F., Grando S.,Ibáñez J., Lacombe T., Laucou V., Magalhães R., Meredith C.P., MilaniN., Peterlunger E., Regner F., Zulini L., Maul E. Developmentof a standard set of microsatellite reference alleles for identificationof grape cultivars. Theor. Appl. Genet. 2004;109(7):1448-1458. DOI10.1007/s00122-004-1760-3.This P., Lacombe T., Thomas M.R. Historical origins and genetic diversityof wine grapes. Trends Genet. 2006;22(9):511-519. DOI10.1016/j.tig.2006.07.008.van Heerden C.J., Burger P., Vermeulen A., Prins R. Detection of downyand powdery mildew resistance QTL in a ‘Regent’ × ‘RedGlobe’population. Euphytica. 2014;200:281-295. DOI 10.1007/s10681-014-1167-4.VIVC. Vitis International Variety Catalogue. Microsatellites by profile.Last modified: 2022-08-10. Julius Kühn-Institut, 2022. Available at:https://www.vivc.de/index.php?r=eva-analysis-mikrosatelliten-vivc%2FindexZdunić G., Lukšić K., Nagy Z.A., Mucalo A., Hančević K., Radić T.,Butorac L., Jahnke G.G., Kiss E., Ledesma-Krist G., Regvar M.,Likar M., Piltaver A., Žulj Mihaljević M., Maletić E., Pejić I., WerlingM., Maul E. Genetic structure and relationships among wildand cultivated grapevines from Central Europe and part of theWestern Balkan Peninsula. Genes. 2020;11(9):962. DOI 10.3390/genes11090962.Zendler D., Schneide P., Töpfe R., Zyprian E. Fine mapping of Ren3reveals two loci mediating hypersensitive response against Erysiphenecator in grapevine. Euphytica. 2017;213(68):1029. DOI 10.1007/s10681-017-1857-9


We observed that 17β-estradiol did not change the basal level of lipid peroxidation  in thyroid homogenates, and no differences were found between sexes. The lipid peroxidation level in response to Fe2+ + H2O2 plus 17β-estradiol was lower in male thyroids. In turn, in male thyroids, 17β-estradiol reduced experimentally induced lipid peroxidation in as low of a concentration as 0.1 μM, whereas in female thyroids the lowest effective concentration of 17β-estradiol was 10 μM, i.e., 100 times higher than in males. In conclusion, the protective effects of exogenous 17β-estradiol against experimentally induced oxidative damage to membrane lipids is stronger in male than in female thyroids. Our observation suggests that female tissue is less sensitive to the protective effects of exogenous 17β-estradiol. This sexual dimorphism of oxidative processes in the thyroid may constitute one of the mechanisms of the different prevalence of thyroid diseases in women and in men.It is well-known that thyroid diseases are more prevalent in women than in men. The contribution of sex hormones may explain such disparity. The aim of this study was to check if there are any differences between sexes concerning the effects of 17β-estradiol on oxidative damage to membrane lipids (lipid peroxidation) in porcine thyroid homogenates under basal conditions and in the presence of Fenton reaction (Fe The presence of sex differences in the incidence, prevalence and severity of many human diseases is well documented. This phenomenon affects a wide spectrum of disorders such as cancer, diabetes and neurological, cardiovascular and autoimmune diseases ,2,3. RegDespite the importance of sex dimorphism in case of thyroid diseases, the biological factors and mechanisms that drive these differences are still poorly understood. However, evidence from an increasing number of studies shows that sex hormones—mainly estrogens—may be responsible for this phenomenon. Such a hypothesis is supported, among others, by the recent observation that the genetic sex-specific molecular profile did not differ between women and men with well-differentiated thyroid cancer .In the context of the parameters of oxidative damage measured in the present study, it should be mentioned that gender differences in oxidative stress in relation to diseases are documented .2O2 production . They were in good body condition and considered free of pathologies by the veterinary medical officer responsible for the health of the animals and the hygiene of the slaughterhouse.2 and stored at −80 °C until the assay.The mean mass of the collected thyroid was 32 ± 1.5 (SD) g. Collected thyroids were frozen on solid COw/v) and then incubated for 30 min at 37 °C in the presence of examined substances.Thyroid tissue was homogenized in ice-cold 20 mM Tris-HCl buffer (pH 7.4) .4 (30 µM) plus H2O2 (0.5 mM). The reactions were stopped via cooling the samples on ice. Each separate experiment was repeated independently three times. Therefore, for each sex, three tissue pools were prepared, with seven (7) thyroid glands used for each homogenate pool.In the second set of experiments, homogenates of male or female thyroids were incubated in the presence of 17β-estradiol  with the addition of Fenton reaction substrates, i.e., FeSOThe concentration of malondialdehyde + 4-hydroxyalkenals (MDA + 4-HDA), as an index of LPO, was measured in tissue homogenates, as described elsewhere . Proteint-test. Statistical significance was determined at the level of p < 0.05. Results are presented as means ± SE.The data were statistically analyzed, using a one-way analysis of variance (ANOVA), followed by the Student–Neuman–Keuls’ test, or using an unpaired p = 0.444).There was no difference in protein concentration between male and female thyroid homogenates under basal conditions  plus H2O2 (0.5 mM) increased lipid peroxidation to a similar extent in both sexes. In turn, 17β-estradiol added to either male or female porcine thyroid homogenates together with Fe2+ (30 µM) plus H2O2 (0.5 mM) decreased—in a concentration-dependent manner—lipid peroxidation induced by the Fenton reaction  + H2O2 (0.5 mM)]  . However0.5 mM)] . The pro0.5 mM)] ,16 and aAll these findings, which indicate the anti-oxidative role of 17β-estradiol, support the point of view that this hormone plays an important role in the response to enhanced oxidative stress. This role can be particularly evident in cases of gender-related disorders. It has been shown that in females, menopause creates a systemic pro-oxidant state due to decreased production of estrogens. This enhanced oxidative stress is directly related to an increased risk of cardiovascular diseases  and to a2O2 production [Considering the results of our study and those cited above, one can conclude that 17β-estradiol can have a fully beneficial effect on the oxidative status of the thyroid gland. On the other hand, however, in our previous study, carried out on primary cell cultures derived from the thyroid glands of adult male or female Wistar rats, we have shown that exogenous 17β-estradiol has a major negative impact on the redox state