The Arabian oryx was the first species to be rescued from extinction in the wild by the concerted efforts of captive programmes in zoos and private collections around the world. Reintroduction efforts have used two main sources: the ‘World Herd’, established at the Phoenix Zoo, and private collections in Saudi Arabia. The breeding programme at the Al-Wusta Wildlife Reserve (WWR) in Oman has played a central role in the rescue of the oryx. Individuals from the ‘World Herd’ and the United Arab Emirates have been the main source for the WWR programme. However, no breeding strategies accounting for genetic diversity have been implemented. To address this, we investigated the diversity of the WWR population and historical samples using mitochondrial DNA (mtDNA) and single nucleotide polymorphisms (SNPs). We found individuals at WWR contain 58% of the total mtDNA diversity observed globally. Inference of ancestry and spatial patterns of SNP variation shows the presence of three ancestral sources and three different groups of individuals. Similar levels of diversity and low inbreeding were observed between groups. We identified individuals and groups that could most effectively contribute to maximizing genetic diversity. Our results will be valuable to guide breeding and reintroduction programmes at WWR. However, due to excessive and persistent poaching, the wild population soon dropped sharply to 108 males and 28 females by 1998 [2); however, individuals are continuously rotated at random between the three enclosures. Since the WWR programme did not implement a specific genetic management strategy, studbook information and pedigree records are not available. The lack of this information poses various challenges for the management of this population, especially because one of its major sources, the ‘World Herd’, originated from a small number of founders. This founder effect and a skewed reproductive success in males have probably increased levels of inbreeding in the population [A reintroduction and captive breeding programme for the Arabian oryx was established at the Al-Wusta Wildlife Reserve (WWR) in Jalooni in Oman in the 1980s using individuals from the ‘World Herd’ . Eventua by 1998 . Action  by 1998 , were trRandom mating, as currently performed at WWR, is a type of management that can maintain adequate levels of genetic diversity . However. 2. 2.1Blood samples were obtained from 138 randomly selected individuals at WWR . Individ. 2.2et al. [mtDNA CR sequences (638 base pairs) were amplified using the primers and PCR conditions described in El Alqamy et al. , and PCR. 2.3SphI and SbfI  and a 320–590 bp gel excision, which is described in Bourgeois et al. [−1. Each sample was processed in triplicate to enhance evenness of coverage of samples within the library. Individual genomic DNA was restriction-digested using both SbfI and SphI enzymes, and Illumina-specific sequencing adaptors (P1 and P2) were then ligated to fragment ends. The pooled samples were size selected (320–590 bp fragments) by gel electrophoresis and PCR amplified (15 cycles), and the resultant amplicons (ddRAD library) were purified and quantified. Combinatorial inline barcodes (five or seven bases long), included in the P1 and P2 adaptors, allowed each sample replicate to be identified post-sequencing. The ddRAD library was sequenced on the Illumina MiSeq Platform . Positive control samples were run so that comparisons could be made between all libraries.Double digest restriction-site associated DNA (ddRADseq) libraries were generated using a modified protocol  with ress et al. . In shor. 2.4viz. A to L [Consensus mtDNA CR sequences were obtained for each individual and aligned using the global alignment algorithm of free-end gaps at a cost matrix of 93% similarity . Variabl. A to L .h), the haplotype diversity (Hd), the average number of nucleotide differences (K) and the nucleotide diversity (π) using the model described by Nei [The genetic diversity and differentiation of the WWR and historical samples were analysed in this study using DnaSP v. 5.1 . This ind by Nei . Relatiod by Nei  using a d by Nei –31.. 2.5. 2.5.1process_radtags command in STACKS v. 1.09 [p-value of less than 0.05. To calculate deviation from Hardy–Weinberg proportions, we assumed five groups: USA founder, Oman founder, WWR-Mix, WWR-Oman and WWR-UAE (see §1 for a description of these groups). Additionally, we discarded loci with sequences that are too similar which might be possible paralogues  by using the function gl.filter.hamming from the R package dartR and a threshold of less than 10 base pairs of difference between sequences.Raw FASTQ files from the MiSeq runs were demultiplexed into unique reads for each individual sample using the  v. 1.09 . The rea v. 1.09 , we disc. 2.5.2K). The model's Bayesian algorithm, in essence, attempts to find the number of populations or sources (K) at which population genetics parameters (i.e. Hardy–Weinberg equilibrium within populations and linkage equilibrium between loci) are maximized.To infer the genetic ancestry proportion of each individual, we used a Bayesian clustering model implemented in the software fastSTRUCTURE . The algK) from one to eight, with 20 replications for each. We then used a pipeline framework developed by Clumpak [K using the software Clumpp [K and averaged it across the 20 replications.In fastSTRUCTURE, we modelled the number of populations  with the help of the R package dartR . PCA is . 2.5.3q-profile [q = 0), Shannon information (q = 1) and heterozygosity (q = 2). Allelic richness was standardized to be used among groups of unequal sample sizes using the technique of rarefaction [FST following Weir and Cockerham [p-values were generated by bootstrapping across loci. All the above statistics were calculated using the R packages: dartR, Poppr [To measure genetic variation within groups we used the -profile , a spectefaction  implemenefaction . These mefaction , observeockerham , and CIsR, Poppr  and HierR, Poppr ) as implNe) is related to genetic drift and thus predicts rates of loss of neutral genetic variation, fixation of deleterious and favourable alleles and the increase in inbreeding experienced by a population [Ne was estimated using the linkage disequilibrium method of Waples and Do [Ne Estimator v. 2.1 [The effective population size  if they are identical copies of the same ancestral allele in a base population. IBD was calculated by an additive relationship matrix approach developed by Endelman  as impleet al. [Falt, where homozygous loci are weighted with the inverse of their allele frequency using the software GCTA [hF, which is a deviation in homozygosity from its Hardy–Weinberg expectation using the software PLINK [We constructed a pedigree by identifying parents and siblings using the R package Sequoia  and plotet al. : (i) Falare GCTA  and the re PLINK  using th. 3Relatedness analyses allowed us to identify that four of the samples  were assigned to the wrong groups within the WWR, possibly due to mistakes in the tagging of individuals or during sampling . After confirming with WWR management, these samples were assigned to the correct groups for all the downstream analyses.. 3.1mtDNA control region sequences of 638 base pairs in length were generated for all 174 oryx samples studied here. Two WWR samples were excluded due to the low-quality reads. A total of 24 polymorphic sites were found across the sequences of the Arabian oryx population in Oman which resulted in nine haplotypes . Haplotype sequences are available from GenBank (accession numbers KX696999 to KX697007). Among the seven haplotypes (A–G) found in the WWR groups, haplotype A was the most frequent (49%). Haplotype E had the lowest frequency (less than 1%) and was present in a single individual of the WWR-Mix group. This haplotype was also present in the KSA, USA and UAE groups.n = 15; π = 0.005; K = 3.81) than in the WWR-Oman  and WWR-UAE  groups, but still lower than those observed among historical samples . Compared with other Arabian oryx captive programmes in the Arabian Peninsula and historical samples , the WWR as a whole contains 58% of the extant mtDNA haplotype diversity.The other five haplotypes  were shared by three or more of the WWR individuals and were present in 2% to 20% of the samples. Molecular diversity indexes of the WWR individuals were higher in the WWR-Mix group  that best explained the genetic structure of the dataset according to tool provided by fastStructure (i.e. chooseK) were between 2 and 6. However, at close inspection of the marginal likelihood of each run of K, we observed that K = 3 had the greatest marginal likelihood, suggesting that the genetic structure of the dataset is best explained by the presence of three different genetic population ancestries (The inferred number of populations (PCA  indicate. 3.4n = 15), which shows a higher inbreeding as measured by the statistics ISF, hF and Falt . Measurements of private alleles indicate that most of these, even those from the founder groups, are present in the WWR-Mix group . AMOVA analyses and FST values were significantly different from zero  and three pairs of parent–offspring relationships .Ne = 44; Ne = 2). Electronic supplementary material, table S9 presents the inbreeding estimates of all the individuals analysed, ordered from the most inbred individual to the least inbred.From the three contemporary groups , WWR-Oman is the group that was the least inbred  and had the largest effective population size Ne = 44; . By contIBD results  between the WWR individuals and the historical samples from the USA captive breeding programmes suggest that the ‘World Herd’ has been a major contributor to the WWR programme. The additional four haplotypes identified in the WWR individuals were sourced from the Arabian Peninsula, including KSA and UAE ,12,15. F. 4.2Here, we describe relatively similar genetic diversity among the founders of the ‘World Herd’, the population sources of the WWR programme (WWR-Oman and WWR-UAE) and the offspring of these sources (WWR-Mix). Our results reveal that the genetic composition of the Arabian oryx individuals at the WWR is best explained by the presence of three different genetic groups, two represented by UAE individuals and the other from the founders of the ‘World Herd’. The two different ancestry sources from the UAE individuals are consistent with microsatellite data (six loci) that the Arabian oryx population in UAE have different ancestors from those of the ‘World Herd’ . FurtherHe in comparison with Ho in all groups analysed except for the WWR-Oman group. It is important to note the challenges of comparing results obtained through SNP-based data (as in this study) with that from microsatellites (most of the previous studies). For instance, and in general, heterozygosity as measured by SNPs  tends to be lower than heterozygosity measured by microsatellites . Furthermore, SNP data provide a more accurate representation of the genome, because current sequencing technologies allow for genotyping thousands of markers compared with tens of markers for typical microsatellites datasets. After acknowledging the above caveats, our results showed the same trend as in microsatellite-based studies [et al. [He = 0.470) compared with individuals from other programmes . Interestingly, our SNP results showed that there was a slight increase of heterozygosity in the WWR-Mix group compared with the parental sources (WWR-Oman and WWR-UAE) and the founders of the ‘World Herd’. This result is encouraging as it shows that a management based on random mating can conserve genetic diversity at adequate levels.Genetic diversity of the WWR-Mix group shows a slight increase compared with the historical/founder individuals. Our analyses showed slightly lower levels of  studies ,7,11,12.q-profile in this study provides additional insights into the genetic composition and variation of the WWR groups. For instance, q = 0 is sensitive to rare alleles , but its estimates are highly dependent on sampling size, whereas q = 2 shows the opposite trend [q-values, that is, we expect to observe an evenness of the allelic distributions across the three q-values. What we observed in the WWR is a decline in q = 0 compared with q = 1 and q = 2 which could be an indication that genetic variation, especially rare alleles, has been lost and is not distributed evenly across each group but harboured within individuals. This observation is probably a reflection of the species' history of frequent founder effects which generally result in a depletion of genetic diversity. Together, these results suggest that the main source of variation is mostly within groups and not between groups. Furthermore, we observe that most alleles, including private alleles from the founder groups, are present in the WWR-Mix. Our results demonstrate the advantage of using the q-profile. For instance, we observe that genetic variation measured by heterozygosity is relatively uniform between groups, except in the USA founder group. This effect is probably because heterozygosity reflects the variation of the most frequent alleles, which is unaffected even after population bottlenecks [q-profile also revealed that the WWR-Mix group harbours more genetic variation than the other groups. This has been a fortunate outcome given the random mating approach and absence of genetic understanding of the founders used at WWR to date. This study generated baseline information regarding several measures of genetic diversity and relatedness  in the USA . More survival;  from theThe most intensive strategy would be based on breeding of specific individuals. For instance, this strategy could involve the reproduction of individuals carrying low frequency haplotypes such as Haplotype E, which was identified in a single individual at WWR. For this strategy, some of our results such as heatmaps of identity by descent . This important resource will make it possible to map the SNPs used in this study, which will allow comparisons across experiments, investigators and time. We acknowledge that it is also important to retain samples, especially historical samples, for future analyses at the genome level.A long-term and global goal for the Arabian oryx conservation community should be to develop a management plan whose aim is not just the increasing of population size but the maximization and retention of genetic diversity over time . We recoClick here for additional data file.


