Approximately 4% of the world’s population suffers from rare diseases. A vast majority of these disorders have a genetic background. The number of genes that have been linked to human diseases is constantly growing, but there are still genetic syndromes that remain to be discovered. The diagnostic yield of genetic testing is continuously developing, and the need for testing is becoming more significant. Due to limited resources, including trained clinical geneticists, patients referred to clinical genetics units must be accurately selected. Around 30–40% of genetic disorders are associated with specific facial characteristics called dysmorphic features. As part of our research, we analyzed the performance of classifiers based on deep learning face recognition models in detecting dysmorphic features. We tested two classification problems: a multiclass problem (15 genetic disorders vs. controls) and a two-class problem (disease vs. controls). In the multiclass task, the best result reached an accuracy level of 84%. The best accuracy result in the two-class problem reached 96%. More importantly, the binary classifier detected disease features in patients with diseases that were not previously present in the training dataset. The classifier was able to generalize differences between patients and controls, and to detect abnormalities without information about the specific disorder. This indicates that a screening tool based on deep learning and facial recognition could not only detect known diseases, but also detect patients with diseases that were not previously known. In the future, this tool could help in screening patients before they are referred to the genetic unit.  Rare diseases occur with a population frequency of less than 1:2000 [Genetic diseases can affect any system or organ in the human body. Around 30–40% of these disorders are associated with specific facial characteristics called dysmorphic features [Recently, high-throughput molecular biology techniques, such as NGS, have been introduced into clinical testing. This extended the number of indications for genetic consultations and genetic trials. However, in many countries, the number of trained clinical geneticists is insufficient, e.g., in Poland, a country with a population of 38 million, there are 146 clinical geneticists. In some national genetic centers, the waiting time for an appointment is 2.5–3 years. Moreover, the diagnostic yield of genetic testing is still limited in many ailments. For example, congenital heart disease is the most common birth defect, affecting nearly 10 to 12 per 1000 live-born infants (1–1.2%). The diagnostic yield of high-throughput genetic testing in nonsyndromic congenital heart disease is only ~10%. In syndromic cases, where heart disease is associated with other abnormalities, the success rate of genetic testing increases by ~30% [We performed a series of experiments to evaluate whether computer-aided facial analysis techniques could be used to detect genetic syndromes with dysmorphic features. Many studies that have been performed in the field were focused on the challenging task of establishing diagnosis based on facial pictures only. As there are still new genetic diseases to be discovered, in the approach we undertook, we have focused on the ability of the system to classify as abnormal syndromes that were not present in the trial dataset. The multiclass classification into 15 diseases and a control class had the best accuracy of 84% in our setup. The binary classification (disease vs. control) accuracy of ~96% was achieved on our dataset composed of patients suffering from 15 genetic diseases and control individuals. The system performed well in classifying patients suffering from diseases that were not removed from the training dataset, indicating that a sensitive screening tool is actually very feasible.The system presented can be easily moved to the Raspberry Pi computer platform. Various components, such as a high-quality camera, can be attached to the Raspberry Pi. The camera would take a photo of the patient, and then a neural network would analyze the features of the picture to classify for genetic disease. Such a solution will allow the implementation of a system that operates on a mobile device, which we are currently working on.The diagnostic analysis of facial pictures started with manual annotation of landmark points and use of neural networks for binary classification of images into Down syndrome and healthy controls . A geomeFacial images of patients suffering from 15 genetic disorders were collected by searching PubMed and social media sites of foundations and organizations devoted to specific diseases. The resulting dataset was composed of 101 pictures of patients with 22q11 microdeletion syndrome, 90 with Angelman syndrome, 25 with Coffin–Lowry syndrome, 35 with Cornelia de Lange syndrome, 33 with Crouzon syndrome, 86 with Down syndrome, 26 with Fragile X syndrome, 118 with KBG syndrome, 31 with Kabuki syndrome, 63 with Mowat–Wilson syndrome, 86 with Noonan syndrome, 65 with Pitt–Hopkins, 112 with Smith–Lemli–Opitz syndrome, 38 with Wideman–Steinert syndrome and 32 with Williams syndrome. Face images of individuals aged between 5 and 12 years old from the UTKFace were used as the control dataset . In addiIn the first stage of face classification , a detecThe 3D Face Alignment Network detected 68 3D landmark points from 2D images . The algThe results of automatic anthropometric measurements based on localization of 3D landmark points on the face  can be fThe results of the multiclass classification system based on deep learning face recognition models are shown in In binary task classification into disease and control groups, SVM based on MTCNN detector and DeepFace embeddings was the most successful, with an accuracy of 96% . In addiWe have evaluated several approaches to construct a classifying system for detecting genetic disease from images of the face. There were significant differences between models generating face embeddings in our experiments. We achieved the best results in multiclass problems with ArcFace. DeepFace gave the best results for the two-class classification. The best accuracy of a multiclass classifier detecting one of 15 genetic syndromes was 84%. The two-class classifier, intended to detect the presence or absence of genetic disease, had an accuracy of 96% in our experiments. More importantly, the binary classifier detected disease features in patients with diseases that were not present in the training dataset. Thus, the classifier was able to generalize differences between patients and controls, and detect abnormalities without the need for information about the specific disorder. This indicates that the system does not have to be trained with all genetic diseases to detect ‘genetic’ features of a face and, therefore, may be used as a first-line screening tool. A machine learning face recognition system outperformed a simple approach based on our experiments’ geometric characteristics of face landmark points. The clinical examination performed by a trained dysmorphologist is based on individual experience . A trainFace2Gene is a state-of-the-art tool that clinicians use in many countries. The solution was built based on a database of 26,000, including patients with more than 200 syndromes. The application was designed to point out the most probable diseases, with a score labeled as high, medium, or low . HoweverA single dysmorphic feature is usually not unique for a specific disorder. For example, hypertelorism is not only present in Coffin–Lowry syndrome but also, among others, in Crouzon syndrome. This overlap in dysmorphic features most probably enables the correct classification of patients with a disease unknown to the system in the binary task. The system analyzes only face image information without knowledge about symptoms, other test results, or family history. Seventy-two percent of rare diseases have a genetic background, while others result from infections, allergies, and environmental causes. One of the limitations of our study is the lack of training on images of patients with non-genetic etiologies. There are usually no dysmorphic features in these cases, and therefore, facial images are not published and available. Fetal alcohol syndrome (FAS), caused by an environmental factor, is associated with specific dysmorphic changes, and represents an example of a disease that could be misclassified as a genetic syndrome. Therefore, cases of such diseases should be included in the final training dataset before clinical application. However, it is essential to note that in the case of a screening system, the number of patients with genetic diseases classified as control is more concerning than a false positive misclassification.Ethnic background and age are essential factors in face recognition and genetic syndrome classification. Ethnicity has been shown to have a high impact on the performance of Face2Gene in the classification of Down syndrome patients of Caucasian and African origin . There iFor many countries, the time needed for diagnosis is counted in years, particularly for many genetic diseases. This is due to the complex nature of genetic disorders, the high cost of laboratory testing, and limited human resources, i.e., trained clinical geneticists. Moreover, most of the patients that have genetic tests remain undiagnosed despite significant progress in the field. For example, a majority of patients with isolated congenital heart disease will have normal results in genetic tests. However, if the heart disease is not isolated and there are additional symptoms or dysmorphic features, the probability of a positive test is significantly increased by ~30% ,5,24. ThOur analyses were based on a relatively small dataset of 15 disorders out of thousands of genetic syndromes. However, the same approach with an extended dataset could have clinical application. Furthermore, once the training of the deep learning algorithm is performed, the testing of individual images is fast, and can be performed on a desktop computer.It is important to remember that there are more than 7500 genetic diseases, and probably still hundreds to be discovered. Therefore, the screening system has to detect abnormalities associated with diseases that were unknown at the time of its construction. Our results suggest that such a system is possible. However, a more extensive study, including a larger dataset with more diseases, is needed.


