Up to 40% of rare disorders (RD) present facial dysmorphologies, and visual assessment is commonly used for clinical diagnosis. Quantitative approaches are more objective, but mostly rely on European descent populations, disregarding diverse population ancestry. Here, we assessed the facial phenotypes of Down (DS), Morquio (MS), Noonan (NS) and Neurofibromatosis type 1 (NF1) syndromes in a Latino-American population, recording the coordinates of 18 landmarks in 2D images from 79 controls and 51 patients. We quantified facial differences using Euclidean Distance Matrix Analysis, and assessed the diagnostic accuracy of Face2Gene, an automatic deep-learning algorithm. Individuals diagnosed with DS and MS presented severe phenotypes, with 58.2% and 65.4% of significantly different facial traits. The phenotype was milder in NS (47.7%) and non-significant in NF1 (11.4%). Each syndrome presented a characteristic dysmorphology pattern, supporting the diagnostic potential of facial biomarkers. However, population-specific traits were detected in the Colombian population. Diagnostic accuracy was 100% in DS, moderate in NS (66.7%) but lower in comparison to a European population (100%), and below 10% in MS and NF1. Moreover, admixed individuals showed lower facial gestalt similarities. Our results underscore that incorporating populations with Amerindian, African and European ancestry is crucial to improve diagnostic methods of rare disorders. This corresponds to approximately 500 million people. Although as a whole genetic and RD are a significant cause of morbidity and mortality in the pediatric population3, by separate each disorder affects a very reduced number of people. Depending on the country, the prevalence to consider a disease as rare ranges from 1 affected individual in 50,000 people to 1 in 200,000. This low prevalence has limited the research on rare disorders.According to the Online Mendelian Inheritance in Man (OMIM) databank, there are more than 10,000 genetic and rare diseases (RD) affecting 7% of the world's population3. As orphan diseases, many RD are chronic and incurable, representing severe and debilitating conditions4. The diagnosis and management of genetic RD is currently a clinical challenge5. Precise and early diagnosis is crucial for individuals and their families to get effective care and to reduce disease progression. However, due to the limited knowledge and complexity of these pathologies, diagnosis may take several years6. People often suffer during a long diagnostic odyssey, with delays in their correct treatment and management7. For most rare diseases, there are no reliable biomarkers for early diagnosis8.Currently, there is limited knowledge on the etiology of these disorders. A reduced percentage of diseases (20%) presents a known molecular basis associated to a detailed phenotype description, and treatment is only available for 0.04% of RD10. These phenotypes are highly prevalent6 and are commonly used for diagnosis, management and treatment monitoring of genetic and RD6. Up to 40% of these disorders present characteristic craniofacial phenotypes, including Down, Morquio, Noonan, Apert, Rett, Fragile X, Williams-Beuren and Treacher-Collins and velocardiofacial syndromes, as well as other conditions such as microcephaly, holoprosencephaly, palate/lip cleft, and other 2,000 rare genetic disorders11.Among the wide constellation of clinical symptoms associated to genetic and rare disorders, craniofacial dysmorphologies emerge as potential biomarkers14, including Fibroblast Growth Factor (FGF), Hedgehog (HH), Wingless (WNT) and Transforming Growth Factor Beta (TGF-β) and Bone Morphogenetic Proteins (BPMs). Disruptions in the regulation of any of these signaling pathways can lead to facial dysmorphogenesis15.The genetic and environmental factors causing these disorders alter the complex process that orchestrates facial morphogenesis during pre- and postnatal development, inducing facial dysmorphologies. Facial development is highly regulated by multiple signaling pathways16. In the clinical practice, craniofacial dysmorphology is commonly assessed through qualitative visual assessment and basic anthropometric measurements. However, this approach may not capture with optimal precision the anatomical complexity of the facial dysmorphologies associated with these disorders. Qualitative descriptions of facial phenotypes are sometimes based on general terms such as coarse face, large and bulging head; saddle-like, flat bridged nose with broad, fleshy tip; or malformed teeth19. Accurate identification of dysmorphic features for diagnosis thus depends on the clinician’s expertise, and only highly trained dysmorphologists are able to recognize the facial “gestalt” characteristic of the rarest disorders19.The facial patterns associated with each disorder are unique, but vary within and among diagnostics, ranging from subtle facial anomalies to severe malformations25. Automated systems have been developed to improve and accelerate the diagnostic process26. Within the clinical practice, Face2Gene is the most commonly used system , a community-driven phenotyping platform trained over 17,000 people representing more than 200 syndromes9. Face recognition is performed on 2D images that can be collected with any type of digital camera or phone, without previous training. Syndrome classification is achieved using DeepGestalt, a cascade Deep Convolutional Neural Network (DCNN)-based method that achieved an average 91% top-10 accuracy in identifying the correct syndrome9.Recent research seeks to incorporate into the clinical diagnosis of RD the use of objective and quantitative tools to assess facial phenotypes21. The advantage of 3D facial models is that they are more efficient than 2D images in capturing the complexity of facial phenotypes, but their widespread use is limited because the photographic equipment required for generating 3D models is not commonly available in the clinical practice. Hallgrímsson et al. (2020)10 analyzed 3D facial models from 7,057 subjects including subjects with 396 different syndromes, relatives and unrelated unaffected subjects (https://www.facebase.org/). Deep phenotyping based on quantitative 3D facial imaging and machine learning presented a balanced accuracy of 73% for syndrome diagnosis20.Other diagnostic approaches based on 3D photogrammetry have been developed more recently26. These tools present high accuracy diagnosis in European and North American populations, that are the populations in which the machine learning algorithms have been trained and validated. However, these tools have not been thoroughly tested in populations with different ancestries, and it is not well understood the how facial phenotypes associated with genetic and RD might be influenced by the complex patterns of population ancestry characterizing human populations.Automated methods have thus demonstrated high potential to facilitate the diagnosis of facial dysmorphic syndromes27. Facial differences between populations are detected in the shape of the forehead, brow ridges, eyes, nose, cheeks, mouth and jaw28. These facial phenotypes result from divergent evolutionary and adaptive histories of human populations occurred during the evolution of Homo sapiens over the last 200,000 years. Nowadays, continuous migration and admixture keep shaping the facial phenotypes of human populations. Depending on dominance and epistatic interactions between alleles fixed or predominant in each parental group30, admixed populations can display a variety of craniofacial morphologies, ranging from resemblance to one of the parental groups to a combination of both parental phenotypes and the evolution of novel phenotypes29. Therefore, the evolutionary and population dynamics of human populations result in genetic and phenotypic patterns that surrogate population ancestry32, and can modulate the facial phenotypes associated to disease.Facial shape shows wide variation across world-wide human populations36, leaving African, Asian and Latin-American populations often disregarded and underrepresented. Unfortunately, there are no reliable representations of facial phenotypes in genetic and rare diseases in populations of non-European descent. However, it is crucial to account for the influence of population ancestry on facial variation to develop quantitative approaches that efficiently diagnose these disorders in populations from all over the world.Few studies to date have analyzed the craniofacial phenotypes associated with genetic and RD in populations of non-European descent37. Peopling of the Americas likely started 12–18,000 years ago39 by migration waves coming from North and South East Asia30, following coastal and continental routes41. Amerindian populations established all over the continent and adapted to a variety of environments over thousands of years. During the last 600 years, admixture with European and African populations further shaped the genetic ancestry of Latin-American populations43. In particular, the population from the region of Cali is the result of diverse migratory processes44. Admixture with the indigenous Amerindian population began in the sixteenth century with the arrival of Spanish colonizers. In the eighteenth century, large colonial settlements of slaves brought from Africa were established in Cali for the exploitation of sugar cane that significantly changed the population structure of Valle del Cauca. Nowadays, the population of Cali is characterized by indigenous and mestizo communities, with Amerindian and African ancestry components predominating over the European ancestry contribution44.To cover this gap, here we assessed the facial dysmorphologies associated to prevalent genetic and RD in a Latin-American population from the Southwest of Colombia. Latin-Americans are fascinating cases of hybrid/admixed populations that evolved over relatively short periods of time36. Here, we quantitatively assessed the facial phenotypes associated to these syndromes, and compared our results in a Colombian admixed population with those reported in European descent populations. We also assessed the diagnostic accuracy of automatic methods currently used in the clinical practice, and detected evidence suggesting that further research is needed to optimize these methods in admixed populations of non-European descent.In this study, we compared the facial phenotypes associated to four genetic and RD, including Down syndrome (DS), Mucopolysaccharidosis type IVA metabolic disorder known as Morquio syndrome (MS), and two types of RASopathies, Noonan syndrome (NS) and Neurofibromatosis type 1 (NF1). The facial phenotype of these syndromes has not been previously characterized in Latin-American populations, and differences between populations with different ancestry backgrounds have not been assessedThe Colombian sample comprised 130 individuals from Valle del Cauca, a Southwest region in Colombia  because Colombia presents one of the highest prevalence of MS in the world, probably as a result of founder effects45. Morquio syndrome is a subtype of Mucopolysaccharidosis disorders caused by more than 180 autosomal recessive mutations in the GALNS gene46 that alter the metabolism of the extracellular matrix glycosaminoglycans47. Individuals with MS show coarse facies with an excessively rapid growth of the head48.Down syndrome , caused by trisomy of chromosome 21, was selected because it is one of the most common genetic disorders, and previous studies have shown that the clinical manifestations associated with DS vary across ethnicities49.Finally, we also included in the analyses two RASopathies, Noonan syndrome  and Neurofibromatosis type 1 , which are prevalent in Valle del Cauca and present altered craniofacial development by genetic mutations that cause Ras/MAPK pathway dysregulationTo assess the facial phenotypes associated with these disorders, individuals diagnosed with DS, MS, NS and NF1 and age matched controls were recruited for photographic sessions at educational