of thyroid cells; this effect is induced through the stimulation of NADPH oxidase expression and, consequently, stimulation of Hoduction . A similoduction . Howeveroduction ,25 were These seemingly contradictory results showing pro-oxidative or anti-oxidative effects of 17β-estradiol are in agreement with the general hypothesis that in contrast to high levels of 17β-estradiol, low levels of 17β-estradiol might have a pro-oxidant-like effect. It has been postulated that the pro/anti-oxidative effect of 17β-estradiol is largely dependent on the specificity of the cell in which estrogens generate their hormone response . It shou2+ + H2O2 plus 17β-estradiol was again lower in male than in female thyroids. Such an observation suggests the higher sensitivity of male tissue to the protective anti-oxidative effects of exogenous 17β-estradiol. This phenomenon can be explained by the fact that throughout its life, the female thyroid is exposed to much higher 17β-estradiol concentrations than the male thyroid, resulting in a kind of adaptation of female tissues to hyperestrogenism. Therefore, the male thyroid may be more sensitive to increased 17β-estradiol levels, associated with any additional conditions, especially resulting from the exposure to exogenous hormones.The main observation from the present study is that in male thyroids, protective effects against experimentally induced lipid peroxidation were caused by lower concentrations of 17β-estradiol when compared to female thyroids. Additionally, the lipid peroxidation level in response to FeIt should be stressed that concentrations of 17β-estradiol, which revealed protective effects in the present study, can only be achieved under experimental conditions. The highest physiologically achievable concentration of 17β-estradiol is estimated to be approx. 25 nM . HoweverTaking into account the experimental model we have used in this study, i.e., tissue homogenates, we can also conclude that the anti-oxidative effect of 17β-estradiol resulted most likely from a direct neutralization of excess ROS. Moreover, in the case of homogenates, we should rather not expect strong adverse effects from high 17β-estradiol concentrations. It is due to the fact that in such an environment, the metabolism of this hormone is limited, and thus the formation of its more harmful pro-oxidative metabolites, such as semiquinone and quinone, is restricted.It should be stressed that the present study is the first one showing sex differences regarding the protective effects of 17β-estradiol against experimentally induced lipid peroxidation. However, because it is a preliminary one, further research is required with the use of other experimental models, possibly other species, and with the examination of other biological molecules, such as DNA and proteins. As natural anti-oxidants have recently been recommended to eliminate the toxic effects of radioiodine used in the treatment of thyroid cancer , 17β-estIn conclusion, whereas exogenous 17β-estradiol exhibits protective effects against experimentally induced oxidative damage to membrane lipids, in the present study, we have observed for the first time that this effect is stronger in male than in female thyroids. Our observation suggests that female tissue is less sensitive to the protective effects of exogenous 17β-estradiol. This sexual dimorphism of oxidative processes in the thyroid may constitute one of the mechanisms of the higher prevalence of thyroid diseases in women than in men.


A global survey indicates that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses can only be performed after babies are born. Abnormal facial characteristics have been identified in various genetic diseases; however, current facial identification technologies cannot be applied to prenatal diagnosis. We developed Pgds-ResNet, a fully automated prenatal screening algorithm based on deep neural networks, to detect high-risk fetuses affected by a variety of genetic diseases. In screening for Trisomy 21, Trisomy 18, Trisomy 13, and rare genetic diseases, Pgds-ResNet achieved sensitivities of 0.83, 0.92, 0.75, and 0.96, and specificities of 0.94, 0.93, 0.95, and 0.92, respectively. As shown in heatmaps, the abnormalities detected by Pgds-ResNet are consistent with clinical reports. In a comparative experiment, the performance of Pgds-ResNet is comparable to that of experienced sonographers. This fetal genetic screening technology offers an opportunity for early risk assessment and presents a non-invasive, affordable, and complementary method to identify high-risk fetuses affected by genetic diseases. Additionally, it has the capability to screen for certain rare genetic conditions, thereby enhancing the clinic’s detection rate. Genetic diseases, which make up around 80% of rare diseases, are caused by variations in the genome. They can result in disabilities, deformities, and intellectual disabilities in patients, and in severe cases, they can even lead to the death of children. According to a global survey, genetic syndromes affect approximately 8% of the population . More thPrenatal screening has been used to assess the risk of a fetus affected with genetic diseases since the 1970s, with the initial focus on Trisomy 21 . MaternaUltrasonography is a low-cost, real-time, non-invasive technique for malformation diagnosis and is widely used in prenatal screening . With thThere has been significant progress in the development of deep-learning-based artificial intelligence (AI) algorithms for aiding in prenatal diagnosis ,10, espeIn this research, we developed Pgds-ResNet, a fully automated prenatal screening algorithm based on deep neural networks, to detect a variety of genetic diseases, especially some rare genetic diseases. The Grad-CAM visualization technique  was thenn = 333), from the initial list of 1000 cases, those without genetic results, 3D volume images, and complete clinical data. Thus, our dataset contained 556 normal pregnancies and 111 pregnancies with genetic abnormalities. This is a retrospective study with genetic test results available in all cases, and the genetic results were used as the gold standard for diagnosis. The decision to induce labor or not is up to the patient.From March 2020 to October 2021, the Guangzhou Women and Children’s Medical Center in China enrolled a total of 1000 pregnant women aged 23 to 38 years who underwent prenatal diagnosis . All ultThe static 3D volume acquisition of the fetal face was performed with a high-frequency probe (6–12 MHz) equipped by a GE Voluson 730 Expert/E10 . The volumes in midsagittal view were