IS. The genetic structure analysis revealed the distinctness of Kangayam, Vechur and Punganur cattle from the rest of the zebu breeds. The results showed the influence of Hallikar breed in the development of most Mysore type cattle breeds of South India with the exception of Kangayam. Bayesian clustering analysis was performed to assess the taurine admixture in South Indian zebu cattle using purebred Jersey and Holstein-Friesian as reference genotypes. Relatively high levels of taurine admixture (>6.25%) was observed in Punganur, Vechur, Umblachery and Pulikulam cattle breeds. Two major maternal haplogroups, I1 and I2, typical of zebu cattle were observed, with the former being predominant than the later. The pairwise differences among the I2 haplotypes of South Indian cattle were relatively higher than West Indian  zebu cattle. The results indicated the need for additional sampling and comprehensive analysis of mtDNA control region variations to unravel the probable location of origin and domestication of I2 zebu lineage. The present study also revealed major concerns on South Indian zebu cattle (i) risk of endangerment due to small effective population size and high rate of inbreeding (ii) lack of sufficient purebred zebu bulls for breeding and (iii) increasing level of taurine admixture in zebu cattle. Availability of purebred semen for artificial insemination, incorporation of genomic/molecular information to identify purebred animals and increased awareness among farmers will help to maintain breed purity, conserve and improve these important draught cattle germplasms of South India.The present study is the first comprehensive report on diversity, population structure, genetic admixture and mitochondrial DNA variation in South Indian draught type zebu cattle. The diversity of South Indian cattle was moderately high. A significantly strong negative correlation coefficient of -0.674 (P<0.05) was observed between the effective population size of different breeds and their estimated F Ever since domestication, animals have been used as an important source of draught power for agricultural production. Among them, cattle have been used for a variety of farm operations like ploughing, carting, tilling, sowing, weeding, water lifting, threshing, oil extraction, sugarcane crushing and transport. Over time, with increasing mechanization of agriculture, the use of draught cattle in farm operations reduced significantly and became negligible in advanced economies. However, in developing countries, particularly in small holder production systems and in hilly and mountainous regions, agricultural production still relies on draught animal power . For exath livestock census [Bos indicus) breeds have been classified into three major groups: dairy type, draught type and dual-purpose cattle. The dairy type and dual-purpose cattle breeds are predominantly distributed in North and North-Western India, while most indigenous draught type cattle breeds are located in Southern and Eastern India. A wide diversity of zebu cattle breeds exists in South India with body size ranging from dwarf  to compact  and medium to large  size animals; draught characteristics varying from heavy load pulling capability  to speed and endurance in trotting ; utilities ranging from draught to penning for dung .India is home to the largest cattle population (13.1% of world’s cattle population) in the world which constitutes 37.3% of its total livestock. As per the 20k census , India hk census . Of thesApart from rapid mechanization of agriculture in South India, the increasing cost of maintaining draught type cattle forced the farmers to replace these animals with crossbred cattle that fetched better returns from the sale of milk. During late 1980s and early 1990s, the draught cattle breeds started losing their economic relevance and the farmers increasingly resorted to crossbreeding with commercial taurine cattle like Jersey, Holstein-Friesian and Brown Swiss. Such crossbred cows approximately yielded more than double the milk as that of indigenous draught breeds. Combined with farmers’ preference for crossbreds, the relatively easy access to breeding services  and availability of better infrastructure for artificial insemination and veterinary care, the proportion of crossbred cattle among the breedable females became much higher in South Indian states than rest of India. The crossbred cattle constitute 35.9% of total cattle in Andhra Pradesh, 41.9% in Karnataka, 76.1% in Tamil Nadu and 94.6% in Kerala as against the national average of 27.7% [Indiscriminate crossbreeding, lack of stabilization of exotic inheritance and absence of selection among F2 hybrids led to negative impacts like increased disease susceptibility, decreased fertility and drop in production levels among crossbred cattle , 8. DuriBlood samples were collected by jugular venipuncture into EDTA vacutainer tubes from various purebred zebu, commercial taurine and crossbred cattle. Blood sampling was performed by local veterinarians in the respective native breed tracts following the standard good animal practice. The blood samples were collected as part of routine veterinary surveillance and consent was obtained from animal owners for the usage of the samples in this study. Therefore, no further license from the “Institutional Committee for Care and Use of Experimental Animals” of the Tamil Nadu Veterinary and Animal Sciences University, Chennai, India and the Joint FAO/IAEA Division, International Atomic Energy Agency, Vienna, Austria was required.Joshi and Phillips  classifiAll the DNA samples were subjected to PCR amplification using 27 FAO recommended microsatellite markers  S1 Tabl. The forhttp://bioinfo.ut.ee/primer3-0.4.0/). The amplified mtDNA control region included nucleotide positions 15167 to 30 of the complete bovine mitochondrial genome sequence available at NCBI GenBank accession number AF492350 [5’ AGGACAACCAGTCGAACACC 3’) and BTMTD1-R 5’ . The amplicon included complete D-loop (control region) flanked by partial cytochrome B, complete t-RNA-Thr and complete t-RNA-Pro coding sequences at 5’ end while partial t-RNA-Phe sequence flanked the 3’end. Polymerase chain reaction was performed in a total reaction volume of 40μl with the following cycling conditions: initial denaturation at 95°C for 15 min followed by 30 cycles of 95°C for 1 min; 58°C for 1 min; 72°C for 1 min with final extension at 72°C for 10 min. Purified PCR products were sequenced using Big Dye Terminator Cycle Sequencing Kit  on an automated Genetic Analyzer ABI 3100 .Primers were custom designed and synthesized to amplify 1207 bp bovine mitochondrial DNA control region using Primer 3 version 4.0 . Bayesian clustering analysis was performed to evaluate population structure and genetic admixture among South Indian cattle using STRUCTURE 2.3.4 [The presence of null alleles in the dataset was checked using Microchecker version 2.2.3 . The neu windows . Basic d windows . A Neigh windows . F stati windows  was calc windows . Analysi windows . Non-metRE 2.3.4 . The proRE 2.3.4  was usedST were estimated using ARLEQUIN 2.1. MEGA version 6.0 [ST derived from mtDNA haplotype frequency were utilized to perform non-metric multi dimensional scaling (MDS) analysis using SPSS version 13.0. To examine the possibility of a past demographic expansion, a mismatch analysis was performed by calculating frequency distributions of pairwise differences between sequences using ARLEQUIN 3.1. Three parameters viz. Ʈ, ϴ0 and ϴ1 were estimated under the sudden expansion model as follows: Ʈ = 2Tu, ϴ0 = 2N0u and ϴ1 = 2N1u  and ‘N1’ being the size of the new population (after the expansion)). The tests for goodness of fit were performed based on sum of square deviations (SSD) between the observed and expected mismatch and Harpending’s raggedness index. The South Indian cattle breeds were subjected to tests for selective neutrality through estimation of Tajima’s D and Fu’s FS. Tajima’s D is the scaled difference in the estimate of θ = 4Neμ  based on the number of pairwise differences and the number of segregating sites in a sample of nucleotide sequences. Fu’s FS statistic is based on haplotype distribution and has low values with the excess of singleton mutations caused by the expansion. Tajima’s D and Fu’s FS were also estimated using ARLEQUIN 3.1. Median Joining (MJ) network of South Indian cattle haplotypes was reconstructed using NETWORK 4.5.1.2 [Mitochondrial DNA sequence editing, assembly of contigs and multiple sequence alignments were performed using CodonCode Aligner version 3.7.1 . Mitochondrial DNA diversity parameters including nucleotide diversity, haplotype diversity, parsimony informative sites and average number of nucleotide differences were calculated using DnaSP, version 4.10 . Frequension 6.0  was util 4.5.1.2 .Bos indicus) cattle in Indus valley region. Despite the relatively low allelic diversity, the observed and expected heterozygosity of South Indian cattle were moderately higher (>60%) and comparable to that of West-Central, North and East Indian cattle [In the present study, a total of 13932 genotypes were generated across 27 microsatellite loci in 516 cattle belonging to 10 draught type zebu breeds, 2 commercial taurine breeds and 2 crossbred populations. The mean basic diversity indices of different South Indian cattle breeds are presented in n cattle –27  and 0.108 (Pulikulam). Breeds like Umblachery, Vechur, Ongole and Bargur showed mean FIS greater than 7% indicating significant heterozygosity deficit, possibly due to close breeding and potential inbreeding. The mean FIS for Vechur and Ongole cattle observed in the present study was significantly lower than reported previously by Radhika et al. [The mean estimated Fa et al.  and Shara et al.  respectiST outlier approach was followed. This selection detection strategy evaluates the relationship between FST and expected heterozygosity under an island model of migration and describes an expected distribution of FST vs He under selective neutrality. The results revealed two loci, HEL5 (FST = 0.215) and HEL13 (FST = 0.216) as significant outliers with high FST values, indicating positive selection in force  to 0.223 (ETH152), whereas among zebu cattle breeds, it varied between 0.024 (INRA005) and 0.114 (HAUT24). The global FST among South Indian zebu cattle observed in the present study was comparable to that of East Indian zebu [ST and Nei’s genetic distance among the draught type zebu, taurine and crossbred cattle are presented in ST ranged from 0.0234  to 0.107 (Kangayam-Punganur) among the South Indian zebu cattle breeds. Significantly high genetic differentiation (FST = 0.0919) was observed among the two dwarf/miniature type Vechur and Punganur cattle. The NeighborNet tree constructed based on pairwise Nei’s genetic distance is presented in The global F statistics among the South Indian cattle breeds are presented in ian zebu  cattle. ian zebu  and Nortian zebu  cattle bST values are presented in Understanding genetic relationship and detecting the cryptic genetic structure is an important first step to elucidate the demography and ancestral history of cattle populations. The non-metric multidimensional scaling (MDS) analysis was used to investigate the relationship among the studied cattle breeds. The MDS plots derived from pairwise FBayesian clustering of individuals without prior population information was performed to assess the cryptic genetic structure in the studied cattle breeds. Among K = 2 to 15, the dataset was best described with K = 2 genetic clusters at which the second order rate of change in LnP(D) was maximum . At K = To further understand the degree of phylogeographic structure in the South Indian zebu cattle breeds, analysis of molecular variance (AMOVA) was performed to assess the distribution of microsatellite variation as a function of both breed membership and geographic origin . When noA 1207 bp long region of bovine mtDNA covering partial cytochrome B, t-RNA-Thr, t-RNA-Pro, D-loop (control region) and partial t-RNA-Phe coding DNA sequence were amplified in 523 South Indian cattle. After trimming and editing, the 951bp long final sequence corresponded to 15240 bp to 16190 bp of the complete mitochondrial genome . Two majST varying between zero and 0.293 while I2 haplotypes showed pairwise FST varying between zero and 0.255. The largest FST among I1 haplotypes was observed between Punganur cattle and other zebu breeds  followed by Vechur and other breeds . The largest FST