This review covers recent findings in the genomics of obsessive-compulsive disorder (OCD), obsessive-compulsive symptoms, and related traits from a dimensional perspective. We focus on discoveries stemming from technical and methodological advances of the past five years and present a synthesis of human genomics research on OCD. On balance, reviewed studies demonstrate that OCD is a dimensional trait with a highly polygenic architecture and genetic correlations to multiple, often comorbid psychiatric phenotypes. We discuss the phenotypic and genetic findings of these studies in the context of the dimensional framework, relying on a continuous phenotype definition, and contrast these observations with discoveries based on a categorical diagnostic framework, relying on a dichotomous case/control definition. Finally, we highlight gaps in knowledge and new directions for OCD genetics research. Obsessive-compulsive disorder (OCD) is a chronic, neuropsychiatric condition with an estimated lifetime prevalence between 1% and 3% . It is cGenetic investigations of OCD typically apply a conceptual framework that relies on either clinical diagnosis  i.e., D.Table 1DApproaches to identify genes associated with OCD typically take advantage of these frameworks and often attempt to either narrow the phenotypic definition to reduce heterogeneity or broaden the phenotypic definition to leverage observed genetic or clinical relationships between OCD and other traits. Methodologies aimed at narrowing the phenotypic definition may use a strict case definition or only include cases with clinically diagnosed OCD , 17, 18,Here we review genetic evidence obtained using approaches that employ both a diagnostic (case/control) and a dimensional (continuous phenotype) framework and discuss the implications for future genetic research of OCD. We only briefly address the genetic findings from studies using the diagnostic conceptual framework in OCD, as recent reviews have discussed these in depth –29. The Refers to the concept of phenotypic variation which lies along a continuum from no symptoms through subclinical symptoms to severe and impairing symptoms.This model assumes that there is an underlying distribution of genetic risk in the general population for OCD and other complex traits that contributes to varying levels of symptom expression, with clinically recognized disease at one end of the distribution .OCD is phenotypically heterogeneous, and different individuals can express distinct sets of characteristic symptoms. The different symptom subtypes may have diverse underlying biological disease mechanisms, depending on symptom characteristics, severity, etc. Creating subgroups of symptoms that commonly co-occur for genetic and other studies may decrease the underlying heterogeneity and facilitate gene-finding efforts.OCD shares certain characteristics, such as intrusive thoughts and/or repetitive behaviors with other related disorders such as hoarding disorder, body dysmorphic disorders, excoriation disorder, and trichotillomania. Based on these phenotypic similarities, these disorders are categorized as obsessive-compulsive and related disorders (OCRDs) in the DSM-5 .An approach used in genetics research to associate specific genetic variations with disease but cannot on its own specify which genes are causal. GWAS approaches usually search for single nucleotide polymorphisms (SNPs) across the entire genome that are consistently associated with the trait of interest. Traditionally, the allele frequencies of affected vs. unaffected individuals are compared but quantitative phenotypic data can also be analyzed .A cross-disorder GWAS aims to identify cross-phenotype genetic influences that transcend diagnostic boundaries by including genetic data from various disorders into one GWAS. This approach can identify SNPs with pleiotropic effects, meaning that they influence more than one distinct phenotypic trait or a common mechanism underlying more than one disorder.Heritability refers to the proportion of trait variation in a population that can be explained by variation genotypes in that population.Genetic correlation is the proportion of variance that two traits share due to genetic causes. It arises from the pleiotropic effects of genes on multiple traits or through linkage disequilibrium between distinct loci that each affect one trait. Traits can be either positively correlated (meaning that the underlying genetic variants contribute to increased risk of both traits), or negatively correlated (meaning that the underlying genetic variants contribute to increased risk of one trait and decreased risk of the other).A polygenic (risk) score is a value that summarizes the estimated effect of many genetic variants on an individual’s phenotype, typically calculated as a weighted sum of trait-associated