Red dairy cattle breeds have an important role in the European dairy sector because of their functional characteristics and good health. Extensive pedigree information is available for these breeds and provides a unique opportunity to examine their population structure, such as effective population size, depth of the pedigree, and effective number of founders and ancestors, and inbreeding levels. Animals with the highest genetic contributions were identified. Pedigree data included 9,073,403 animals that were born between 1900 and 2019 from Denmark, Finland, Germany, Latvia, Lithuania, the Netherlands, Norway, Poland, and Sweden, and covered 32 breeds. The numerically largest breeds were Red Dairy Cattle and Meuse-Rhine-Yssel.The deepest average complete generation equivalent (9.39) was found for Red Dairy Cattle in 2017. Mean pedigree completeness ranged from 0.6 for Finncattle to 7.51 for Red Dairy Cattle. An effective population size of 166 animals was estimated for the total pedigree and ranged from 35 (Rotes Höhenvieh) to 226 (Red Dairy Cattle). Average generation intervals were between 5 and 7 years. The mean inbreeding coefficient for animals born between 1960 and 2018 was 1.5%, with the highest inbreeding coefficients observed for Traditional Angler (4.2%) and Rotes Höhenvieh (4.1%). The most influential animal was a Dutch Meuse-Rhine-Yssel bull born in 1960. The mean inbreeding level for animals born between 2016 and 2018 was 2% and highest for the Meuse-Rhine-Yssel (4.64%) and Rotes Hohenvieh breeds (3.80%).We provide the first detailed analysis of the genetic diversity and inbreeding levels of the European red dairy cattle breeds. Rotes Höhenvieh and Traditional Angler have high inbreeding levels and are either close to or below the minimal recommended effective population size, thus it is necessary to implement tools to monitor the selection process in order to control inbreeding in these breeds. Red Dairy Cattle, Vorderwälder, Swedish Polled and Hinterwälder hold more genetic diversity. Regarding the Meuse-Rhine-Yssel breed, given its decreased population size, increased inbreeding and low effective population size, we recommend implementation of a breeding program to prevent further loss in its genetic diversity. The modern European red dairy cattle breeds are widely used both within and outside Europe. The European red dairy breeds are known for their functional characteristics and good health including a higher fertility, fewer claw and leg problems, excellent udder health, easy calving, and lower incidence of still birth compared to the Holstein–Friesian breed –4. In adAnimal husbandry in Europe has historically been characterized by a large phenotypic variation between cattle populations from different geographical regions . BecauseMaintaining the genetic diversity of breeds is crucial for future improvements of European livestock in order to respond to changes in climate, emerging diseases, or consumers’ preferences , 9. The Pedigree analysis is an important tool to describe the genetic diversity of a breed and its evolution across generations . InbreedAnother threat to the genetic integrity of dairy cattle breeds is the use of bulls from higher-yielding breeds as a shortcut to improve performance. Crossing and subsequent selection may erode the genetic composition of the recipient breed. Pedigrees contain (or should contain) information about such genetic introgression.Animal numbers have been declining in many European red dairy breeds e.g., , 21) and and21]) Pedigree information was collected from the Animal Breeders Association of Latvia , CRV (the Netherlands), Geno (Norway), Lithuanian Red Cattle Improvement Association , Viking Genetics , Vereinigte Informationssysteme Tierhaltung w.V. , and Wroclaw University of Environmental and Life Sciences . The submitted records included information on the animal, sire, and dam ID and birth dates. Pedigrees were created according to the Interbull international ID structure  and allThe total number of unique animals in the resulting database was 9,073,403. Data on the number of animals in the pedigree for each breed and for each sex are in Table The country of origin and breed code for each animal were derived from the international animal ID. The total number of countries of origin was 30 and the number of registered breed codes was 32. The breeds were defined according to the breed codes assigned by the Interbull Centre . Some of these breed codes are collective codes for several national breeds, e.g., Red Dairy Cattle (RDC), which includes Swedish Red, Danish Red, Finnish Ayrshire, Polish Red, Norwegian Red, Lithuanian Red and Latvian Brown among others. The Swedish polled (SKB) includes the Swedish native breeds: Swedish Polled, Swedish Mountain Cattle and Swedish Red Polled. Finncattle (FIC) includes the Finnish native breeds: Eastern, Western and Northern Finncattle. In the current study, some national breeds were submitted to the pedigree with their national breed codes, e.g., Rotes Höhenvieh (RVH), Traditional Angler (RVA), Vorderwälder (VWD) and Hinderwälder (HWD). The two numerically largest breeds were RDC with 6,014,920 animals and Meuse-Rhine-Yssel (MRY) with 2,498,187 animals , Simmental (SIM) and Aberdeen Angus were also present in the pedigree, either as parents of a targeted red dairy animal that had been crossed for improved milk production, or for dual-purpose breeding. Data from the non-targeted breeds are not representative for the red dairy cattle breed and were excluded from further analyses, mainly because of their high level of cross breeding and of their low pedigree completeness level. Unless otherwise stated, the populations used in the analyses included the targeted red dairy breeds  with more than 500 animals in the pedigree, born between 1960 and 2018.We used the software package RelaX2  for pediOne way to characterize pedigree data is to estimate the number of complete generation-equivalents, defined as the sum of the proportion of known ancestors over all traced generations. The equivalent complete generation (ECG) was estimated as the sum of ancestor . EquivalSubpopulations were identified within the entire database. The definition of a subpopulation is a group of animals that are connected via a common ancestor or via descendants . AnimalsGeneration intervals were calculated along the four gametic pathways: sire to son  are assumed to be unrelated and have an inbreeding coefficient of 0. The effective number of founders . The exMarginal contributions, i.e., the contribution not yet explained by the other ancestors, were computed to account for relationships among ancestors by selecting the animal with the largest contribution to the population, then iteratively selecting animals with the largest contribution not accounted for by previously selected individuals. Marginal contributions  or “RED” (Red Holstein).The proportion of ge gRain  for the ge gRain ). PartiaGene flow between breeds and between countries of origin was studied in animals born between 2010 and 2018. This was performed by studying the breed of the animals born between 2010 and 2018, which belonged to the targeted red dairy breeds, and the breed of the parents, which could belong to any breed. The results were illustrated by an alluvial plot produced using the R-package “ggalluvial-package” version 0.11.1  in R 2424. Gene The number of animals born between 1960 and 2018 was 9,036,935. At the population level, the number of individuals with unknown parents was 1,559,777. Data on the population structure including Regarding the subpopulations, the largest one included 8,926,186 animals from all 30 countries of origin and from all 32 breeds . Seven of the other subpopulations included more than 100 individuals and several subpopulations included only one breed originating from one country.Complete generation equivalents by year of birth are shown in Fig. Sixty-eight percent of the 2,233,059 animals born between 2010 and 2018 were purebred animals (i.e. breed proportion of one breed higher than 80%). The breed purity and inbreeding level of the observed purebred breeds are in Table Almost 2.4% 53,885) of the purebred animals in the pedigree had an inbreeding coefficient greater than 6.25%, and all breeds had at least one purebred animal with an inbreeding coefficient greater than 12.5%  and the longest for MRY (6.5 years). The mean generation interval for all animals born between 1960 and 2018 was longer (5.5 years) compared to animals born between 2010 and 2018 (4.8 years). Mean generation intervals per 10 years are shown in Fig. Table Gene flow between breeds is illustrated in Figs. RVA and RHV descendants were more influenced by other breeds compared to the other targeted red dairy breeds Figs.  and 5. ABreeding organizations within the countries included in the current study  have used sires from other countries than their own, for breeding purposes. In Germany, the Netherlands, Norway and Poland, more than 93% of their animals were from sires born within the country. In Sweden, Denmark and Finland, the number of animals with sires born within country ranged from 68 to 76%. Only 52% of the Lithuanian animals and 28% of the Latvian animals had sires born within country, with sires from other countries being used.In contrast to the numerous pedigree analyses on Holstein cattle –37 and oMinor and traditional breeds are in danger of being replaced by higher-yielding modern breeds in highly efficient breeding programs. They are also in danger of losing their genetic integrity due to admixture with higher-yielding breeds. Several of the modern red dairy cattle breeds in Europe produce high-quality milk and in large quantities, display functional and health traits of interest, and show good adaptation to different environments . Proper The level of inbreeding depends on the breed’s pedigree completeness. In particular, if the fraction of missing parents is large in a pedigree, this can lead to substantial underestimation of the inbreeding level , 44. TheGenetic drift and loss of breeds are the most important factors that affect genetic diversity. Uncontrolled inbreeding leads to inbreeding depression which could lead to different negative consequences. In particular, smaller and endangered populations are very exposed to inbreeding. The greatest value of The inbreeding coefficient for an individual is very sensitive to the quality of the available pedigree information ; thus, areported , 41. Thenish Red  which isnish Red  which isSince the 1960s, the inbreeding level has steadily increased for the MRY, RDC and SKB breeds Fig. . For theThe parameters derived from the probabilities of gene origin, as described by Boichard et al. , are use animals  could beg et al. , in whicg et al. , both thentclass1pt{minimaThe average generation interval in the MRY, RDC and HWD breeds has decreased since the 1960s Fig. . DecreasThe effective number of founder animals for each population is proportional to