among I2 haplotypes was observed between Nepalese zebu and South Indian zebu  followed by Vechur and other breeds . The pairwise FST for each of the haplogroups were further utilized to perform non-metric multidimensional scaling analysis (MDS) and understand the evolutionary relationships among the zebu cattle breeds , analysis of molecular variance among zebu mtDNA haplotypes of cattle from South India, Gangetic plains  and Indus valley (West India) sites were conducted . When noTo obtain further insight into the demographic history of South Indian draught type zebu cattle, the mismatch distribution of pairwise nucleotide differences among haplotypes within I1 and I2 maternal lineages was analysed for each of the investigated breeds . Sudden Median Joining (MJ) network of mtDNA haplotypes from South Indian zebu cattle was reconstructed. Additionally, 298 mtDNA control region sequences of North Indian and Nepalese (Gangetic Plains), West Indian , Chinese, Bhutanese and Vietnamese cattle were also included for haplotype reconstruction. The network results  revealedOur study presents the first comprehensive genetic and phylogeographic analysis of draught type zebu cattle breeds of South India using nuclear and mitochondrial DNA variations. Historically, the focus of cattle breeding in South India was towards improving draught characteristics, mainly for agricultural operations and transport, including movement of military equipment. The diversity, characteristics and morphological classification of South Indian cattle breeds  are presIS in the present study. The relatively higher mean FIS estimates of some of the breeds reflected the breedable male and female populations [IS among the breeds investigated in the present study was observed in Pulikulam cattle (FIS = 0.108). Vechur cattle with second highest estimated FIS (0.089) had only few hundred breedable females and <5 breedable males with an effective population size of <10. Similarly, Umblachery and Bargur breeds had higher mean FIS estimates of 0.089 and 0.076 respectively whose effective population size was 1580 and 1581 respectively. Although the total population of both these breeds were relatively higher (39050 and 14154 heads of Umblachery and Bargur respectively), only few hundred breedable males (586 and 624 for Umblachery and Bargur respectively) were present in their respective native breed tracts. Lack of availability of sufficient breeding bulls was one of the main reasons for low effective population size, higher estimates of FIS and high rate of inbreeding in these breeds [IS estimate was observed in Hallikar cattle (FIS = 0.021), whose total population was greater than 1.2 million heads with breedable male and female population of 5524 and 397357 respectively. Interestingly, Punganur cattle with a total population of less than 3000 and effective population size of ~200 showed positive, but relatively lower estimate of FIS (0.041). This could be possibly due to a better breedable male to breedable female ratio observed in this breed (0.0715) as compared to Vechur (0.0020), Pulikulam (0.0084) and Umblachery (0.0387) cattle [With rapid mechanization of agriculture in South India, the need for rearing draught cattle came down drastically in the last few decades. This led to reduction in effective population size and consequent increase in rate of inbreeding in many of these breeds . A signiulations  in theirulations . The hige breeds . Among tSingh and Sharma  assessedST was lowest among Hallikar and Alambadi (FST = 0.023), Alambadi and Bargur (FST = 0.026), Alambadi and Pulikulam (FST = 0.024), Bargur and Umblachery (FST = 0.031) breeds. Further, significant level of Hallikar ancestry was observed in Alambadi, Bargur, Pulikulam, and Umblachery cattle breeds (Most South Indian cattle breeds belong to Mysore type cattle (Group IV) , of whice breeds .Alambadi cattle bred in the hilly tracts of Western ghats have close resemblance to Hallikar breed that are reared in the adjoining province of erstwhile Mysore. Ware  expresseST = 0.070) and little or no traces of Kangayam ancestry in Umblachery cattle. Similar to Pulikulam cattle, significant admixture was observed in Umblachery cattle with traces of Hallikar and Deoni ancestry in them. This could be possibly due to trade influx of cattle into native tract of Umblachery more than a century ago, from Southern Tamil Nadu (Cumbum district) where the now extinct breed called “Kappiliyan” or “Tambiran Madu” of Canarese (Mysore) origin was in abundance [Pulikulam, is a migratory cattle breed, maintained in a zero-input system in southern parts of Tamil Nadu state of South India. These cattle resemble Kangayam in physical appearance but are significantly smaller and compact in size. Gunn  opined tbundance , 46.rd Pattagar (community chief) of Palayakottai who selectively bred the local cattle, only with sires available in his herd. The appearance of some of his cows/heifers gave the impression that they had a distinct strain of Ongole blood in them, as the Pattagar was known to possess purebred Ongole cows [ST indicated marked differentiation of Kangayam from Hallikar (FST = 0.093) and Ongole (FST = 0.075) cattle. Further, Kangayam was also significantly differentiated from other Mysore type breeds  with pairwise FST ranging from 6.3% to 7.7%. The Bayesian clustering analysis showed Kangayam as a genetically distinct entity from both Ongole and Hallikar cattle. Low levels of admixture observed in Kangayam cattle, when assumed under K = 4 to K = 8  with the exception of Kangayam. Kangayam is one of the popular draught breeds of South India known for their power (0.8hp per pair of bullocks) and sturdiness . Althougole cows . But no ole cows . Howeverto K = 8 , was proST showed Vechur and Punganur to be genetically distinct from other South Indian draught type zebu cattle. Although, Bayesian clustering analysis indicated the influence of Hallikar ancestry in both the breeds (K = 3 to K = 6), they clustered independently at K = 7 and K = 8 (ST = 0.092). This could be due to restricted gene flow between the two populations , genetic drift due to small populations size and of late, selective breeding practiced by farmers.The two bantam cattle breeds, Vechur and Punganur have retained moderate levels of diversity despite the small population size and being classified in the critically endangered category. Genetic distance based phylogenetic analysis and non-metric multidimensional scaling plot derived from pairwise Fnd K = 8 . It is avice versa. Often, the choice of semen used for artificial insemination (AI) is left to the inseminators and depends on the availability of purebred zebu, purebred taurine or crossbred semen straws, irrespective of the breed/genotype of the cows  brought for AI. This resulted in dilution of purebred zebu germplasm with varying levels of taurine admixture in well described zebu cattle populations, thus affecting their breed purity.With the mechanization of agricultural operations and increasing demand for dairy products in the last few decades, the focus of cattle breeding in South India shifted towards improving milk production. Beginning with the Operation Flood program in 1970s, intensive cattle development projects in Southern India were targeted towards genetic improvement of local cattle for milk through crossbreeding with exotic commercial taurine cattle. Although the official breeding policy emphasized the need for selective breeding of recognized zebu cattle breeds, indiscriminate crossbreeding took place in the native tracts of many indigenous breeds. In the absence of pedigree recording under small holder production set up, it is not uncommon that the local cows inseminated with purebred taurine or crossbred semen were subsequently served by locally available sires or Bos taurus X Bos indicus crossbreds were also genotyped to evaluate the range of taurine inheritance under field conditions. The proportion of membership coefficient obtained for individual animals in the inferred zebu and taurine clusters were utilized to estimate taurine admixture and purity of the investigated zebu cattle breeds , Vechur (42.3%), Umblachery (33.3%) and Pulikulam (23.5%) cattle. Significant proportion of individuals belonging to these breeds also had >12.5% taurine admixture. Among these, Vechur and Pulikulam have been reported to have severe shortage of breedable males , 39 in tThe present study revealed no additional maternal haplogroups in the South Indian cattle other than the typical I1 and I2 zebu lineages reported earlier . A relatBos tauus and Bos indicus cattle based on mtDNA variations [Bos taurus cattle were domesticated in the Near East during the Neolithic transition about 10 thousand years before present [Several studies have demonstrated independent domestication and origin of riations . It is a present –62. Simi present  shed muc present  identifi present  reportedST derived from frequency of I2 haplotypes showed the distinctness of Vechur and Kangayam cattle, thus indicating the diversity of this lineage within South Indian cattle. The results showed the need for additional sampling of zebu breeds from North, East and North-East Indian cattle to explore and gather new evidence on the potential location for the origin and domestication of I2 lineage in the Indian sub-continent.The present study based on comprehensive sampling of diverse breeds of South Indian zebu cattle, showed the mismatch modal value of I1 haplogroup to be higher (two mismatch) than reported by Chen et al. . More inThe present study revealed moderate levels of genetic diversity existing in South Indian zebu cattle. It also established the genetic distinctness of Kangayam, Vechur and Punganur cattle from the rest of the South Indian zebu cattle. The influence of Hallikar breed was observed in the development of most Mysore type cattle breeds of South India with the exception of Kangayam. Analysis of mtDNA control region sequences revealed two major maternal haplogroups, I1 and I2, with the former being predominant than the later. The results indicated the need for additional sampling and comprehensive analysis of mtDNA control region variations to unravel the probable location of origin and domestication of I2 zebu lineage. The present study revealed two major concerns about the status, conservation and improvement of South Indian draught type zebu cattle breeds: (i) Four of the ten investigated zebu cattle breeds are under the risk of endangerment due to small effective population size and high rate of inbreeding (ii) the lack of sufficient purebred zebu bulls for breeding and increasing level of taurine admixture in the native breeds tracts. Recently, the state governments of South India have started establishing conservation units and research centres for improvement of breeds like Alambadi, Bargur, Kangayam and Umblachery. Parental stocks of these breeds are being established by procuring true to the type animals from small holder farmers. However, in the absence of pedigree records, it has been difficult to estimate genetic purity or taurine admixture in those animals. Considering the relatively higher levels of taurine admixture in some of the breeds, it is advisable to perform molecular/genomic evaluation of local cattle to improve breed purity in these units/centres. It also needs to be mentioned that additional efforts are necessary to make the farmers aware of scientific breeding practices and limit the levels of inbreeding and taurine admixture in purebred zebu populations. Availability of purebred semen for artificial insemination, incorporation of genomic/molecular information to identify purebred animals and increased awareness among farmers will help to maintain breed purity, conserve and improve these important draught cattle germplasms of South India.S1 FigFST outlier approach using LOSITAN showed that two marker loci (HEL13 and HEL5) deviated from selectively neutrality.Selection detection based on the (DOCX)Click here for additional data file.S2 FigDetermination of correct number of clusters in Bayesian STRUCTURE analysis  (a) Mean L (K) over 10 runs for each K value of 1 to 15 (b) Distribution of ΔK with the modal value (K = 2) indicating the true K or the uppermost level of structure.(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table. Global F statistics among South Indian cattle (a) draught type zebu, taurine and crossbred cattle and (b) draught type zebu cattle only(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.