acquired using high or max quality mode and the lowest angle sweep, which allowed the inclusion of the total facial surface from the forehead to the chin. Following the collection of the ultrasound images, the sound beam angle was tilted as much as possible to 45°, the scanning volume angle range was adjusted to 50–70°, and the high-3 to max quality mode was used to set a three-dimensional volume acquisition frame of appropriate size.All fetal facial volume images obtained were analyzed offline, using 4D View software  and activating the Volume Contrast Imaging function to optimize the volume contrast resolution. This study utilized selected 3D ultrasound volume data, ensuring that the fetal head occupied 3/4 of the screen image by adjusting the magnification. Subsequently, the image was rotated by 7° and 14° to capture the fetal facial information, with the sagittal position serving as the initial plane of interception. An automated preprocessing workflow was used to remove the identification of the information and eliminate unintended human labels. Each ultrasonic image was cropped and masked to remove text, electrocardiogram and respirometer information, and other information outside the scanning sector.Overall, our dataset contained 845 normal images and 275 genetic disease images (including 12 genetic diseases) from 1000 pregnancies , with geThis study also employed data augmentation techniques to enhance the feature representation of images and prevent overfitting . Our expSpecific screening models were obtained using the fetal ultrasound images. The ResNet-18, ResNet-34, VGG-19, and VGG-16 models were trained to choose the appropriate network architecture. They are widely used in medical image analysis, especially for facial recognition ,24. ResNThe ResNet network model was obtained with 18 and 34 layers and the VGG model with 16 and 19 layers as the deep neural network model of this experiment. Initially, the training set was used following data enhancement to train the four aforementioned models, and the initial learning rate was set to 0.0001. Because there have been no relevant studies or models available for transfer learning or parameter initialization before, this study selected the random initialization method. The convolution kernel was set to , and Adam was employed as an optimizer. Subsequently, ReLU was selected as the activation function, and we applied binary cross-entropy to the output of the last dense layer, and the batch size was set to 32. The batch normalization module was added to each layer of the model, and weight decay (L2 regularization) was applied by us to avoid model overfitting. Subsequently, the epoch was set to 25, 50, 100, 150, and 200, and 5 models for each network structure were saved. The model with the smallest verification set loss was selected as the optimal model of the network structure.All the functionality, experiments, and analyses were implemented using Python  and Google TensorFlow (for the implementation of the deep learning architecture).The original patient data were divided into a training set and a test set in the experiments. TensorFlow, Keras, and Python were used for statistical analysis. The performance of the genetic diseases screening model was evaluated by calculating the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and F1*-score on the test set. The 95% CIs were the Wald CIs for sensitivity, specificity, and AUROCs, which were calculated with empirical bootstrap containing 1000 replicates. In addition, by analyzing the output results of the model on the training set, the highest F1-value was used as the selection criterion for the best threshold value.Furthermore, receiver operating characteristic curves (ROCs) were plotted to demonstrate the screening performances and the identification of genetic diseases using the Pgds-ResNet models. The ROCs were drawn by plotting the sensitivity against the 1–specificity at different operating thresholds. Additionally, ROC analysis was performed to determine the optimal operating thresholds by using the outputs of the models on the tuning dataset. To further evaluate the performance of the deep learning model, we also used ANOVA to record the significant difference between the performance of the deep learning model and the gold standard.The Grad-CAM visual interpretation technique was employed to generate a heatmap. This heatmap highlights the areas within the images that have a significant impact on the final classification judgment, thereby enhancing the interpretability of the model algorithm. By heatmap visualization, we were able to observe the contribution of different areas of the image to the final prediction and establish potential associations between facial features and genetic diseases.The facial features of various genetic diseases are not clear. In order to make the prediction effect of Pgds-ResNet more convincing, the heatmap was compared with the ultrasound image.Three levels  of seniority sonographers participated in a comparison study. Initially, Pgds-ResNet and each sonographer received 100 images from the test set at random and determined whether the fetus exhibited certain genetic abnormalities independently. Secondly, the statistical analysis compared the diagnostic results with the genetic counseling report. We gave instructions to the sonographers that the examination time for each image was within 10 min. The sonographer was allowed to take a break for every 20 images examined.The accuracy, sensitivity, and specificity were used to compare Pgds-ResNet’s screening performance with those of the human doctors. Apart from the images, the doctors were masked from all information provided.The overview of the acquisition and pretraining processing of datasets workflow is shown in The overall framework of this research is shown in Pgds-ResNet yielded the following values on the test dataset after training: 0.98, 0.89, 0.96, and 0.92, corresponding to AUROC, sensitivity, specificity, and F1, respectively . When thp-value and F-value presented in p-value is 0.658, indicating no significant difference in performance compared to the gold standard. The F-value is 0.196, suggesting a relatively small difference. In general, the results indicate that the predicted outcomes of the models are fairly close to the gold standard, suggesting good predictive performance of the models. On the other hand, the corresponding F-values for ResNet-34, VGG-16, and VGG-19 are 15.921, 30.014, and 28.125, respectively, indicating a larger difference between the performance of these algorithms and the gold