the size of the population for all breeds. The same pattern was found in the Irish breeds studied by Mc Parland et al. . In spitFounders and individuals near the top of the pedigree are favored in the estimation of marginal contributions. Although the individuals further down the pedigree also make large genetic contributions to the population, these are diluted because they have already been partly accounted for by their ancestors. In our study, a large part of the animals were born in distant years, i.e., near the top of the pedigree and, thus may have a large impact on the estimation of marginal contributions. The uneven number of animals within each breed also influences the estimates of marginal contributions across breeds. Since the sizes of the MRY and RDC populations were the largest, they may have suppressed the contributions of animals in other smaller breeds in the analysis of marginal contributions.When we analyzed the gene flow among countries, we found that foreign sires and dams were rarely used in the Norwegian population. This is in line with the closed isolated subpopulations observed here, which could be due to the farms being geographically distant in Norway. To test this hypothesis, further studies based on data about the geographical location of the animals are needed.We provide the first detailed analysis of the genetic diversity and inbreeding levels of the European red dairy cattle breeds. Based on our results, it is necessary to control the inbreeding level of both the RHV and RVA breeds to prevent larger losses in their genetic diversity. The RDC, VWD, SKB and HWD breeds hold considerably more genetic diversity and are therefore less prone to inbreeding problems. Regarding the MRY breed, because of its decrease in population size, its increasing inbreeding level, and


Rangifer (Reindeer and caribou: Cervidae) was last revised in 1961. Recent literature shows that many of the subspecies and several species synonymised out of existence are, in fact, valid, some names have been misapplied, and new subspecies-level clades are in need of description. This paper reviews available names for recently defined ecotypes of reindeer and caribou in compliance with ICZN rules for zoological nomenclature.Advancements in molecular and phylogenetic analysis have revealed the need for greater taxonomic resolution since  Rangifer species or subspecies have been named in North America; 31 in Europe and Asia  , dated to the middle Pleistocene (Günz) period, 680,000 to 620,000 BP  its European range was extensive (The oldest North American ent (BP) . A fossi-shaped”  antlers 0,000 BP . Rangifeciations , the secxtensive , supplyiR.t.fennicus or R.t.angustirostris, an eastern Siberian forest form , in Fennoscandia and western Russia, and possibly also eastern Russia  and occur along the fringes of the Rocky Mountain and Laurentide ice sheets as far south as northern Alabama; and in Sangamonian deposits  from western Canada  that have adapted to high-elevation alpine tundra with snow characteristics similar to Arctic tundra: hard-packed, shallow snow that they can paw through to reach terrestrial lichens. The haplotype composition of reindeer from southern Norway is similar to, but "substantially genetically different from” that of the tundra reindeer from western Russia, R.t.sibiricus  (The "mountain reindeer” of Norway (and formerly Sweden and Finland) are tundra reindeer  .Rangifert.sibiricus includes 19 herds, named for their calving grounds, from Arkhangelsk in European Russia to Chukotka, Siberia  caribou derived from ancient Beringian-Eurasian pre-glacial populations, based on shared mtDNA haplotypes, but evolved in separate refugia during the LGM  are the most genetically distinct of all Rangifer subspecies ; they are not, however, closely related to each other, with a 69% genetic differentiation between them . The range of Kamchatka reindeer therefore should be restricted to the Kamchatka Peninsula.The Okhotsk or Kamchatka reindeer, ptations . RozhkovR.t.fennicus Lönnberg, 1909, was described from Finnish Lapland  pairwise FST values, using a "genome-wide bovine SNP genotyping array”, averaged 3.8%–9.4%, "consistent with their morphological and ecological differences” ; guettardi was later replaced by R.constantini, a more evolved tundra form  is a montane form whose ecology parallels that of British Columbia’s mountain caribou (see below). It migrates altitudinally in dense coniferous forests at elevations of 400–1,500 m, where snow cover is 130–250 cm, and forages arboreal lichens in winter  and NAL (North American) lineages, respectively.Early genetic analyses showed two major lineages of rdillera . COSEWICBEL barren-ground caribou subspecies are R.t.groenlandicus sensu lato of the mainland tundra, R.t.caboti of Labrador, R.t.osborni of the northern cordillera, R.t.pearyi of the High Arctic, and the extinct insular R.t.dawsoni  bones of the extinct, insular Dawson caribou, originally R.dawsoni Seton-Thompson, 1900, were similar to barren-ground caribou but smaller, evidence of island dwarfism  and a little less closely to Alaska barren-ground caribou .Rangiferexcelsifrons Hollister, 1912, Rangifermcguirei Figgins, 1919, and Rangiferogilviensis Millais, 1915, under R.stonei Allen, 1901  is the Porcupine Herd (named for a river that flows from Yukon into Alaska) of barren-ground caribou that winters mainly in the Ogilvie Mountains, Yukon, and calves on the Alaska-Yukon coastal plain. Their migratory, rutting and calving-aggregation behaviours and small size  herds, all geographically small, isolated mountain herds, "appeared relatively discrete with > 0.50 population assignment to one cluster, rather than several [and] had high pairwise differentiation”  and maintains separate alpine rutting and calving areas. R.t.osborni, clustered into four groups based on microsatellite DNA analysis and three based on mtDNA . It has seven recognised herds on mainland Canada, defined by calving grounds, and extends into Alaska. Barren-ground caribou are smaller and paler than woodland caribou, but have longer, thinner antlers that, in males, sweep back, up and forward, main beams reaching > 135 cm with multiple tines at the top, often palmated  expedition  from North West Territories … suggesting common origin of the ancestors of these populations”  "is evidence of evolutionary significance and points to Baffin Island caribou as a candidate for consideration as a DU”  with those in Labrador and two each with Dolphin and Union and Bluenose herds of barren-ground caribou .Greenland caribou is neither of the BEL lineage, from which descend all Eurasian and Canadian tundra reindeer and caribou, nor the NAL lineage of woodland caribou: it clusters outside of the BEL cluster, as do Svalbard reindeer  Greenland reindeer . Yannic L., 1767 .NAL lineage as other woodland caribou, but from the BEL lineage. They quoted caribou north of 60° latitude were more likely "splinter populations of barren ground caribou, which have adapted to a more sessile life-style, increased in body size, and assumed some ‘woodland mannerisms’”. Since the best-supported model of R.a.osborni, it should be considered an ecotype of Osborn’s caribou.th edition of Linnaeus’ ‘Systema Naturæ’, carried forward Cervustarandus γ caribou. Rangifer taxonomists  during the LGM, giving rise to two clades of boreal woodland caribou east and west of a "suture zone” in Manitoba . He gave its range as west of Hudson’s Bay and James Bay in rocky (Precambrian Shield) habitat, west to Lake Superior and Lake Athabasca. In Fig. Contrary to the practice of the Barren-Ground Caribou, the Woodland variety travels to the southward in the spring. They cross the Nelson and Severn Rivers  in immense herds in the month of May, pass the summer on the low, marshy shores of James’ Bay, and return to the northward, and … retire more inland in the month of September.”"caribou south of the Barrenlands. He did not mention having examined museum specimens back in London, did not figure the animal or give metrics supporting its being "much larger than the Barren-Ground Caribou [and] has smaller horns” and most of his account of R.t.sylvestris is hearsay. His description of "immense herds” in migration precludes its application to woodland caribou.caribou of the woodland ecotype DU6, sampled in Alberta north and south of the Peace River, clustered separately and were genetically distant from each other (average FST = 5.9% between pairs). Could those north of the Peace River be the newly discovered BEL clade of boreal "woodland” caribou in the Mackenzie Valley  between migratory and montane herds, whereas woodland caribou of southern Quebec were significantly differentiated (FST = 1.8%–4.8%). caribou. Labrador caribou are also smaller than Quebec woodland caribou  and pelage . Forest reindeer and woodland  caribou . Woodlanorms cf. .There are no generally accepted thresholds of genetic distance to distinguish species within a genus or subspecies within a species. Genetic distances comparing mtDNA sequences among cervid genera are generally 12%–18%  subspecies  ranged from 27.6% to 32.1%  that are at least as distinct as species in other taxa, and (2) many previously named subspecies and some previously unidentified ecotypes are distinct enough to be recognised as subspecies, or even as full species.Genetic distances and FRangifer:The following names are available and should be used for ecotypes and phylogenetic clades of ST = 44% from all other caribou  and barren-ground (BEL) clades range from 135,600 years ago during the penultimate (Illinoian) interstadial , ecology, and behaviour. This clearly supports that the woodland caribou should be restored to species level, Rangifercaribou Gmelin, 1788  results from isolation in at least four glacial refugia south of the ice sheets  of 20% to wild R.t.tarandus from Norway, but only 5% to Alaskan barren-ground caribou  have revealed distinct subspecific clades of R.arcticusandersoni 1913. AMNH M-34433 would be a suitable neotype specimen, type locality "south side of Coronation Gulf”.Dolphin and Union barren-ground caribou, DU2. Unnamed Baffin subspecies. Rangifertarandus” whether they were from domestic or wild types, or R.t.tarandus, R.t.sibiricus or R.t.fennicus .Eurasian reindeer diversity is clouded in the English literature because many geneticists labelled their samples as "Domestic reindeer, a large, multi-faceted industry throughout Russia and Siberia, show little genetic exchange with wild reindeer and their population identities are mutually exclusive e.g., .R.t.tarandus Trouessart, 1898, mountain reindeer, is restricted to Norway, Sweden, Finland and Russia (Murmansk).R.t.sibiricus von Schreber, 1784. Siberian reindeer, ranges from Arkhangelsk eastwards.R.t.taimyrensis Michurin, 1965. However, its genetic separation  from the Yakutsk population ofR.t.sibiricus, > 1,000 km to the east, is only FST = 0.5% angustirostris awaits genetic sampling and phylogenetic analysis.