Crassostrea virginica. We also investigated the impact of hatchery practices on neutral genetic diversity of restored reefs and quantified the broader genetic impacts of large‐scale hatchery‐based bivalve restoration. Restored reefs showed similar levels of diversity as natural reefs, and striking relationships were found between planting frequency and broodstock numbers and genetic diversity metrics (effective population size and relatedness), suggesting that hatchery practices can have a major impact on diversity. Despite long‐term restoration activities, haphazard historical translocations, and high dispersal potential of larvae that could homogenize allele frequencies among populations, moderate neutral population genetic structure was uncovered. Moreover, environmental factors, namely salinity, pH, and temperature, play a major role in the distribution of neutral and adaptive genetic variation. For marine invertebrates in heterogeneous seascapes, collecting broodstock from large populations experiencing similar environments to candidate sites may provide the most appropriate sources for restoration and ensure population resilience in the face of rapid environmental change. This is one of a few studies to demonstrate empirically that hatchery practices have a major impact on the retention of genetic diversity. Overall, these results contribute to the growing body of evidence for fine‐scale genetic structure and local adaptation in broadcast‐spawning marine species and provide novel information for the management of an important fisheries resource.The release of captive‐bred plants and animals has increased worldwide to augment declining species. However, insufficient attention has been given to understanding how neutral and adaptive genetic variation are partitioned within and among proximal natural populations, and the patterns and drivers of gene flow over small spatial scales, which can be important for restoration success. A seascape genomics approach was used to investigate population structure, local adaptation, and the extent to which environmental gradients influence genetic variation among natural and restored populations of Chesapeake Bay eastern oysters  Understanding the extent of genetic variability in natural and restored oyster populations and how the variation is structured across broad environmental gradients could provide important information for planning future bivalve restoration programs and their management.22.1N = 556 individuals).Oysters were collected between 2015 and 2018 from nine sites throughout the Chesapeake Bay . Freebayes v1.2.0‐dirty  ; the neutral dataset consisted of all remaining SNPs. Any SNP identified as an outlier in at least one method was removed from the neutral dataset. SNPs that were detected as outliers using all methods constituted the putatively adaptive dataset. All outlier analyses were repeated using the same criteria excluding the Virginia coastal Bay Wachapreague sample to test for selection within Chesapeake Bay samples (inner Bay dataset).R2 separately for SNPs on the same chromosome  and for unlinked SNPs . The critical R2 was estimated from the unlinked loci by root transforming the R2 values and taking the 95th percentile of the distribution as the threshold beyond which the LD is caused by physical linkage  can lead to biases in downstream analyses if independence of loci is assumed  and expected (He) heterozygosity, allelic richness (Ar), and the FIS inbreeding coefficient in hierfstat v0.04‐22  was used to calculate observed  was estimated using the single‐sample linkage disequilibrium method  as suggested by Waples , which balances effects of precision and bias  = 2  using generalized linear models. The association between 2.5FST  as a function of water distance between sampling sites  as implemented in adegenet v.2.1.1  and the Bayesian clustering algorithm implemented in STRUCTURE v.2.3.4  and the Chesapeake Bay Program (http://data.chesapeakebay.net/) from buoys located closest to each of the eleven sampling sites .A RDA was performed as a genotype–environment association method to detect SNPs putatively under selection based on correlations with environmental variables as described in Capblancq et al.  using thes Table . Variances Table . After v2.7C. virginica is uniquely explained by environmental variables, how much is uniquely explained by geography, and how much is due to the combined effect of the two, variance components of the RDA were partitioned by running 3 models: a full model with environmental and geographic variables, a partial model in which geography explains genetic data conditioned on important environmental variables, and a partial model in which important environmental variables explain genetic data conditioned on geography. This analysis allowed for distinguishing between how much of the total explainable neutral and adaptive variance was due to the environment , how much was due to geography , and how much was due to the joint effect of both factors. Additional details of this approach are given in Supplementary Methods Redundancy analysis was conducted on the neutral and putative outlier datasets separately to assess the influence of environmental variables and geographic distance on observed patterns of genetic variation  on the protein sequences of C. virginica. We used GO terms generated in Johnson and Kelly  annotation of SNPs identified as outliers in at least two differentiation‐based outlier detection methods and identified in RDA. The resulting flanking regions (100 bp) of these SNPs were extracted from the eastern oyster genome that we previously used for the bioinformatics pipeline and BLASTed  were not thinned based on this criteria due to loess curve being below critical R2 shown in Figure The critical RR2 calculated from the intrachromosomal LD analysis for the inner Bay dataset was 0.1026  were not thinned based on these criteria due to loess curve being below critical R2). After removing linked SNPs, the thinned neutral dataset consisted of 4922 SNPs.The critical 3.2He), observed heterozygosity (Ho), allelic richness (Ar), inbreeding coefficients (FIS), relatedness, and effective population size (Ne) were calculated for each sampling site using the thinned neutral dataset. He was similar between sampling sites, ranging from 0.218 to 0.239, while Ho differed more substantially among sites ranging from 0.183 to 0.246 (Table Ho than He except HCS (restored), HCW , LC , BR, and TS . The Harris Creek restored sample HCS displayed the lowest Ho overall (0.183), while the restored Harris Creek site HCR1 displayed the highest Ho overall (0.246). Excluding the single cohort HCS sample, all Harris Creek restored samples displayed slightly higher levels of Ho than natural Maryland and Virginia populations  to 0.177 (HCS), and about half of all FIS coefficients were negative. The restored mixed‐cohort sites had the lowest FIS overall  as well as the coastal Bay (W) site. Global relatedness trends ranged from 0.0022 to 0.0619  to 501.4 (LC) (Table Nb from a single cohort of juveniles was the lowest (67.3), the upper confidence limit was higher than all MD samples except LC and HCR4. The Ne estimates for the restored Harris Creek samples increased as hatchery‐planting frequency increased and were higher than Ne estimates from the natural MD populations HCW and TB  Table . The natC) Table . While tTB Table . OverallNe  positive association between the average broodstock sex ratio and Ho . Overall, the relationships between genetic diversity metrics and hatchery practices (planting effort and broodstock size) were positive, strong, and highly predictive.The number of broodstock used for hatchery plantings was significantly positively associated with 3.3FST estimates between natural and restored populations were small, ranging from 0.001 to 0.032  site and all other sites (0.019 < FST < 0.030). Pairwise FST estimates between HCR1 and the inner Bay sites were higher than comparisons among other inner Bay populations (0.002 < FST < 0.012). Similar to FST results, analyses of population structure via DAPC (49 PCs retained) revealed four major population clusters, with the coastal Bay (W) sample grouping distinctly from all natural and restored inner Bay sites , a total of 208 SNPs were significantly associated with