standard.The This study conducted separate statistical analyses for each type of genetic disease. The sensitivity, specificity, and F1-score of each type of genetic disease were obtained by defining the target category (a certain type of genetic disease) as positive and the other categories as negative. In screening for Trisomy 21, Trisomy 18, and Trisomy 13, Pgds-ResNet achieved sensitivities of 0.83, 0.92, and 0.75 and specificities of 0.94, 0.93, and 0.95, respectively. With the classified threshold at 0.16, the percentage of correctly classified images into positive cases was 0.83 (10/12) in Trisomy 21, 0.92 (12/13) in Trisomy 18, and 0.75 (12/16) in Trisomy 13. In total, two Trisomy 21 images, four Trisomy 13, and one Trisomy 18 were misclassified by Pgds-ResNet as normal fetuses . The misclassification is primarily due to the poor image quality of those samples , and theDuring the image classification, we found Pgds-ResNet focused on abnormalities within the facial region. The most prominent areas among 86 images of genetic diseases in our test set were the areas of the jaw, flat frontal bone, and nasal bone.The ultrasound images were used to demonstrate the screening performance of Pgds-ResNet . The conPgds-ResNet also detects rare genetic diseases and achieves a surprisingly high degree of accuracy, especially in cases without apparent fetal structural abnormalities. Pgds-ResNet detected 43 of the 45 other genetic disease images in the test set. The sensitivity and F1-value were 0.96 and 0.87, respectively . The heaWe also compared Pdgs-ResNet’s performance with three sonographers . The accTo develop a deep learning model for predicting the brain age of preterm neonates using routine clinical brain MR images, we enrolled 281 preterm infants aged 28 to 37 weeks (We propose a deep learning model called Pgds-ResNet for detecting high-risk fetuses affected by genetic diseases, especially some rare genetic diseases. Pdgs-ResNet analyzes ultrasound images and makes decisions based on the imaging characteristics that are associated with genetic diseases. Pdgs-ResNet discovers fetal facial abnormalities as the most effective features in detecting genetic diseases. The screening accuracy is on par with the senior sonographers who received genetics training before this study. Comparing the heatmaps to the ultrasound images also confirms that Pgds-ResNet is able to correctly identify Trisomy 21, 18, and 13 syndromes and a number of rare types of genetic diseases prenatally.10q25.3, 8q24, VAX1, IRF6, and other genes are associated with cleft lip disease and affect the development of the human jaw and maxilla, resulting in abnormalities in the nasal wing, cheek, lips, and other parts of the face. Rs287104 locus in the KCTD15 gene is related to the morphology of the nasal tip and alar; Rs9995821 locus in the DCHS2 gene is related to nostril aperture; Rs2977562 locus in the 3q21.3 gene is associated with the thickness of the upper lip; and Rs10176525 locus in the 2q36.1 gene is related to the height of the nasal bridge [Recent genetic studies have shown that facial abnormalities in patients with genetic diseases are closely related to the mutation of certain genes ,27. For l bridge ,31.In fact, craniofacial manifestation has aided in the screening for genetic diseases . Recent However, these genetic disease screening and diagnosis methods cannot be applied to prenatal diagnosis because the set of key facial points commonly used in facial recognition algorithms cannot be obtained in fetal ultrasound images. Our research addressed this limitation and revealed an association between fetal facial features and various genetic diseases, especially in rare genetic diseases, which enables automated screening and identification of genetic diseases based on fetal ultrasound images.The “fetal profile” plane is a required component for standard examination during pregnancy . MicrognTo better interpret Pgds-ResNet’s results and minimize the black-box effect of deep learning models, we used Grad-CAM’s visualization technique to highlight the identified abnormal regions. Pgds-ResNet discovers abnormalities primarily in the areas of the facial forehead, nasal part, mouth, and jaw in different types of cases. Comparative studies on humans and AI further showed that Pgds-ResNet could discover features imperceptible to operators. Moreover, Trisomy 21, 18, and 13; Turner syndrome; 1q21.1 microdeletion; 15q11–q13 duplication; Helsmoortel–Van der Aa syndrome; 15q26.1–q26.3 deletion; and 20p13 duplication all show distinctive variations in the heatmap.Being the first study that applies AI techniques to detect facial features in fetal ultrasound images for prenatal screening of genetic diseases, there is certainly ample room for improvement. Firstly, the limited availability of samples of fetal genetic diseases resulted in a relatively small sample size for this study. Moreover, due to the small dataset used for algorithm validation and the absence of external test sets, the results may be overly optimistic . SecondlDespite these limitations, further development of our method could make a profound impact on prenatal genetic disease screening. It is well known that training a qualified sonographer is costly and time-consuming. Rapid detection and accurate diagnosis also largely depend on a clinician’s experience. A well-verified AI model with good robustness is expected to relieve the shortage of qualified sonographers, a challenge especially in under-developed regions, and hopefully reduce the financial burdens of the patients. It will also assist clinicians in making prenatal care decisions and thus improve early intervention outcomes. It can further be used as a triage scheme in clinical practice to reduce the application of NIPT or invasive procedures and save social resources. This type of screening tool could be extremely useful and is currently not available to clinicians by any other means.In summary, this study has successfully developed an AI framework that utilizes fetal faces from ultrasound images for effective and automated screening of genetic diseases. Additionally, the framework provides informative heat maps in the context of fetal genetic conditions. Pgds-ResNet found that the fetal nose, jaw, forehead, etc., contained diagnostic information. It could help with prenatal ultrasound diagnosis, reduce false-negative results, and compensate for the lack of medical resources. However, it is worth noting that this deep-learning-based algorithm serves as an aid to doctors in diagnosis, saving them time rather than replacing them.