The criteria of the comparison were the cumulated gain and reliability of the genomic estimated breeding values (GEBV).Single-step genomic best linear unbiased prediction (ssGBLUP) allows the inclusion of information from genotyped and ungenotyped individuals in a single analysis. This avoids the need to genotype all candidates with the potential benefit of reducing overall costs. The aim of this study was to assess the effect of genotyping strategies, the proportion of genotyped candidates and the genotyping criterion to rank candidates to be genotyped, when using ssGBLUP evaluation. A simulation study was carried out assuming selection over several discrete generations where a proportion of the candidates were genotyped and evaluation was done using ssGBLUP. The scenarios compared were: (i) three genotyping strategies defined by their protocol for choosing candidates to be genotyped ; (ii) eight proportions of genotyped candidates (p and as TOP with high p. However, the reliability of GEBV was higher with RANDOM than with TOP. This disparity between the trend of the gain and the reliability is due to the TOP scheme genotyping the candidates with the greater chances of being selected. The extra gain obtained with TOP increases when the accuracy of the selection criterion to rank candidates to be genotyped increases.The genotyping strategy with the greatest cumulated gain was TOP followed by RANDOM, with EXTREME behaving as RANDOM at low The best strategy to maximise genetic gain when only a proportion of the candidates are to be genotyped is TOP, since it prioritises the genotyping of candidates which are more likely to be selected. However, the strategy with the greatest GEBV reliability does not achieve the largest gain, thus reliability cannot be considered as an absolute and sufficient criterion for determining the scheme which maximises genetic gain.The online version contains supplementary material available at 10.1186/s12711-022-00712-y. Genomic prediction  uses information that is derived from high-throughput genotyping of single nucleotide polymorphisms (SNPs) in the genetic evaluation . It has Several methods that are defined by their assumption on the prior distribution of the SNP effects have been proposed and implemented  generation, see below). In the founder population, approximately 5000 loci per chromosome were segregating at a minor allele frequency (MAF) higher than 0.05 .To simulate a genome in linkage disequilibrium (LD), a founder population was simulated using a mutation–drift-equilibrium algorithm as suggested by Meuwissen et al. . BrieflyThe breeding population was assumed to have discrete generations, with 900 individuals  per generation. At each generation, 30 males and 300 females were selected and mated, with each selected female having three offspring . All individuals were assumed to have one phenotypic record available for their genetic evaluation. The aim of the simulated population structure was to mimic a sheep breeding population with large paternal and small maternal families. The assumption of a litter size of 3 can be considered as a little unrealistic in a sheep population, but it was the minimum possible size in order to assume discrete generations while still being able to have selection pressure on both male and female candidates. However, the assumption of discrete generations means that the results would not be affected by differences in generation interval, and the observed differences between scenarios would be due only to the quality of the estimates. Hence, the decision of assuming discrete generations.The first two generations (G(− 2), G(− 1)) were assumed to be the initial reference population, so all individuals were genotyped. Thereafter, nine additional generations were simulated (G0 to G8), with only a proportion The genome was divided into 26 autosomal chromosomes , each with 1000 loci used as the SNP panel and 100 loci used as the quantitative trait loci (QTL) affecting the trait. The genome of the animals in the base population ) was simulated by randomly sampling haplotypes from the founder population, and in the following generations (i.e. G(− 1) to G8), an animal’s genome was sampled assuming Mendelian inheritance given its parents’ haplotype.To select the loci included in the SNP panel and the QTL, we calculated the frequency of all segregating loci at G0 and, within each chromosome, we selected 1100 with the highest MAF. The distribution of MAF at G0 for all loci selected across replicates is shown in Additional file The heritability of the trait was assumed to be 0.2 . The additive effect for each QTL was sampled from a standardised normal distribution and the favourable allele was assigned randomly with equal probability. The true breeding value (TBV) of each individual was calculated as the sum of the QTL effects, given its genotypes and the additive effect. The QTL effects were rescaled, such that the genetic variance in G(− 2) was 20. The phenotype for each individual was simulated as the sum of its TBV and an environmental effect sampled from a normal distribution with a mean of zero and a variance of 80.Simulating the genome of the base population (G(− 2)) by sampling from the permuted haplotypes of the expanded founder population ensured that all the replicates had the same expected LD pattern, but still allowing for the replicates to be independent from each other. The size of the founder population  was sufficiently large so that the permutation of the founders’ haplotypes resulted in all base animals having a unique genome structure . Furthermore, the loci assigned as SNPs or QTL (and their effects) were sampled within each replicate, further ensuring that replicates were different, and thereby, independent.The genetic evaluation was done using ssGBLUP, but scenarios using BLUP and GBLUP evaluations were also simulated for comparison. All three methods of selection were implemented using Henderson’s mixed model equation .The assumed linear model is:The BLUP, GBLUP and ssGBLUP evaluation methods are defined by the ormation , 19. Forenotypes . Finallyormation . In thisVanRaden , and thea et al. . In addia et al. .The selection scheme simulated here assumed multiple discrete generations of selection. Hence, at a given generation, the genetic evaluation of the current candidates to be selected was done with a model including them plus all the information (phenotypes and available genotypes) of animals from previous generations .For the period of selection (G0–G8), the candidates were selected assuming standard truncation based on the genomic breeding values calculated using the linear model described above.The ssGBLUP scenarios, which were compared here, included eight proportions of candidates being genotyped, three genotyping strategies to select the candidates to be genotyped, based on two ranking criteria. In addition, the BLUP and GBLUP scenarios were also included as they are equivalent to the situations where none or all candidates are genotyped and can be considered as the respective lower and upper limit for the expected performance of the ssGBLUP scenarios.The proportion (The three genotyping strategies were: (i) RANDOM, where the proportion of candidates to be genotyped were chosen at random from all those available; (ii) TOP, where the genotyped animals were the 2, LD pattern) were fixed to a given value. This was done in order to focus the study on the main goal about testing the effect of genotyping strategy and the proportion of genotyped candidates on the performance of ssGBLUP in a population with large paternal families and small maternal families (as in sheep populations). We expect that the magnitude of these parameters should have a relative minor impact on the results and that the conclusions drawn here should be general enough so that they may extrapolate to some degree the changes on these parameters.The magnitudes for some other assumptions/parameters needed in the simulation that were related to population structure  and the genetic architecture , corresponding to the first generation where some candidates were ungenotyped and ssGBLUP needed to be implemented. The reliability of the GEBV is the square of the Pearson’s correlation between the GEBV and the TBV in the candidate group at a given time of selection. The retained genetic variance at a given generation is the variance of TBV of all candidates born in that generation.Because BLUP and GBLUP are equivalent to the scenarios with 0 and 100% of candidates being genotyped, they can be considered to be the respective lower and upper limit of the expected performance of ssGBLUP. Hence, a parameter denoted as Efficiency , which suggests that the values on the reliabilities are not an artefact due to differences on the scale (results not shown).Several published studies have reported the effect of genotyping strategies on GEBV reliability/accuracy using simulation or real data e.g. –26). Alt. Alt26])ment}x′x . Then, sHowever, the observation that the greatest genetic gain is not achieved by the strategy with the highest GEBV reliability is counter-intuitive. Our simulations assumed discrete generations, so the differences in cumulated gain should only be due to how well the GEBV were estimated. A recent study assessed the effect of genotyping strategies under ssGBLUP scenarios similar to ours, where only a proportion of the current candidates are genotyped . Under vHence, when only a proportion of the candidates are genotyped, the optimality to maximise gain is not only about how much the GEBV reliability increases, but also which candidates benefit from the enhanced reliability. For instance, on the one hand, a candidate with very poor performance/EBV probably would be too far below the ranking order to have a significant chance to be selected, so attempting to estimate its GEBV with higher accuracy (by genotyping the candidate itself) would likely not change any preliminary selection decision. On the other hand, a candidate with good performance/EBV would likely rank high, so genotyping the candidate to improve its GEBV reliability may prove beneficial when comparing with other candidates of similar selective advantage. In our study, the TOP scheme genotypes the candidates with the highest phenotype/EBV, meaning that they are also likely to have the highest genetic merit (so increasing the reliability of candidates with a high chance of having high GEBV and being selected). However, the genotyping protocols used with RANDOM and EXTREME mean that some genotyped candidates may have a poor genetic merit (and lower GEBV and less chance of being selected). Hence, the selected candidates chosen with TOP are more likely to be based on more reliable GEBV than with RANDOM and EXTREME with low Our conclusions on the ranking of the selection strategies to maximise gain agree with those reported by Howard et al. , but notThe disagreements between our results and those from Howard et al.  can be pd et al.  concludeIn practice, selected populations most likely have an overlapping population structure, so the post-selection genotyping of any selected ungenotyped animal would be an attractive decision to achieve the extra benefit reported by Howard et al. . Howeverd et al. , which a. Conversely, our assumption of a finite number of loci in the genome has shown faster decline in the genetic variance than that observed in real selection programmes [Granleese et al.  also repd et al.  since the et al.  was 0.15entclass1pt{minimaogrammes , thus fuHence, if only a proportion of the candidates is to be genotyped, the consensus conclusion  dictate) (as in ) may furWhen only a proportion of the candidates are to be