Passerina ciris) using a genetic‐based approach. We started by mapping patterns of genetic variation across geographic space  using 386 individuals from 25 populations across the breeding range. We then genotyped an additional 230 samples from 31 migration stopover locations and 178 samples from 16 wintering locations to map patterns of migratory connectivity. Our analyses of genetic variation across the breeding range show the existence of four genetically distinct groups within the species: Eastern, Southwestern, Louisiana, and Central groups. Subsequent assignment of migrating and wintering birds to genetic groups illustrated that birds from the Central group migrated during the fall via western Mexico or southern Texas, spent the winter from northeastern Mexico to Panama, and migrated north via the Gulf Coast of Mexico. While Louisiana birds overlapped with Central birds on their spring migratory routes along the Gulf Coast, we found that Louisiana birds had a more restricted wintering distribution in the Yucatan Peninsula and Central America. Further estimation of the straight‐line distance from the predicted breeding location to the wintering location revealed that individuals sampled at lower winter latitudes traveled to greater distances , confirming that these species exhibit a leapfrog migration pattern. Overall, these results demonstrate the utility of a genoscape‐based approach for identifying range‐wide patterns of migratory connectivity such as leapfrog migration with a high degree of clarity.Technological advances in migratory tracking tools have revealed a remarkable diversity in migratory patterns. One such pattern is leapfrog migration, where individuals that breed further north migrate to locations further south. Here, we analyzed migration patterns in the Painted Bunting ( Passerina ciris) using genetic data. We found four genetically distinct groups: Eastern, Southwestern, Central, and Lousiana. Passerina ciris has a leapfrog migration pattern.We studied migration in the Painted Bunting ( The assumption is that migration occurs because the conditions in the breeding range become adverse, and species move to regions where conditions are more favorable, and food is more abundant Newton, . Long‐diOne classic pattern of migration, which has been described, but only rarely documented with clarity, is leapfrog migration. This occurs when the most northerly breeding populations of a species winter the furthest south, while the populations that breed further south migrate a shorter distance to winter Welty, . As a rePasserella ilica unalaschcensis), did not support the leapfrog pattern. In contrast to expectations, individuals wintering further south overlapped on the breeding grounds with individuals that wintered further north and there was unexpectedly high degree of mixing between the two wintering ranges  are distributed from North Carolina to Florida. The western subspecies  has a distribution area that is 25 times larger than the eastern subspecies, which ranges from northeastern Mexico  to central Texas, southeast Kansas, east to west Tennessee, Mississippi and Oklahoma , collaborators from universities in Latin American countries, the Institute for Bird Populations, and independent banding stations. One hundred and twenty‐six individuals from 13 populations were previously used to test for genome‐wide population structure  for the preamplification of the SNPs and the Juno GT Preamp Master Mix for the final amplification. For each run, we screened 94 individuals and two nontemplate controls. We imaged the results on an EP1 Array Reader and called alleles using Fluidigm's automated Genotyping Analysis Software (Fluidigm Inc.) with a confidence threshold of 90%. In addition, we visually inspected all SNP calls and removed any calls that did not fall clearly into one of three clusters (heterozygote or either homozygote cluster). As DNA quality can affect call accuracy, we employed a stringent quality filter and dropped variants with missing calls exceeding 20%.2.3To create a genoscape with the best quality data, we filtered out samples with high values of relatedness to eliminate closely related samples from further analyses. To do this, we conducted a kinship analysis by formatting our whole genome data in the PLINK package , where K ranged from 1 to 5  and clipped to the Painted Bunting breeding range using the latest eBird shapefile  to the genetically distinct groups characterized by the final genoscape using 33.1Our STRUCTURE analysis of 386 samples from 25 populations across the breeding range showed that the breeding populations could be robustly divided into four genetically distinct groups: an eastern cluster represented by sampling in North Carolina, Georgia, and Florida, a southwest genetic cluster , a coastal south‐central cluster in Louisiana, and a central cluster that ranges from an isolated New Mexico population to eastern Arkansas Figure . Not sur3.2Most birds captured outside the breeding season were assigned to the Central group Figure . UltimatThe predicted breeding location of wintering individuals supports the population assignments for the most part, as most wintering birds had predicted locations within the distribution of the Central genetic group Figure . Howeverp > .001; Figure Lastly, to better understand the observed migratory patterns in space, we estimated the straight‐line distance from the predicted breeding location to the wintering location of each individual. If Painted Buntings exhibit a leapfrog migration pattern, we would expect birds further north traveling to winter further south, thus traveling greater migration distances. Here we found that, indeed, individuals sampled at lower winter latitudes traveled greater distances  in southwestern Mexico. Alternatively, leapfrog migration may be explained by variations in habitat quality at breeding and wintering location. Specifically, high‐quality breeding habitats further north may support longer‐distance migration, allowing birds to overwinter further south where they may also have greater access to resources ; formal analysis ; methodology ; writing – original draft ; writing – review and editing . Christen M. Bossu: Data curation ; formal analysis ; methodology ; supervision ; validation ; visualization ; writing – review and editing . Thomas B. Smith: Writing – review and editing . Andrea Contina: Conceptualization ; visualization ; writing – review and editing . Ricardo Canales del Castillo: Data curation ; investigation ; writing – review and editing . Kristen Ruegg: Conceptualization ; data curation ; funding acquisition ; investigation ; methodology ; project administration ; resources ; supervision ; validation ; visualization ; writing – review and editing . Blanca Estela Hernandez‐Baños: Conceptualization ; investigation ; supervision ; visualization ; writing – original draft ; writing – review and editing .Appendix S1Click here for additional data file.


Nonsyndromic orofacial clefts belong to a class of congenital malformations characterized by a complex and multifactorial etiology. During early facial development, multiple factors can disrupt fusion leading to a cleft; this includes the shape of the embryonic face. The face shape hypothesis (FSH) of orofacial clefting emerged in the 1960s, influenced by morphological differences observed within affected families, comparative studies of mouse models, and advances in modeling genetic liability for complex traits in populations. For the past five decades, studies have documented changes in the shape or spatial arrangement of facial prominences in embryonic mice and altered post-natal facial shape in individuals at elevated risk for orofacial clefting due to their family history. Moreover, recent studies showing how genes that impact facial shape in humans and mice are providing clues about the genetic basis of orofacial clefting. In this review, I discuss the origins of the FSH, provide an overview of the supporting evidence, and discuss ways in which the FSH can inform our understanding of orofacial clefting. It is well established that nonsyndromic orofacial clefts have a complex multifactorial etiology . Genes, Three lines of converging evidence led to the development of the FSH: the first is observational and relates to the presence of distinctive orofacial traits in individuals whose baseline genetic risk for clefting is higher than average; the second is experimental and relates to work done using embryos of mouse strains with varying natural susceptibility to clefting; and the third is theoretical and relates to the establishment of models to conceptualize disease liability in populations.th century  concept emerged in the 1930’s as a way to model the inheritance of discrete traits in mammals that did not follow strict Mendelian patterns ; 1934b. By the start of the 1970’s, the notion that certain aspects of facial shape may contribute to cleft liability was beginning to gain acceptance. Since these early efforts, there have been over 30 quantitative studies comparing facial morphology between the ostensibly unaffected parents and/or siblings of individuals with clefts and controls with no family history of the defect. These studies employ a variety of methods including cephalometry , direct What we can state is that every study conducted thus far has reported at least some significant differences in facial morphology between unaffected relatives and controls. Moreover, these differences have been reported for every part of the craniofacial complex and many appear to be sex-specific. However, sorting out which specific facial features are reliable indicators of cleft-risk has been a challenge. There have been a few narrative reviews on the topic , but it NOG (associated with philtrum shape), THADA  and PAX7, MSX1, and PTCH1 .One expectation of the FSH is that the genes that drive face shape variation in humans will also be implicated in orofacial clefting (and vice-versa). PRDM16, PAX7, PKDCC, SATB2, COL8A1, MSX1, FAT4, FGFR2, PAX9, BMP4, GREM1, NOG, BMP2, PAX1, and BMP7. Moreover, in many cases, phenotypic effects at the cleft-related GWAS signals were localized to anatomical regions directly relevant to clefting, like the central midface. For example, the rs227727 SNP near NOG was strongly associated with normal-range upper lip shape  11.25 in mispositioned and misshapen medial nasal prominences that can no longer make contact to form an intact primary palate. When examined at a slightly later timepoint (GD 17), treated mice showed midface hypoplasia and broader upper facial dimensions, and many developed clefts  present in families , although at least one study has shown differences along these lines . Similar