Eretmochelys imbricata) from the Hawaiian archipelago form a small and genetically isolated population, consisting of only a few tens of individuals breeding annually. Most females nest on the island of Hawai'i, but little is known about the demographics of this rookery. This study used genetic relatedness, inferred from 135 microhaplotype markers, to determine breeding sex-ratios, estimate female nesting frequency and assess relationships between individuals nesting on different beaches. Samples were collected during the 2017 nesting season and final data included 13 nesting females and 1002 unhatched embryos, salvaged from 41 nests, of which 13 had no observed mother. Results show that most females used a single nesting beach laying 1–5 nests each. From female and offspring alleles, the paternal genotypes of 12 breeding males were reconstructed and many showed high relatedness to their mates. Pairwise relatedness of offspring revealed one instance of polygyny but otherwise suggested a 1 : 1 breeding-sex ratio. Relatedness analysis and spatial-autocorrelation of genotypes indicate that turtles from different nesting areas do not regularly interbreed, suggesting that strong natal homing tendencies in both sexes result in non-random mating across the study area. Complexes of nearby nesting beaches also showed unique patterns of inbreeding across loci, further indicating that Hawaiian hawksbill turtles have demographically discontinuous nesting populations separated by only tens of km.Hawksbill sea turtles ( PopulMost sea turtle populations worldwide have been dramatically reduced in size from historical levels, and virtually all still face challenges to persistence posed by a variety of human-related impacts –17. The Eretmochelys imbricata) is a circumtropically distributed marine reptile with nesting colonies scattered across the Atlantic and Indo-Pacific. In the past, their numbers have dwindled for many of the same reasons as other sea turtle species, such as human harvesting for food, and fisheries bycatch, but also for being disproportionately targeted by the international shell trade, which is illegally ongoing in many countries )https://github.com/ngthomas/microhaplot) that uses both genotype calls and mapped reads to produce short phased haplotypes of all genetic variation found on each PCR amplicon. Additional filtering parameters were applied to microhaplotypes using MICROHAPLOT's R Shiny app., including a minimum total microhaplotype read depth of 12, and an initial minimum allelic ratio of 0.50 . Afterwards, the allelic ratios were refined for each amplicon locus individually by examining the relative depths of alleles for homo and heterozygous microhaplotype calls. The acceptable allele ratio for a homozygous call was never more than 0.09. The acceptable allele ratio for a heterozygous call was never less than 0.20. A minimum fraction of 0.7 microhaplotypes with acceptable allelic ratios across all individuals was required for each amplicon. If any individual had more than two possible microhaplotypes for the same locus (possibly indicating DNA contamination) the locus or sample was either removed, or noisy low-frequency microhaplotypes were excluded. At this point, microhaplotype calls arising from the FREEBAYES, BCFTOOLS and GATK_HC outputs were combined into a single dataset. Microhaplotype loci were removed from the analysis if more than 7% of allele calls were mismatched with replicate samples. Linkage among amplicon microhaplotypes (LD) and departures from Hardy–Weinberg expectations (HWE) were assessed using the program GENEPOP [p-values for multiple hypothesis testing using the method of Benjamini & Hochburg [Microhaplotyping was performed using the R package MICROHAPLOT v. 1.0.1  were computed for all turtles with the R package RELATED v. 1.0 [r for the empirical data. Final analysis was run with 1000 bootstrap replicates to generate 95% confidence intervals for each pairwise value, and used an error-rate parameter of 0.02 for each locus. Inbreeding was set to ‘allowed'.Genetic diversity statistics for our samples, including heterozygosity and D v. 1.0  using saD v. 1.0 , therefoPairwise relatedness was also inferred using the R package CKMRsim v. 0.1 , which uNe) of the breeding population using the sibship assignment method [Lastly, relatedness analysis was performed in COLONY v. 2.0.6.6 , which dt method .a priori. However, because there were known relationships in our data  we relied on relatedness inferences between these individuals to determine if inbreeding was adversely impacting relatedness estimates.Due to small population size, a strong signal of background inbreeding was suspected for Hawaiian hawksbill turtles, which can confound the accuracy of standard pairwise relatedness metrics ,69. Thou. 2.6K-means clustering algorithm, using 20 principal components as predictors. Discriminant analysis of principal components (DAPC: [rtest included in the ADEGENET package) and 1000 permutations. The p-value of this test indicates the proportion of permuted statistics that exceed or are equal to the maximum observed value.In addition to relatedness inferences, several methods available in the R package ADEGENET  were uses (DAPC: ) was thes . A total of 2201 variants were detected by all programs. After removing variants with poor genotyping consistency across replicate samples and between variant calling programs, and filtering for missing data, there were 281, 202 and 256 variants from each of the callers, respectively. Only one of the final variants was an indel. Binary SNPs made up 85–98% of the other variants with the rest being trinary or quaternary SNPs. The final mean numbers of variants per PCR amplicon were 1.66, 1.72 and 1.58, for each of the callers, respectively . Preliminary analyses using only binary SNPs and a minor allele frequency threshold of 0.05 returned results consistent with the final dataset that included all variant types.a). The mean missing data across all markers was 0.5% (b). The mean genotyping consistency of the final marker panel across replicate samples was 97.7%, and only two microhaplotype loci needed to be removed for having less than 93% consistency. Genotyping error rates estimated by COLONY2 suggest that the mean allelic drop rate across all markers was 3.5% (95% CI = 1.77%–6%), and the mean rate of all other errors was 0.4% . Estimated allelic drop rates were associated with the amount of missing data in each locus.A total of 170 PCR amplicons from all three callers were used for microhaplotyping and 135 final microhaplotype loci passed all filtering parameters, including linkage disequilibrium. The mean number of microhaplotype alleles per locus was 2.61, and the maximum number of alleles was seven a. The mewas 0.5% b. The mewas 0.4% c,d. EstiPatterns of genetic diversity differed between nesting complexes, especially across the southern point of the island (South Point) separating the ‘Āpua and Kamehame complexes in the east from the Pōhue complex in the west figures  and 3, wa). A similar relationship was seen for allele frequencies (b). The variance around these positive associations represents genetic differentiation that in terms of population structure was estimated