genotyped, TOP is the best strategy to ensure that genetic gain is maximised, since it prioritises the genotyping of candidates which are more likely to be selected. However, in this study, the Efficiency of ssGBLUP on realising the extra genetic gain of GBLUP does not improve as fast with proportion of genotyped candidates as reported in other studies. The choice of EBV rather than phenotypes to rank candidates to be genotyped improves the extra genetic gain of ssGBLUP using TOP. Since in our study, where not all candidates were genotyped, the highest genetic gain was not achieved by the strategy with the greatest GEBV reliability, the latter parameter cannot be considered as an absolute and sufficient criterion to determine which scheme would maximise genetic gain over multiple generations. Hence, to assess the best strategy to maximise gain in a specific situation, any feasibility study should not be restricted to quantifying the gain in GEBV reliability for the alternative schemes, but also their expected genetic progress.Additional file 1: Figure S1. Histogram of the minor allele frequency (MAF) at G(− 2) for all loci chosen to be QTL or as part of the SNP panel across all 100 replicates.Additional file 2: Table S1. Cumulated genetic gain, genetic variance and reliability observed in the GBLUP and BLUP scenarios over generations. Table S2. Cumulated genetic gain over generations for all ssGBLUP scenarios. Table S3. Genetic variance over generations for all ssGBLUP scenarios. Table S4. Reliability over generations for all ssGBLUP scenarios. Table S5. Efficiency of ssGBLUP in terms of their cumulative genetic response and reliability for all ssGBLUP scenarios when the genotyping criterion was the candidates’ own phenotypes. Table S6. Efficiency of ssGBLUP in terms of their cumulative genetic response and reliability for all ssGBLUP scenarios when the genotyping criterion was the candidates’ estimated breeding values. Table S7. GEBV reliability estimates observed for all ssGBLUP scenarios. Table S8. Proportion of genotyped candidates that were selected as parents for all ssGBLUP scenarios.


Although current guidelines prefer the use of targeted testing or small-scale gene panels for identification of genetic susceptibility of hereditary endocrine tumour syndromes, next generation sequencing based strategies have been widely introduced into every day clinical practice. The application of next generation sequencing allows rapid testing of multiple genes in a cost effective manner. Increasing knowledge about these techniques and the demand from health care providers and society, shift the molecular genetic testing towards using high-throughput approaches.In this expert opinion, the authors consider the molecular diagnostic workflow step by step, evaluating options and challenges of gathering family information, pre- and post-test genetic counselling, technical and bioinformatical analysis related issues and difficulties in clinical interpretation focusing on molecular genetic testing of hereditary endocrine tumour syndromes.Considering all these factors, a diagnostic genetic workflow is also proposed for selection of the best approach for testing of patients with hereditary genetic tumour syndromes in order to minimalize difficult interpretation, unwanted patient anxiety, unnecessary medical interventions and cost. There are potential benefits of utilizing high throughput approaches however, important limitations have to be considered and should discussed towards the clinicians and patients. A joint position paper of the European Reneference Network on rare endocrine diseases (Endo-ERN) was recently published about genetic testing in inherited endocrine disorders . This guGenerally, whole-exome sequencing results in definitive diagnosis in ~11–40% of the patients depending on disease type . Many raGermline genetic testing influences the proband’s disease on the one hand  and the proband’s family  on the other hand. Therefore, germline genetic testing has an important role in tumour syndromes considering all the relevant legal regulations in force in each country.https://www.nccn.org/professionals/physician_gls/default.aspx#detection). For assessment of potential hereditary cancer risk, probands’ medical history and expanded family history have to be investigated.The importance of genetic counselling in oncology is highlighted by several guidelines from ACMG, American society of clinical oncology (ASCO) and national comprehensive cancer network (NCCN) .The minimum information required for an adequate cancer family history is defined by ASCO expert statement . BrieflyHowever, most guidelines are based on studies performed predominantly on white populations and may have limited validity in other ethnicities , 22.A genetic test should be offered if—apart from susceptibility based on personal and family history—the following criteria are met: (1) a genetic test is available that has sufficient sensitivity and specificity; 2) the result can be correctly interpreted; (3) a positive result will influence the proband’s diagnosis, therapeutic or cancer risk management; and/or (4) give useful information about cancer risk for family members  the resu.The aim of the pre-test counselling is to assess the probability of hereditary cancer predisposition syndrome, to help clinicians develop a differential diagnosis, and offer genetic testing if it is justified. During counselling, it is determined which the most appropriate test is and what information can be expected based on the result. Counsellors explain the benefits and limitations of genetic testing (including possible outcomes) and the possibility of not testing as well. This information helps patients to decide whether or not to have a genetic test and to give informed consent, including signing a consent form .Once a genetic diagnosis is made, counsellors provide recommendations for surveillance, prevention and potential management informed by ref. . They inGenetic testing performed without appropriate pre- and post-test genetic counselling can result in unnecessary genetic testing, misinterpretation of genetic test results leading to potentially inappropriate medical management (prevention/surveillance/intervention) –25. As hEven during genetic counselling, there are limitations in cancer risk assessment. In the family history, there are several factors: small family size, lack of knowledge of family history, deaths in early age, gender imbalance, consanguinity or misattributed parentage that complicate the recognition of inheritance pattern , 26, 27.Different tests of the same gene have varying levels of sensitivity and specificity. In addition, genetic heterogeneity makes the selection of the best genetic test challenging Table  28–36].–36.28–36For targeted gene testing, conventional Sanger sequencing is the most widely used as a gold standard method . Small rNGS is able to investigate more than 50 genes simultaneously, often at a lower cost than single-gene testing in terms of cost/gene. When NGS multigene panels are planned it is important to include discussion of which genes will be tested during counselling . In addiAlthough laboratories now offer various cancer susceptibility gene panels, not all genes included in the panels are of unequivocal clinical relevance. Findings identified in genes without clearly established clinical value may be difficult to interpret (see below).NGS comprises of library preparation, sequencing and data analysis (see details in ). Each pDuring NGS a huge amount of sequence data is produced that requires special bioinformatics handling and analysis, hence bioinformatics probably represents more challenges compared to the sample analysis , 43–45. During a WES test 15,000–20,000 variants can be expected . This liIt is possible that a pathogenic variant is not identified in an affected individual. Apart from technical problems, the reasons for this include a pathogenic variant in another gene that was not tested or in a gene not yet identified, phenocopy and additionally, sporadic occurrence. In these cases, affected probands and first-degree relatives can be kept under clinical surveillance.To estimate the relevance of variants of uncertain significance (VUS) is challenging , 51, 53.Due to constantly increasing data and knowledge, reevaluation and reanalysis of genetic test results are possible. Moreover, laboratories should have a protocol regarding variant-level and case-level reevaluation that is supported by ACMG guidelines .Laboratories should also have a communicated policy about reporting incidental (secondary) findings  guided bScreening for hereditary cancer syndromes is only recommended in high-risk patients due to higher VUS rate, the equivocal clinical value of variants with low-penetrance or newly discovered genes and secondary findings .As noted above, handling large data sets generated by high-throughput technologies can result in high numbers of false-positive results and incidental findings. This risk correlates with the number of genes tested simultaneously. As a consequence overtesting, overdiagnosis, and overtreatment were described as major side effects of high-throughput approaches .Whole-genome sequencing (WGS) covers the whole coding and noncoding regions of the genome, and for this reason, it may be the preferred genetic test. However, among NGS approaches it gives the least average coverage and it is the most expensive technology. In addition, the interpretation of noncoding variants and VUSs results in the most uncertainty. Whole-exome sequencing (WES) covers all protein-coding regions of the human genome. Although this comprises ~1–2% of the genome, it includes approximately 85% of known disease-causing mutations, making it a more feasible option . It can for diagnostic purpose, only genes with a known  relationship between the aberrant genotype and the pathology should be included in the analysis’ and ‘for the sake of comparison, to avoid irresponsible testing, for the benefit of the patients, ‘core disease gene lists’ should be established by the clinical and laboratory experts’ [Consequently, based on the guideline recommended by the ESHG ‘experts’ . The addexperts’ , 42. TheTherefore, although high-throughput sequencing  are technically available and have many advantages they still represent diagnostic challenges that have to be considered in clinical practice , 59.The clinical value  of genetic testing differs among endocrine tumour syndromes, hence disease-specific guidelines have to be followed.Establishing a diagnosis is usually based on clinical criteria in a proband, and confirmed by the result of the genetic test. Also, in atypical cases (for example due to decreased penetrance or variable expression) genetic testing can be an important part of the diagnosis. However, genetic testing in asymptomatic family members , establishes an early diagnosis which may have significant consequences.VHL variant is confirmed surgeons prefer nephron-sparing surgery over total nephrectomy in order to spare organ function as long as possible since tumour re-occurrence in the same or on the other side is more likely compared to sporadic cases [While genetic tests in nearly all endocrine tumour syndromes contribute to tumour risk assessment, overall, therapeutic decisions in endocrine tumour syndromes are rarely based on germline genetics. Therapy includes chemo-, hormone, targeted- radio-, radionuclide therapy and surgical interventions . Targeted oncological therapy—if used in individual cases—is rather based on somatic alterations. However, germline variants can influence surgical approaches and timing for specific surgeries. For instance, in a