and research centers in Cali (Colombia) in 2021. The photographic material was taken under the protocol approved by ethics committee “Human Research Ethics Committee of the Icesi University” with Approval Act No. 309. To photograph the participants and to record relevant clinical information, we obtained informed consent from the participants or from their parents or legal guardians in the case of minor children, in accordance with national guidelines and regulations.Facial shape was captured from 2D images taken using a professional digital camera (SONY Alpha 58 + 18–55) that was attached to a tripod and placed at one-meter distance in front of the participants. To capture a natural facial gesture, the images were acquired in an upright position with facial neutral expression. Participants were asked to sit still, looking towards the front, with open eyes and closed mouth. Although this was challenging in children with Down syndrome, who usually show hyperactivity and tongue protrusion due to hypotonia, several photographs were taken until a neutral facial expression was achieved.50. The automatic landmarking process is explained in detail in Supplementary Information. In brief, from the set of 68 landmarks registered by Dlib, 15 landmarks directly matched our configuration of 18 facial landmarks  . This method was first validated with the 2D facial images of 20 control subjects, and the average RMSE was 1.75 mm. To validate the automatic landmarking method with images of syndromic patients, we manually landmarked 20 patients, including 5 individuals diagnosed with each syndrome represented in our sample. The RMSE for syndromic patients was slightly higher (RMSE = 1.96 mm), but below 2 mm . Considering that this error threshold is widely accepted in studies of biological anthropology for craniometric measurements52 by detecting linear distances that significantly differ between pairwise sample contrasts and comparing patterns of significant differences across samples.We used Euclidean distance matrix analysis (EDMA) to describe the facial phenotype associated to each syndrome. EDMA is a robust morphometric method for assessing local differences between samples53. After scaling, as EDMA represents shape as a matrix of linear distances between all possible pairs of landmarks, a total of 153 unique facial measurements were calculated for each individual. Linear distances were compared for each group of DS, MS, NS and NF1 syndromes with control individuals by performing a two-tailed two-sample shape contrasts on all unique inter-landmark linear distances from each sample. Relative differences between patients and controls were computed as (mean distance in controls—mean distance in patients) / mean distance in controls.To account for size differences between subjects, the 2D coordinates of the facial landmarks of each subject were scaled by their centroid size, estimated as the square root of the sum of squared distances of all the landmarks from their centroid54. To pinpoint specific local shape differences and to reveal the unique morphological pattern of variation associated with each disorder, the ten longest and shortest significant relative differences were plotted on facial figures.Statistical significance was assessed using a non-parametric bootstrap test with 10,000 resamples. EDMA statistically evaluated the number of significant local linear distances in each two-sample comparison based on confidence interval testing. We used the default α level in EDMA (α = 0.10), and a 90% confidence interval was calculated for each linear distance. The shape differences were sorted in increasing order, and the first 5% and the last 5% differences were discarded. The resulting minimum and maximum differences were used to set up the lower and upper confidence limits for each linear distance. Interlandmark distances were considered non-significantly different between controls and patients when the resulting interval contained the value zero. Otherwise, the equality null hypothesis was not accepted, and we assumed that a significant shape difference existed at the α level55. First, we estimated from the EDMA results a facial dysmorphology score (FDS) as the percentage of significantly different distances between patient and control groups. Then, we ran simulations with random samples of controls and patients generated by iterative bootstrapping to assess the statistical significance of the patterns revealed by EDMA. For each disorder, we first created subsamples of N randomly chosen controls . Then, using a subsampling approach, we automatically generated random pseudo-subsamples containing a known number of patients (namely M). This procedure was repeated with increasing numbers of patients and resulted in a series of staggered pseudo sub-samples that contained from M = 0 to M = N patients. A total of 150 simulations were run in each round, and in each of these simulations, we computed an EDMA analysis and an FDS score.To confirm that results were not random due to the small sample sizes available in rare diseases, we combined the results from EDMA with an iterative bootstrapping method that further assessed whether the facial dysmorphologies associated to each syndrome were statistically significantP-value assessing the statistical significance of the comparison was computed as the ratio between the number of simulations containing no patients that provided a higher FDS than the observed FDS divided by the total number of simulations. P-values below 0.05 indicated that the FDS obtained using the real dataset was higher that the FDS obtained randomly in a sample of control subjects.The results from each round of random groups were separately represented in histograms. The first round of simulations contained no patients (M = 0) and only included control individuals, representing facial differences that can be found randomly in the general population. To assess whether the FDS value obtained using the complete patient dataset was significantly different or similar to the FDS resulting from a random sample, we compared the distribution of FDS random values with the FDS observed in the whole sample. The https://www.face2gene.com). Following Gurovich9, we assessed the top-one and top-five accuracies for each disorder, estimated as the percentage of cases where the Face2Gene model predicted the correct syndrome as the first result or within the five first results from the sorted list of probable diagnoses. We also calculated these accuracies expanding the diagnostic range to the disorder family.To assess the accuracy of automated diagnostic methods in the Colombian sample, we compared the clinical diagnosis based on clinical and genetic testing with the diagnosis estimated from the frontal facial 2D images of the patients using the Face2Gene technology  and the most inferior medial point of the lower right eyelid , and a 7.5% increase between the right palpelabre inferius and the outer commissure of the right eyes (exocanthion), indicating hypertelorism. Additionally, in this Colombian sample, people with DS exhibited longer measurements in the buccal portion, with a 6–8% increase of mouth width as measured from the crista philtri to the chelions Fig. A. HoweveThe facial pattern associated with Morquio syndrome was also characterized by wider and shorter midfacial traits, as observed in Down syndrome. However, facial dysmorphologies were more abundant and severe in MS than in DS, with 65.4% of facial traits significantly different in diagnosed individuals and higher percentages of relative change Fig.  A, B. ThIn Noonan syndrome, facial dysmorphologies were abundant and concentrated in the orbital and nasal regions. EDMA detected significantly increased distances in the upper face, but decreased distances in the midface Fig. A.Figure Patients presented a lower position of the eyes, with 9 to 13% increased distances between the glabella or sellion and the landmarks located in the eyes. The mouth also showed a more inferior position, with 8–10% increased relative distance between the tip of the nose and the superior lip, but the shape of the mouth did not show large differences between patients and controls. The reduction of midfacial heights in individuals with NS ranged from 5 to 11%, with a similar magnitude as in DS Fig. A. FDS inNeurofibromatosis type 1 was associated with minor facial dysmorphologies, which were less abundant and less severe than in the previous syndromes Fig. A. IndiviFor the other syndromes, the simulation tests confirmed that the facial dysmorphologies associated with Down, Morquio and Noonan syndromes were significant and different from random comparisons in control subjects. Few simulations resulted in a higher FDS than the FDS obtained with the complete real sample . However, a Levene test detected a significant difference in the variance of gestalt similarity scores (P = 0.01). Whereas in the Colombian population the gestalt similarity in DS ranged from very high to very low; in the European population the range of variation was limited from very high to medium . Due to the small sample size and genetic heterogeneity of the patients, it was not possible to test whether different genetic variants were associated to different facial phenotypes. Comparative European samples were not available.In Morquio syndrome, the top-1 accuracy of Face2Gene was 0%, as the specific diagnostic of mucopolysaccharidosis type IVA (MPSIVA) was never listed as a first prediction Table . In our P = 0.09), the comparison between populations showed that in Europe, both the diagnostic accuracy and the gestalt similarity were higher than in Colombia. Using 2D images of patients from European origin, the Face2Gene top-1 accuracy for NS was 100% and the average gestalt similarity was 5.5 , demonstrating that ancestry is a relevant factor when assessing craniofacial variation associated to rare disorders.However, in contrast to European and North American populations45. As a mucopolysaccharidosis syndrome, the typical alterations of MS involve the supporting tissue and the osteoarticular system73. Individuals with MS display abnormalities such as skeletal dysplasia, short stature and trunk, kyphoscoliosis, pectus carinatum, genu valgum, and joint hyperlaxity74. Oral diseases often include periodontal disease, malocclusions, caries, and premature tooth loss46. Individuals with MS show coarse facies, with an excessively rapid growth of the head48. Craniofacial features include a prominent forehead, hypertelorism, prognathism, wide mouth and nose, depressed nasal bridge, plump cheeks, and lips with an oversized tongue48. In the Colombian population, the facial dysmorphologies observed were consistent with traits reported in the literature, which included hypertelorism, prognathism, wide nose, and wide mouth48.In Morquio syndrome, the worldwide prevalence ranges from 1 case per 75,000 to 1 per 200,000 live births; whereas in Colombia the prevalence rises up to 0.68 per 100,000 live births46. Further research is required to test this hypothesis and to assess whether pharmacological treatments can slow down the progression of the disease and reduce the facial dysmorphologies associated with MS. This is especially relevant in Colombia, which is a country with one of the highest prevalence of MS in the world45.In the Colombian sample, Morquio syndrome was associated with the most severe facial dysmorphologies. Considering that keratan and chondroitin sulfate alterations associated with MS cause irreparable damage to leukocytes and fibroblasts, and accumulate over life inducing extreme deformations of the osteoarticular system, facial dysmorphologies associated with MS are expected to increase with age, becoming more severe in adult individuals73. There is no consistent evidence regarding the genotype–phenotype correlation in MS, and whether different GALNS mutations are associated with the degree of severity in facial dysmorphology. In our Colombian sample, we detected four genetic variants . Two of these