the five environmental variables tested, with a large proportion of SNPs being more specifically attributed to mean salinity (74 SNPs), pH variables , minimum water temperature (41 SNPs), and minimum dissolved oxygen . Interestingly, most of the SNPs associated with environmental variables were located on chromosomes one through six. However, many of the SNPs associated with environmental variables were located across all ten chromosomes , two geographic variables using principal coordinates of neighbor matrices (PCNM1 and PCNM2) and four environmental variables  were selected for RDA. The RDA explained a small but significant portion of the genetic variation for all sites  and for the inner Bay sites (5 SNPs), two geographic (PCNM1 and PCNM2) and four environmental variables  were selected for RDA. The RDA explained a moderate but significant portion of the genetic variation for all sites , 85 of which had gene ontology (GO) annotations, while 35 were uncharacterized proteins. For the inner Bay dataset, SNPs identified in at least two genome‐scan methods and in RDA (90 SNPs) were distributed across all 10 chromosomes, 68 had significant hits, 47 of which had gene ontology (GO) annotations, while 18 were uncharacterized proteins. For the full dataset and inner Bay dataset, most of the genes were involved in ion binding and transmembrane transporter activity. A complete list of significant GO terms and candidate genes for the full dataset is in Table The SNPs identified as outliers in at least two genome‐scan methods and in RDA from both datasets (208 SNPs) were distributed across all 10 chromosomes. Of all the SNP‐containing sequences that were BLASTed against the protein sequences of the eastern oyster genome, 128 SNPs had significant hits  across sampling locations were negative, indicating heterozygosity excess, and those that were positive were lower than those observed using SNP datasets in Canadian eastern oyster populations  was within the range of or slightly lower than what has been observed in studies using similar markers (SNPs) in oysters. For example, observed heterozygosity levels were similar to those observed in Canadian eastern oyster populations  of the estimated inbreeding values  are consistently an order of magnitude lower than Ne estimates from Canadian populations  are still much reduced compared to what they likely were in the past. Therefore, maintaining diversity of extant Chesapeake Bay natural populations should only be a minimum target.In general, z et al.  for Canaz et al. . Point e4.2Ne and relatedness), as well as a positive correlation between broodstock male‐to‐female ratio and observed heterozygosity. To date, few studies have assessed how hatchery production techniques can directly (and positively) impact genetic diversity of cultured and supplemented populations of bivalves. A somewhat similar result was found in a recent study of eastern oysters, in which the ratio of males‐to‐females in broodstock was positively correlated with metrics of genetic diversity of hatchery‐produced eastern oyster cohorts  can increase diversity. Furthermore, results from this study contribute to the growing body of evidence that adaptive differentiation can occur over very fine geographic scales in marine species and suggest that this structuring is at least partly driven by spatial heterogeneity in environmental parameters such as salinity, temperature, pH, and dissolved oxygen. The eastern oyster is a commercially exploited species with large‐scale restoration efforts underway in the Chesapeake Bay and in other regions (e.g., Brumbaugh & Coen, None declared.Table S1–S6Click here for additional data file.Supplementary MaterialClick here for additional data file.


Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis affecting the mainly axial joints in both East Asia and Europe. To date, the pathogenesis of AS is still unknown, although we know that genetics play a vital role in it. The HLA-B27 allele is found in over 85% of AS patients. However, strong evidence suggests that other major histocompatibility complex (MHC) and non-MHC genes are also involved in the pathogenesis. In addition, current data showed that there were significant differences in both genomics and metagenomics among the different ethnic populations. The investigation of the key role of the microbiome in AS pathogenesis also highlighted the host–microbiome genetic interactions. Here, we systematically review current AS genetic research data and further compare genetic differences, especially between East Asian and European groups, which may highlight the challenge in future genetic studies. Ankylosing spondylitis (AS) is one of the commonest rheumatic diseases in both Asia and Europe. It is a highly heritable chronic inflammatory disease that mainly affects the axial joints, but also has peripheral joints and various organs involvement , 2000. PHLA-B27 and AS were first reported in 1972 (which were some of the earliest described genetic associations), and it remains the most substantial risk factor for AS   . A recen   .CDKAL1, and several suggestive and secondary loci , whereas 23 achieved genome-wide significance in white Europeans . Additioary loci . EllinghERAP1, GPR35, HHAT, HLA-B, ICOSLG, IL23R, IL27, NOS2, NPEPPS, RUNX3, TBX21, TYK2, UBE2E3, UBE2L3, and ZMIZ1, and two intergenic regions (2p15 and 21q22) (HAPLN1-EDIL3 and ANO6)  . Besidesnd ANO6) . HoweverIL23R associated with AS in Caucasians, was not polymorphic in East Asians   gene, for example, the primarily associated variants indicated diversity between Europe and Asia. rs11209026, a critical non-synonymous SNP in t Asians . No othe Chinese . The samulations . It may , 11.8%) . The finIt has been drawing increasing attention that gut inflammation plays a pivotal role in the pathogenesis of AS. IBD, as a paradigm for microbiota effects on the pathogenesis of the immune-mediated disease, is strongly related and significantly overlaps with AS in genetic predisposition. HLA-B27 transgenic rats did not develop gut and joint inflammation when bred in a germ-free environment, while inflammations present when they were exposed to healthy gut bacteria .via a series of cytokines, thus participating in the pathogenesis and progress of AS.Several studies have investigated how the microbiome as a key role in driving the pathogenesis of AS. Intestinal dysbacteriosis may affect the permeability of the intestinal wall, the expression of related inflammatory factors, the intestinal mucosal immune status in AS patients, and molecular mimicry of HLA-B27 . IntestiRUNX3) and microbiome, highlighting a non-MHC host genotype influencing AS via the microbiome potentially  is aiming to use genotype data from thousands of genetic variants to quantify an individual’s genetic risk for specific diseases and has potential as a diagnostic and screening test for common heritable diseases, including AS . In our The similarities and differences in the genetic features of AS between East Asia and other ethnic populations have demonstrated the utility of gene mapping in probing the genetic diversity among different ancestry groups. It is of great importance to confirm the associated loci in populations of different ancestries, which is a crucial indicator of the overall significance of defining the true disease-causing variant. The identification of genetic signatures in both the East Asian and European populations will provide additional details for unraveling the genetic basis of AS and other autoimmune diseases.PRS will be of clinical use, particularly for a disease of low prevalence and high heritability, like AS. Given the low cost of microarray, even next-generation sequencing, PRS makes it possible for population screening. Modified PRS models of ethnic specificity and multi-omics, including epigenomics and metagenomics, need more comprehensive training cohorts and more accurate evidence for better disease prediction of AS. To date, most genetics studies have been undertaken in European ancestry. Therefore, further studies of multi-omics analyses, microbiota, and environmental factors will require undertaking or expanding trans-ethnic cohorts.XW, GW, LZ, and HX made substantial contributions to draft and revise the manuscript.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.