The birthplace (the place where an athlete was born) effect (BPE) has been found to be one of the environmental variables associated with early talent development and the achievement of a high level of proficiency in sport. The purpose of the current study is twofold: (1) to calculate the BPE in 14–18-year-old athletes who participated in individual and team sports and (2) examine how coaches perceived this effect. The participants were 1397 athletes  who competed in 5 individual  and 5 team  sports, as well as 147 coaches who provided their preliminary thoughts about the BPE. Data analyses revealed that although the BPE was not found to be associated with cities of a similar size, it was observed that growing up in cities of small and medium sizes was more beneficial than growing up in towns or cities of other sizes. Most of the coaches believed that certain characteristics of the place or city where the athlete grew up  could contribute positively to the athlete’s development. We discuss how the BPE data can aid policymakers in developing a sport policy associated with early phases of talent development. One of the environmental factors linked to the development of expertise in sport is the place where an athlete was born ) . A tyBPE data were collected on male athletes in major sports, among them being baseball, basketball, golf, hockey , footbalSupport for the above-mentioned BPE findings was also found in quantitative studies using an alternative approach to examining this effect: population density. In one study conducted on male Danish team handball and soccer players, it was reported that the overall participation rates were higher among players who were born in low-density communities rather than high-density communities . In anotA number of arguments has been made to explain why it was more beneficial for young athletes to grow up in cities of a small-to-medium size than in cities of a different size. Although large cities can provide children and youths with enhanced conditions such as well-designed and equipped sporting facilities, as well as better coaching guidance, it appears that the large cities’ sport programs are heavily structured around and hindered by the lack of space and time in which young athletes can participate ,12. The Similar explanations for the benefits athletes can gain in cities of small-to-medium sizes were also provided in a number of qualitative studies ,14. In aOf particular interest to the aims of the current BPE study are two studies in which BPE data were collected on elite male and female Israeli athletes. In one study , data weGrowing up in a city of a medium size was also found to be advantageous for team handball players. The likelihood of players who were born in a medium city to play in Division 1 was higher than that for players who were born in a city of a different size. For the volleyball players, it was indicated that the likelihood of players who were born in a small place (<2000 people) to play in Division 1 was higher than that for players who were born in a city of a different size. The main finding of Lidor et al.’s  study reIn another study on female elite athletes , BPE datDevelopmental Model of Sport Participation (DMSP) (see )As such, the purpose of the current study was twofold: (1) to examine the contribution of Israeli athletes’ (ages 14–18 years) city sizes  to sports participation and performance and (2) collect preliminary data on how the coaches perceived this effect. The data on the BPE of the young athletes obtained in the current study were compared with the BPE data collected in two previous studies on elite male  and femaSD = 1.54), among them being 390 females  and 1007 males . The athletes were part of 10 sports: 5 individual  and 5 team  sports. The number of athletes in each sport is described in The BPE was assessed in 1397 14–18-year-old athletes  participated in the study. Among these coaches, 68  worked directly with the 14–18-year-old athletes who participated in our study, and 79 worked in the same sports programs as the other coaches but did not work with the athletes who participated in the current study. All the coaches were certified by their sports federations.This study was approved by the ethics committee of the Academic College at Wingate (Ethic Code # 112).n = 68) and sent them the questionnaires via electronic mail.Information about the athletes’ birthplaces, genders, and types of sport was obtained via questionnaires, which were given to the athletes by their coaches. Each director of the sport program where the athletes who participated in the study practiced received a letter providing the background and objectives of this study. In addition, informed consent was obtained from the parents of the participants. After approval was obtained, the first author approached the coaches of the athletes (www.cbs.gov.il/shnaton60/st02_11x.pdf (accessed on 10 February 2022)). Four categories of city size were used in the study, as performed in previous BPE studies on Israeli athletes  were were16])ils, see ,16). TheThe selection procedure we used in the current study was also applied in two previous studies on Israeli elite male  and femaSince the mean age of the participants  was 16.32 years and the majority of the data in our study were collected in 2016, we used the census data presented in 2014. That year was the most appropriate one to reflect the ages of 14 of the athletes  who took part in our study.In order to collect preliminary information on how the BPE is perceived by coaches, 150 coaches were approached by the first author. The coaches were asked to answer in writing a closed question related to the BPE: “Do you think that the athletes’ places of birth affect their development?” There were two options for the answer: “yes” or “no”. Only if the answer was “yes” was the coach asked another (open) question: “How does the place of birth contribute to the athletes’ development?” The coaches were asked to outline how the BPE contributed to the athletes’ development. Out of the 150 coaches who were approached, 147 responded (rate of responsiveness = 98%).To test the BPE, odds ratios (ORs) were calculated to determine the likelihood of participating in each of the sport programs  for each city size, and 95% confidence intervals (CIs) were calculated around each OR. An OR greater than 1 (with upper and lower limits higher than 1) implied that an athlete born in the given city size was more likely to become a participant in the sport program in the given sport than if they had been born in any other city size. An OR of less than 1 (with upper and lower limits less than 1) implied that an athlete born in the given city size was less likely to become a participant in the sport program than if they had been born in a city of a different size. ORs including the value of 1 within their CI range were not considered to be statistically significant.As indicated previously, we were aware of the fact that the sample size of the female athletes was relatively small (390 participants). However, it was our aim to analyze their data across the different sports in order to strengthen our understanding of the existence of the BPE in the various sport programs available to female children and youths in the country. The responses of the 147 coaches who provided exploratory information about the BPE were descriptively analyzed. The coaches’ responses were also thematically analyzed.The results are presented separately for the BPE and the responses of the coaches.The representations of the Israeli population aged 14–18 years, the male athletes, and ORs and CIs across cities of different sizes are presented in Data are presented separately for individual sports and team sports.In only two sports was the BPE found to be significant: gymnastics and swimming. In gymnastics, it was found that small towns of up to 2000 people yielded an OR significantly higher than 1. This means that the likelihood of participating in a competitive sport program for those gymnasts who were born in a small place was higher than that for those who were born in a city of a different size. In addition, cities with a population of 50,000–200,000 yielded an OR significantly lower than 1. The likelihood for gymnasts who were born in a medium-sized city to be part of a competitive sport program was lower than that for those who were born in a city with a smaller or larger population.Similar results were obtained in swimming; small towns of up to 2000 people yielded an OR significantly higher than 1. This means that the likelihood of participating in a competitive swimming program for swimmers who were born in a small place was higher than that for those who were born in a town or city of a different size. In addition, cities with a population of 50,000–200,000 yielded an OR significantly lower than 1. The likelihood for swimmers who were born in a medium city to be part of a swimming program was lower than that for those who were born in a city with a smaller or larger population.The BPE was found to be significant in each sport; however, mixed results were observed. In basketball, cities with a population of 50,000–200,000 yielded an OR significantly higher than 1. This means that the likelihood for basketball players who were born in a medium-sized city to be part of a basketball program was higher than that for those who were born in a city of a larger or smaller size. In soccer, the findings for cities of the same size  were found to be the opposite; cities of this population size yielded an OR significantly lower than 1, and the likelihood for the soccer player who was born in a medium-sized city to participate in a competitive soccer program was lower than that for those who were born in a city with a smaller or larger population.For the team handball players, cities with a population of 50,000–200,000 yielded an OR significantly higher than 1. This means that the likelihood for team handball players who were born in a medium-sized city to be part of a team handball program was higher than that for those who were born in a city of a larger or smaller size. It was also found that small towns of up to 2000 people yielded an OR significantly lower than 1. The likelihood of participating in a competitive sport program for those team handball players who were born in a small place was lower than that for those who were born in a place with a higher population.For the volleyball players, small towns of up to 2000 people yielded an OR significantly lower than 1. This means that the likelihood of participating in a competitive sport program for those volleyball players who were born in a small place was lower than that for those who were born in a city of a different size. Finally, for the water polo players, cities with a population of more than 200,000 people yielded an OR significantly higher than 1. This means that the likelihood for water polo players who were born in a big city to be part of a water polo program was higher than that for those who were born in a city of a different size.Data are presented separately for individual sports and team sports.The BPE was found to be significant in three sports: gymnastics, judo, and track and field. For the gymnasts, cities with a population of 2000–50,000 or 50,000–200,000 people yielded an OR significantly lower than 1. This means that the likelihood for gymnasts who were born in small-to-medium-sized cities to be part of a competitive sport program was lower than that for those who were born in a city of a different size. For the judokas, it was found that small towns of up to 2000 people yielded an OR significantly higher than 1. This means that the likelihood of participating in a competitive judo program for those individuals who were born in a small place was higher than that for those who were born in a city with a larger population.For the track and field athletes, being born in a small town was also found to be beneficial; small towns of up to 2000 people yielded an OR significantly higher than 1. This means that the likelihood of participating in a competitive sport program for those track and field athletes who were born in a small place was higher than that for those who were born in a city with a larger population. In addition, it was found that cities with a population of 50,000–200,000 people yielded an OR significantly lower than 1. The likelihood for those who were born in medium-sized cities to be part of a competitive sport program was lower than that for those who were born in a city of a different size.The BPE was found to be significant in all team sports but water polo. . Mixed results were indicated. For the basketball players, cities with a population of more than 200,000 people yielded an OR significantly higher than 1. This means that the likelihood for basketball players who were born in a big city to be part of a basketball program was higher than that for those who were born in a city with a smaller population. In addition, cities with a population of 2000–50,000 people yielded an OR significantly lower than 1. This means that the likelihood for those who were born in small cities to be part of a competitive sport program in basketball was lower than that for those who were born in a city of a different size.For the soccer players, cities with a population of 50,000–200,000 yielded an OR significantly lower than 1. The likelihood for soccer players who were born in a medium-sized city to be part of a competitive sport program was lower than that for those who were born in a city of a different size.For the team handball players, cities with a population of 50,000–200,000 people or more than 200,000 people yielded an OR significantly higher than 1. The likelihood for team handball players who were born in medium- and large-sized cities to be part of a competitive sport program was higher than that for those who were born in a city of a smaller size. In addition, cities with a population of 2000–50,000 people yielded an OR significantly lower than 1. This means that the likelihood for those who were born in small cities to be part of a competitive team-handball program was lower than that for those who were born in a city of a different size.For the volleyball players, small towns