genetic variants, p.Gly301Cys and p.Arg386Cys, that are the most frequently reported mutations in cases of Morquio syndrome; specifically in Colombia, but also in other American , and European countries 77. The high prevalence of the p.Gly301Cys mutation in the Colombian population could result from founder and migration effects45. The p.Arg386Cys variant has been further detected in China and Turkey77; whereas the p.Arg94Cys allele has been previously reported in Middle East, Brazil, and Italy77. Other genetic variants, such as p.Ile113Phe, which are more frequently reported in British and Irish populations77, were not detected in our Colombian sample. Further tests with larger samples associated to each genotype are needed to test whether the population-specific genetic variants can be associated to different facial phenotypes in Morquio syndrome.Moreover, dysmorphologies associated with MS vary among individuals. Typically, MS patients present severe phenotypes, although less severe forms have been described as mild or attenuated phenotypes49. NS is the most common type of RASopathy, and is a rare genetically heterogeneous autosomal dominant disorder caused by mutations in either the PTPN11, SOS 1, KRAS, BRAF or RAF1 genes. Individuals with NS display facial features such as hypertelorism, epicanthic folds, strabismus, downward slanting palpebral fissures, ptosis, high arched palate, deeply grooved philtrum with high peaks of upper lip vermillion border, midfacial hypoplasia and micrognathia, broad flat nose, low-set posteriorly rotated ears, curly/sparse/coarse hair, and short webbed neck60. In the Colombian population, we detected hypertelorism, downward slanting palpebral fissures, and midfacial hypoplasia in cases of NS, as reported in populations of European descent60. In addition, our results quantified relative changes in the position of the mouth in Colombian individuals diagnosed with NS not reported before78.Regarding Noonan syndrome, the worldwide prevalence of NS is 1 per 1,000 to 1 per 2,500 live births49. NF1 is an autosomal dominantly inherited neurocutaneous disorder caused by a mutation in the neurofibromin gene. The clinical manifestations of NF1 are variable, and the timing of the onset has a major influence49. Regarding craniofacial traits, individuals with NF1 present macrocephaly, facial asymmetry caused by dysplasia of the sphenoid wings61, as well as bone deformities caused by plexiform neurofibromas, enlarged mandibular canal, retrognathic mandible and maxilla, and short cranial base79. The facial pattern associated with NF1 in individuals from Colombia was also compatible with typical traits of NF1, such as midface hypoplasia49. However, our results did not detect facial asymmetry or hypertelorism as prominent facial differences between diagnosed individuals and controls in the Colombian population49.In Neurofibromatosis type 1, the worldwide incidence is 1 per 2,500 to 1 per 3,000 individuals36. However, comparative facial quantitative analyses including subjects from different world regions are not usually available for most genetic and rare disorders, and reference data for diagnosis is mainly based on phenotypes defined on populations of European descent. In fact, almost no images of individuals of Latin American origin are included in reference medical texts16. Our results underscore the need to extend the analyses to populations from all over the world to achieve a complete and more accurate phenotypic representation of genetic and RD to optimize the diagnostic potential of facial biomarkers in the clinical practice.Overall, our results support previous evidence demonstrating that rare disorders present distinctive facial traits that are population specific, with clinical features that are significantly different in Africans, Asians, and Latin Americans81. In the Colombian sample analyzed here, Face2Gene diagnosed Down syndrome with 100% accuracy, with the same accuracy as in the European sample. This result suggests that in a relatively common genetic disorder such as DS, in which the machine learning algorithm is likely trained in a large sample of individuals with a distinctive and well-represented facial phenotype, Face2Gene shows high diagnostic accuracy, independently from the genetic ancestry.Deep learning algorithms such as Face2Gene have shown potential as a reliable and precise tool for genetic diagnosis by image recognitionHowever, we found that this result cannot be extrapolated to other rare disorders. For instance, we detected a lower accuracy in the diagnosis of Noonan syndrome in the Colombian sample as compared with the European sample. Although Face2Gene correctly identified the disorder in most Colombian subjects, especially when considering the top5-accuracy within Noonan syndrome-like disorders (88.9%), the percentage of top1-accuracy was reduced from 100% to 66.7% in the Colombian sample. We hypothesize that when machine learning algorithms are trained in a relatively small sample of individuals with homogeneous European ancestry, the accuracy of diagnosing rare disorders might be more sensitive to population ancestry. Individuals from diverse populations may show lower gestalt similarity scores when assessed with predictive models that are trained on a population with different genetic and facial variation, and this may lead to reduced diagnostic accuracy.Unfortunately, no data was publicly available on European samples to compare the diagnostic accuracy of Face2Gene in Morquio and Neurofibromatosis type 1 syndromes. Our results showed that the top1-accuracy for exact diagnosis of Mucopolysaccharidosis type IVA was 0% in the Colombian sample, despite Morquio syndrome was associated with the most severe facial dysmorphologies. Only a low percentage of cases (36.4%) were identified as a mucopolysaccharidosis-like syndrome in the first prediction. In the case of NF1, the top1-accuracy was also very low (8.3%), although the facial dysmorphologies in this disorder were less abundant and severe, and this result could just reflect the difficulty to diagnose NF1 from facial traits.56 were ordinary photos with uncontrolled lighting, pose, and image quality. In Colombia, 79% of individuals diagnosed with DS were associated with very high gestalt similarity values, but in 21% of subjects the gestalt similarity was lower, and ranged from medium–high to very low values. Specifically, individuals with the lowest scores exhibited traits that suggested an admixed ancestry, a hypothesis that needs further assessment.Finally, in the Colombian sample we detected a wide range of variation in gestalt similarity scores for most disorders, even for Down syndrome. In European subjects, the gestalt similarity for DS was high or very high in 95.5% of cases, and only 5% of subjects showed a medium gestalt score, even when the images included in Ferry et al. (2020)16. Experts in dysmorphologies can identify the facial “gestalt” distinctive of many dysmorphic syndromes16. However, this facial assessment relies on the expertise of the clinician, and is very challenging because there is no clear one-to-one correspondence between disorders and facial dysmorphologies. Different genetic mutations can cause the same syndrome or similar phenotypes, whereas the same mutation can induce different phenotypes82. In addition, within the same rare disease there may be several subtypes, and symptoms may vary even within individuals of the same genetic disorder and the same family3. This complex biology generates confusion at the time of diagnosis and warrants the development of efficient, objective and reliable diagnostic methods.Qualitative visual assessment of facial dysmorphologies is frequently employed for diagnosis, clinical management and treatment monitoring of RD6. In this automated approach, methods can be based on 2D or 3D images26. The advantage of 2D methods is that data collection is easy and can be readily translated into the clinical practice, as physicians can take facial images even with simple digital cameras or smartphones. The collection of 3D models is more sophisticated and requires specialized equipment but provides more accurate phenotype descriptions by incorporating the depth dimension.Computer-assisted phenotyping can overcome these pitfalls and provide widely accessible technologies for quick syndrome screening3; although in other disorders facial dysmorphologies develop later, during postnatal development. Male and female faces present sexual dimorphism at adulthood83, and diseases can differently affect the facial phenotype depending on sex differences84.To further improve the methods of craniofacial assessment to diagnose individuals with genetic syndromes and RD that exhibit facial dysmorphologies, it is crucial to assess the large morphological variation displayed by human populations in facial phenotypes. Factors such as age, sex and ancestry should be accounted for in diagnostic methods. Clinical manifestations in some genetic disorders usually begin at an early age, with two thirds of patients expressing symptoms before the second year of birth6 that may take several years, leading to the so-called diagnostic odyssey7.The role of population ancestry in the facial phenotype associated with genetic and rare disorders also needs to be further investigated in future analyses, assessing the reliability and validity of automatic diagnostic tools in admixed populations with diverse contributions of Amerindian, African and European ancestry components. This is critical in rare disorders with heterogenous clinical presentation and phenotype, where clinical diagnosis is a challenging process6. Even though gene-based technologies have greatly improved diagnostic procedures25, the mutations causing many rare diseases are still not known and access to genetic testing is limited3. Genetic consultations may become a long process, and broad molecular testing such as exome and genome sequencing represent a high expense that is not affordable for all families and health care systems, especially in low-medium income countries7. In this context, faster, non-invasive and low-cost diagnostic methods based on facial phenotypes emerge as complementary tools for providing earlier first reliable diagnoses26.Accurate and early diagnosis of genetic and rare disorders are crucial for adequate health care and clinical management. Without a diagnosis, individuals and their families must proceed without basic information regarding their health and future developmental outcomes85. Including the morphological variation of faces from such different ancestry backgrounds is key to pinpoint the facial dysmorphologies associated with diseases in worldwide diverse populations86. Our simulation analyses further highlight the importance of maximizing the recruitment of diagnosed and control individuals, as results considerably change depending on the cohort and sample sizes.Therefore, in future research the recruitment of participants must be expanded to include as many individuals with RD as possible, together with large comparative samples of age-matched controls, from both sexes, and from diverse world regions that faithfully represent the complex craniofacial variation and evolutionary histories of human populations. For instance, the population in Southwestern Colombia is characterized by high levels of admixture from people with Native American, African, and European ancestry36. Our ancestry comparisons highlight that diverse genetic background variation can modulate the phenotypic response to disease, affecting the accuracy of current tools of clinical diagnosis. In the future, deep learning algorithms including a high variety of populations with different ancestry backgrounds will optimize the precision and accuracy of diagnosis in an unbiased approach. Such predictive models will support clinicians in decision-making across the world.Facial phenotypes associated with genetic and rare disorders can be influenced by population ancestrySupplementary Information.