patient with renal cell carcinoma when a pathogenic germline ic cases .RET mutation [When genotype-phenotype correlation is strong, surveillance and preventive measures can be based on genetic findings. In MEN2 syndrome, the timing of prophylactic thyroidectomy is based on the particular mutation . On the mutation . Startinmutation , 63.Regarding family screening or so-called cascade testing, knowing the pathogenic variant characteristic for a family, targeted genetic tests provide a simple and cheap method for cascade testing. Family screening has to be voluntary no one can be forced against his/her will. Advantages and disadvantages have to be discussed during genetic counselling. However, cascade testing has an indisputable role in prevention. Involving asymptomatic carriers in relevant surveillance protocols helps the early diagnosis and favours early medical interventions. Also, it gives the opportunity to decide about family planning. Each person has the right to make informed decisions regarding his/her life management in the light of potential carrier status of a pathogenic germline variant , 24. TheTo assess the clinical relevance of a VUS is challenging . This caThe genetic test is only one criterion in the establishment of the diagnosis of tumour syndromes. In some cases, a patient despite having a clear phenotype characteristic for an endocrine tumour syndrome the genetic test fails to identify the pathogenic alteration. This phenomenon is called a phenocopy. Of endocrine tumour syndromes, it frequently occurs in relation to MEN1 . If the As we described above, the more genes we test the more uncertainty we have to deal with. Selection of the proper test is the task of genetic counsellors after patients are referred to genetic consultation by clinicians .TP53 gene—that can complicate the picture) [Whenever it is possible  targeted gene testing  is suggested Fig. . Targetepicture) , 72, 73.In the last decade, NGS based strategies have been revolutionised genetic testing and high-throughput techniques are widely introduced into every day clinical practice. The application of NGS allows rapid and cost-effective testing of multiple genes. Therefore, an increased demand from health care providers and society has been pressing to shift genetic tests towards high-throughput approaches. Besides all advantages, however, the application of NGS is associated with increased uncertainty in result interpretation. Challenges in technical, bioinformatical processes and in interpretation presented in this work highlight the increased uncertainty of the clinical value of the results. Therefore, following ACMG and ESHG guidelines, it is still recommended phenotype-based targeted testing and to use as small-scale approach as possible in order to minimalize difficult interpretation , unwanted patient anxiety, unnecessary medical interventions and cost. Selecting the most suitable genetic test is the part of the work of clinical geneticists. Fortunately, endocrine tumour syndromes are quite well defined, and only selected cases require WES. Still, genetic heterogeneity, decreased penetrance, variable expression or mosaicism can challenge diagnostic workflow. As the result of germline genetic test influences the life of the proband, including treatment and surveillance and the life of his–her family through family screening/cascade testing and family planning, germline genetic testing has an important role in the management of endocrine tumour syndromes.


The interplay between age and symptoms intensity on antibody response to SARS-CoV-2 infection has not been studied in a general population setting.We explored the serologic profile of anti-SARS-CoV-2 antibodies after the first wave of the pandemic, by assessing IgG against the spike protein (ELISA-S), IgG against the nucleocapsid protein (ELISA-NP) and neutralizing antibodies (SN) in 82,126 adults from a French population-based multi-cohort study.ELISA-S positivity was increased in 30- to 49-year-old adults (8.5%) compared to other age groups . In the 3681 ELISA-S positive participants, ELISA-NP and SN positivity exhibited a U-shaped relationship with age, with a lower rate in 30- to 49-year-old adults, and was strongly associated with COVID-19-like symptoms.Our study confirms the independent role of age and symptoms on the serologic profile of anti-SARS-CoV-2 antibodies, but the non-linear relationship with age deserves further investigation.The online version contains supplementary material available at 10.1007/s15010-021-01731-5. Since the emergence of SARS-CoV-2 in China in December 2019 and its spread worldwide, numerous studies have focused on determining the antibody status of the population at different times during the course of the pandemic. Consistent associations between seropositivity and young age, low socio-economic factors and high population density have been reported  had a positive ELISA-S, 1.6%  an indeterminate ELISA-S and 93.3%  a negative ELISA-S.We found a strong association between ELISA-S and age with increased positivity in 30- to 49-year-old adults [8.5% ] compared to younger [5.6% ] or older adults [2.8% ] Fig. A. ELISA-A positive ELISA-NP was found in 1839 (2.3%) and a positive SN in 1200 (1.5%) participants, and it was lower with increasing age . The association with prior symptoms was independent of age and sampling month.A total of 1450 ELISA-S-positive participants (39%) reported CLS a median of 3.9 months  before DBS, 63% were ELISA-NP positive and 49% were SN-positive while 1234 (34%) reported other symptoms, 28% ELISA-NP-positive, 21% SN-positive and 997 (27%) had "No symptoms reported", 25% ELISA-NP-positive and 21% SN-positive Below is the link to the electronic supplementary material.


Chamaecyparis formosensis is an endemic species of Taiwan, threatened from intensive use and illegal felling. An individual identification system for C. formosensis is required to provide scientific evidence for court use and deter illegal felling. In this study, 36 polymorphic simple sequence repeat markers were developed. By applying up to 28 non-linked of the developed markers, it is calculated that the cumulative random probability of identity (CPI) is as low as 1.652 × 10–12, and the identifiable population size is up to 60 million, which is greater than the known C. formosensis population size in Taiwan. Biogeographical analysis data show that C. formosensis from four geographic areas belong to the same genetic population, which can be further divided into three clusters: SY (Eastern Taiwan), HV and GW (Northwestern Taiwan), and MM (Southwestern Taiwan). The developed system was applied to assess the provenance of samples with 88.44% accuracy rate and therefore can serve as a prescreening tool to reduce the range required for comparison. The system developed in this study is a potential crime-fighting tool against illegal felling. Illegal logging is a severe problem in many timber-producing countries. Unplanned felling results in forest degradation, affects forest ecosystems and promotes the spread of pests and pathogens. In addition, large-scale deforestation causes forests to lose their soil and water conservation functions, thus leading to water shortages in the dry season and floods in the rainy season. These disasters brought by illegal or unplanned felling cause significant damages to the country.Incidents of theft of valuable timber continue to occur, yet the crime investigations are complicated due to the lack of effective measures to present court evidence for conviction. To obtain the molecular evidence linking seized timber to illegally felled stumps, it is necessary to develop an individual identification system for tree species of high economic value, which are often the target of illegal felling.Chamaecyparis formosensis Masam., also known as False Cypress, is endemic tree species in Taiwan distributed majorly in the cloud forest, a zonal forest type in the mid elevation (1700–2600 m) with extremely high biodiversity1. Gigantic C. formosensis is known for its superb timber quality. Due to its high quality and market value , C. formosensis is critically threatened by illegal felling. Unplanned felling and poor management compromised the ecosystem and endangered these endemic species. Similar scenarios also happened to Dalbergia spp. (Leguminosae: Papilionoideae)3, Fraxinus excelsior (Oleaceae)4, Swietenia macrophylla (Meliaceae)5, and Intsia palembanica (Fabaceae)6. Moreover, timber production countries suffer from illegal felling, particularly in South-east-Asian, African and South American countries7. Although suspects were arrested on some occasions, lack of direct scientific evidence to link seized timber and stump had led to a failure of conviction in the majority8.9, and DNA length difference-based techniques SSR (Simple Sequence Repeat)11, and INDEL(Insertion/Deletion). SNP, SSR, and INDEL are all co-dominant molecular markers classified into heterozygous and homozygous. Diversified types can be found at the same loci according to the pairwise characteristics of genes in the same allele. For example, human ABO blood type contains three genotypes: IA, IB, and i. When the genotype of the individual is IAIB, IAi, and IBi, it is a heterogeneous combination, showing blood types AB, A, and B respectively. When the genotype of the individual is IAIA, IBIB, ii, it is a homogenous combination, showing blood types A, B, and O, respectively. When reaching enough numbers of the polymorphic molecular markers, they can be used for individual identification and thus can be used to compare the seized timber to the illegally felled stumps. The reported individual identification systems have demonstrated their potentials to provide scientific evidence for court cases11.In recent years, countries suffering from serious illegal logging have successively begun to develop DNA-based timber individual identification systems that can provide court evidence for a conviction. The reported technology for individual identification systems includes DNA point difference-based technique SNP (Single Nucleotide Polymorphism)13. SSR marker is a co-dominant and highly reproducible DNA marker with the following characteristics: it has a high degree of polymorphism; it is abundant and evenly distributed in eukaryotic genome; most of them are not functional and can be efficiently and economically tested by PCR (polymerase chain reaction); last, the length is generally short which provides a higher opportunity to be amplified when applied to the lysed sample14. Therefore, SSR is the most commonly used method for individual identification systems16. To protect the cypress resources in Taiwan, we have developed and adapted several polymorphic SSR markers17 for individual identification.The SSR individual identification technique has been developed for more than 30 years and has been widely used in DNA paternity testing, forensic examination, victim identification, and animal individual identificationC. taiwanensis8 and F. excelsior4, it is demonstrated that the SSR individual identification system can provide scientific evidence that is considered acceptable by court. In these cases, the individual identification system developed with genetic markers for those species were used to link seized timbers and victim trees, while considering the random probability of the same genotype appearing in the population. Therefore, convincible scientific proof with a confidence level close to 100% was accepted as court evidence for crime conviction.In the illegal felling crime case reports 18. SSR is also often used in genetic diversity and population structure analysis of species19, and can further predict