Diaporthe citri is a major cause of diseases in citrus. One common disease is melanose, responsible for large economic losses to the citrus fruit industry. However, very little is known about the epidemiology and genetic structure of D. citri. In this study, we analyzed 339 isolates from leaves and fruits with melanose symptoms from five provinces in southern China at 14 polymorphic simple sequence repeat (SSR) loci and the mating type idiomorphs. The genetic variations were analyzed at three levels with separate samples: among provinces, among orchards within one county, and among trees within one orchard. The five provincial populations from Fujian, Zhejiang, Jiangxi, Hunan, and Guizhou were significantly differentiated, while limited differences were found among orchards from the same county or among trees from the same orchard. STRUCTURE analysis detected two genetic clusters in the total sample, with different provincial subpopulations showing different frequencies of isolates in these two clusters. Mantel analysis showed significant positive correlation between genetic and geographic distances, consistent with geographic separation as a significant barrier to gene flow in D. citri in China. High levels of genetic diversity were found within individual subpopulations at all three spatial scales of analyses. Interestingly, most subpopulations at all three spatial scales had the two mating types in similar frequencies and with alleles at the 14 SSR loci not significantly different from linkage equilibrium. Indeed, strains with different mating types and different multilocus genotypes were frequently isolated from the same leaves and fruits. The results indicate that sexual reproduction plays an important role in natural populations of D. citri in southern China and that its ascospores likely represent an important contributor to citrus disease.The fungal pathogen  Diaporthe citri (anamorph Phomopis citri) is one of the most important pathogenic fungi on citrus. It induces melanose on the leaves, twigs, and fruits; stem-end rot of postharvest fruits; and gummosis and blight of shoots, perennial branches, and trunks of citrus. Among these diseases, melanose brings perhaps the most significant economic loss since it significantly reduces the marketability of citrus fruits [D. citri infection, with grapefruit and lemons being the most severely affected [s fruits ,2,3,4,5.s fruits . All citD. citri produces both sexual spores (ascospores) and asexual spores (conidia) on dead twigs and branches, but it is not known to sporulate on living tissues [D. citri, on decaying wood on the ground or on the dead branches on trees [ tissues ,8. Severon trees ,8,9. Oncon trees ,7,8. Conon trees ,5,7,8.Diaporthe belongs to the order Diaporthales in class Sordariomycetes of the Ascomycota phylum. Species in Diaporthe usually have broad host ranges, with most species capable of infecting multiple plants [D. citri, other species in Diaporthe can also cause diseases such as necrosis spots, diebacks, cankers, decay, or wilts on different plant hosts including cultivated crops, trees, and ornamental plants, which could lead to host tree deaths or significant economic losses. Examples of diseases caused by Diaporthe species on common crops include the soybean stem blight by Diaporthe phaseolorum; stem canker of sunflower by Diaporthe helianthi; leaf spot, swelling arm, and perennial cankers of grapevines by multiple Diaporthe species; and branch cankers and fruit stem-end rot of avocado by Diaporthe foeniculina and Diaporthe sterilis [D. citri and other pathogens in the Diaporthe genus is well-recognized, our knowledge about their diversity, population genetics, reproduction strategies, and pathogenicity is very limited [The genus e plants ,11,12. Asterilis ,14,15. A limited ,17. Know limited ,19,20.Diaporthe, including those of three isolates of D. citri , the heterothallic mating system and the structures and DNA sequences of both mating types (MAT) were elucidated [D. citri contained only one mating type (MAT) idiomorph: strain ZJUD14 contained MAT1-1 and strains ZJUD2 and Q7 contained MAT1-2 [D. citri. Specifically, in this study, we aimed to analyze the patterns of genetic variation at three spatial scales: (i) at the provincial level among five provinces  in southern China, (ii) among four orchards within one county in Hunan Province, and (iii) among five trees from a single orchard, also in Hunan. In total, we obtained 339 isolates and analyzed their genotypes at 14 newly developed polymorphic simple sequence repeat  markers. In addition, the mating type of each isolate was determined using a mating type-specific PCR. We hypothesize that geographic separation plays a major role in genetic relationships among subpopulations of D. citri. Specifically, we hypothesize that the five provincial populations of D. citri in southern China will be genetically differentiated, while relatively limited differences will be found among subpopulations of D. citri from different orchards within the same county and among samples from different trees within the same orchard. In addition, based on what has been reported before about the dominance of the asexual reproduction of D. citri in nature [D. citri isolates from the same tree and the same orchard. Furthermore, based on the observed broad host range of D. citri [Recently, with the analyses and release of the genomic sequences of 12 species in the genus ucidated ,22. DNA n nature ,8,9, we D. citri ,11,12, wD. citri were collected between June and September in 2019 from Citrus reticulata (Ponkan) and C. sinensis (navel orange) in 21 orchards in five provinces of southern China  in one orchard in Luxi county, Hunan Province. For each tree, five leaves and five fruits with melanose symptoms were sampled and 10 melanose spots per leaf/fruit were individually treated for isolation of D. citri samples from four orchards within Luxi county in Hunan Province. Two of the four orchards had C. reticulata while the other two were planted with C. sinensis. Here, four leaves or fruits with melanose symptoms were randomly collected from each of 10 random trees (at least 10 m apart) in each orchard. A total of 102 isolates were obtained from these four orchards. At this level, we analyzed the potential effects of intermediate spatial separation and host tree species on the total genetic variations.The second spatial level analyzed C. reticulata located in five provinces. These included 24 isolates from four orchards in Fujian (FJ) Province, 30 isolates from three orchards in Guizhou (GZ) Province, 30 isolates from four orchards in Jiangxi (JX) Province, 28 isolates from five orchards in Zhejiang (ZJ) Province, and 30 isolates from two orchards in Hunan (HN) Province. Similar to the two smaller scales, each isolate was derived from an independent melanose spot on either leaves or fruits. Here, the isolates were derived from at least five trees in each orchard. For each tree, 1–4 leaves or fruits with melanose symptoms were randomly sampled for isolation of D. citri. However, to reduce the potential over-representation of isolates from certain trees, only one isolate from each tree was randomly chosen for genotyping and subsequent data analyses.The third level is the largest spatial scale at the provincial level, where 142 isolates were collected from 18 orchards of D. citri were isolated following the method described previously [To minimize confounding factors, the three levels of analyses used separate samples. Strains of eviously . CultureFor each isolate, its genomic DNA was extracted from mycelia scraped off with a sterile blade from a colony growing on PDA at 25 °C for about 7 days. The scraped mycelia were lyophilized, and then the DNA was extracted using a SIMGEN Fungi Kit  according to the manufacturer’s protocol. The extracted genomic DNA samples were quantified using a Nanodrop spectrophotometer  and stored in sterile water in a −20 °C freezer.D. citri-like isolates were confirmed based on their colony morphology, microscopic conidia features, and molecular characteristics [D. citri-specific primer pair Dc-F/Dc-R (5′-CCCTCGAGGCATCATTAC-3′/5′-ATGTTGCAGATGGTCAAATGG-3′) designed based on sequence variations in the Tub gene among 21 Diaporthe species from citrus [w/v) agarose gel and visualized under UV light after ethidium bromide staining. Genomic DNAs from two strains (ZJUD2 and ZJUD14), which were identified as D. citri [Diaporthe citriasiana strain ZJUD31 and D. citrichinensis strain ZJUD40 [The obtained eristics . SpecifiD. citri strain ZJUD2 [https://wheat.pw.usda.gov/demos/BatchPrimer3/, accessed on 5 November 2020) [D. citri  [The genome sequence of in ZJUD2  was screer 2020)  to identer 2020) . The selBased on amplification success and the level of polymorphisms, we selected 14 markers to genotype the subset of 14 isolates from our recent collection. The optimum annealing temperature of the selected 14 pairs of primers was derived by gradient PCR using the genome DNA of ZJUD2. The PCR reaction system was the same as described above. The PCR cycling conditions were: 94 °C for 5 min, 31 cycles of 94 °C for 30 s, 50~62 °C for 30 s, 72 °C for 30 s, with a final extension of 5 min at 72 °C. The PCR products were separated and visualized as described above for confirmation of amplification success and polymorphisms among the 14 representative strains. All 14 primer pairs successfully amplified a single product from each of the 14 representative strains. These 14 primer pairs were then used to genotype all 339 isolates following protocols described below.For each of the 14 SSR markers, the forward primer was labeled with either FAM, HEX, or TAMRA fluorophore. The PCR reaction system was the same as described above. PCR cycling was performed as follows: 94 °C for 5 min, 35 cycles of 94 °C for 30 s, 53, 55, or 56 °C (depending on the primer pair) for 30 s, 72 °C for 30 s, with a final extension of 10 min at 72 °C. The amplicons were separated using capillary electrophoresis. Capillary electrophoresis was performed in multiplex reactions of three amplicons with three different fluorescent dyes per reaction. The PCR products were analyzed using an ABI 3730xl Genetic Analyzer  with a GS500LIZ Size Standard (Applied Biosystems). The output files were analyzed using GeneMapper v4.1 software (Applied Biosystems) to identify the lengths of amplified fragments at each locus.D. citri isolation successes [For most diversity estimates, two sets of samples were analyzed: non-clone-corrected  and clone-corrected. For clone-corrected datasets, only a single isolate of each multilocus genotype (MLG) was retained from each sub-population. Isolates with the same alleles at all loci were considered as belonging to the same clone. Clone-corrected datasets were used to