Agropyron has an important role in soil protection and forage production in rangelands. The investigation utilized 37 ISSR primers, resulting in the detection of 956 loci within the A. elongatum genome and 705 loci within the A. cristatum genome. The findings revealed a high level of polymorphism, with 97% of loci in A. elongatum and 84% of loci in A. cristatum exhibiting variability. Notably, the primer (AC)8GCT emerged as a promising candidate for evaluating genetic diversity due to its ability to amplify numerous loci in both species. Using both the UPGMA algorithm and Bayesian analysis, the examined Agropyron accessions were categorized into two subgroups based on their respective species. The Q values associated with these subgroups suggested that certain accessions, namely "G16," "G19," "G20," "G21," "G22," "G23," "G24," and "G25," displayed potential admixture genomes. An analysis of molecular variance (AMOVA) underscored the significance of within-species variability, which accounted for 69% of the overall diversity, compared to between-species variability at 31%. Various genetic diversity parameters, including Na, Ne, I, He, and the number of private loci, were found to be higher in A. elongatum when compared to A. cristatum. Furthermore, Jaccard similarity coefficients ranged from 0.33 to 0.66 in A. cristatum and from 0.25 to 0.7 in A. elongatum, indicating the extent of genetic relatedness among these species. Intriguingly, the study identified two and three heterotic groups in A. cristatum and A. elongatum, respectively, which could be harnessed in the development of synthetic varieties to exploit heterosis. The results also indicated that a small proportion of ISSR loci pairs (5.2% in A. elongatum and 0.5% in A. cristatum) exhibited significant levels of linkage disequilibrium (LD) (P≤0.05), suggesting the potential utility of LD-based association mapping in Agropyron species. In conclusion, this research sheds light on the genetic diversity of Agropyron species and provides valuable insights into their potential applications in soil protection and forage production, as well as the prospects for enhancing genetic variability and heterosis in these species.The genus  Agropyron, from the family Poaceae, is one of the important pasture plants that is native to Europe and Asia. Agropyron spp. is a perennial herb with diploid (2n = 2x = 14), tetraploid (2n = 4x = 28), and hexaploid (2n = 6x = 42) genomic formulas [Agropyron plants have beneficial traits such as resistance to biotic and abiotic challenges such as low temperature, salt, drought, diseases, and pests, which protect the plant’s gene pool and lead to enhanced pastures and greater fodder production [The genus formulas . Moreoveoduction .Agropyron contains several species, 19 of which are found in Iran [Pseudoroegneria spicata and the Y genome from an unknown donor in various allopolyploid combinations, including the StP genome from Douglasdeweya wangii and the StYP genome from the genus Kengyilia [A. cristatum , known as crested wheatgrass, is a perennial plant with medium height that is known as a cool-season plant and is resistant to drought as well as cold stresses [A. cristatum has a divergent and deep root system, which makes it a suitable choice for soil stabilization. There are some reports about crossing A. cristatum with bread or durum wheat with the aim of introducing desirable genes like disease resistance as well as grain yield from A. cristatum. By acquiring wheat-A. cristatum disomic addition lines, it is possible to utilize these desired genes for improving wheat varieties. Till now, wheat-A. cristatum 1P, 2P, 3P, 4P, 6P, and 7P disomic addition lines have been successfully created, and many excellent genes were located in specific chromosomes and transmitted into wheat [A. cristatum 6P addition line confers gene clusters related to yield, such as multiple florets and grains per spike, and the 2P addition line possesses gene clusters related to disease resistance, including powdery mildew, leaf rust, and stripe rust [A. cristatum 5P disomic addition line has not been documented. Another notable species belonging to the Agropyron genus is A. elongatum, commonly referred to as tall wheatgrass, renowned for its robust resistance to salinity stress and its ability to endure challenging environmental conditions [A. elongatum or A. cristatum [A. elongatum. As a result, several valuable wheat genotypes have been released, including Xiaoyan 6, Xiaoyan 759, Xiaoyan 22, Gaoyou 504, and Shanrong 3 in China, as well as Oasis and Seri in Mexico [Lr24/Sr24, Lr19, Sr26, and Sr43 genes controlling rust resistance and the Fhb7 gene controlling Fusarium head blight resistance from Thinopyrum ponticum and Thinopyrum elongatum, as well as powdery mildew resistance genes including Pm21, Pm62, and Pm55 from Dasypyrum villosum, have been introgressed into wheat [The genus  in Iran . The ideengyilia . A. crisstresses . A. crisipe rust . Neverthristatum . Diseaseto wheat .Agropyron spp. accessions is relevant for genetic resource conservation, widening the genetic base, and practical applications in breeding programs. Several molecular marker systems, such as simple sequence repeats (SSR) [In order to guarantee future food and nutritional security, plant genetic resources are crucial . Thus, ets (SSR) , 16, gents (SSR) , and funts (SSR)  have beets (SSR) , the gents (SSR) .Agropyron spp. in Iran, it is hypothesized that significant genetic diversity will be exhibited by the domestic accessions of Agropyron spp. when they are assessed using ISSR markers. Furthermore, it is expected that distinct patterns in the population structure of these accessions will be revealed, which could contribute to the conservation of genetic resources and provide information for breeding programs concerning this important pasture plant.Given the limited reports regarding the genetic variability of Agropyron spp. from two species, A. cristatum (7 accessions) and A. elongatum (25 accessions), were generously provided by the Agricultural and Natural Resources Research and Education Center’s Gene Bank, Isfahan, Iran (https://esfahan.areeo.ac.ir/). Regarding the plant material, the collection of plant specimens adhered to the pertinent national guidelines and regulations. Agropyron is a cross-pollinated plant, each of the derived plants may have different characteristics. In the greenhouse, seedlings from each accession are grown in individual plastic pots. Once the plants had matured sufficiently, leaves from all of the pots were collected and combined to obtain genomic DNA. The genomic DNA was isolated using the technique described by Doyle and Doyle [2, Taq DNA polymerase), 5 μL ddH2O, and 1 μL primer. The manufacturing of mater mixes and the synthesis of primers are accomplished by Sina Clone Company, Tehran, Iran. The PCR program was as follows: an initial step of 4 min at 94°C, followed by 35 cycles of 94°C for 45 s, 52°C to 54°C for 45 s , 72°C for 2 min, and 10 min at 72°C. The reaction products were mixed with an equal volume of formamide dye  and resolved in 2% (w/v) agarose gel (0.5X TBE). Then, they were stained with ethidium bromide (1.0μg ml-1) and photographed under UV light by the Gel Documentation System . Here, all of the presented chemical reagents were prepared by Sigma-Aldrich.In this study, 32 native accessions of nd Doyle . The DNAnd Doyle  presenteAgropyron spp. accessions. In this regard, 10 independent runs were performed with the setting the number of subpopulations (K) from 1 to 10, burn in time and MCMC (Markov Chain Monte Carlo) replication numbers both set to 500,000, and a model for admixture and correlated allele frequencies. Delta K (ΔK) based on the second-order rate of change in the likelihood [A. cristatum as well as A. elongatum in Darwin software [A binary data matrix was constructed by assigning a value of 0 (absence) or 1 (presence) to each band in the PCR amplification products for each of the 32 accessions. The POPGENE version 1.31 software  8GCT) and ISSR2 ((GA)8YT) respectively. Also in the species A. cristatum, ISSR23 ((AC)8GCT) and ISSR3 ((GA)8YG) had the maximum and minimum values of total loci, respectively. Results showed that 97% of amplified loci in A. elongatum and 84% of amplified loci in A. cristatum are polymorphic  verified the suitability of the distance measurement method and also the classification algorithm. A model-based Bayesian technique in the program STRUCTURE was utilized to reveal the genetic structure of the analyzed germplasm. After using Evanno’s approach, the most likely value of K was found to be two. Agropyron spp. accessions were partitioned into two main subgroups, including “green” and “red” which