species' geographic provenance and distribution. Genetic methods have been applied to confirm the source and trade routes of protected species21. However, filing the DNA of every individual is not feasible even if C. formosensis has been listed as an endangered species. Therefore, it is important to analyze the genetic variation and population structure of C. formosensis. Genotyping can reveal the provenance of the timber and greatly reduce the range of possible plant sources.The legality of wood products usually depends on their sourceC. formosensis has been listed as an endangered species by the IUCN Red Book . It has become one of the most concerning issues whether C. formosensis has lost its genetic diversity due to excessive logging. From conservation perspective, SSR marker can be used to find high population genetic diversity areas, to understand the local species’ genetic structure composition of these areas, and to further provide necessary conservation measures and management to these areas. SSR marker is an effective tool for studying the diversity of populations and providing harmonised standards , observed heterozygosity (Ho), expected heterozygosity (He), the inbreeding coefficient (Fis), the fixation index (Fst)) for comparison with other species22.Due to the extensive planned logging and rampant illegal logging in the last century, C. formosensis SSR individual identification system, which provides high discrimination power against genetic variation, in order to prevent the occurrence of illegal felling. Moreover, the biogeographical analysis of C. formosensis also facilitates providing provenance information of the seized timber. In addition to being a long-developed individual identification tool16, SSR is also often used in plant conservation and breeding24. The SSR markers developed in this study can also support the future C. formosensis afforestation by selecting mother trees with higher diversity.The primary purpose of this study is to develop 27, the reported detection rates for dried timber were only 20–40%9. Therefore, it is necessary to develop more SSR markers as a contingency plan. When the sample is in a poor condition, more markers can be applied in order to achieve the threshold of combined power of discrimination (CPD) required for successful comparison between seized timbers and victim trees. In order to maximize potential loci, Next generation sequence (NGS) methods were used. In this study 3 DNA library were constructed. We used the Illumina MiSeq platform  to sequence the DNA libraries . After quality-trimming to the raw reads with CLC Genomics Workbench version 10 , 70,319,509 contigs were generated with the length between 133 and 146 bp on average. De novo assembly was conducted with the following parameters: contig number 208,467, minimum length of contigs 18 bp, maximum length contigs 108,928 bp, and average length contigs 491 bp. The sequence was assembled with software CLC Genomics Workbench version 10, and the length of the assembly sequence was 102,281,642 bp.MicroSAtellite)28. We newly designed 100 candidate SSR primer pairs for testing in C. formosensis by BatchPrimer329.A sum of 78,250 SSR containing sequences was screened by MISA  were registered in GenBank in NCBI Table  and pass30. However, transforming candidate SSR primer pairs into validated SSR markers is still a time-consuming and expensive step. Qualified SSR markers need to succeed in PCR amplification, have good peak pattern quality with minor stuttering, and be free of non-amplifying  alleles. The turnover rate from candidate SSR primer pairs to validated SSR markers varies from species to species31. The success rate in Chamaecyparis plants is between 5.24% and 9.27%32.Unlike the traditional SSR cloning method, with next-generation sequencing technology, it is easy to obtain a significant amount of SSR containing sequences from sequenced genomes17 polymorphic SSR markers were analyzed against 92 individuals from 4 geographic areas 33 are from 0.000 to 0.891, with an average of 0.414. The levels of expected heterozygosity (He)33 range from 0.103 to 0.906, with an average of 0.565. Significant (P < 0.001) deviations of Hardy–Weinberg equilibrium (HWE)34 were detected in 23 SSR loci: Cred47, 225, 231, 236, 242, 248, 249, 250, 253, 260, 262, 276, 277, 280, 603, 610, 628, 640, 641, 674, 678, 682, 683. Ho is the actual proportion of heterozygous individuals in each locus within the population, whereas the He is the expected value estimated per HWE. Ho and He are among the most widely used parameters in estimating genetic diversity in a population. The population structural and even historical information can be obtained from Ho and He. When Ho = He, it means that the population is random mating. When Ho < He, it means that the population is inbreeding. When Ho > He, it means that the population is outcrossing33. Most of these loci (36 tested) are Ho < He , suggesting the population of C. formosensis has a low genetic divergence and is an inbred strain. HWE describes that under ideal conditions, there exists no mutations, no natural selection, no individuals moving in or out, the population is infinitely large, and random mating within the population. Therefore, gene frequency does not change over time or generation. However, there will always be one or more interfering factors  affecting gene frequency in nature35. Therefore, HWE is difficult to achieve in nature. In this study, 23 loci out of 36 markers deviated from HWE (63.89% deviation rate). The reason for this deviation could be artificial selection, non-panmixia or genetic drift.In this study, newly developed 9 validated SSR markers and other 27 validated SSR markersPIC), is an index of the relative ability of the SSR marker's genetic variability. The higher the polymorphism of marker's genotype, the higher the PIC value36. Polymorphic markers were highly informative (PIC > 0.50), reasonably informative (0.50 > PIC > 0.25), and slightly informative (PIC < 0.25). Power of discrimination (PD)37 refers to the ability of genetic markers to distinguish individuals within a population. Obviously, in a population with more allele types and evenly distributed genotypes, the low probability of two random individuals having the same genotype, and the system can identify the greater probability of two random individuals. Probability of identity (PI)38 is the probability of two individuals with the same genotype. PD = 1-PI . The value of PIC, PD and PI of individual markers reflects its identification ability in the individual identification system. The greater PIC and PD, the lower PI in value, suggesting the higher identification ability of the marker, and vice versa. The levels of PIC range from 0.097 to 0.876, with an average 0.528. The levels of PD range from 0.102 to 0.885, with an average 0.567. The levels of PI range from 0.114 to 0.897, with an average 0.431. There were 19 out of 36 markers with PIC greater than 0.5, and the mean of these 36 markers PIC values was greater than 0.5, suggesting the markers have a high identification ability. The results of PD and PI correspond to those of PIC. The highly informative markers presented in PIC also show higher identification ability in PD and PI.Polymorphism information content, or power of information content (P < 0.001) were detected among Cred35/229/277 (Group 1), Cred47/298 (Group 2), Cred231/249/253/262 (Group 3), Cred281/297 (Group 4), Cred603/683 (Group 5) and Cred640/678/682 (Group 6) with GENEPOP 4.239, suggesting the abovementioned group located in the same linkage group (Table CPI) is the product of the PI of each genetic marker. At this time, CPI will be greatly reduced, and the combined power of discrimination (CPD) will become very high. As defined above, CPI + CPD = 1. The credibility of the individual identification system is calculated based on "Random match probability in population size and confidence levels’" published by Budowle et al.40. Confidence levels (CL) = (1 – CPI)N, where N is number of individuals.Significant linkages . In this study, the locus with the lowest PI within a linkage group was used to calculate the CPI  dictated by court, this system can be used to identify 60 million C. formosensis, with CPI as low as 1.652 × 10–12, CPD as high as 0.999999999998348 , beyond the known population size of 32.06 ± 3.20 million C. formosensis42. Under ideal conditions, a minimum of 6 loci can be applied to the system, with an identifiable C. formosensis population of 2,900 under 95% confidence level. The CPI is as low as 1.728 × 10–5, and CPD is as high as 0.999982712603209 44 reviewed 233 publications by examining how authors detect and deal with null alleles and the methods used to estimate the frequency of null alleles across articles. The authors demonstrate that the frequency of simulated null alleles is usually overestimated, which will lead to underestimating the usability of this marker. It was misunderstood that the existence of null alleles will reduce the availability of paternity testing, individual identification, and population genetic research. However, it has been demonstrated that null alleles do not change the overall result on assignment testing44. Compared with the presence of null alleles, increasing the number of loci and the degree of genetic differentiation has a more significant impact on the accuracy of assignment testing. This argument is valuable for studying SSR markers and populations prone to invalid alleles, as it allows researchers to use loci affected by invalid alleles44.Dakin et al. (2004)8 where C. taiwanensis individual identification system was applied to an illegal felling conviction case, CPD calculations exclude any markers that show homozygous PCR results per ISO/IEC 17,025. The CPDs are calculated only from the possibilities of the markers found in timber and tree samples simultaneously. Null alleles and PCR fail will only reduce the identification rate but will not cause seized timber and victim tree from different individuals to be identified as the same source. However, this is not to say that efforts should not be made to use loci that display low-frequency null alleles. On the contrary, markers that are less prone to invalid alleles should always be preferred because they are less ambiguous and are more potent in assignment testing. However, before many individual identification markers are developed and optimised, the impact of null alleles should not be overemphasized, as it reduces the usability of markers44.In Huang et al.is, by definition Fis = 1-Ho/He, is the inbreeding coefficient of an individual concerning the local subpopulation. When Ho < He, then Fis > 0, indicating that the population is an inbreeding. The 36 polymorphic SSRs were used to evaluate the genetic diversity parameters of the four groups  (Table A) for each locus is 4.417 and 5.444. Ho and He are ranged from 0.376 to 0.506 and from 0.474 to 0.583, respectively. All the groups show positive inbreeding coefficients, suggesting these four groups are inbreeding lines.FY) Table . The numst) estimates population differentiation due to genetic structure47. A higher Fst value means a higher degree of difference between populations. When Fst is less than 0.05, there is no differentiation among populations. When Fst is between 0.05 and 0.15, there is low differentiation among populations. On the other hand, the estimation of the number of migrants (Nm) is gene flow value47. If Nm is more than one, genes frequently exchange, which counteracts the genetic drift and prevents the population differentiation48. If Nm is greater than four, the population is a random mating49. The analyses of Fst and Nm of the four