minimize potentially distorted estimates of allelic diversity due to differences in sampling efforts and in uccesses .To determine gene diversities of the subpopulations and populations at different spatial scales, the following diversity estimates were calculated using the program GenAlEx v. 6.5.2 : number poppr [poppr [To determine the genotypic diversities in subpopulations and populations at each of the three spatial scales, the number of MLGs was determined in R v. 3.6.3 (R Core Team 2013)  using thpoppr . Geneticr [poppr .poppr.Population genetic structure was analyzed using several approaches. In the first, we analyzed the sources of genetic variation at different spatial scales using GenAlEx version 6.501 . The relStructureHarvester .In the third approach, we used the program STRUCTURE v. 2.3.4  to inferC. reticulata vs. C. sinensis among geographically closely located orchards within Luxi county in Hunan) and two host organs (leaves vs. fruits of the same five trees within one orchard) on the observed genetic variations, respectively.At the two smaller spatial scales, we also used GenAlEx to identify the potential influences of two host species (MAT1-1-1 and MAT1-2-1 in D. citri were identified by BLASTP searches against the D. citri genomes of strains ZJUD14 (GCA_014872985.1) and ZJUD2 (GCA_014872965.1), which contained MAT1-1-1 and MAT1-2-1, respectively [Diaporthe W-type  and Diaporthe G-type  [D. citri-specific MAT1-1-1 primer pair DcM1F/DcM1R (5′-ATGTGGCACAAAGAAATCC-3′/5′-TCACTGGAGGTCCCAATTG-3′) and MAT1-2-1 primer pair DcM2F/DcM2R (5′-ATGTGGCACAAAGAAATCC-3′/ 5′-TCACTGGAGGTCCCAATTG-3′), respectively, using the Primer Premier 5 software [D. citri in this study. Each PCR reaction had a final volume of 25 µL, containing 1 µL of template genomic DNA (~20 ng), 1 µL forward primer, 1 µL reverse primer, 12.5 µL of× Taq Plus Master Mix (Vazyme Biotechnology Co. Hangzhou), and 9.5 µL sterile water. PCR cycling was performed as follows: 94 °C for 5 min, 35 cycles of 94 °C for 30 s, 55 °C (MAT1-2-1) or 56 °C (MAT1-1-1) for 30 s, 72 °C for 1 min, with a final extension of 7 min at 72 °C. After the amplification products of MAT1-1-1 and MAT1-2-1 were mixed, 4 μL of the mixture was separated on 1% agarose gel for electrophoresis  and visualized under UV light after ethidium bromide staining.The sequences of ectively . In thesB199325)  as querysoftware . These pMAT1-1-1 and MAT1-2-1 mating types among the isolates at each subpopulation and in the total population were calculated based on both the non-clone-corrected and clone-corrected datasets. A χ2 test with a critical value of p < 0.05 was performed to determine whether the mating type frequencies deviated significantly from a 1:1 ratio [2 test when analyzing small sample sizes, an exact binomial goodness-of-fit test (two-tailed) was also performed to assess whether the observed mating type ratios for analyzed populations deviated from 1:1. The χ2 test and the exact binomial test (two-tailed) were performed using IBM SPSS Statistics 20 . Occurrence of the two mating types in equilibrium would be an indication of sexual reproduction and recombination.The ratios of the :1 ratio . The nulA) [d) [poppr [p < 0.05) [Linkage disequilibrium was analyzed by calculating the index of association (IA)  and the A) [r¯d)  after 99) [poppr . The nul) [poppr ,41. The  < 0.05) .D. citri genome showed that these 14 SSR loci were all located on different contigs and not closely linked. The details about these 14 loci are presented in The fourteen primer pairs we screened showed polymorphisms in the subset of 14 isolates (data not shown). The BLAST analyses of the 14 SSRs using the assembled At the largest spatial scale that includes the five provincial populations, all 14 loci were polymorphic, and the number of alleles per locus among the 142 isolates ranged from 4 to 20, Nei’s gene diversity ranged from 0.40 to 0.89, Simpson index ranged from 0.39 to 0.89, and Evenness ranged from 0.42 to 0.86. At the next spatial scale among the four orchards used for analyzing potential local population structuring and host tree effects on pathogen gene diversity, 13 of the 14 loci were polymorphic, while the DC1 locus was monomorphic among the 102 isolates. The number of amplified alleles, Simpson index, Nei’s gene diversity, and Evenness per locus among the 102 isolates ranged from 2 to 15, 0.04 to 0.78, 0.04 to 0.79, and 0.35 to 0.88 in the orchard population, respectively . SimilarThe above results on gene diversities described the variations among the 14 loci at different spatial scales. When all loci are considered, individual populations at all three spatial scales also showed high diversities. For example, the numbers of different and effective alleles per locus ranged from 2.957 (tree level) to 4.104  and 1.920 (orchard level) to 2.466 ; the Shannon’s information indices were 0.908, 0.684, and 0.681; and the unbiased Nei’s diversities were 0.472, 0.348, and 0.363 for populations at provincial, orchard, and tree scales, respectively. The values of unbiased allelic diversities were overall similar to Nei’s diversity .Among the five provincial subpopulations, that from Fujian Province showed the highest gene diversity, with Nei’s gene diversity and Shannon’s information index being 0.638 and 1.252, respectively, followed by the Zhejiang subpopulation with Nei’s gene diversity and Shannon’s information index at 0.569 and 1.132, respectively. The levels of gene diversity of the Hunan, Jiangxi, and Guizhou subpopulations were similar, with Nei’s gene diversity and Shannon’s information index ranging from 0.403 to 0.424 and 0.839 to 0.874, respectively .Similar to gene diversity analyses, results from the genotype diversity analysis also revealed high genotypic diversities within all examined populations, with 139 (98%), 99 (97%), and 87 (92%) MLGs identified among 142, 102, and 95 fungal isolates analyzed from the provincial, orchard, and tree levels, respectively . At the p > 0.05), consistent with limited host tree specialization in D. citri genotypes. At the tree level within the orchard in Luxi, 1% of the total genetic variations were found among the subpopulations from the five trees, while 99% were found within the individual tree subpopulations (D. citri populations from the two different organ types (leaf vs. fruit)  .p = 0.001), with the PhiPT values ranging from 0.104 (HN vs. GZ) to 0.421 (FJ vs. JX)  . PCA ana vs. JX) . The min vs. JX) .The STRUCTURE analysis also identified two genetic clusters (K = 2). However, isolates in the two genetic clusters showed different frequency distributions among the five provincial subpopulations. Isolates of one genetic cluster were distributed mostly in the Fujian subpopulations, and the other genetic cluster was mainly distributed in subpopulations from Hunan, Jiangxi, and Guizhou provinces. The Zhejiang subpopulation had similar representations of both genetic clusters . The resp = 0.001), consistent with geographic separation playing a significant role in the genetic differentiation among these five provincial populations.Mantel analysis showed significant correlation between genetic and geographic distances  and the exact binomial analysis   . Indeed,≤ p ≤ 1) . Further≤ p ≤ 1) .p > 0.05), except for five subpopulations: GZ (p = 0.001), ZJ (p = 0.001), O2 (p = 0.012), T1 (p = 0.001), and T5 (p = 0.015). Similarly, Prp values were consistent with phylogenetic incompatibility in most of the subpopulations (p > 0.05), except for FJ (p = 0.020), GZ (p = 0.025), ZJ (p = 0.001), T1 (p = 0.010), T4 (p = 0.001), and T5 (p = 0.043). In the clone-corrected data, the T1 subpopulation was in linkage equilibrium (p = 0.126), with a Prp value of 0.807. For both the non-clone-corrected and clone-corrected data, phylogenetic incompatibility was found in all subpopulations  of the subpopulations did not reject the null hypothesis of linkage equilibrium  were distributed at similar frequencies in most subpopulations of D. citri at all three spatial scales. In combination with results from linkage equilibrium and Prp analyses, this result is consistent with the frequent mating and recombination of D. citri in nature. Similar results have been reported in several plant fungal pathogens such as Phyllosticta citricarpa [Zymoseptoria tritici [Dothistroma septosporum [C. truncatum [P. citricarpa, the causal agent of citrus black spot, and found that the populations from China and Australia were sexually reproducing populations with very high genetic diversity. In contrast, the population from United States was consistent with asexual reproduction, with only one multilocus genotype (MLG), and all strains had the same mating type, MAT1-2-1. Our research showed that D. citri populations have high levels of genetic diversity at all three studied spatial scales in China, likely due to frequent sexual recombination in orchards. Similar results have been reported another citrus fungal pathogen P. citriasiana [We found that the two mating types .Interestingly, we observed very few shared MLGs among the 339 isolates. Previous studies have suggested that the large number of conidia of fections . If thisD. citri likely has multiple modes of nutrient acquisition, as a biotroph that can cause diseases, a saprophyte that grows on a variety of dead branches and twigs, and as endophyte living under the epidermis in the intercellular space of trees [D. citri populations could have existed in these orchards for an extended time and represented populations in relative equilibrium. In such equilibrium populations, sexual reproduction and local adaptation are likely common for populations of D. citri, potentially making it difficult for immigrants to establish in the endemic niches. However, the observation of significant genetic differentiations among the five provincial populations does not mean that there was no migration. As shown in the minimum spanning tree, closely related strains were found from several geographically distinct areas. In addition, STRUCTURE analyses identified evidence for genetic admixture between the two highly differentiated genetic clusters in most geographic populations . Together, these results suggest that the D. citri populations are likely changing. With increasing human influences, both directly through the exchange of citrus seedlings and indirectly by impacting climate change, the changes in D. citri populations will likely accelerate.A previous study suggested that of trees ,7,8. As D. citri from two Citrus tree species in 21 orchards in five provinces in southern China. Many issues about the ecology, evolution, and population genetics remain unanswered. For example, at present, the exact host range of D. citri outside citrus trees remains undefined. Further sampling and analyses of D. citri populations from host plants outside of Citrus species, from other Citrus species , and from decaying twigs on the ground over time are needed in order to provide a more robust understanding on the origins and epidemiology of the D. citri populations causing citrus melanose.Though the sample size was relatively large, our study only analyzed samples of