corresponded to two species 8GCT) could be effectively applied for the evaluation of genetic variability among Agropyron accessions.Beside the feeding application of the genus ristatum  and A. elongatum  possess ron spp. , and thu, Panahi  and Moha, Panahi . As infe reports , 37 eachron spp.  in additA. elongatum and A. cristatum) have different genetic structures. Hence, it is mandatory to exert population structure and kinship relationships in association mapping studies of Agropyron spp. to avoid false positive results in identifying significant trait-marker relationships [Agropyron spp. differentiation is important because each species has its own growth habit; for example, A. elongatum has a longer days to flowering period as well as a shorter plant height than A. cristatum [A. elongatum from Iran. Among the studied Agropyron species, A. elongatum had more private alleles than A. cristatum, which implies promising selection potential in A. elongatum. Evidence of private alleles in both analyzed accessions demonstrates their evolutionary importance [A. elongatum, which has a higher proportion. The population structure, a tool employed to elucidate the connections between individuals within and among populations [Agropyron spp. could also lead to this phenomenon. Close to population structure, the existence of LD is a prerequisite for a given plant population to be utilized in association mapping analysis [A. elongatum was higher than the LD value calculated for A. cristatum. Such a level of LD could guarantee the success of marker-trait association identification through an association mapping approach in Agropyron spp. [A. elongatum.The categorization of the examined accessions, employing both the UPGMA method and Bayesian analysis, unveiled the presence of two distinct groups, with each group comprising accessions belonging to the same species. So, it can be exploited that these species Click here for additional data file.


Pterois spp.) records along the equatorial Southwestern (SW) Atlantic revealed a new expansion of these potentially damaging invasive populations, which could impact over 3500 km of Brazilian coastline over the next few years, as well as unique ecosystems and marine protected areas in its path. To assess the taxonomic status, invasion route, and correlation with other centres of distribution, we investigated the genetic diversity patterns of lionfish caught in 2022 at the Amazonia, Northeastern Brazil, and Fernando de Noronha and Rocas Atoll ecoregions, using two molecular markers, the mitochondrial COI and the nuclear S7 RP1. The data indicate that all studied lionfish belong to what is generally accepted as P. volitans, and share the same genetic signature as lionfish present in the Caribbean Sea. The shared haplotypes and alleles indicate that the SW Atlantic invasion derives from an active movement of adult individuals from the Caribbean Sea into the Brazilian coast. The Amazon mesophotic reefs likely served as a stepping-stone to overcome the biogeographical barrier represented by the Amazon-Orinoco River plume. New alleles found for S7 RP1 suggest the onset of local genetic diversification, heightening the environmental risks as this bioinvasion heads towards other South Atlantic ecoregions.In 2020, multiple lionfish ( One of the best-documented, as well as most damaging, invasions recorded to date involves the Indo-Pacific lionfish (Pterois spp.), which are known to have negative impacts on the environment, economy, and human health in the Western Atlantic and Mediterranean Sea6. The establishment and expansion of lionfish populations across their non-native range have caused economic losses estimated at US$ 24 million per year7.Biological invasions have become one of the leading drivers of biodiversity loss worldwide and pose a serious threat to aquatic life 8. Another possible component of this “ecological success equation” is that all Atlantic lionfish are likely to have a hybrid origin, which could have provided adaptive advantages for the founders of the invasive population10.The rapid spread and successful establishment of lionfish in their non-native range is related to their broad dietary breadth, predation efficiency, large environmental niche, high fecundity, fast growth, resistance to parasites, and lack of natural predators in the invaded ecosystemsPterois volitans) were first detected in Arraial do Cabo , one in 2014 and another in 201513. Due to the distance of this region from the Caribbean (5500 km), it remained unclear whether these sightings represented a long-distance dispersal event or a secondary release of animals from the aquarium trade12. As no further lionfish specimens were detected anywhere in this Brazilian region during the subsequent 8 years, an aquarium release would appear to be the most likely explanation13.In the South Atlantic Ocean, two lionfish  and depth range (1–110 m) including Great Amazon Reef System (GARS), the northeastern coast of Brazil (NEB), and the oceanic archipelago of Fernando de Noronha (FNA)Pterois spp. sequences available from the public NCBI and BOLD databases for identification purposes and to better elucidate the invasion and establishment of lionfish in the SW Atlantic.Given this background, the present study applied genetic markers to describe the early stages of the lionfish bioinvasion of the equatorial SW Atlantic and the Brazilian coast, to determine the origin and diversity of the invading fish, and potential dispersal routes, as well as to aid in the management of this ongoing bioinvasion. Mitochondrial and nuclear DNA markers were sequenced in invasive lionfish specimens from the Brazilian coast and compared with Pterois spp. were generated from samples obtained at various Brazilian locations, within three ecoregions  and 26 sequences of intron 1 of the S7 ribosomal protein gene (S7 RP1) of P. miles allele Ind07 (KT358620), which has been recorded in P. volitans as well10.For S7 RP1, sequences of 503–728 bp , namely Amazonia, Northeastern Brazil and Fernando de Noronha and Rocas Atoll. Our novel results indicate that: (1) the species is Pterois volitans with a strong genetic affinity with the Caribbean population; and (2) despite the wide area (1800 km) and multiple shallow and mesophotic habitats sampled around Brazil, the same genetic signature was found in all specimens analysed, which indicates a single, rapid and successful invasion. Furthermore, previously unreported alleles found in the lionfish sampled in the study area could indicate population expansion, which would be consistent with the high invasive capacity of these fish, verified previously in the Western Atlantic and Mediterranean Sea.Our results provide the first molecular description of the early stages of lionfish invasion (2020–2022) in three ecoregions of the SW Atlantic  P. volitans17, which was later identified as a hybrid10. Recently, an analysis of the specimens from the Caribbean Sea, Florida, and northern USA showed alternative invasion scenarios that suggest separated secondary introductions18. These findings should be further investigated to better understand population boundaries and dispersal patterns, which are essential parameters for the effective control and management of future invasions. Nevertheless, mitochondrial COI data presented here indicate that all captured lionfish in the equatorial SW Atlantic belong to what is generally considered Pterois volitans. Furthermore, it supports the hypothesis of a single route of invasion from the Caribbean, because the only two haplotypes found for this gene are the same as those previously reported for the Bahamas and the Caribbean10.A survey of genus P. volitans native range, while six alleles were found in a North Carolina sample, two of them unique to the invaded location10. However, it cannot be said that P. volitans source populations have been fully described for S7 RP1. The data presented here describes an initial sample of the invading fish. Nevertheless, it revealed three previously unrecorded alleles. In this scenario, our data seems likely to underrepresent S7 RP1 variability in the Brazilian ecoregions. A high variability in S7 RP1 would be consistent with the rapid expanding lionfish invasion observed now15. It seems clear that in order to understand the demography of the invading lionfish using S7 RP1, a deeper understanding of its mutation rates and a wider and continued genetic survey is necessary. Finally it is important to highlight that we found the predominance of an S7 RP1 allele previously attributed to P. miles from the Indian Ocean, which seems to support a hybrid origin of the SW Atlantic lionfish, as proposed by Wilcox et al.10.The number of alleles