of up to 2000 people yielded an OR significantly higher than 1. This means that the likelihood to participate in a competitive volleyball program for those who were born in a small place was higher than that for those who were born in a place with a higher population.The coaches’ responses associated with the BPE are presented in The discussion is composed of three parts. In the first part, we discuss the BPE data from the 14–18-year-old athletes, and in the second part, we examine the reflections of the coaches on the contribution of the place of residence to the young athletes’ development. Based on the BPE data obtained in our study, in the third part, we elaborate upon a number of aspects associated with the establishment of a national sport policy for talent detection and early phases of talent development. Mixed BPE results were found for the male and female athletes across the individual and team sports. The main BPE findings in the male athletes were that (1) for those who participated in individual sport programs—gymnastics and swimming—growing up in a small place (less than 2000 people) had greater benefits than any other sized place of residence; (2) for the athletes who were part of competitive basketball and team handball programs, it was more beneficial to grow up in cities of a medium size  than in cities of other sizes; and (3) only for the water polo players was it more beneficial to grow up in a large city .The main BPE findings in the female athletes were that (1) for those who participated in individual sport programs—gymnastics, judo, and track and field—it was more beneficial to grow up in a small place or in a city of a medium size (gymnastics) than in a place of a different size, and (2) for the athletes who played team sports—basketball and team handball—it was more beneficial to grow up in a large city than in a city of any other size. For the team handball players, it was also beneficial to grow up in a medium-sized city. Living in a small place was found to beneficial for only the volleyball players.The mixed BPE results of our study on the 14–18-year-old athletes were similar to the ones reported in Lidor et al.’s studies ,16 on elTherefore, the contribution of small places and cities of a medium size to the development of the young athletes in our study can presumably be explained by the instructional and social characteristics of these places, as discussed in previous BPE studies e.g., ,13,16). ,16. 13,1From a 10-year perspective, it appears that in Israel, it has been more beneficial for athletes to grow up in towns and cities of a small-to-medium size ,16. EvenHowever, the explanations favoring growing up in places or cities of small or medium sizes may not tell the whole story of the findings obtained in our study, since we also found that athletes who participated in 7 sport programs (out of the 20 programs) did not benefit from living in places or cities of small or medium sizes. It appears that additional characteristics of the place or city, rather than only its size, may be associated with the athletes’ development. The analysis of the coaches’ BPE reflections can add another dimension to the quantitatively analyzed BPE data in our study.Most of the coaches pointed out that the place of birth did in fact contribute to the athletes’ development; however, they did not focus on the size of the city but rather on other characteristics. A number of their reflections indeed strengthened the finding that it is more beneficial to grow up in small- and medium-sized cities than in cities of a greater size. For example, the coaches emphasized ways that athletes can benefit from living close to the sports facilities: they can save time on the roads, arrive independently to the facility, and feel secure on their way to and from practice. These observations are typically associated with small towns and cities of a medium size and therefore provide support for the benefits of such places to a young athlete’s development . However, the coaches also mentioned that the socioeconomic status of the place or city where the athlete grew up had the potential to influence his or her development. More specifically, they claimed that children and youths are provided with both more formal and informal opportunities to develop their sport skills in wealthy places or cities rather than those available to them in poorer places or cities. The coaches did not relate to cities of a given size. In addition, they did not mention a specific sport program that might be negatively or positively influenced by the socioeconomic status of the place or city where the athlete lived. These thoughts of the coaches reflect the idea that certain conditions, among them the availability of sport facilities, instructional support, and social support , resulted in a higher rate of sport participation among children . In addiAnother perspective that was shared by the coaches was the one related to how popular a sport program was in a given city. The coaches felt that children are initially attracted to popular sport activities; the more popular the sport, the more chances there are that the children will select it for themselves. For example, it was found in our study that the likelihood of participating in competitive sport programs for the female volleyball players who were born in a small place (up to 2000 people) where the game was popular was higher than that for those who were born in a city of a different size. These data can be explained by the fact that in Israel, the game of volleyball was originally played in places like the Kibbutz (cooperative farming settlements) and small towns , and theThe combined qualitative reports given by the coaches and the quantitative BPE analysis provide further support for the notion that small-to-medium-sized towns and cities, rather than large cities, are considered to be more suitable sports environments for children and youths  to develop their sports skills. The coaches felt that urban conditions, such as living in close proximity to the sports facilities, and the socioeconomic status of the city are detrimental contributors to a young athlete’s early development phases. The coaches argued that such conditions can help young athletes focus on the training program.Finally, the observations made by the coaches in our study can add dimensions to the concept of BPE, other than the size of the city or the population density. As noted by Wattie et al. , “… furtUp to now, a national sports policy associated with early phases of talent identification and development has not been established in Israel. In one study evaluating a number of aspects of the elite sports policy in Israel , only onBased on the data of the current study, as well as on similar data collected in previous BPE studies on adult Israeli athletes ,16 and oIn order to increase the number of children who choose to participate in a sport program, it is proposed to sport policymakers that they further develop sport programs in individual and team sports in small- and medium-sized cities by increasing (1) the number of sports facilities available to the public at large and (2) the subsidization of children’s participation in the sport program(s). The public investment in sport program(s) in small- and medium-sized cities may not only benefit the young members of the local community but also those children who live in a large city which is located near the small community. In many areas in Israel, the geographical distance between small communities and large cities is small , and therefore, those who live in a large city  may also benefit from the sport programs taking place in a nearby small- or medium-sized city.BPE data can also be used by policymakers when decisions about prioritization of sport programs are made . For exIn summary, mixed BPE findings were obtained from our study. No clear-cut conclusions can be made on the contribution of one city of one size to the development of athletes across the observed individual and team sports; that is to say, different sizes of cities were indicated to be linked to attaining success in individual and team sports.It might be of interest to policymakers in Israel to develop a national sports policy in order to maximize the potential to recruit children to sport programs and provide them with the appropriate conditions to achieve success. If this is the case, knowing about the contribution of the BPE to an athlete’s success can help policymakers implement these evidence-based data in the decision-making processes. However, if such a national sport policy is not the main interest of the ministry due to its motivation to enable the different sport federations to establish their own sport policies, then the data of the BPE can be used by the federations as well.


Monitoring the change in genetic diversity over time and space in crop species is essential to facilitating further improvement. As the world’s most important tuber crop for human consumption, and an ideal candidate to help address global food security, the cultivated potato deserves in-depth study in this regard. In this overview, some aspects of spatiotemporal diversity assessment in the cultivated potato are examined with the aim of promoting appropriate strategies for breeding programs in line with challenges relating to sustainable crop production.Solanum tuberosum L.) for the period pre-1800 to 2021. A substantial panel of 1219 potato varieties, belonging to different spatiotemporal groups, was examined using a set of 35 microsatellite markers (SSR). Genotypic data covering a total of 407 alleles was analyzed using both self-organizing map (SOM) and discriminant analysis of principal components (DAPC) de novo and a priori clustering methods, respectively. Data analysis based on different models of genetic structuring provided evidence of (1) at least two early lineages that have been maintained since their initial introduction from the Andes into Europe in the 16th century, followed by later ones coming from reintroduction events from the US in the mid-1800s; (2) a level of diversity that has gradually evolved throughout the studied time periods and areas, with the most modern variety groups encompassing most of the diversity found in earlier decades; (3) the emergence of new genetic groups within the current population due to increases in the use of germplasm enhancement practices using exotic germplasms. In addition, analysis revealed significant genetic differentiation both among and within the spatiotemporal groups of germplasm studied. Our results therefore highlight that no major genetic narrowing events have occurred within the cultivated potato over the past three centuries. On the contrary, the genetic base shows promising signs of improvement, thanks to extensive breeding work that is gaining momentum. This overview could be drawn on not only to understand better how past decisions have impacted the current genetic cultivated potato resources, but also to develop appropriate new strategies for breeding programs consistent with the socio-economic and sustainability challenges faced by agrifood systems.We investigated the changes in genetic diversity over time and space of the cultivated potato ( Crop breeding plays a crucial role in the production of high-quality food and, therefore, in improving global food security. In this regard, increasing genetic diversity is essential to provide opportunities for further improvement of crop species and to maintain agroecosystem functioning . To achiThe assessment of the trends in a crop’s genetic diversity over time and space can be addressed through a number of approaches, which depend on the different views and interpretations that exist of the concept itself . In thisThe first traces the evolution of the cultivated potato from its native to its modern form, particularly in terms of how introductions and subsequent past decisions have shaped modern lineages. Information on the parental origin of potato varieties that have been cultivated, or themselves used as parental lines for further selection, provides a wealth of information on historical breeding choices . Such hiA second approach assesses a possible reduction of potato genetic diversity caused either by modern agricultural practices (or by fortuitous decisions in the past) or by environmental changes. The concept of genetic erosion relies on the loss of alleles that severely affect the genetic vulnerability of crops to biotic and abiotic