at p < 0.005; p < 0.005; and Jost's D = 0.027, p < 0.005. When only the 12 most polymorphic microhaplotype loci, with four or more alleles, were assessed for population structure the indices were: D = 0.06, with all values being statistically supported at p < 0.005.The per-locus heterozygosity estimates for east and west nesting complexes were similar, and show a positive, though loose, correlation when plotted against each other a. A simiquencies b. The vaGIS) for both the east and west nesting complexes had no discernible relationship when plotted against each other (c). Values for both complexes ranged between −0.7 and 0.7, with mean values falling below zero in both cases, and being significantly different from zero after 1000 bootstrap replicates. In spite of many extreme GIS values, departures from HWE were not statistically supported for any locus when both nesting complexes were analysed jointly.In contrast to other metrics of genetic diversity, and fixation indices, the locus-specific inbreeding coefficients . Examination of pairwise relatedness between the offspring of these two nesting females corroborates this, indicating that they are half-siblings. Therefore, the operational breeding sex ratio for Hawaiian hawksbill turtles in 2017 appears to be near parity, with some female bias in the Pōhue nesting complex. There was no evidence of any half-sibling relationships among any of the nest-mates, and no indication of polyandry during the 2017 breeding season. Mean estimates of Ne from COLONY2 were 8 and 16 for non-random and random mating scenarios, respectively. The upper and lower 95% confidence bounds for Ne ranged from 4 to 34 across all estimates.Paternal genotype reconstructions in COLONY2 indicate that there were 12 breeding males for 13 breeding females, with father P07 having mated with mother-119 and mother-176 b. Examinr for all the nesting females was noticeably less than the mean r values for the east and west nesting complexes individually. Closer inspection revealed that turtles breeding on opposite sides of South Point, both males and females, tended to be more related to each other than to turtles nesting across the point (b). Many breeding pairs also had high pairwise r values between them, especially from the ‘Āpua and Kamehame nesting complexes (One unexpected result from the pairwise relatedness coefficients was that mean he point b. Many bomplexes . These d. 3.3c). Thus, while both male and female hawksbill turtles are observed throughout the Hawaiian archipelago [The K-means clustering algorithm implemented in ADEGENET split nesting females and inferred paternal genotypes between eastern and western nesting complexes, corroborating genetic diversity indices and relatedness analysis, and suggesting that these genetic differences are biologically meaningful. Genetic differences may also separate the eastern ‘Āpua and Kamehame nesting complexes, the latter of which was poorly sampled in the current study, but the available data indicate that the strongest genetic differentiation divides Hawai'i Island nesting turtles across South Point . In bothfigure 3hipelago , to repr. 4Dermochelys coriacea), for example, are not as strictly philopatric as other species [Though all sea turtles exhibit natal homing behaviours, there is considerable variation among species, populations, and individuals in nesting site fidelity. Female leatherback turtles . However, contrasting signals of inbreeding and outbreeding in different nesting complexes at each locus (c) indicate that non-random mating is not unique to the 2017 nesting season alone, because this pattern would require generations to form. Additionally, mean GIS values would return to zero after only one generation of random mating among groups. More data will be needed to make sense of the distinct east-west inbreeding patterns, but one explanation could be that Hawaiian hawksbill nesting colonies are currently trying to balance trade-offs between inbreeding and outbreeding depression [Not only was there significant genetic population structure detected within the study area, but relatedness analysis revealed that both males and females appear to be mating assortatively by nesting complex . The coaancestry a,b. Howech locus c indicatpression .et al. [Inbreeding and outbreeding depression require an association between genetic diversity and reproductive fitness. In sea turtles, one measure of this fitness is likely the successful hatch rate of eggs. In the present study, all sampled offspring were unsuccessful hatches, and the rate of successful hatches was assessed from empty egg shells in the nest chamber . These data from the 13 females included in this study were not large enough for robust statistical analysis, but a study by Phillips et al.  that waset al. .For breeding units as small as the nesting complexes of Hawaiian hawksbill turtles, even infrequent gene flow between them may be enough to prevent the loss of genetic diversity and stave off the worst effects of inbreeding ,84–86. Tb and The mating system of Hawaiian hawksbills may also be important for genetic load management in this small population. This is because assortative mating within breeding groups can allow lineages to purge deleterious mutations more efficiently, in a similar fashion as subdivided populations . In othefigure 3Because of the demographic discontinuity between nesting complexes there could in reality be multiple mating systems, and differences in breeding sex ratios, within the Hawaiian hawksbill metapopulation. At the outset of this research this degree of population complexity was not anticipated and more samples will be needed to elaborate on the patterns uncovered in this work. For example, more turtles from the Kamehame complex are required to determine genetic structure with nearby ‘Āpua , and the. 4.1The genetic drift and mating system that determine the level of structure between two populations also determine the level of relatedness between two individuals, thus genetic population structure and genetic relatedness are two different aspects of the same variation . Just asr values of many related individuals were also depressed compared to distributions simulated under optimal conditions  relative to the reference allele frequencies [An overabundance of negative relatedness coefficients was observed for unrelated Hawaiian hawksbill turtles, and the nditions . For thequencies ,94.r values persisted even when the reference was generated exclusively from the same nesting complex. For the eastern nesting complexes, structure between Kamehame and ‘Āpua could be partly responsible, but the western Pōhue complex was also affected so other hidden structures could exist in the data. Family structures embedded within nesting complexes (or even nesting beaches) that create groups of highly related individuals could be the distortion. More data are required to determine if there could also be assortative mating within nesting complexes, as well as between them.One source of skew in the data could be genetic structure in the reference allele frequencies, which can negatively bias relatedness estimates ,94. SignN where N is the number of samples. Therefore, when only the adult turtles in