and haplotypes are likely to increase in rapidly expanding populations, especially for quasi-neutral regions of the genome, such as introns. A large number of alleles have been reported for S7 RP1 over 14. Similarly, there is no evidence of recent transactions involving adult lionfish in the ornamental fish trade in any of the regions affected, and they would be especially unlikely in Fernando de Noronha (oceanic region). As a matter of fact, after the first lionfish was found in Brazilian waters12, it raised concerns on the risk of bioinvasion related to possible aquarium releases, and the Brazilian environmental authority banned the import of five Pterois species. Before that, starting in 2008, all imports had to be registered15. On the other hand, larval dispersion from the Caribbean would require countercurrent movement over thousands of kilometres and a successful crossing of the Amazon plume, which flows northward into the Caribbean16. Therefore, the most parsimonious explanation would appear to be that adult lionfish have migrated from the Caribbean Sea, possibly overcoming the porous Amazon-Orinoco biogeographical barrier using the local mesophotic reefs as stepping stones, as previously suggested by Luiz et al.16. This hypothesis is also strongly supported by the spatio-temporal trends recently observed for the lionfish invasion in Brazilian waters and by the genetic data presented here. In the past three years (2020–2023) 352 animals, including juveniles, adults, and egg-bearing females, were reported on 2766 km of Brazilian coast, which strongly indicates a fast and extensive new stage of the SW Atlantic invasion15. While some diversification is suggested by the nuclear S7 RP1, the two COI haplotypes observed in our samples have been previously reported in the United States and the Caribbean, seemingly connecting the population studied here to that present in the Caribbean. Population bottlenecks due to founder effect are expected in the early stages of an invasion. Herein, no genetic evidence of bottleneck was found, and the observed diversity does not seem to hinder the invasive capability of these first specimens recorded in Brazilian waters.Four hypotheses can be proposed to account for the recent occurrences of lionfish in the equatorial SW Atlantic (Brazil): (1) passive transfer in ballast water discharged by ships in ports, (2) aquarium release, (3) larvae dispersal, and (4) adult movement from the Caribbean, a neighbouring region interconnected to the equatorial SW Atlantic. The first two hypotheses seem unlikely. There are no records of lionfish presence associated with major Brazilian ports, such as Mucuripe harbour, on the Ceará coastP. volitans that crossed the biogeographic barrier of the Amazon River and reached the equatorial SW Atlantic through the reefs located underneath the estuarine river plume in a stepping-stone movement. The turbid and brackish waters of the Amazon-Orinoco plume extend some 2300 km along the coast of northern South America and represent an effective barrier for taxa with reduced dispersal capacity. However, the water that lies under the plume is more saline, which, along with the large Amazon reef system below, may facilitate the exchange of marine species in these subsurface waters19. Moreover, the North Brazil Current, flowing from the North of Brazil towards the Caribbean, probably plays a major role in this biogeographical boundary, driving the flow of larvae and other planktonic organisms with reduced mobility northwestward, and hindering their movement in the opposite direction. Nevertheless, pelagic taxa with active swimming capacity may be able to cross it19, as our genetic data suggest.Indeed, environmental conditions and historical records indicate that the ongoing lionfish invasion likely initiated with adult individuals of 22, including for the lionfish invasion16. The main direction of coastal currents would not prevent the movement of adult individuals from one habitat to the next in a stepping-stone pattern16. This hypothesis was first proposed by Luiz et al.16 and Soares et al.14 and is supported by the results of genetic analyses herein. The expansion pattern suggested here seems similar to what is happening with the invading Pterois miles in the Mediterranean23, where lionfish followed a typical Lessepsian immigrant pattern starting in the Levantine Sea then gradually spreading into the eastern Mediterranean in a stepping stone pattern23. This pattern reflects the adaptive capacity of these invasive fish and the risks of their expansion around the world.The function of mesophotic reefs in the Amazon and northeastern coast as stepping stones to migration in both directions of the Caribbean and Brazilian Province has been predicted earlier13, and then to the Brazilian northeastern coast14, is also consistent with a stepping-stone invasion of the equatorial SW Atlantic through the Amazon reef corridor underneath the estuarine river plume and the semi-continuous mesophotic reef system along the Brazilian equatorial coast24. Moreover, larger animals (20–40 cm) are found off the Amazon coast in comparison with the populations from northeastern Brazil and Fernando de Noronha archipelago (20–30 cm15). This evidence suggests an earlier arrival and corresponding older lionfish population in the Amazon coast, which supports the adult dispersal hypothesis indicated by our genetic results.The temporal and spatial distribution of the recent records, with lionfish first being sighted on Amazon mesophotic reefs in 2020, gradually spreading into the oceanic archipelago24. This is the largest cluster of seamounts in the equatorial SW Atlantic and the closest plateau to the continent25, which forms part of the volcanic mountain chain of the Fernando de Noronha Archipelago, providing extensive shallow and mesophotic reef habitats for lionfish adult dispersal. Therefore, lionfish have likely used the mesophotic reefs and seamounts from the equatorial SW Atlantic (Ceará and Piauí)26 as stepping-stones to arrive in Fernando de Noronha Archipelago. It is also likely that they are using artificial reefs  as additional habitats, which help them recruit and establish populations in new environments along the equatorial SW Atlantic coast, as already observed for the invasive coral, Tubastraea spp.27.We found mostly the same COI haplotypes and S7 RP1 alleles in samples from three ecoregions  despite distances of almost 1800 km between our genetic samples. This could be explained by currents , as well as reefs and seamounts that link these environments in the SW Atlantic. The Brazilian equatorial margin (Ceará plateau) has extensive seamounts and reef habitats located 20–50 m deep on the continental shelf between the GARS and the Eastern Brazilian Reef System15; we report here that a few months ago, lionfish was seen for the first time in the remote Rocas Atoll , the semiarid coast of northeastern Brazil , and the Fernando de Noronha Archipelago, covering three marine ecoregions  that was amplified by PCR using the primers FishF2 and FishR1 described by Ward et al.28. The second marker was intron 1 of the nuclear S7 ribosomal protein (S7 RP1), which was amplified using RP1F and RP1R primers described by Chow and Hazama29. Each PCR contained 10 ng of DNA, 1 × Buffer, 1.5 mM of MgCl2, 0.2 mM of dNTP, 0.2 mM of each primer, 1 U of Taq DNA polymerase, and ultrapure water to complete the final volume of 25 µL. Following purification, amplicons were sequenced in both directions using the fluorescent dideoxynucleotides Sanger method at a third-party partner. All sequences were deposited in GenBank under the accession numbers OP882709–OP882735 and OQ750554–OQ750564 (COI sequences); and OP894998–OP895014 and OQ800937–OQ800945 (S7 RP1 sequences) (NCBI Bioproject PRJNA954392).Genomic DNA was extracted using Phenol–Chloroform Extraction, followed by genomic DNA precipitation and resuspension in TE. The final concentration and purity of DNA samples were determined using a Nanodrop spectrophotometer . Two loci were used as genetic markers to distinguish 30. The haplotypes and alleles identified in each sample were used to produce reticulate trees in PopART31 using the Median-joining method. Pterois spp. publicly available sequences from NCBI (https://www.ncbi.nlm.nih.gov/) and BOLD databases (https://www.boldsystems.org/) were included in this analysis (see Supplementary Table Sequencing reads were checked manually, edited, and aligned in MEGA11The authors declare no conflict with ARRIVE guidelines since the samples did not originate from experimentation on live animals. The species in question is an invasive species, which is neither endangered nor threatened.Supplementary Tables.