alleles , 33.Genomic Structural Equation Modeling is a multivariate method that can be used to model the joint genetic architecture of complex traits, identify SNPs with effects on a common factor of cross-trait liability and identify SNPs that cause divergence between traits .OCD has a multifactorial etiology and results from a combination of genetic and environmental impacts across the lifespan , 35–37. Modern genomic methods demonstrated that the genetic contribution to OCD is primarily polygenic, meaning that hundreds or thousands of genetic variants each contribute a very small amount to the overall genetic predisposition to OCD , 53. TheWhile OCD patients vary widely with respect to a range of factors , symptom type has been most extensively discussed as a source of potential phenotypic and genetic heterogeneity. It is hypothesized that each symptom subtype dimension may be associated with a distinct set of underlying psychological and biological mechanisms, however, it remains unclear whether these clinical subtypes represent genetic subtypes as well , 6, 61. Several studies have used data reduction techniques such as factor analyses to identify symptom-specific OCD subtypes , 6, 15 aFamily and twin studies have reported mixed results regarding the heritability of specific OC symptom dimensions , 65–69. r = 0.56, and non-additive r = 1) and environmental factors (r = 0.43) [Longitudinal symptom stability provides another lens through which to examine the genetic and environmental contributions to OCD and OC symptoms. Multivariate analyses of the Padua Inventory Revised assessed in 2002 and again in 2008 in the population-based Netherlands Twin Register indicate that OC symptoms are longitudinally stable in adults . Longitu = 0.43) . Among c = 0.43) . These fA corollary of the liability threshold model previously described, which assumes an underlying normal distribution of genetic risk in the general population, is that it contributes to varying levels of symptom expression, from no symptoms to subclinical symptoms to severe and impairing symptoms see Fig. . Indeed,If this model is correct, including population-based samples of participants with some level of OCS in genetic studies would increase the available sample size and thus the power to detect genetic variants. However, this approach assumes that the OCS phenotype being used  indeed reflect the underlying genetic liability and hence correlate with OCD as clinically defined. But what is the evidence that this is true? In addition to determining whether OCS are in fact heritable, it is important to define the genetic relationships between the dimensional  and categorical  traits. There are two commonly used ways to determine if such a relationship exists. The first is to use polygenic risk score (PRS) analysis, which is a single value estimate of an individual’s SNP-based genetic liability in one trait or disease, to estimate the relative risk of having the second trait based on its SNP-based genetic liability profile. The second is genetic correlation analysis, which estimates the proportion of variance that two traits share based on their genetic similarity.p = 0.19, N = 5,018), to 10% in the Smit et al. sample  [p = 0.003, N = 6,931) [Twin studies indicate that, like OCD, subclinical OCS are heritable, with genetic factors accounting for 36–42% of its phenotypic variance , 76. Sim= 8,267) , and 14%= 6,931) , the latrG = 0.83, SE = 0.43, p = 0.073) [rG = 0.61, p = 0.017) [If OCD and OCS are indeed part of the same genetic and phenotypic continuum, one would expect to see significant genetic correlation between OCS and OCD. These relationships have been explored using a variety of methods including linkage disequilibrium regression score (LDSC) analysis. LDSC analysis allows the use of summary results in tests of genetic correlation (see Box). Burton et al.  reported= 0.073) , whereas= 0.017) .R² = 0.28%, p = 0.0045) [Polygenic risk score (PRS) (see Box) analyses performed in the three population samples discussed above provide an alternative approach to test the genetic correlation between subclinical OCS and clinical OCD. Using this method, PRS trained on genetic associations from OCS in the Burton et al.  sample w 0.0045) . This as 0.0045) , 75, 79 N = 650, R² = 2.28%, p = 0.002) [R² = 0.49%, FDR-adjusted p = 2.42 × 10−5) as well as the ‘contamination/cleaning’ dimension . An independent pathway analysis by Burton et al. [Only two GWAS studies to date have examined the unique and shared genetic architecture of OC symptom factors, one of which used data from clinical OCD patients , and the= 0.002) , and aft= 0.002)  they alsn et al.  showed tn et al. . In othen et al. , 78, whiThese studies, although not conclusive, do suggest that subthreshold obsessive-compulsive symptoms share genetic risk with