this study are used to generate the references, we can expect the strength of the downward bias to be between 0.04 and 0.08. When including hatchlings, which theoretically have the same allele frequencies as their parents, the strength of the bias is between 0.01 and 0.001. Neither scenario fully explains the observed skew, though using hatchlings for the references might exacerbate negative biases due to family structures in the data.Relatedness coefficients can also be underestimated when the same samples for which relatedness is being estimated are used to estimate reference allele frequencies ,94. The E. imbricata, however, a Hawaiian hawksbill turtle was recently selected by the Vertebrate Genomes Project (vertebrategenomesproject.org) to represent this species as the reference genome, and this assembly will enable future research to explore the genomic complexity of this precariously small and non-randomly mating population.Two more things that could be causing pairwise relatedness coefficients to be downwardly biased are selection and genetic admixture in the population founders ,95. Bala


Artificial selection has been applied to domesticated birds for many decades. More recently, this selection has made use of so-called single-nucleotide polymorphism (SNP) markers—simple variants in a DNA sequence. These SNPs can be used for whole-genome screening to detect the unique traces of areas of the genome that are subject to selection. Doing this may help to shed light on the evolutionary and family history (phylogeny) of domestic Japanese quails of different breeds and utility types . In this study, 99 birds were used, representing eight breeds (11% of the world’s quail gene pool) and various purposes of use to gather genetic (whole-genome) data in the first-ever analysis of its kind performed on domestic quails. We thereby uncovered evolutionary relationships and points of divergence of individual quail breeds, gleaning important insights into the genetic diversity of domestic quail breeds and their future breeding potential.Traces of long-term artificial selection can be detected in genomes of domesticated birds via whole-genome screening using single-nucleotide polymorphism (SNP) markers. This study thus examined putative genomic regions under selection that are relevant to the development history, divergence and phylogeny among Japanese quails of various breeds and utility types. We sampled 99 birds from eight breeds  of egg , meat (Texas White and Pharaoh) and dual-purpose (Estonian) types. The genotyping-by-sequencing analysis was performed for the first time in domestic quails, providing 62,935 SNPs. Using principal component analysis, Neighbor-Net and Admixture algorithms, the studied breeds were characterized according to their genomic architecture, ancestry and direction of selective breeding. Japanese and Pharaoh breeds had the smallest number and length of homozygous segments indicating a lower selective pressure. Tuxedo and Texas White breeds showed the highest values of these indicators and genomic inbreeding suggesting a greater homozygosity. We revealed evidence for the integration of genomic and performance data, and our findings are applicable for elucidating the history of creation and genomic variability in quail breeds that, in turn, will be useful for future breeding improvement strategies. The study of molecular genetic principles that determine the degree of manifestation of economically significant traits is of crucial importance for increasing agricultural. In so doing, it can be applied to produce the most effective and cost-efficient agricultural products for domestic and world consumption. The poultry industry is one of the key sectors of agricultural production, and its important products are meat and eggs. Poultry meat accounts for 45% of the total global meat production  and virtCoturnix japonica Temminck & Schlegel, 1848), is a migratory bird native to East Asia. Domesticated Japanese quail are a common poultry type used for meat and eggs in Europe, Asia and throughout the world. Quails have been used in genetic research since 1940 [The progenitor of contemporary quail breeds, the Japanese quail  and brown (br), can be employed for autosexing (sex sorting) of newly hatched chicks [To create a competitive breeder stock for quails, it is necessary to use modern methodologies of genetic and genomic analysis aimed at increasing the efficiency of selection and breeding work. For instance, identified sex-linked genes for phenotypic traits, e.g., recessive genes for imperfect albinism  populations of Kenya [Coturnix coturnix) in the Azores archipelago. To the best of our knowledge, however, there have been no studies using this technique to characterize the genomic architecture and diversity in the divergently selected breeds of the domesticated Japanese quail.The integration of high-throughput, next-generation sequencing-based genomic technologies into practical quail breeding should therefore be carried out, at the initial stage, by assessing the genomic architecture characteristic of a particular breed. A subsequent comparison of the genomic structure of quail breeds of different origin and direction of selective breeding is necessary. This can prove to be an effective approach to identify specific genomic regions that are either related to recent divergence and/or earlier breeding differentiation. In addition, such an investigation facilitates a genome-wide assessment and refinement of the diversity and phylogeny amongst various quail breeds. Genotyping by sequencing (GBS), one of the restriction enzyme-based enrichment approaches designed initially for plants , is a prof Kenya . Ravagniof Kenya  employedIn the current investigation, we thus aimed to examine and compare the variability and phylogeny among the genomes of eight divergently selected breeds of egg-type, meat-type and dual-purpose quails that represented a significant portion (~11%) of the global gene pool of quail breeds. In accordance with this goal, the GBS approach was applied to characterize the genetic structure of these quail populations. This information is essential for maintaining their genomic diversity and facilitating efficient breeding in the future.Quails were hatched from fertile eggs purchased from the Genofond LLC , grown n) was taken into account, for which the following performance indicators (as mean ± standard deviation) were collected: egg number (EN) for 180 days from the start of lay; egg weight (EW) obtained at the age of 180 to 210 days ; and body weight (BW) of females at the ages of 6 weeks and 6 months. These data were subsequently assessed and compared with calculations of interbreed genetic variability resulting from the GBS analysis. Herewith, we proposed a hypothesis that a certain degree of “congruence”  between phenotypic and genomic data can take place for this sample of quail breeds. For this purpose, an appropriate mathematical analysis was undertaken using a new index, Narushin’s IPI . The latter was recently established by Vakhrameev et al. [For each breed, the number of females . Student’s lculator .Feather samples containing pulp were obtained from 106 quails of all the breeds studied. DNA extraction