Results from these analyses indicated a positive correlation between increased introgression from japonica rice varieties and genetic differentiation in weedy rice. In addition, increased crop-to-weed introgression formed a parabola pattern of dynamic genetic diversity in weedy rice. Our case study indicated that human activities such as the frequent change in crop varieties can influence the evolution of their conspecific weeds through crop-to-weed introgression, which promotes their genetic differentiation and dynamics of genetic diversity in agroecosystems.To generate knowledge on how human activities influence plant evolution in agroecosystems, we analyzed allelic introgression from japonica rice cultivars into the indica type of weedy rice. We also analyzed the impact of crop-to-weed introgression on the genetic differentiation and diversity of weedy rice, using InDel (insertion/deletion) and SSR (simple sequence repeat) molecular fingerprints. Results based on the STRUCTURE analysis indicated an evident admixture of some weedy rice samples with indica and japonica components, suggesting different levels of introgression from japonica rice cultivars to the indica type of weedy rice. The principal coordinate analyses indicated indica–japonica genetic differentiation among weedy rice samples, which was positively correlated with the introgression of japonica-specific alleles from the rice cultivars. In addition, increased crop-to-weed introgression formed a parabola pattern of dynamic genetic diversity in weedy rice. Our findings based on this case study provide evidence that human activities, such as the frequent change in crop varieties, can strongly influence weed evolution by altering genetic differentiation and genetic diversity through crop–weed introgression in agroecosystems. As an important driving force, introgression plays an essential role in shaping the evolution of plant species. However, knowledge concerning how introgression affects plant evolution in agroecosystems with strong human influences is still limited. To generate such knowledge, we used InDel (insertion/deletion) molecular fingerprints to determine the level of introgression from  Evolution is one of the most important concepts in biology, in which four essential forces, including mutation or genetic variation, selection, gene flow or introgression, and genetic drift, drive the evolutionary process ,7,8,9,10Domesticated plant species, which are commonly referred to as crops, and their phylogenetically close wild relatives provide excellent cases for studying the influences of introgression on evolution, because of their intimate relationships, allowing natural introgression to occur among crops and their wild relatives ,15,16,17Sorghum halepense L.), weedy sunflowers (Helianthus annus), and weedy rice (Oryza sativa f. spontanea) ,,44,indicer pairs  from theil 2023)  to analy2+), 200 µM dNTPs, 4 µM of each primer, 0.5 U Taq polymerase, 40 ng template DNA, and ddH2O to the final volume. Reaction conditions comprised an initial denaturation step for 4 min at 94 °C, followed by 30 cycles of 30 s at 94 °C, 30 s at 52~58 °C, and 30 s at 72 °C, and a final extension step at 72 °C for 10 min. According to the size of the PCR products, labeled PCR products of 3~5 InDel or SSR primer pairs were mixed together in a ratio of FAM:HEX:ROX:TAMRA = 1:3:1:3, then were electrophoresis separated on ABI 3730xl Analyzer . For genotyping, the separated InDel or SSR fragments of each sample were scored using the software GeneMapper version 4.1 .Polymerase chain reactions (PCR) were performed in a total volume of 20 µL containing 1 × PCR buffer  of each weedy and cultivated rice sample was calculated based on the “InDel molecular index” [indica rice variety (93–11) and the japonica rice variety (Nipponbare) were used as the reference to determine the homozygote indica (II), japonica (JJ), and heterozygote (IJ) genotypes, respectively , intermediate type (0.25 ≤ jF < 0.75), and japonica type (jF ≥ 0.75), respectively, based on the average values of jF or “InDel molecular index” of the 38 InDel loci [The frequency of r index” . For eacu et al. ). In thiDel loci ,44. japonica-specific alleles was examined based on the InDel data matrix, including JS weedy rice that originally had an indica genetic background and the reference rice samples , using the software STRUCTURE v.2.3.4 [https://taylor0.biology.ucla.edu/structureHarvester/ accessed on 20 April 2023). Introgression of the  v.2.3.4 . The STR v.2.3.4  using thjaponica-specific alleles (jF) for measuring the levels of crop-to-weed introgression from japonica rice varieties, we analyzed the correlation between the jF values [japonica genetic components  derived from the “inferred ancestry of individuals” cluster data matrix generated in the STRUCTURE analysis [jF values and the proportion of introgressed japonica genetic components  were visualized by a linear model of Pearson’s correlation method using Prism v.8.0.2 [To estimate the possible application of the frequency of j values  and the analysis . Results v.8.0.2 . indica–japonica genetic differentiation pattern of JS weedy rice, we performed a principal coordinate analysis (PCoA) of the genotypic data matrix based on the 38 InDel molecular fingerprints, using the software GenAlEx v.6.5 [indica and japonica rice varieties as references. Results from the first two principal coordinates of all studied samples were graphed in a 2-dimensional scatterplot to illustrate the genetic differentiation of JS weedy rice samples. To determine the Ex v.6.5 . The datjF < 0.25), middle (0.25 ≤ jF < 0.75), and high (jF ≥ 0.75) levels of introgression, respectively. Consequently, there were 10 low-introgression, 10 middle-introgression, and 10 high-introgression ISWPs established for further correlation analyses. In addition, we randomly selected eight naturally sampled weedy rice populations (NSWPs) from the field sampled JS weedy rice populations based on their jF values, ranging from 0.05 (lowest) to 0.34 (highest), to analyze the relationships between genetic differentiation of weedy rice and crop-to-weed allelic introgression revealed by ISWPs.To estimate correlations between genetic differentiation and crop-to-weed allelic introgression of weedy rice, we established 30 ideally sampled weedy rice populations (ISWPs), each containing 16 samples randomly drawn from the JS weedy rice pool with a total of 1116 samples. These populations represented the low  between ISWPs based on the genotyping data matrixes of both InDel and SSR molecular fingerprints, respectively, using the software GenAlEx v.6.5 [jF values were calculated to represent the differences in introgression (j-dF) between ISWPs based on the same data matrixes. The correlation between genetic differentiation and crop-to-weed allelic introgression was calculated based on the obtained stF and j-dF values of the 30 ISWPs and eight NSWPs, respectively. The obtained correlation was visualized using Pearson’s correlation method (linear model) packaged in the software Prism v.8.0.2 [The pairwise Wright’s t values  were calEx v.6.5 . Moreove v.8.0.2 . eH and Shannon’s information index, I) [jF), we calculated the two genetic diversity parameters using the same ISWPs and NSWPs (see the eH and I) with jF values was obtained using the quadratic equation fitting regression analysis for the ISWPs. The obtained results were visualized using a nonlinear model packaged in the software Prism v.8.0.2 [eH and I) with the jF values in the eight NSWPs using Pearson’s correlation method (linear model) packaged in the software Prism v.8.0.2 [To estimate the correlation of genetic diversity  ,60 with  see the , respect v.8.0.2 . In addi v.8.0.2  to confiindica varieties until the early 1950s, when japonica rice varieties were introduced to this province due to their favorable commercial quality (tastes) and high yield. Statistical data between the 1950s and 2010s evidently demonstrated a relatively rapid change in rice cultivation from indica to japonica rice varieties in this province  was essentially province . Startinprovince .indica to japonica rice varieties started in the southern part of JS. Then, the cultivation areas of japonica rice varieties were gradually extended northward to cover the rice cultivation areas in the whole province. The cultivation areas for japonica rice varieties increased from only 3.4 kha (1000 hectares) in the 1950s to 2015.3 kha in the 2010s. In contrast, the cultivation areas for indica rice varieties dramatically decreased from 1988.5 kha in the 1950s to 290.0 kha in the 2010s  characteristics  at the 38 InDel (insertion/deletion) loci across the rice genome. The jF values of the typical indica rice varieties and weedy rice samples from GD were equal to 0 or close to zero, whereas the jF values of the typical japonica rice varieties and weedy rice samples from NEC were equal to one or close to one , ~7% were the intermediate type (0.25 ≤ jF < 0.75), and ~2% were the japonica type (jF ≥ 0.75) , northeast China (NEC), and JS had their distinct eristics . This che to one . These r to 0.97 ; althougeristics . In the  ≥ 0.75) . In addiica type . These rjaponica-specific alleles into weedy rice in JS rice fields, where weedy rice was originally composed of the indica genetic background, we conducted the STRUCTURE analysis using the admixture model based on the data matrices of the InDel molecular fingerprints. Results from the STRUCTURE analysis demonstrated the distinct genetic components of the typical indica (red and blue) and japonica (green) rice varieties (references) at the most optimal K value (K = 3) and their neighboring K values . The findings suggested the distinct genetic components and substantially diverged genetic relationships of the indica and japonica cultivated rice samples used as references  into the indica genetic background (red and blue) of weedy rice samples/populations.To determine the level of introgression of ferences . The disindica: red and blue, japonica: green), the weedy rice samples from JS showed both indica and japonica genetic components  with nearly no admixture; although, the typical indica rice reference varieties showed somehow other genetic components with an extremely low level of admixture at the K = 3 and K = 4  obtained based on the “InDel molecular index” could be directly used for estimating the level of allelic introgression from japonica varieties into the indica type of weedy rice, we analyzed the correlation between the obtained jF values and the ratios of the introgressed japonica-component of weedy rice samples extracted from the “ancestry of individuals” cluster data matrix in the STRUCTURE analysis. Results from the correlation analysis indicated a significant positive correlation  between the jF values and the ratios of introgressed japonica components , at the positive loads of the first principal coordinate  based on the InDel molecular fingerprints, using typical ca types . Obvious left in . Therefos <0.25, . A smallright in . Therefos >0.75, . Noticeaand 0.75 .indica and japonica rice varieties. This finding was supported by the gradually increased japonica-specific allelic frequency (jF) in the JS weedy rice samples that should originally be the indica type. In general, the PCoA results clearly indicate the genetic differentiation of JS weedy rice samples that are scattered between the typical reference stF) and differences in allelic frequency (j-dF), based on the data matrices of InDel  and NSWPs . These results generated from the ISWPs and NSWPs of JS weedy rice suggest that crop allelic introgression would cause considerable genetic differentiation of its conspecific weed. To estimate the correlation between the extent/level of genetic differentiation and crop-to-weed introgression, we calculated the pairwise genetic differentiation  and Shannon’s information index (I) to represent genetic diversity, based on the