OCD. However, even among individuals who endorse no OC symptoms, there may be appreciable genetic risk for OCD, suggesting statistical power for studies invoking a dimensional framework remains a concern Fig. . MoreoveThe heterogeneity, and perhaps also the dimensionality associated with OCD, can also be seen in the phenotypic overlap and comorbidity patterns with other psychiatric disorders, and in particular, with the Obsessive-Compulsive Related Disorders  , 90, mooN = 3494). All eight symptom dimensions were highly heritable in this sample . They found that TS was correlated with symmetry/exactness, aggressive obsessions, and fear of harm obsessions, anxiety disorders were correlated with contamination/cleaning symptoms, disruptive behavior disorders were associated with aggressive obsessions and correlated with disruptive behavior disorders, and ADHD was correlated with hoarding obsessions/compulsions and with aggressive obsessions. Other studies corroborate relationships between OCD symptom dimensions and psychiatric comorbidities, most consistently with aggressive obsessions and hoarding symptoms, which are associated with multiple psychiatric illnesses in several studies [For example, although genetic correlations between the OC dimensions and other psychiatric disorders have not yet been examined, some work has been done to assess the phenotypic correlations between these symptom subtypes and psychiatric illness. Hirschtritt et al  systemat studies –104. ThiTwin and family studies indicate that many of these conditions are not only highly comorbid, but are also heritable –108 and N = 7567 [N = 5293 [Few studies have examined the genetic relationships between the OCRDs, partly because several of these disorders were not recognized as distinct illnesses until their inclusion in the DSM-5 in 2013. However, one such study that used multivariate twin modeling to examine genetic relationships between quantitative symptom-based measures of the OCRDs identified two primary latent factors, one of which loaded significantly on all five OCRD phenotypes , but most strongly on OCD, HD, and BDD, while the other was specific to the body focused repetitive behavior (BFRB) disorders (TTM and ExD). The additive genetic variance was 63.3%  for the first (common) factor, and 73.7%  for the second, BFRB specific factor . Two twiN = 7567  and N = N = 5293 ) that exN = 5293  and 0.41N = 5293 .Of the non-OCRD psychiatric or neurodevelopmental disorders that have been examined in conjunction with OCD, AN and TS have the most consistent and strongest evidence for genetic correlations with OCD. The genetic correlation between OCD and AN is around 0.53 , 115, alThe possible shared genetic basis of OCD and other neuropsychiatric disorders has also recently been a focus of attention , 117. CrFactor analysis as implemented in genomic structural equation modeling (Genomic SEM) , which mrG = 0.37, SE = 0.21, p = 0.07, [rG = 0.28, SE = 0.20, p = 0.13 [Although OCD was originally classified as an anxiety disorder, and anxiety is a prominent symptom, OCD is now recognized as a disorder with biological and clinical characteristics that are distinct from anxiety disorders. Nevertheless, these disorders are highly comorbid, and thus there is continuing interest in determining whether they share a genetic etiology. Genetic studies of anxiety disorders are still in the early stages, and data on the correlation between the anxiety disorders and OCD are also limited. While the point estimates for genetic correlations between OCD and anxiety disorders , and PTp = 0.13 ) are higIn sum, while the degree and nature of the genetic relationship between OCD and related disorders are yet to be determined with precision, even these early results necessitate an open-minded approach to the nosology of OCD guided by genetics Fig. . CertainThere is now an emerging body of molecular evidence to suggest that OCD shares genetic features with many other psychiatric disorders and with subclinical OCS in the population. Nevertheless, it is clear that increased sample sizes and creative approaches to phenotypic data collection will be necessary to move the field forward. OCD and its related psychiatric disorders appear to vary along continuous, biobehavioral, and psychological dimensions, including, but not limited to, cognition and beliefs, habit and behavior, reward and reinforcement, error monitoring response inhibition, as well as neuroticism and emotion sensitivity. It has been suggested that genetic variants responsible for susceptibility to mental illness may not always line up with current diagnostic categories, and there are initiatives to identify shared dimensional phenotypes that “lie in between” diagnostic labels and risk genotypes –127. Dis