Isoëtes cangae is a critically endangered species occurring in a single lake in Serra dos Carajás, Eastern Amazon. Low genetic diversity and small effective population sizes (Ne) are expected for narrow endemic species (NES). Conservation biology studies centered in a single species show some limitations, but they are still useful considering the limited time and resources available for protection of species at risk of extinction. Here, we evaluated the genetic diversity, population structure, Ne, and minimum viable population (MVP) of I. cangae to provide information for effective conservation programs. Our analyses were based on 55 individuals collected from the Amendoim Lake and 35,638 neutral SNPs. Our results indicated a single panmictic population, moderate levels of genetic diversity, and Ne in the order of thousands, contrasting the expected for NES. Negative FIS values were also found, suggesting that I. cangae is not under risk of inbreeding depression. Our findings imply that I. cangae contains enough genetic diversity to ensure evolutionary potential and that all individuals should be treated as one demographic unit. These results provide essential information to optimize ex situ conservation efforts and genetic diversity monitoring, which are currently applied to guide I. cangae conservation plans.The quillwort  The quillwort Isoëtes cangae is a critically endangered species occurring in a single lake in Serra dos Carajás, Eastern Amazon. To provide information for effective conservation programs, we evaluated its genetic diversity and population structure using neutral SNPs. Our results indicated a single panmictic population, moderate levels of genetic diversity, and effective population size in the order of thousands, contrasting to the expected for NES. Geneticcampinaranas, cangas, and campos rupestres  is a cosmopolitan genus of lycophytes with about 250 species found in lakes, wetlands , and terrestrial habitats  and reproductive cycles dependent on water  species  using genomic data to provide information about minimum viable population (MVP), a primordial data for species long‐term survival. Furthermore, with thousands of SNPs, we evaluated whether I. cangae is a Neotropical NES with low genetic diversity as classical studies supposed or, according to Santos et al. . The FLONA Carajás was created as a protected area for sustainable use, conciliating conservation, and mining activities of the ferruginous mountain outcrops from Serra dos Carajás. It sits 720 m above the sea level with an area of 1.23 kmI. cangae distributed along the whole area of the Amendoim Lake (Table n = 13), south (n = 10), east (n = 11), and west (n = 8), besides a fifth group to cover extra individuals located more centrally in the lake  as shown in the Figure We collected leaf samples of 55 specimens of ke Table  in five ke Table : north 2.2I. cangae individuals was performed using the genome skimming approach  was assembled using a total of 4.3 Gb paired‐end reads  sequenced by NextSeq 500 Illumina platform. The draft de novo genome assemblies were generated using SPAdes v3.10.0  in SAMtools; (ii) binary alignment map (.bam files); and (iii) sequence dictionary file (.dict files) in Picard v.2.22.9 (http://broadinstitute.github.io/picard/). All these files were used to perform the variant calling step  to convert the final.gvcf into a.vcf file.We employed the 30,000 contigs to align and build three files for each sample: (i) reference sequences file . Afterward, we selected SNPs by quality, keeping only SNPs with (i) with 10% of maximum missing data value because a large amount of missing data may affect some demographic parameters   that would correspond to the groups or genetic clusters  and Discriminant Analysis of Principal Components (DAPC). Both methods are model‐free, and there are no assumptions regarding the population model, unlike other clustering methods  were tested to evaluate possible changes in the best K value  were used to select the best K using the Bayesian information criterion (BIC) in the function find.clusters  with "adegenet" to plot sample genotypes in the multivariate space according to the five sampled areas and thus visually assess any potential clustering following a spatial pattern , expected heterozygosity (Heexp), inbreeding coefficient (FIS) at the individual and population levels, and nucleotide diversity (π) only for population level. Significance of different diversity values  was assessed with Tukey's test using Query function as well. Also, the Yang's Relatedness coefficient  were also calculated for Ne) and its confidence interval (C.I. 95%) using the linkage disequilibrium (LD) method , without replacement and missing values, producing datasets with different number of SNPs, between 5,000 and 30,000. We calculated Ne for all the resampled datasets to assess the sensitivity of our estimates to missing SNPs.We estimated effective population size /Ne considering 50 as the minimum population size necessary to avoid inbreeding problems, and MVP500 = (500 × Nc)/Ne using 500 as the minimum population size to maintain species evolutionary potential  and was estimated through a survey according to Santos et al. . Densities were correlated with estimated coverage visually to establish a mathematical relationship between the coverage and the density. We applied this correlation to estimate the density of specimens in the remaining 170 squares sampled. Not all specimens were directly counted in all squares due to the limited time and divers available to carry out the sampling. Nc for all lake up to a depth of 4 m was calculated by multiplying the average estimated density of specimens (specimens of I. cangae per m²) with the size of the sampled area (in m²). To measure the size of the sampled area, the Google Earth Pro geometric area measurement tool (Google LLC. V7.3.3.7796) was used, using the satellite image and the coordinates of the sampled points to define the limits of the geometric area measured. All R scripts for the methodological steps are available in https://github.com/jdalapicolla/Isoetes_Scripts.R.To calculate the minimum viable population (MVP) for s et al.  by free‐33.1The draft genome adopted as a reference, namely "ITV2008_illumina," contained 881,782 contigs : 268,606,676). From this reference, "icangae_ref30k" : 16,698,786) was selected with 30,000 contigs Table . Each savcf file for 55 individuals, which after filtering by quality, were reduced to 71,621 SNPs . The observed heterozygosity of 0.267 and the expected heterozygosity of 0.224 lead to a negative inbreeding coefficient value (FIS = −0.194)  = 0.005], indicating panmictic conditions  individuals , and considering the minimum population size as 50 (MVP 50), the number of individuals necessary to the long‐term viability of the only population of I. cangae was between 92 and 220. Considering the minimum population size as 500 (MVP 500), a population 10 times larger would be necessary with the number of individuals ranging between 919 and 2,195  used in analyses which allowed us a broader explanatory power on neutral genetic structure. SNPs are codominant markers that may represent different portions of the genome. SNPs contain enough information for population genetics analyses while providing a refined and accurate genomic data source to access the genetic structure and diversity at a low cost  that consists into three categories ranging between 0 and 0.5: low diversity from 0 to 0.166, moderate diversity from 0.167 to 0.333, and high diversity from 0.334 to 0.500. In this rank, I. cangae showed a moderate genetic diversity (Heexp). The previous study using SNPs for an Isoëtes species  and nucleotide (π) diversities than the annual herb Monogereion carajensis Barroso & King , an endangered NES from Serra dos Carajás    and Dioclea apurensis Kunth , wider distributed species in the Amazon  reproductive mechanisms preventing inbreeding or enhancing the breeding of unrelated individuals or (ii) hybridization between different species or distant populations  is not sympatric in the same habitat, being found in temporary lakes, and intense fieldworks in the area never registered any hybrids between them  a significant genetic pool of I. cangae ensured ex situ and (ii) a support for several ongoing studies, including plant performance in field trials aiming to define more suitable habitat for translocation/reintroduction and optimize conservation efforts.In short, our results showed that the only known population of None declared.Jeronymo Dalapicolla: Data curation ; Writing‐original draft . Ronnie Alves: Data curation ; Formal analysis ; Validation . Rodolfo Jaffe: Conceptualization ; Formal analysis ; Investigation ; Writing‐original draft . Santelmo Vasconcelos: Conceptualization ; Data curation ; Writing‐review & editing . Eder Soares Pires: Formal analysis ; Methodology ; Resources . Gisele Lopes Nunes: Formal analysis ; Methodology . JOvani Bernardino de Souza Pereira: Investigation ; Methodology ; Validation . José Tasso Felix Guimarães: Formal analysis ; Methodology . Mariana Costa Dias: Formal analysis ; Methodology . Tais Nogueira Fernandes: Funding acquisition ; Project administration ; Validation . Daniela Scherer: Funding acquisition ; Project administration ; Validation . Fernando Marino Gomes dos Santos: Funding acquisition ; Project administration ; Validation . Alexandre Castilho: Funding acquisition ; Project administration ; Validation . Mirella Pupo Santos: Conceptualization ; Investigation ; Validation ; Visualization . Emiliano Nicolas Calderón: Data curation ; Investigation ; Methodology . Rodrigo Lemes Martins: Funding acquisition ; Project administration ; Validation . Rodrigo Nunes Fonseca: Conceptualization ; Project administration ; Validation . Francisco de Assis Esteves: Funding acquisition ; Project administration ; Validation . Cecilio F Caldeira: Conceptualization ; Data curation ; Funding acquisition ; Investigation ; Project administration ; Writing‐review & editing . Guilherme Corrêa Oliveira: Conceptualization ; Funding acquisition ; Investigation ; Project administration ; Validation ; Writing‐review & editing .Isoetes cangae; R scripts for all analyses; raw VCF and Filtered VCF files; and the Supplementary Information are available in Dryad (https://datadryad.org/stash/share/3rWc5i‐8oYxy‐I_NCl44FHy8ElweMsztdvvv0e2_bv0).Draft genome for Supplementary MaterialClick here for additional data file.