geographic areas were conducted by GeneAlex 6.50350  was found between HV and MM. The Fst values ranged from 0.056 to 0.065 were found between the rest geographic areas, indicating a low differentiation in these geographic areas. The highest Nm value (6.832) was found between HV and GW, whereas the lowest value (3.141) was between HV and MM. The Nm values of four geographic areas were greater than 1 (between 3.141 and 6.832), suggesting a frequent gene exchange between the four geographic regions, which offsets genetic drift and prevents population differentiation. For GW/MM (Nm = 4.022), GW/HV (Nm = 6.382), the Nm values of the population are more significant than four, suggesting that these populations are random mating.The fixation index 56, Abies kawakamii (Pinaceae)57, Castanopsis carlesii (Fagaceae)58. After the glaciation, species spread from the refuge to the surrounding areas and created species diversity across the latitude gradient59. In our results, the low polymorphism of C. formosensis probably indicates that they originally derived from the same large population during the glaciation. After the glacial retreat, these four C. formosensis clusters spread out from the refuge and formed the four populations due to geographic isolation.STRUCTURE analysissis Fig. , and theers Fig. b: the SYst Table  show tha60 also show that, based on molecular evidence, many plants  in Taiwan island have high genetic diversity, higher than that of mainland China and Japanese archipelago. This remarkable high genetic diversity is associated with the Ice Age history in Taiwan60. The low genetic diversity of C. formosensis differs from most Taiwanese plants but is similar to another endangered plant of the genus Cypress, C. taiwanensis, in Taiwan Island 8. Compared to C. obtusa, an endangered cypress plant in the Japanese archipelago, C. formosensis is also inbreeding (Fis = 0.034), but the degree of genetic diversity (A = 23.9) is significantly lower. One possible explanation of the low genetic diversity is that a large population of C. formosensis was divided into several smaller populations after ancient glacial retreat in Taiwan, and then they were recently overexploited by humans (REF).Studies61 was applied to analyse the provenance of 92 individuals independently. The probability of samples returning to the correct provenance is 95.00% (MM), 88.00% (HV), 69.57% (GW), and 100.00% (SY), with an overall mean correct rate of 88.04%  and SY  were used for DNA library preparation. To build three DNA libraries, genomic DNA was extracted from fresh leaves using the VIOGENE plant DNA extraction kit . The DNA libraries were sequenced using the Illumina MiSeq System  at Tri-I Biotech .In order to develop SSR markers for individual identification, we constructed three DNA libraries. Three Bioinformatics analysis was conducted with CLC Genomics Workbench version 10 . The raw reads were prescreened to remove adapter sequences and reads with greater than 0.01 error or an average quality less than QV20. The trimmed sequences were further subjected to de novo assembly.MIcroSAtellite v 1.0)28 was applied to screen the SSR containing sequences from contigs. To design SSR primers, sequences with at least five di-, tri-, tetra-, penta-, and hexa-nucleotide repeats were selected using BatchPrimer329, with optimized conditions set length at 18–23 bp, melting temperature 45–62 °C, and product size of 80–300 bp.MISA . The samples DNA used in marker validation were extracted using the VIOGENE plant DNA extraction kit . The PCR reaction was conducted with a final volume 20 μL containing 2 ng of genomic DNA, 0.25 μL of 10 μM each primer and 10 μL of Q-Amp 2 × Screening Fire Taq Master Mix . The following PCR process was conducted: an initial denaturation of 95 °C for 2 min; 30 cycles of 95 °C for 45 s, a primer-specific annealing temperature for 45 s, and 72 °C for 45 s; followed by a 15-min extension at 72 °C , observed heterozygosity (Ho), expected heterozygosity (He), Hardy–Weinberg equilibrium (HWE). PowerMarker V3.2562 was used to calculate polymorphism information content or power of information content (PIC)63. Power of discrimination (PD)37, PD = 1 − ΣPi2, where Pi is the frequency of genotype i . Probability of identity (PI)38, PI = 1 − PD. The combined power of discrimination (CPD)37, here we calculated CPD of 28 markers. CPD = 1 – [(1 – PD1)(1 – PD2)…(1 – PD28)].The combined probability of identity (CPI)38. Microsoft Excel (Microsoft Office 2016) was used to calculate PD, PI, CPD, CPI. GENEPOP 4.239 was used to test for linkage disequilibrium.Marker analysis was conducted by combining 27 pairs published SSR markersis, Fst and Nm by GenAlex 6.50350.Genetic diversity parameters, genetic differentiation and gene flow among 4 geographic areas  were analyzed using F52. The program was run for K = 1 to 5 clusters with 20 independent runs to assess simulation stability. Each simulation was run for an initial 1,000,000 burn-in period followed by 100,000 replications based on the Markov chain Monte Carlo (MCMC)64. The best grouping was evaluated by Delta K64 in Structure Harvester Web v0.6.9465. Bar graphs were generated by CLUMPP 1.1.266 for K ideal.The population genetic structure was analyzed using STRUCTURE 2.3.461 on every 92 individuals independently. A pairwise simulation was also conducted on the pooled database deducted the sample itself.Individual provenance simulation was conducted with GENECLASS v. 2.0With legislation compliance of experimental materials, we hereby declare that all of our experimental research and field studies on plants, either cultivated or wild, including the collection of plant material, comply with relevant institutional, national, and international guidelines and legislation.In this research, the software and the data generated by the software  are used legally in accordance with regulations, which allow to reproduction, distribution, transmit and modification works .Supplementary Information.


Triticum aestivum L.) is an allohexaploid species resulting from the natural hybridization and chromosome doubling of allotetraploid durum wheat (T. turgidum) and a diploid goatgrass Aegilops tauschii Coss (Ae. tauschii). Synthetic hexaploid wheat (SHW) was developed through the interspecific hybridization of Ae. tauschii and T. turgidum, and then crossed to T. aestivum to produce synthetic hexaploid wheat derivatives (SHWDs). Owing to this founding variability, one may infer that the genetic variances of native wild populations vs improved wheat may vary due to their differential origin and evolutionary history. In this study, we partitioned the additive variance of SHW and SHWD with respect to their breed origin by fitting a hierarchical Bayesian model with heterogeneous covariance structure for breeding values to estimate variance components for each breed category, and segregation variance. Two data sets were used to test the proposed hierarchical Bayesian model, one from a multi-year multi-location field trial of SHWD and the other comprising the two species of SHW. For the SHWD, the Bayesian estimates of additive variances of grain yield from each breed category were similar for T. turgidum and Ae. tauschii, but smaller for T. aestivum. Segregation variances between Ae. tauschii—T. aestivum and T. turgidum—T. aestivum populations explained a sizable proportion of the phenotypic variance. Bayesian additive variance components and the Best Linear Unbiased Predictors (BLUPs) estimated by two well-known software programs were similar for multi-breed origin and for the sum of the breeding values by origin for both data sets. Our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.Cultivated bread wheat ( Triticum aestivum) is an allohexaploid (2n = 42) with three genomes, A, B, and D, each with seven chromosomes and with a large genome of 16 × 109 bp  and a diploid goatgrass Aegilops tauschii Coss. . When these crossings occurred is not well known, but they most probably involved spontaneous and casual crossings of a few individuals from two distantly related grasses. Ae. tauschii is not well represented in wheat germplasm  has been reduced (SHW) . This artificially created hexaploid represents a wider genetic diversity gene pool that should enable wheat researchers to introduce novel genetic variation lost during the process of origin and domestication of wheat  are treated as a random effect with a covariance matrix defined by A represents the numerator (additive genetic) relationship matrix  estimate the additive genetic variances of the three populations comprising the synthetic wheat, as well as the extra variances that result from crossing them; and (3) predict breeding values of both SHW and SHWD data sets using two different well-known software programs when considering the multi-breed origin versus considering only one-breed and comparing their results.Considering the decomposition of the multi-breed genetic covariance matrix described by SHW and SHWD lines. In this study, two data sets comprising SHW and SHWD were used. Data set 1 included 13 cycles (1997-2010) of the Semi-Arid Wheat Yield Trial (SAWYT) from the Global Wheat Breeding Program of CIMMYT. The data consisted of SHWD wheat yield trials in dryland environments with breeds denoted as diploid Ae. tauschii (D), tetraploid T. turgidum (T), and hexaploid T. aestivum (V) of SHWD. The total number of locations reporting data was 170. Data set 2 had wheat synthetic lines (SHW) derived from 422 crosses between Ae. tauschii (D) and T. turgidum (T) evaluated in one environment for one disease.Throughout the years, the International Maize and Wheat Improvement Center (CIMMYT) has produced thousands of https://mc-stan.org)  and “maternal” (iM) breed group, and p1 and p2 stand for the ploidy of these groups. For example, if the paternal line is Ae. tauschii, a diploid species, it will only contribute one allele (p1 = 1) and its effect will be denoted as 1 derivatives. Next, we use T. aestivum.Assume a population with individuals pertaining to one of the several breed groups described in Ae. tauschii and adapting expression (1) , the genotypic value is: Starting with diploid T. turgidum (tetraploid) and T. aestivum (hexaploid) are: Similarly, genotypic values for i within pure breed PB , i.e., the probability that at any locus the paternal and maternal gametes are identical by descent  variance in D, T and V breed groups, respectively, In these cases, all individuals are pure breeds and the genotypic variance is a function of a single parameter, the additive genetic variance of the pure breed. In addition, one may consider inbreeding within pure lines. Let ent IBD, . Then, wHence, the additive genotypic variances for the pure breeds become: Equivalent derivations, assuming only alleles from different gametes may be IBD, lead to: Ae. tauschii (D) × T. turgidum (T) hybrid line. Using expression (1), the genotypic value i belonging to this synthetic (SHW) breed group could be represented as: j = 1 or 2, are, respectively, the additive effects of paternal (iP) and maternal (iM) alleles in the breed group D or T denoted in the superscript. We will assume that diploid Ae. tauschii (D) is the male parent and tetraploid T. turgidum (T) is the female parent. As the synthetic lines were produced by two distinct alleles from T. turgidum, there are two different code numbers in the alleles inherited from tetraploid T. turgidum maternal alleles. In fact, because the three inherited alleles were doubled (either induced by colchicine or spontaneously), they are identical to their copies and have the same code number.Now consider the synthetic derivative wheat produced after doubling chromosomes in an