stress . The impS. demissum [S. vernei and S. spegazzini [S. acaule, and to virus Y from S. stoloniferum [A third approach measures the effectiveness of the use of exotic germplasm in modern selection, particularly that of wild crop relatives and landraces. Such breeding practices are key means of restoring genetic diversity that has been lost over time as result of domestication, migration, disease, and other causes of genetic bottlenecks . This aldemissum , resistademissum , but alsegazzini , resistaoniferum . Other woniferum . Agronomoniferum . Althougoniferum ,40. It aoniferum . Neverthoniferum . Hence, To adequately address these three approaches to evaluating the fluctuations in the spatiotemporal genetic diversity of the cultivated potato, we applied two clustering methods, self-organizing map (SOM)  and discS. tuberosum L. and 3 accessions of native potato from Chiloe Island, all used as references from the study of Esnault et al. 2014 [The potato germplasm used in this study consisted of 1219 potato accessions, originating from Europe (1100), North America (70), South America (12), Asia (27), Africa (8), and Oceania (2). The plant material, released between pre-1800 and 2021, was obtained from several European breeder and maintainer collections as well as gene banks (see acknowledgments). Both the date of release and the geographical origins of each variety were registered in accordance with breeder information. For cultivars whose origin and/or date of release was uncertain, first cited appearance or description from historical documents and literature was registered as such. The panel was set up to cover as equally as possible each year during the period studied. The potato germplasm comprised 30 heirloom varieties (released before 1850), 205 old cultivars (released between 1850 and 1950) and 984 modern cultivars (released after 1950) . The panal. 2014 , as wellThe online pedigree database  was usedGenomic DNA isolation was performed for each potato variety from tubers, leaves, or in vitro plantlets using the QuickPick Plant DNA kit as per the manufacturer’s protocol . DNA extracts were quantified using a ND-3300 NanoDrop spectrofluorimeter .Thirty-five SSR markers were selected from the literature based on the following criteria: (i) homogeneous repartition on the chromosomes; (ii) optimal amplification and resolution; (iii) ability to detect high rates of polymorphism; (iv) adequacy of observed fragment sizes with those reported in selected studies ,49,50,51Genetic diversity of SSR loci was estimated using the number of detected SSR alleles (Na) and the polymorphic information content (PIC) according to the Nei’s statistic . The fren = 1219) with no prior information on data organization. The probability density function of the input data was estimated by performing a nonlinear projection of the genetic data, here expressed as a vector in a 1219-dimensional space, onto a two-dimensional network. The two-dimensional network consisted of 81 (9 × 9) output neurons arranged on a hexagonal network onto which the accessions could be assigned. A hexagonal network was preferred because it does not favor horizontal or vertical directions [N of output neurons necessary to cluster the accessions, the SOM was trained using different map sizes and we selected the those that fitted best, according to the global quality criterion of the result based on the topographic error [pbatch) mode and Manhattan-type distance functions. Manhattan was preferred over default Euclidean distance functions because it gave clearer clustering patterns (greater number of empty neurons), although both methods led to similar outcomes.The self-organizing map (SOM) is a type of artificial neural network commonly used to model complex nonlinear relationships of large multidimensional data and sort it into decipherable clusters, while preserving the original topology of input data and following unsupervised learning rules . SOM anarections . Since tic error . Detailsic error . A learnic error  and consn = 1219), using prior information on data organization. DAPC analyses were carried out using the R adegenet package version 2.1.4 [Xval.dapc) was used to define the correct number of principal components (PCs) to be retained, being the lowest root mean square error according to R adegenet procedure [n discriminant eigenvalues, where n = number of a priori groups − 1. The resultant clusters were plotted in a scatterplot of the first and second linear discriminants (LD) of DAPC, with potato accessions as points and inertial ellipses around predefined groups.Discriminant analysis of principal components (DAPC) is a multivariate method combining two analysis methods, discriminant analysis (DA) and principal component analysis (PCA), to assess population structure with the aim of maximizing among-group variation and minimizing within-group variation . DAPC waon 2.1.4 . Four a rocedure . Discrimpegas 1.0–1 [The analysis of molecular variance (AMOVA)  was perfas 1.0–1  to assesas 1.0–1 ). The siA total of 407 alleles were detected in the potato germplasm from the 35 SSR loci, of which 184 were rare alleles (45.2%) and 41 unique alleles (10.1%) . The numSOM analysis was processed using the complete dataset with no prior knowledge of data organization. During the learning process, SOM was trained with different assorted sizes of output layer, and the grid arranged into an output layer of hexagonal 9 × 9 neurons was selected as the optimal resolution for discerning plausible structured clusters . As illustrated in One way to analyze the SOM clustering chronologically is to replot the neuron numbers for all accessions, as a function of time b. We canVitelotte Noire and Johnny Gunter in neuron 8, native potatoes from Chiloe Island in neurons 1 and 22, and heirloom varieties  in neurons 10 and 19. Between 1801 and 1850 , potato accessions mainly remain around the previous heirloom cluster (neuron 10), implying that little genetic differentiation happens during this period. It should be noted that almost all other neurons have emptied. Between 1851 and 1900 , new clusters appear mainly bottom right and top left. This closely follows the major crises of potato late blight epidemics and corresponds to new introduction events. Between 1901 and 1950 , a reinforcement of preexisting clusters is found. From 1951 until 2021 , numerous new accessions appear both around preexisting clusters and in new regions of the SOM.n = 1219) using four a priori spatiotemporal models of genetic structuring:Discriminant analysis of principal components (DAPC) was conducted to assess the relationships between potato accessions within the entire population  of PCA and six discriminant eigenvalues. Separation between continent clusters comprising potato accessions of Europe, North America, and South America (further divided between Andigena accessions and accessions from Chiloe Island) was visualized in the DAPC scatterplot with respect to the two first linear discriminants (LD) . In partDAPC analysis using germplasm originating in various countries was carried out retaining 60 first PCs (65% of variance conserved) of PCA and seven discriminant eigenvalues. In this model of structuring, only countries comprising more than 25 potato accessions were used to assess spatial interrelations, while the other countries were grouped into a single cluster called “Rest”. Although there is a partial overlap of the points and their resulting ellipses, the tendency of countries to be grouped according to their continent of origin is also found here . Thus, aGaumaise) from the modern cluster are found in the heirloom cluster. Again, this is no surprise as, e.g., modern variety Gaumaise was created in 1955 from a self-fertilization of Rosa (syn. Plate de Forenville), a French heirloom. As expected, more potato accessions from the old cluster are found in the heirloom cluster. The higher proximity between the old and modern clusters explains why that many varieties from one cluster can be found in the other.DAPC analysis using germplasm of three temporal groups  was carried out retaining 60 first PCs (75% of variance conserved) of PCA and two discriminant eigenvalues. Separation between the three temporal clusters is visualized in the DAPC scatterplot with respect to the two first LD , especiaDAPC analysis using germplasm of 50-year breeding groups was carried out retaining 60 first PCs (65% of variance conserved) of PCA and five discriminant eigenvalues. The clusters comprising potato accessions belonging to 50-year breeding groups appear to be distributed in accordance with the chronology of the periods in the DAPC scatterplot with respect to the two first LD . ClusterThe four previous models of genetic structuring were examined using AMOVA analyses to assess the genetic variability into within and among-group variation components. Results are summarized in In a changing world, an environmentally sustainable increase in the productivity and nutritional value of crops can play a crucial role in ensuring global food security. Furthermore, human societies will remain vulnerable to the tragedies encountered in the past, for example, great famines caused by late blight. Additionally, climate change is predicted to have dramatic consequences for crops, including changes in geographical distribution, phenology, crop-pest synchrony, and increased risk of attacks by invasive pest species. In this study, three interconnected aspects of the spatiotemporal evolution of cultivated potato genetic diversity were examined to provide insights into its past and current genetic resources, with the aim of promoting appropriate strategies for breeding programs in line with the challenges of sustainable crop production and with regards to available genetic resources, either in the field or conserved in collections.Native of the Canary Islands, Makah Ozette, Yam, Raudar Islenskar, Lumpers, Raeburn’s Gregor cups, Rocks, Skerry Blue, Fransen, Pink Fir Apple, and Rosa. A second one (neuron 2) comprises Myatt Ashleaf, Highland Burgundy Red, Blanchard, Lagad Glas, Quarantaine de la Halle and Ratte. Our results suggest that these two early branches have been maintained until today since the genetic groups to which they belong also include modern varieties which are closely related to them. In addition, no descendants of the Andigena potatoes were found in modern potatoes, while several were from the native potato accessions from Chiloe Island. Although the first identified lineages appeared to be closer to the accessions from Chiloe Island (neuron 22) and the second to Andigena accessions , it remains difficult to connect these two lineages to strains historically recorded, the originally introduced ancestors having most likely disappeared.According to historical records, the germplasm of the modern cultivated potato is based on only a small number of introductions . It has Lumpers, Myatts Ashleaf, Pink Fir Apple, Ratte, Fransen have A-type ctDNA, which is typical of Andean potatoes, whereas Yam, released before late blight epidemics, has T-type ctDNA, typical of Chilean potato, as the vast majority of existing modern potato cultivars. However, since our SOM clustering has shown that Yam and Lumpers are grouped in neighboring clusters 10 and 19, the relationship between any resulting SOM grouping and the available ctDNA-type information cannot be established de facto. Consequently, it is at this stage nearly impossible to establish the source, Andean or Chilean, of these two identified lineages. To unravel this issue, it would be of interest to further differentiate these two identified earlier groups by identifying the cytoplasmic type for each accession concerned, for instance using the method of Hossaka and Sanetomo (2012) [A logical extension of this study would be to identify the source of these detected early lineages, which could be of upland “Andean” or/and lowland “Chilean” origin. Indeed, these two genetic groups result from an adaptation process targeting distinct loci ,21. Despo (2012) , which mRough Purple Chili, introduced by C. Goodrich in the 1800s from Chile, appears in the pedigree of numerous modern varieties, and subsequent selection led to it becoming a founder of a line of descent which includes Early Rose [Early Rose is itself an ancestor of a substantial proportion of American variety pedigrees spanning a century and a half of breeding work. Early Rose and its offspring were then used widely in the production of European