data matrices of InDel and SSR molecular fingerprints. In addition, we used the frequency of japonica-specific alleles (jF) to represent the level of crop-to-weed introgression. The correlation analysis was conducted based on the 30 ISWPs with their respective low, middle, and high levels of introgression (R2 = 0.98 for eH and 0.99 for I) for the InDel molecular fingerprints (R2 = 0.81 for eH and 0.78 for I) for the SSR molecular fingerprints  and crop-to-weed introgression (jF) was also analyzed based on the eight NSWPs (eH and I) significantly increased with the increases in the jF values for the InDel molecular fingerprints  and the jF values for the SSR molecular fingerprints  generated based on these results from NSWPs generally agreed with those revealed from ISWPs, particularly for InDel molecular fingerprints.In addition, the correlation between the level of genetic diversity  and high yield [indica rice varieties were almost completely replaced by japonica rice varieties in different rice cultivation areas in this province. As a consequence, weedy rice with japonica characteristics was reported to appear frequently in the rice fields of this province [indica to japonica varieties has considerable influences on the genetic compositions of weedy rice as indicated by its indica and japonica characteristics. Such influences are hypothetically through gene flow or allelic introgression from japonica rice varieties to weedy rice individuals. Our results in this study based on the historical archives clearly demonstrated the pattern of traditional rice cultivation in Jiangsu Province (JS), where gh yield ,36,37. Aprovince ,33,42. Aindica type (~91%) and the rest of the weedy rice samples were the intermediate and japonica types, based on the InDel molecular index [japonica-specific alleles (jF). These results were obtained based on a relatively large number of weedy rice samples  collected from 36 populations across the JS rice cultivation areas. This finding indicates the change in characteristics of JS weedy rice gradually from the original indica type to the japonica and indica–japonica intermediate types, associated with the rapid changes in rice cultivation patterns from indica to japonica varieties in this province. In other words, the rapid alteration of rice cultivation from indica to japonica varieties promoted the divergence of JS weedy rice characteristics, essentially through crop-to-weed introgression: although, other factors, such as direct-seeding and mechanic harvesting, cannot be completely excluded. The changes in the indica–japonica characteristics of JS weedy rice found in this study are similar to those revealed either by phenotypical characterization [indica, japonica, and intermediate types, although predominated by the indica type. All results clearly indicate a wide range of variation regarding the indica–japonica characteristics of JS weedy rice, owing to the rice cultivation change from indica to japonica varieties.In fact, our results showed that most examined JS weedy rice samples  were the ar index  or the arization ,61 or morization ,34, in wjaponica type that was associated strongly with the typical japonica rice varieties, whereas those from GD were essentially the indica type that was associated intimately with the typical indica rice varieties. Our finding is consistent with the previous reports in which weedy rice is genetically closely associated with its cultivated counterparts co-occurring in the same regions [indica and japonica rice varieties cultivated during a historical period of time in this province. Successive crop-to-weed gene flow or introgression, coupled with the independent assortment and genetic recombination in the self-pollination process, promoted the admixture of indica and japonica genotypes in JS weedy rice. In this study, we also found that the reference weedy rice populations from NEC were essentially the  regions ,23,27,31indica type to the current japonica types with evident admixture genetic components, as indicated by the different bar-plots with three K values  [indica–japonica characteristics and genetic components of JS weedy rice, but also in shaping the evolution of JS weedy rice as an important driving force. These observations agree with the previous reports concerning weedy rice genetic diversity and evolution [Results from the STRUCTURE analysis based on the InDel molecular fingerprints in this study clearly demonstrated the change in genetic compositions in JS weedy rice from the original K values . The pregression ,66,67,68 decades . The relte (~1%) ,66,67,68volution ,20,21,22japonica-specific alleles (jF) of the weedy rice samples with considerably high confidence , as well as genetic diversity (eH and I), using the ideally sampled weedy rice populations (ISWPs) established according to their levels  of introgression determined by the jF values. Our results clearly indicate that the level of gene flow or crop-to-weed introgression can be measured by the frequency of the nfidence . Resultsprinting , we analjaponica-specific allelic (crop-to-weed) introgression was highly significantly correlated  with the level of jF values of the examined weedy rice samples. Therefore, the jF values can represent japonica-specific allelic introgression and be directly utilized to determine the relationships between the level of crop-to-weed introgression and the genetic differentiation of weedy rice. Results obtained based on ISWPs with low, middle, and high levels of introgression, using both the InDel and SSR molecular fingerprints, clearly demonstrated the correlation patterns, in which crop-to-weed introgression substantially prompted the genetic differentiation of JS weedy rice. Interestingly, similar correlation patterns were obtained based on the randomly selected naturally sampled weedy rice populations (NSWPs) with low to middle levels of introgression in JS, although with a much lower level of correlation for the results obtained using the SSR molecular fingerprints. Therefore, we can conclude based on all results from this study that crop-to-weed introgression can significantly promote the genetic differentiation of weedy rice populations with the original indica genotype through accumulating japonica-specific alleles from its co-existing japonica rice varieties. In other words, the gradual introgression of different crop-specific alleles into weedy rice populations can considerably cause their within- and between-population genetic differentiation.Further analyses in this study indicated that the level of jF values was significantly associated with the two independent genetic diversity parameters  in both ISWPs and NSWPs (indica–japonica) InDel molecular markers. The differences in genetic diversity revealed by SSR and InDel molecular markers can easily be explained by the reasons that the formation of genetic diversity in weedy rice is not only determined by crop-to-weed introgression involving indica–japonica alleles or characteristics, but also by other types of alleles that are not associated with the indica and japonica characteristics. Therefore, we propose that, based on this case study, the jF values that can represent japonica-specific allelic introgression can also be used to determine the relationships between the level of crop-to-weed introgression and the dynamics of genetic diversity in weedy rice populations. This conclusion is supported by previous studies, in which an increased level of crop-to-weed introgression can promote rapid changes in genetic diversity patterns in weedy populations [Our results based on the InDel molecular fingerprints also suggest that the level of nd NSWPs . Noticeaulations ,20,23,27indica to japonica varieties for the past few decades reveals the human-influenced evolution of weedy rice in agroecosystems. It is well-known that weedy rice infests worldwide rice fields, causing considerable losses in the grain yield and quality of cultivated rice ,26,27. Ajaponica-specific alleles in JS weedy rice, most likely through crop-to-weed allelic introgression based on the InDel molecular fingerprints. Such introgression is closely associated with the rapid replacement of indica rice varieties by japonica rice varieties in JS. This finding evidently indicates that human-influenced cultivation changes in rice varieties (from indica to japonica) alone have already altered the genetic components of the co-occurring weedy rice populations. Our results from the analysis of InDel and SSR molecular fingerprints based on the ISWPs and NSWPs further demonstrated a positive correlation of crop-to-weed introgression, as measured by the jF values, with the genetic differentiation of weedy rice. In addition, these results also confirmed the close association between the levels of crop-to-weed introgression and genetic diversity both in ISWPs and NSWPs, using the two sets of molecular fingerprints. Our results indicated the presence of the japonica-specific alleles detected in JS weedy rice, which is associated with the rapid change in rice cultivation from indica to japonica varieties. Genetic differentiation and genetic diversity are two important elements that are closely associated with the evolution of plant species. Therefore, we consider that human activities or disturbances can promote the rapid adaptive evolution of conspecific weedy rice in rice ecosystems through the change in these elements, which makes the control and management of weedy rice very difficult. Altogether, these findings demonstrated that human activities or disturbances can significantly influence the genetic differentiation and genetic diversity of weedy rice—a conspecific weed—only through consecutive allelic introgression from its cooccurring cultivated counterparts that have constantly been improved by humans at different periods in time. This conclusion is supported by the large number of Such human-influenced rapid evolution of agricultural weeds as revealed in this case study may also be frequently found in many other weedy plant species in agroecosystems ,16,70,71japonica-specific alleles in JS weedy rice that should originally be the indica type with the indica genetic background, based on the insertion/deletion (InDel) molecular fingerprints. The presence of the japonica-specific alleles in the indica type of weedy rice is most likely the result of gene flow or introgression of alleles from japonica rice varieties; although, in the practical rice production, a very low frequency of japonica weedy rice contamination might also happen. Such gradual introgression of japonica alleles is closely associated with the change in rice cultivation from indica to japonica varieties in the past few decades. Our results further indicate that crop-to-weed allelic introgression has considerably changed the genetic components of the cooccurring JS-weedy rice populations. Further analyses based on InDel and SSR molecular fingerprinting indicate a significant positive correlation between the levels of crop-to-weed introgression and genetic differentiation in JS weedy rice. Similarly, increased crop-to-weed introgression promoted a change in genetic diversity in weedy rice with a parabola correlation pattern. Altogether, the above findings demonstrate that human activities, such as the change in cultivated rice varieties, can impose a considerable impact on the evolution of its conspecific weed by promoting genetic differentiation and diversity in rice ecosystems. A similar pattern of crop-to-weed introgression promoting genetic differentiation and genetic diversity is likely found in other crops and their conspecific weeds. Therefore, we conclude that, based on this case study, human activities or disturbances may accelerate the adaptive evolution of conspecific weeds through crop-to-weed introgression in agroecosystems, which may impose a great challenge for the control and management of these weeds. In this study, we detected many