i can be obtained as follows: Applying variance operator rules, the genotypic variance of a synthetic line D and T) are independent; therefore, the covariance between their additive effects is null and has been excluded from expression (10) and all further developments. Now, effects i.e., T. turgidum is allopolyploid, these effects originate in different genomes; therefore, they can be assumed independent and the last term in expression (10) is equal to zero. By gathering these results, using definitions in expression (5) and dropping unnecessary subscripts, we can express expression (10) as: Alleles in different breed groups  and segregation terms (DV and TV) (see Appendix 1). The AQ. AQ are the so-called partial numerator additive relationship matrices  was obtained by As shown by matrices  associati.e., the breeding values by breed origin, aQ, with Q = {D, T, V, DV, TV}, where D, T, and V are Ae. tauschii (D), T. turgidum (T) and T. aestivum (V) breed groups, and DV and TV indicate the segregation effects between Ae. tauschii- T. aestivum and T. turgidum—T. aestivum populations. Segregation effects were added to the model to account for the segregation variance, defined as the additional genetic variance in an F2 population over that in the F1 population  of the Ae. tauschii, T. turgidum and T. aestivum breed groups, respectively. Matrices ZDV and ZTV are, respectively, the incidence matrices for the segregation random effects (aDV and aTV) between Ae. tauschii and T. aestivum, and between T. turgidum and T. aestivum. The random effect of the interaction between genotype and environment is modeled in vector ge, whereas ZGE is the corresponding incidence matrix. Finally, e (n × 1) is the error vector. The main objective was to make inferences about the parameters of the model, especially the additive and segregation genetic variances.To fit the additive genetic covariance structure just described, we defined a linear mixed model with several random genetic effects, pulation . The mod1 and F2 groups. Initially, it is necessary to specify the full conditional sampling density of the phenotypic data vector. Following y, we have: The Bayesian inference approach employed to fit model (13) can be described as a hierarchical construction  and is sb, aQ and ge.We will now describe the prior distribution assigned to each of the location parameters, b with very large variance to avoid the occurrence of an improper posterior distribution, a problem that appears when a Uniform prior is employed for b . Subsequently, by combining the likelihood and the conjugate prior distributions, one can obtain the joint posterior distribution of all the unknowns of the model. Finally, marginalization of this latter distribution to obtain samples of the parameters of interest is performed by means of a standard Gibbs sampler e.g., . This isData on 13 cycles (1997–2010) of the SAWYT  were available for this study. The data consisted of wheat yield trials in dryland environments that belong to international CIMMYT collaborators. The experiments were arranged in an incomplete randomized block design with two replicates. Synthetic derivative lines were progressively included in SAWYT trials as they developed, reaching 46% of the wheat lines tested in 2006. However, most of them were not repeated in consecutive years, causing high temporal discontinuity in their evaluations  (Ae. tauschii and T. turgidum) and the bread wheat lines (T. aestivum) used in the crosses and backcrosses  . Pedigrekcrosses . We usedSHW) lines for which Pyrenophora tritici-repentis (PTR) diseases were recorded. The Pyrenophora tritici-repentis (PTR) causes a disease originally called yellow spot but also known as tan spot, yellow leaf spot, yellow leaf blotch or helminthosporiosis. The 422 wheat lines were evaluated with six replicates in the greenhouse. The total number of observations was 438 This data set contains 422 synthetic wheat lines (AQ) (briefly described in Appendix 3 and codes given in Supplementary File S2) associated with the additive and segregation genetic variances of each breed group (population). The algorithm for calculating the entries was developed according to the rules described by A FORTRAN 90 subroutine was written to compute the entries of the partial numerator relationship matrices  and relationship matrices provided by the user  using the Gibbs sampling algorithm. The hyper-parameters for the prior distributions were set using the default rules implemented in the BGLR-R package (see Appendix B in the BGLR package).The full conditional distributions of the model parameters ]} had low segregation variance (0.178) and the lowest total additive variance (0.425).V×[V ×[V × (V× SHW)]]} to 0.52 (V× SHW) and line mean repeatability varied between 0.96 to 0.98. Considering only one breed group, narrow sense heritability and line mean repeatability were 0.13 and 0.87, respectively. These last results show that partitioning the total additive variance into different breed groups should provide a more precise variance component and heritability estimations.Narrow sense heritability ranged between 0.35 {The BLUP prediction for each genotype breeding value and their breed-by-origin components as well as the sum of the breeding values by breed origin are given in the Excel files in Supplementary File S3. In addition, the BLUPs obtained when considering only one breed group are also included. Supplementary File S3 also contains the pdf files corresponding to the trace plots and the posterior densities of each of the variance components.SHWD lines belonging to the D and T breed groups had a correlation of 0.6174 and 0.6211 with the corresponding BLUPs computed assuming a single additive variance, whereas the BLUPs of V, DV, and TV were strongly correlated: 0.8941, 0.8478, and 0.8384, respectively.Comparing the breeding value (BLUP) estimates when considering one homogeneous group versus the sum of breeding values by breed origin gave a correlation of 0.9079, indicating similar pedigree relationships of both numerator relationship matrices. The breeding values (BLUPs) for the Ae. tauschii and T. turgidum)  and no segregation effects because no F1 and F2 generations were derived. This data set had the lines derived from crosses after doubling the number of chromosomes by colchicine. In Data set 2, there are only two breed groups  for the two categories, and the sum of the breeding value by origin for the 422 lines between BGLR and Stan were high. Comparing the breeding value (BLUP) estimates when considering one-breed versus the recomposing breeding values obtained by fitting the multi-breed model gave a correlation of around 0.90.The BLUP prediction for each breeding T. aestivum population included in the crosses that produced the synthetic derivative lines. Therefore, it was necessary to consider a genetic model that partitions the synthetic derivative breeding value into components that can be attributed to the different genetic sources. Thus, we adapted the theory developed for multi-breed animal populations  procedure to estimate the additive genetic variance for synthetic wheat crosses with a hierarchical Bayesian model. The model we fitted involved a genetic structure based on the population from which the alleles that make up the cross originated  showing less genetic variability than T. turgidum (0.613) and Ae. tauschii (0.616). This finding agrees with the molecular results for genetic diversity of Ae. tauschii and its durum parents, even when compared with the variability present in the derived synthetic lines (T. turgidum-T. aestivum (0.327) and Ae. tauschii-T. aestivum populations (0.161).Partitioning the genotypic variability for Data set 1 revealed that the three wheat species had different additive genetic variances for grain yield, with T. turgidum—T. aestivum and Ae. tauschii- T. aestivum populations (0.327 vs 0.161) suggests that the differences in the allelic frequencies between T. turgidum—T. aestivum populations are higher than those between Ae. tauschii- T. aestivum populations. It is difficult to determine what the causes of these differences are because they may be related to genetic drift, to selection, or to a combination of both  and different when considering one-breed group (0.235) for BGLR and T. turgidum = 0.100 and Ae. tauschii = 0.118, and 0.235 when considering one-breed group for BGLR and 0.233 from Stan software.For Data set 2, which combined only the cross between A, and an additive genetic variance parameter, i.e., they are not “minimum variance.” Therefore, incorrectly specifying the variance parameters will have a negative impact on the estimation of the realized breeding values and prediction errors. This study showed the advantage of using a multi-breed model for assessing the additive variance in the different breeds and planning wheat breeding strategies accordingly. Although in this study we assumed a fully additive model, it should be noted that an important amount of genetic variance in wheat is epistatic.Given that the genetic variances of wheat populations may be different, it is relevant to take these differences into account while predicting the breeding values of synthetic lines and their parents. BLUP methodology is essentially a shrinking technique that requires modeling the genotypic means while accounting for the covariance among genetic effects. Under this methodology, breeding values are modeled as Gaussian distributed random variables with a covariance matrix that results from the product of a genetic relationship matrix, Of all unbiased linear predictors of genetic effects, BLUPs are the ones that have the lowest prediction errors under certain assumptions and with known dispersion parameters . The other one, with a simpler structure, includes only synthetics (Data set 2 with only S). The joint analysis of the SAWYT database that contains historic observations on the grain yield of a considerable number of genotypes and locations produced estimates of the seven variance components of the model  and derived synthetic CIMMYT lines  was 0.0121. The genetic parameters estimated for Data set 1 in the current research were higher than the values reported in the literature for the genotypic variance of wheat estimated using different databases. Ae. tauschii and T. turgidum parents and the hybrids produced by crossing, and the synthetic lines, for which there are no phenotypic observations. In addition, only by evaluating the segregating populations of the crosses of synthetic lines with T. aestivum can new and positive alleles be identified to improve grain yield in wheat  has been identified as being a very important component of the genotypic variability of grain yield of several crops (It is possible to treat a character in different environments as different traits and fit a multi-trait BLUP using correlations between environments. The factor analytic model could provide main environmental causes of G × E, only if factors and loadings are associated with some environmental variables like soil type, moisture availability, altitude, frost prevalence, precipitations, sun radiation, etc. However, the lack of connectedness between locations across years of these experimental data makes it unsuitable to attempt a more complex structure for the G × E effects. This would be possible with additional environmental information about the locations and years in order to model the covariance between environments.T. turgidum and Ae. tauschii were the greatest, whereas the additive variance of T. aestivum was less than half the size of the previous ones, a result that is consistent with previous reports pointing toward less genetic variability in commercial elite wheat varieties. These results are also evidence that synthetic hexaploid wheat is helpful for increasing the genetic diversity of cultivated bread wheat. The analyses of two different data sets comprising either synthetic derivatives of wheat or only synthetic wheat proved the suitability of the statistical model used in this study for accounting for complex genetic variance-covariance structures existing in multi-breed populations and their segregation populations, including breed groups with different ploidy levels. The model used in this study should offer useful results for wheat genetic resource conservation when making wide crosses, establishing correct pre-breeding strategies, and determining efficient wheat breeding schemes for accelerating genetic gains.Estimates of the genetic variance parameters, obtained under a hierarchical Bayesian approach, evidenced that genetic variability is heterogeneous among wheat populations. The variances of