varieties after the Great Famine [From the 1850s, SOM temporal analysis has revealed the appearance of new genetic clusters which were relatively distant from the first lineages detected in the grid. This corroborates theories about reintroduction events from the US after the Great Famine in Europe in the mid-1800s. The genetic dissimilarity between these clusters is easily explained. In North America, potatoes were most likely introduced from Europe at the end of the 17th century. Several important introductions have since added to the original genetic material. For example, the Chilean clone rly Rose . Early Rt Famine . The occFrom the 1900s until today, a reinforcement of preexisting clusters is found with numerous new accessions also appearing around preexisting clusters. Interestingly, the SOM network, especially in relation to the 1950s onwards, begins to further expand by occupying more adjacent neurons. This illustrates more intense activity in potato selection, by the means of increased exchange of varieties between countries and the enhancement of potato germplasm using exotic material as well as by targeting specific market niches ,34,59. SThroughout its history, the cultivated potato has experienced severe and repeated selection pressures, resulting in some sequential putative genetic bottleneck events . In partFrom a temporal point of view, multivariate analyses show that the diversity evolved gradually in a structured way throughout the studied periods. This corroborates the work of Vos et al. 2017) , which s , which Agria (NLD), Daber (GER), Katahdin (USA), Flourball (GBR), Early Rose (USA) and Mira (DEU) are crossed with contrasting material (with the variety chosen depending on the available genetic resources in the breeding country) the resultant hybrids are split into different clusters in the grid. This has been reported in a previous study [From a spatial point of view, a genetic structure is identified in which the potato accessions appear more distant between continents than between countries. Accessions originating from European countries tend to cluster together, except for those from the UK, which fall between those from European countries and those from the US. The genetic proximity between the latter groups can be explained by historical introduction events from the UK to the Americas in the 17th century, and vice versa following European late blight epidemics in the mid-1800s . Accessius study . Althougus study , most ofAs for many cultivated crops, the use of wild relatives or landraces by breeders as a source of adaptative potential for the improvement of existing germplasm can have a significant impact in the cultivated potato . The pro(1)S. Tuberosum ssp. Andigena CPC 1673 in many breeding programs, which confers resistance to G. rostochiensis pathotypes Ro1 and Ro4 [G. pallida pathotype Pa2 [S. tuberosum ssp. Andigena CPC 1673 in their pedigree, including Agria, Alcmaria, Amaryl, Amex, Aminca, Carrera, Cherie, Elkana, Mara, Prominent, and Saturna; and Ro4 ,79 as wetype Pa2 . Many re(2)Maris Piper and Ulster Glade, cultivars that have been bred with a dominant gene for resistance to cyst nematode, derived from S. tuberosum ssp. Andigena;(3)Lenape, which has in its second-generation ancestors a wild potato native to the Chaco region in South America, S. chacoense, selected to provide the qualities of resistance to certain diseases, including late blight;(4)BRA 9089, whose parents are from (Chilote x Svitez) and landrace cross, in the basis of important lineages in China with Mira (syn. Ora) as well as in Germany with Axilia, Leander, Hessenkrone, and Sitta;(5)VTN 62-033-03 and AM 66-0042 using S. vernei and S. demissum for targeted resistance to certain diseases and high starch yield. These two ancestors are highly represented in the pedigree of modern varieties.Improved clones Our results revealed that current diversity is explained both by inheritance from ancestral cultivars and by the introduction of additional gene pools whose effect on the genetic base is noticeable over time. Many genetic clusters have appeared over time within the cultivated potato population, especially from the 1950s onwards. For instance, newly detected clusters at the origin of major emerging lineages have been due to the use of:In addition to the priority targeting of disease resistance and yield, specific market niches have clearly been exploited in parallel, such as fresh market, French fries processing, crisp processing, and starch industry. Thus, the ancestors responsible for the new genetic stratifications identified in the structure of the studied gene pool almost all originate from recent breeding programs using exotic material, either from wild potato relatives or from landraces.By examining spatiotemporal trends in the genetic diversity of cultivated potatoes, our study demonstrates that no loss of diversity in this crop over the past three centuries should be feared. On the contrary, the genetic analysis presented here shows encouraging signs of improvement, thanks to the sensible and significant work which has been carried out by potato breeders, especially over the past century. From a practical perspective, the data collected through this research could be used to guide breeders, who were partners in this study, in the design of appropriate selection strategies, for instance by anticipating, in full knowledge of the facts, possible crop losses resulting from changing environments, and using available genetic resources already adapted to similar constraints. This can be done by filling gaps in the genetic patterns identified in breeding stock germplasm and by proposing parent candidates whose membership in relevant genetic clusters has been clearly identified. Finally, it would be of interest to confirm our data using more abundant and automatized molecular markers, such as SNP arrays. This could circumvent the known limits of microsatellite markers such as homoplasy and the problems associated with determining allele dosage inherent to polyploid data.


Synthetic hexaploid wheats and their derived advanced lines were subject to empirical selection in developing genetically superior cultivars. To investigate genetic diversity, patterns of nucleotide diversity, population structure, and selection signatures during wheat breeding, we tested 422 wheat accessions, including 145 synthetic-derived wheats, 128 spring wheat cultivars, and 149 advanced breeding lines from Pakistan. A total of 18,589 high-quality GBS-SNPs were identified that were distributed across the A (40%), B (49%), and D (11%) genomes. Values of population diversity parameters were estimated across chromosomes and genomes. Genome-wide average values of genetic diversity and polymorphic information content were estimated to be 0.30 and 0.25, respectively. Neighbor-joining (NJ) tree, principal component analysis (PCA), and kinship analyses revealed that synthetic-derived wheats and advanced breeding lines were genetically diverse. The 422 accessions were not separated into distinct groups by NJ analysis and confirmed using the PCA. This conclusion was validated with both relative kinship and Rogers' genetic distance analyses. EigenGWAS analysis revealed that 32 unique genome regions had undergone selection. We found that 50% of the selected regions were located in the B-genome, 29% in the D-genome, and 21% in the A-genome. Previously known functional genes or QTL were found within the selection regions associated with phenology-related traits such as vernalization, adaptability, disease resistance, and yield-related traits. The selection signatures identified in the present investigation will be useful for understanding the targets of modern wheat breeding in Pakistan. Triticum aestivum, also called common wheat) is one of the most important staple cereal crops that feed more than 35% of the world's population  and diploid wild goatgrass . Its large and complex hexaploid genome of approximate 16 Gigabases, had hindered genomic analysis in this species  were classified into three subpopulations, including 145 synthetic-derived wheats , 128 commercially released Pakistan cultivars , and 149 advanced lines . Detailed information on the 422 wheat accessions can be found in MspI-PstI) approach. A GBS analysis pipeline in TASSEL version 5.0  was used for SNP calling  method  of alleles. The LD decays within WP and three subpopulations were evaluated, as was the distance among pairs of SNPs with non-linear regression using a custom R script.PowerMarker version 3.25 software  R package , was used to identify genes/QTL that underlying population genetic differences and to detect candidate regions of the wheat genome under selection in any genetic population . To exclude the effect of genetic drift , denoted as PGC, and was used to identify genomic regions under selection. To determine the threshold of significance of genomic regions under directional selection, the first EV was reshuffled 1,000 times to simulate the null distribution. The 95th quantile of the 1,000 most significant PGC was calculated using 1,000 permutations to determine the cutoff. After log10 (p) transformation, 5.0 was applied for -log10(PGC) of EigenGWAS analyses in all 10 EVs to declare as regions under selection.EigenGWAS implemented in the GEAR software (freely available from https://plants.ensembl.org/. The PANTHER Overrepresentation Test (release 20210224) using Gene Ontology (GO) database (release 2021/05/01) using Triticum aestivum database as a reference list. GO analysis included biological process, molecular function, and cellular component. The raw p < 0.01 was set as the threshold to declare the significant differential expression.To exclude the strong effect of LD, significant SNP loci within 5 Mb both up- and downstream based on the LD level of the WP were merged as potential selected regions. Functional annotations of the target GBS-SNPs were performed using SnpEff software  transitions (A/G and C/T) and (2) transversions . Transition-type SNPs (73.24%) were more frequent than the transversions (26.76%), and transition/transversion (Ts/Tv) ratio was 2.73 . The C/TTo investigate possible population structure and genetic relationships among the 422 wheat accessions, NJ-tree, PCA, and kinship analyses were conducted . Based oTo gain further insights into genetic relationships, we calculated the frequency distribution of kinship coefficients and genetic distances for the WP and the three subpopulations , S5. KinST analysis was computed to investigate subpopulation divergences and presented in ST coefficient among subpopulations varied from 0.0492 to 0.075. The FST coefficients showed that the divergence between the SYN-DER and AL (0.0492) was lowest, while the divergence between PC and AL was highest (0.075). Results suggest a low level of genetic differences and in accordance with the NJ-tree and PCA analyses.Genetic differentiation of predefined subpopulations  was assessed using AMOVA analysis . AMOVA rr2 values ranged from 0.06 (5D) to 0.33 (4B). The average r2 for WP was found to be 0.19. The averaged r2 reached the lowest in the AL subpopulation (0.06), and the highest in the AL subpopulation (0.35)  . The aveNP pairs . The LD ectively .ST  and FST fairly mirrored each other, indicating reasonable grouping as defined by FST. Overall, EigenGWAS detected selection signatures on all 21 chromosomes (10(PGC)] within one region was used to declare as representative. In total, therefore, 38 selection regions were identified and are shown in Rht8) and flowering time (Ppd-D1a.1)  includes one gene and five QTL, which includes a low–molecular weight glutenin subunit Glu-A3 controlling gluten quality of the wheat, while QTL were associated with phenology-related traits such as grain yield, biological yield, flag leaf length, and kernel width (Ppd-A1) controlling flowering time in wheat. The 5A region (558.36–569 Mb) consists of QTL associated with spike-related traits in wheat such as spike number, awn length, and spike length  during wheat improvement, EigenGWAS was conducted based on positive and negative coordinates of the 422 wheat accessions from Pakistan on the corresponding EV, and their selection differentiations were quantified by FST . The aveST . The gen to 7.20 . To facie 10 EVs . Generalomosomes , while 8d-D1a.1) . The regel width . The 5A e length . Similarsistance . NotablyFunctional annotation was carried out to evaluate the genome composition  using the whole-genome SNPs  and