was performed using the Syntol kit for DNA isolation from animal tissues . The DNA solution concentration was determined using a Qubit 3.0 fluorimeter . To check the purity of the extracted DNA, the OD260/280 ratio was tested using a NanoDrop-2000 instrument (Thermo Fisher Scientific).PstI–MspI double-digest was used to generate one GBS library that also contained negative control samples devoid of DNA. Libraries were subjected to a Pippin Prep  to choose fragments with a size between 220 and 340 bp (genomic sequence plus 148 bp adapters). We employed a set of 768 barcodes designed by Integrated DNA Technologies, Inc.  and Illumina  that differed from each other by at least three mutational steps. The corresponding adapter sequences were as follows: PstI_Common_F, AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC; PstI_Common_R, GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCA; MspI(Y)_Common_F, CGAGATCGGAAGAGCGGACTTTAAGC; and MspI_Common_R, GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT. Single-end sequencing (1 × 101 bp) was performed utilizing a NovaSeq 6000 instrument  and the appropriate v1.5 reagents. Raw fastq files were quality checked using a custom QC pipeline, DECONVQC [Quail genotyping was performed using GBS analysis  that incDECONVQC ,34. As oDECONVQC .As a reference genome, we used the Japanese quail genome assembly Coturnix japonica 2.0 , along wJoint genotyping of the resulting files was implemented using bcftools  generatiFor some types of analysis, e.g., principal component analysis (PCA), analysis of genetic diversity and divergence, construction of phylogenetic networks, analysis of population structure and analysis of gene flow (migration events), an additional linkage disequilibrium (LD) filter was applied to remove loci for which LD was identified within a 50 Kb sliding window with a step of 5. After using the LD filter, 27,171 SNPs were included in the analysis.OH), expected heterozygosity (EH), unbiased expected heterozygosity (UHE), rarefied allelic richness  and coefficient of inbreeding (UFIS) based on unbiased expected heterozygosity.To determine within-population genetic diversity, PLINK 1.9 software package was used. QC was performed at both the individual and SNP levels using PLINK 1.9. By implementing various parameters of the R package diveRsity , we compess , coeffiFST values. Visualization of PCA results was carried out in the R ggplot2 package [FST distances were plotted using an agglomerative method for constructing phylogenetic networks, i.e., Neighbor-Net in SplitsTree 4 [For breed clustering, PCA and calculation of identical-by-state (IBS) distances were performed in PLINK1.9. The degree of genetic differentiation of the studied breeds was estimated based on pairwise  package . DendrogtsTree 4 . The softsTree 4  for modetsTree 4  was usedtsTree 4 . Using ttsTree 4 , the NeitsTree 4  trees shOptM R package [Gene flow (migration) events were analyzed using the TreeMix 1.12 program . The ana package . The besThe analysis of homozygous genomic segments  was performed using the detectRUNS R package  with theInformation for the three major performance characteristics, according to which the IPI index was computed, is given in A matrix of interbreed Euclidean distances computed for breed IPI values is presented in EH = 0.263 vs. the maximum value of 0.310, p < 0.001) and allelic richness  as compared to the JAP breed. This can be explained by the higher intensity of breeding work in TUX that was aimed at consolidating the desired breed characteristics. The inbreeding coefficient (FIS) was represented by the maximum value for the JAP population , which may be indicative of a likely growth in gene homozygosity in this population.Using around 100 DNA samples from quails of as many different phenotypes as possible, we generated a GBS panel for genotyping the quail breeds. In terms of genetic diversity values , TUX quaUHE) and expected inbreeding rate (UFIS) adjusted for small samples. The former was highest in EST (0.313) and JAP (0.319). The latter coefficient was represented by positive values for all breeds ranging from the minimum of 0.011 in TEW to the maximum of 0.046 in JAP quails. High rates of UFIS were also found in EST (0.032) and MAG (0.031), as well as in the PHA population (0.035). This enabled us to conclude that there was a significantly higher homozygosity of genes in these four populations as compared to other breeds.Due to the small number of birds in each breed, we also calculated unbiased measures of expected heterozygosity  showed that the optimal number of clusters (K) was equal to 3 a,b. At KThe ENB, ENW and TUX breeds that formed a separate cluster partially overlapped each other. This was consistent with the history of the TUX descent through crossbreeding between ENW and English ENB, as well as the selection of these breeds for egg production. The formation of a joint cluster of JAP, EST, TEW and PHA breeds also conformed to the history of their origin and breeding. In particular, when creating EST, the PHA, JAP and ENW breeds were involved, and when developing TEW, the PHA breed was used.FST genetic distances  are presented in In the era of integrative agriculture, there is a need, in the process of monitoring, breeding and selection, to link genetic and genomic technologies to breeding regimes for economically important traits  of heterozygotes, while FROH conforms to the proportion of homozygous regions in the genome. The latter estimate appears to be more accurate than the former one because it directly assesses genomic homozygosity. These two indicators are normally calculated and reported in similar studies  t,70 tFIS To date, there have been a number of investigations focusing on the genetic diversity in quails. These studies were mainly related to the assessment of the genotypes of domestic and wild quails in order to characterize the genetic structure of populations in these species ,72,73, aCyrtonyx montezumae) using whole-genome sequencing data from 74 quails. Ogada et al. [This approach can sometimes be employed in combination with traditional molecular markers. For example, Mathur and DeWoody  examineda et al.  utilizeda et al. ,91. For a et al.  reportedUHE = 0.265 vs. 0.319; Using GBS analysis for the first time to analyze the genomes of different domestic quail breeds, we observed a lower genetic diversity in TUX as compared to JAP quails  and identified their evolutionary relationships suggesting a possible relation to their performance. In particular, the respective genetic divergence was shown for the egg , meat (TEW and PHA) and dual-purpose (EST) utility types. This study contributes to the identification of genetic differentiation and determination of relatedness between the studied breeds. In addition, it was demonstrated for the first time that the GBS analysis method is instrumental in the intra- and inter-breed assessment of genetic variation in domestic quails. The information reported here facilitates a deeper understanding of the processes of breed formation and selection in quails and can be further used to improve their economically important traits by identifying significant SNPs and candidate genes associated with these traits.