Pogoniopsis schenckii Cogn. is a mycoheterotrophic orchid that can be used as a model to understand the influence of mycoheterotrophy at different stages of the reproductive cycle. We aimed to verify the presence of endophytic and epiphytic fungi at each stage of the reproductive process and investigated how the breeding system may relate to genetic structure and diversity of populations. In this study we performed anatomical and ultrastructural analyses of the reproductive organs, field tests to confirm the breeding system, and molecular analysis to assess genetic diversity and structure of populations.P. schenckii is autogamous, which may explain the low genetic diversity and high genetic structure in populations.During the development of the pollen grain, embryo sac and embryogenesis, no fungal infestation was observed. The presence of endophytic fungal hyphae was observed just within floral stems and indehiscent fruit. Beyond assuring the presence of fungus that promote seed germination, specific fungi hyphae in the fruit may affect other process, such as fruit ripening. As other mycoheterotrophic orchids, P. schenckii is autogamous, which may explain the low genetic diversity and high genetic structure in populations. Altogether, our findings provide important novel information about the mechanisms shaping ecology and evolution of fragmented populations of mycoheterotrophic plant.We discuss an interesting interaction: fungal hyphae in the indehiscent fruit. These fungal hyphae seem to play different roles inside fruit tissues, such as acting in the fruit maturation process and increasing the proximity between fungi and plant seeds even before dispersion occurs. As other mycoheterotrophic orchids, The online version contains supplementary material available at 10.1186/s12870-021-03118-y. Mycoheterotrophy is the parasitic relationship between plants and fungi in which the plants depend on the interaction with fungi to fulfill part of their nutritional needs . In presMycoheterotrophic plants exhibit numerous modifications. They are small, herbaceous, with reduced vegetative organs and, for most of their life cycle, remain underground . InfestaDue to high levels of specialization found in mycoheterotrophic species, most of which are related to reductions in morphological traits  and genoHow mycoheterotrophy can impact the reproduction of these species is still a debatable issue. Autogamy is, in fact, observed in many mycoheterotrophic plants –13. HowePogoniopsis is a Brazilian endemic genus composed of two mycoheterotrophic species. The reproductive data for the genus show the absence of floral rewards and reproduction by autogamy [P. schenckii Cogn. [Pogoniopsis may be used as a model to understand structural, ecological and evolutionary consequences of mycoheterotrophy on reproductive stages and dynamics of the species. Therefore, we answer the following questions: (a) What is the structural pattern of fungal colonization of the fruits of P. schenckii? (b) Does interaction with fungal hyphae occur during the anther, ovule and embryo development? (c) Is autogamy the predominant breeding system of this species, and to what extent limitations in seed and pollen movement shape the genetic structure in this rare and endemic orchid species? Finally, we discuss how fungal colonization may influence the development of reproductive organs in this plant and how the breeding system and pollination interactions may influence the genetic structure of this rare orchid.Orchidaceae, one of the most diverse families of angiosperms, accounts for about 235 mycoheterotrophic species , 24, 25)autogamy , howeverii Cogn. , differiii Cogn. , the genPogoniopsis schenckii presents tetrasporangiate anther , which present evident nucleus. The first phase of the meiotic division of the MiMc, originates a dyad of cells  in the populations ranged from 20 to 29. The expected (HE) and observed (HO) heterozygosity per population ranged from 0.428 to 0.66, and from 0.197 to 0.328, respectively. The inbreeding coefficient (f) ranged from 0.42 to 0.54  differentiation, with global FST and DST values of 0.233 and 0.312, respectively. The Mantel test was not significant (p = 0.163), demonstrating that populations do not show isolation-by-distance. We identified three distinct genetic clusters (K = 3) corresponding to the analyzed populations in the assignment test performed with MAVERICK program  between populations showed high and significant . The obtained data corroborates the low genetic variability that has been described in mycoheterotrophic orchids of Corallorhiza and Gastrodia species, in addition to high inbreeding coefficient for Gastrodia species [Breeding systems are the greatest determinants for genetic variation in plants , 75. For species –22. ThesP. schenckii, may result from loss of alleles, a consequence of genetic drift [P. schenckii presents low genetic variability, it can be deemed high when compared with other autogamous mycoheterotrophic species [Arabidopsis, researchers observed that mutations may occur at higher frequencies in autogamous lineages, increasing values of genetic diversity [P. schenckii. Evolution in species that perform self-pollination may differ from species that perform cross-pollination. Endogamy can reduce the genetic diversity of populations, but it can also slightly increase the likelihood of dominant mutations [Low genetic diversity in rare and typically small species, such as ic drift , 76. Alt species –22. Proxiversity , a proceutations , 79. TheFST = 0.233 and DST = 0.312), and the presence of three genetic groups, which may be explained by the high rate of inbreeding and low pollen flow between populations. This pattern is commonly observed in mycoheterotrophic orchids [Pogoniopsis schenckii presents indehiscent fruit, which damp off on the ground when ripe, subsequently beginning decomposition. Thus, the germination process and the establishment of protocorm probably take place near the mother plant. This may be another process that, when associated with autogamy, reinforces the genetic structure of each population.The obtained results show a strong population structure  1.0% in sodium cacodylate buffer for 12 h in the dark [In order to analyze the main changes that occur in cells during fruit development, fruits at different developmental stages were fixed in 2.5% glutaraldehyde in 0.2 M sodium cacodylate buffer, pH 7.25, for 24 h . Post-fithe dark . The matthe dark  and leadthe dark , then exn = 50 in Pirapitinga and Poço do Pito populations). To document the efficiency of natural pollination, 10 inflorescences with five open flowers were marked and not bagged (n = 50 in two populations). Flowers used in experimental pollination treatments were monitored until fruit maturation or senescence. Floral visitors were observed with a camera (Sony DCR SR21E). A total of 27 flowers were observed in 17 individuals for 168 h.To verify the occurrence of self-pollination, 10 inflorescences with five floral buds in pre-anthesis were marked, bagged, and monitored until the possible formation of fruits (n = 31 and Poço do Pito – n = 37), and one located in the municipality of São Lourenço da Serra, São Paulo – n = 11 (Supplementary file Fig. SA total of 79 individuals from three populations were sampled, two located in Parque Estadual Serra do Mar – Núcleo Santa Virgínia, São Paulo state (Pirapitinga – To quantify cloning occurrences in populations, three distinct methods were used following the recommendations of Meloni et al. : The ratFST and DST among populations, using the ARLEQUIN and GENODIVE [The genetic structure of the populations was estimated through pairwise GENODIVE  programsGENODIVE .Additional file 1: Figure S1. Longitudinal and transversal sections of anthers of Pogoniopsis schenckii. A. Overview of the tetraesporangiated anther biteca. Arrows indicate the microsporangia. B. Microspore mother cell. C. Dyads of microspores. D. Tetrad of microspores. E. Mature pollen grains, with two cells. F. Release of the mature pollen grain by rupture of the epidermis. G-I. Germination of the pollen grain and arrival of the pollen tube in the placental region. G. Detail of the germination of the pollen grain. H. Detail of the pollen tube reaching the placental region. I. Growth of the pollen tube along the column.; dmi= dyads of microspores; e= epidermis; gc= generative cell; mimc= microspore mother cell p= placental region; pg= pollen grain, pt= pollen tube; tm= tetrad of microspores; vc= vegetative cell. Scale bars: C-F= 20µm, B, H= 50µm, A,G,I= 100µmAdditional file 2: Figure S2. Longitudinal sections of ovules of Pogoniopsis schenckii. A. General view of the placenta with ovules in the beginning of differentiation. B. Megaspore mother cell. C. Megaspore mother cell during meiosis I. D. Megaspore dyad. E. Calazal megaspore expanding and degenerating micropylar megospores. F. Binucleated megagametophyte. G. Binucleated megagametophyte with nuclei entering the second cycle of mitosis. H. Megagametophyte with three visible nuclei. I. Mature megagametophyte. cm= calazal megaspore; dm= degenerating megaspore; ec= egg cell; md= megaspore dyad; mmc= megaspore mother cell; ngm= nuclei of megagametophyte; p= placental region; pn= polar nuclei; s= synergid. Scale bars: B-I=20µm, A=100µmAdditional file 3: Figure S3. Longitudinal sections of ovules and seeds of Pogoniopsisschenckii. A. Penetrated and non-penetrated synergids. B. Zygote. C. Embryo with two cells. D. Three cell embryo. E. Five cell embryo. F. Six cell embryo. G. Presence of starch as a reserve in the embryo evidenced by Lugol. H. Presence of proteins in the embryo evidenced by Xylidine. ac= apical cell; bc= basal cell; e= embryo; ec= egg cell; f=funiculus; ps= penetrated synergid; s= synergid; z= zygote. Scale bars: A-E,G= 20µm, F,I=50µmAdditional file 4: Figure S4. Development of the fruit of Pogoniopsisschenckii. A, Fruits in different stages of development, with detail of a sectioned fruit. B, Fruit at the beginning of ripening, with detail of a sectioned fruit. C, Ripe fruit. D, Detail of a sectioned ripe fruit. Scale bars= 1cm.Additional file 5: Figure S5. Genetic structure suggested by the Maverick software in Pogoniopsisschenckii based on eight nuclear microsatellite loci.Additional file 6: Figure S6. Map showing the populations sampled of Pogoniopsis schenckii. The distribution map was created using the ArcMap 10.7 software, intellectual property of Esri, used herein under license. In black, the population collected in São Lourenço da Serra, SP. In red populations collected at the Parque Estadual Serra do Mar, Núcleo Santa Virgínia, SP. TPi = Trilha da Pirapitinga; TPP = Trilha do Poço do Pito.Additional file 7