significant SNPs identified by EigenGWAS  . Of the ad hoc breeding strategies in an attempt to restore part of lost genetic variability  during domestication. This conclusion could be a good explanation of the high levels of GBS-SNPs in the A- and B-genomes observed in this study  using stringent filtering criteria, and used for downstream analysis . In concis study . The filis study . The preis study , which iis study . The abuis study . The hexis study . The higThe molecular characterization of genetic resources remains the most promising option for efficient conservation and sustainable use of their diversity in crop breeding  to explore whether the studied population is genetically homogeneous or contains distinct subgroups, and (2) to find quantitative evidence that supports the presence of these subgroups . The selection for agronomic traits in the Pakistan crossbreeding programs was considered the main reason for this high variation within subpopulations. The low degree of diversity among subpopulations could be due to high genetic exchange or gene flow  as compared to D-genome (29%) and A-genome (21%), a finding consistent with previous reports , end-use quality (Glu-A3), flag-leaf-related traits , biotic resistance (Lr34), and vernalization (Vrn-D3 and Ppd-A1)  within the selection regions in Pakistan wheat germplasm, which supports our observation. Comprehensive knowledge of genetic diversity, population structures, and the identification of selection regions offer the potential to assist plant breeders in better understanding the implications of the selection regions on targeted crop improvement and facilitate the use of germplasm.Although many selection regions had been identified in Pakistan wheat germplasm before  can be found below: HL conceived and designed the experiments. AR performed NGS bioinformatics and genotyping. MA conducted the experiments and wrote the manuscript under the supervision of HL. MA, SD, and HS performed statistical analyses. HL and AR revised the manuscript. JW and ZH reviewed the manuscript. All authors read the final version of the manuscript and approved it for publication.This work was supported by the project of Hainan Yazhou Bay Seed Lab (B21HJ0223) and the National Science Foundation of China (32022064).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Gasterosteus aculeatus) and stone loach (Barbatula barbatula), and the non‐native and invasive topmouth gudgeon (Pseudorasbora parva). We aimed to characterize both natural and anthropogenic determinants of genetic diversity and population genetic connectivity. Genetic diversity was highest in topmouth gudgeon, followed by stone loach and three‐spined stickleback. The correlation between downstream distance and genetic diversity, a pattern often observed in riverine systems, was only marginally significant in stone loach and three‐spined stickleback, while genetic diversity strongly declined with increasing number of barriers in topmouth gudgeon. An Isolation‐By‐Distance pattern characterizes the population genetic structure of each species. Population differentiation was only associated with migration barriers in the invasive topmouth gudgeon, while genetic composition of all species seemed at least partially determined by the presence of migration barriers. Among the six barrier types considered , the presence of watermills was the strongest driver of genetic structure and composition. Our results indicate that conservation and restoration actions, focusing on conserving genetic patterns, cannot be generalized across species. Moreover, measures might target either on restoring connectivity, while risking a rapid spread of the invasive topmouth gudgeon, or not restoring connectivity, while risking native species extinction in upstream populations.Habitat fragmentation impacts the distribution of genetic diversity and population genetic structure. Therefore, protecting the evolutionary potential of species, especially in the context of the current rate of human‐induced environmental change, is an important goal. In riverine ecosystems, migration barriers affect the genetic structure of native species, while also influencing the spread of invasive species. In this study, we compare genetic patterns of two native and one highly invasive riverine fish species in a Belgian river basin, namely the native three‐spined stickleback ( Barriers isolate populations from one another by reducing the number of migrants and effective population sizes. Small (effective) populations are more vulnerable to stochastic events and inbreeding, which in turn results in the loss of genetic diversity  is a small (3–11 cm) eurytopic fish species, occupying various habitats  and high pollution tolerance. It has a short generation time (1–2 years) and low to moderate dispersal capacities. Barriers such as watermills and weirs are significant drivers of genetic divergence  is a benthic and longer‐lived species  with low tolerance to environmental pollution  is a small (3–11 cm) non‐indigenous species, which can carry the highly infectious unicellular Sphaerothecum destruens parasite similarity in genetic patterns between two native and an invasive species, and to determine the relative importance of natural  and anthropogenic (i.e. migration barriers) factors underlying genetic diversity and connectivity. Based on the comparison among three co‐occurring fish species, we provide recommendations to reconcile the preservation of genetic diversity.22.1Fish were sampled during autumn 2017 following a standardized electrofishing protocol under permission of the Agency of Nature and Forest (ANB) of the Flemish Community (Belgium). Fishing covered a river stretch of 100 m at each of the 20 locations in the Demer basin in Flanders , comprising 20 individuals per population of each of the three species. First, genomic DNA was extracted from stored fin clips using a salt precipitation protocol adapted from Cruz et al. . Next, DApeKI for 2 h at 75°C. Both a unique (5–7 bp) and common barcode were ligated to the digested DNA at 22°C for 60 min, followed by 30 min at 65°C. All samples were purified using CleanPCR beads before and after PCR amplification. The concentration of each sample was checked using the Quant‐iT PicoGreen dsDNA kit (Thermo Fisher Scientific Inc.). Based on the concentration, all samples from one pool were combined so that 10 ng from each individual sample was added. Each pooled library was size selected for fragments with a size ranging from 240 to 340 bp (excluding adapters) using a BluePippin (Sage Science). A more detailed protocol is added to the Supplements. Libraries were sequenced 100 bp paired end on a Illumina HiSeq2500 platform at Macrogen, Inc.The final step included the construction of a GBS library following the methods described in Elshire et al. , with mo2.3Read quality of the raw sequencing files was assessed using FastQC software v0.11.9 Andrews, . The rawThe retained reads of three‐spined stickleback were mapped against the reference genome  and a maximum number of four base pair differences between stacks (M = 4) and between catalogue loci (n = 4). A locus catalogue was built, based on a subset of samples, before mapping all samples to this catalogue.For stone loach and topmouth gudgeon, SNPs were called using the Additional SNP filtering, using the VCF file produced by Stacks, was conducted in R v4.0.2 with the package Radiator v1.28.1 Table , for all2.4Spatial (waterway) distances were calculated using the Network Analyst toolbox in ArcGIS v10.8.1 . Upstream distance was defined as the maximal upstream distance from the sampling site and downstream distance was defined as the distance to the Dijle‐Demer river confluence . Network centrality was calculated as the average waterway distance of a sampling location to all other locations. Pairwise and downstream numbers of barriers between the sampling locations were counted, based on a data set of the Flemish Environment Agency (VMM) that keeps track of current and historic migration barriers, and divided into six categories: watermills, weirs, tunnels, sluices, riverbed obstructions, and others.2.5Statistical analyses were implemented in R v4.0.2  and expected (HE) heterozygosity per population using the R package DiveRsity v1.9.90  was calculated for each population using the DiveRsity package. Effective population size (Ne) was calculated for each population with the linkage disequilibrium method . Stepwise model selection (both backward and forward) was performed using the stepAIC function in the R package MASS v7.3–54 , downstream distance (distance to the Dijle‐Demer confluence), and downstream number of barriers as explanatory variables. Collinear variables were removed  using the R package vegan v2.5–7 , we retained 245,890, 114,819, and 258,817 variant sites in 286, 287, and 280 individuals, respectively. After filtering, the final data sets included 17,411 SNPs in 236 three‐spined stickleback, 17,407 SNPs in 255 stone loach, and 23,401 SNPs in 249 topmouth gudgeon Table .3.2HO) ranged from 0.076 (Demer Tongeren) to 0.118 (Melsterbeek Runkelen) in three‐spined stickleback, from 0.117 (Kaatsbeek Diepenbeek) to 0.151 (Melsterbeek Runkelen) in stone loach, and from 0.120 (Herk Hoepertingen) to 0.156 (Winge Blauwmolen) in topmouth gudgeon (Table HE) exceeded HO at five sampling sites of three‐spined stickleback. In stone loach, HE was consistently lower than HO. In topmouth gudgeon, HE was lower than HO at three sampling sites. Across the eight shared sampling locations, observed heterozygosity was correlated between stone loach and topmouth gudgeon . Effective population size ranged from 19.1 (Demer Tongeren) to 392.1 (Mene Tienen) in three‐spined stickleback (average Ne = 98.5); from 130.2 (Begijnenbeek Bekkevoort) to infinite (Zwarte Beek Beringen) in stone loach (average Ne = 679.2); and from 135.5 (Begijnenbeek Bekkevoort) to 518.1 (Kleine Beek Schaffen) in topmouth gudgeon (average Ne = 279.84) (Table Mean observed heterozygosity (FIS was highest in populations of topmouth gudgeon (average = 0.063) varying between −0.024 and 0.079, followed by three‐spined stickleback  and stone loach , followed by stone loach  and topmouth gudgeon . Pairwise FST ranged from 0.047 to 0.329 in three‐spined stickleback, and all pairwise comparisons were significant , but not with three‐spined stickleback .Overall nt Table . Pairwises Table . In topmes Table . At the Principal component analysis revealed clear differentiation among populations of three‐spined stickleback, stone loach, and topmouth gudgeon Figure . Further3.33.3.1F1,11 = 3.703, p = 0.078; downstream distance: F1,11 = 1.739, p = 0.078). In stone loach, upstream  and downstream distances  were included in the final model but were not associated with observed heterozygosity when including all locations. In topmouth gudgeon, total number of downstream barriers  was negatively correlated with observed heterozygosity. Upstream distance  and network centrality  were also included in the final model but did not significantly affect observed heterozygosity. We observed IBD , stone loach , and topmouth gudgeon . When including the eight shared locations, overall patterns remained similar , followed by stone loach . No IBD was observed in three‐spined stickleback across these eight locations .The observed heterozygosity of the three‐spined stickleback populations was not significantly correlated to any spatial variable . Downstream distance was the most important variable of the spatial data set . The most important variables were PCNM3 in the spatial data set . Important variables included downstream distance, riverbed obstructions, and watermills. In all three species only a small portion of the variation was explained by the combined effect of barriers and space  harbour distinct populations of both stone loach and three‐spined stickleback. Both locations appear strongly affected by the downstream number of barriers, indicating a need for barrier removal.5Genetic patterns differed between two native and one invasive riverine fishes. Although barriers influenced the genetic composition of both native species, we observed a strong effect on genetic differentiation in topmouth gudgeon. Hence management measures should focus on restoring connectivity, while minimizing further dispersal of the invasive topmouth gudgeon through improved water and habitat quality.The authors declare no conflict of interest.Appendix S1Click here for additional data file.