Gasterosteus aculeatus), ninespine stickleback (Pungitius pungitus), and tubesnout (Aulorhynchus flavidus). Populations were sampled from the southern and northern edges of each species’ range, to identify patterns associated with latitudinal changes in genetic diversity. Weak correlations in genetic diversity (FST and expected heterozygosity) and three different patterns in the genomic landscape were found among these species. Additionally, no candidate genes for convergent evolution were detected. This is a counterexample to the growing number of studies that have shown overlapping genetic patterns, demonstrating that genome scan comparisons can be noisy due to the effects of several interacting evolutionary forces.Comparing genome scans among species is a powerful approach for investigating the patterns left by evolutionary processes. In particular, this offers a way to detect candidate genes that drive convergent evolution. We compared genome scan results to investigate if patterns of genetic diversity and divergence are shared among divergent species within the stickleback order (Gasterosteiformes): the threespine stickleback ( Genome scans are popular tools for detecting shared evolutionary events among species. However, not all scans will show overlapping results. This article detects three different outcomes of genome scans, and few shared genetic patterns. Many statistics represent genetic diversity  is a good system for comparative genome scans, as several regions of the genome have been identified that are strongly associated with local adaptation in this species  and tubesnout (Aulorhychus flavidus), as an example of how comparisons of genome scan results can identify common genetic patterns.Threespine stickleback  and have yet to explore the patterns of genetic diversity. These species diverged approximately 50 mya  in sea water, the carcases were then preserved in 95% ethanol which was replaced after 24 h. The northern populations of both species and all ninespine samples were donated by collectors. Between 30 and 52 fish were collected per population  or low quantity (concentration <8 ng/μl) were re‐extracted. Any sample that failed three re‐extractions was removed. This quality check was repeated after pooling DNA samples (see below).DNA was extracted from a ~2‐mm clip of the pectoral fin of each fish using a Qiagen DNeasy Blood and Tissue Kit. The protocol was modified slightly to increase yield by washing the fins in dHIndividual DNA samples were pooled together by population before library preparation  and the genetic divergence (FST). FST was calculated for each species using the R package poolFstat .Genetic diversity was measured as two different values, the genetic diversity within a population . Any gene that had more observed FST outliers than the 999th quantile of this binomial distribution was considered a top candidate for local adaptation (using qbinom in R).Genes showing signatures of differentiation across the latitudinal gradient were identified for each species using a top‐candidate approach  using OMA , this should be a conservative approach.To compare population divergence among species, the average 2.7FST outliers in multiple species, however this approach may miss some true signals as it is very stringent . Patterns of FST in ninespines were extremely heterogenous to the point that no peaks could be identified, and H¯E was noticeably lower than the other two species  than within‐species comparisons . This study highlights how the complexities of evolutionary histories, such as genetic bottlenecks or gene flow from unsampled habitats, can complicate the comparisons of genome scans.Comparing the results of three genome scans we found few similarities in genomic patterns among species. Only the tubesnout and threespine had similar genome‐wide average 4.1FST scores typically cluster in several broad peaks in comparisons among freshwater and marine environments, with pronounced peaks around the Eda locus  than were found in this study (range Spearman’s ρ = −0.07–0.09). However, Manhattan plots of FST and genetic diversity also did not show any clear overlapping peaks or troughs ; Data curation (lead); Formal analysis (lead); Investigation ; Project administration (lead); Resources ; Visualization (lead); Writing – original draft (lead); Writing – review & editing . Qiushi Li: Methodology (supporting); Resources ; Supervision (supporting); Writing – review & editing . Dorothea Lindtke: Resources ; Supervision (supporting); Writing – review & editing (supporting). Samuel Yeaman: Conceptualization ; Funding acquisition (lead); Investigation ; Project administration (supporting); Supervision (lead); Writing – review & editing .Supplementary MaterialClick here for additional data file.


Vernaccia is a white grape mostly used to produce a distinct wine protected by the controlled designation of origin (DOC) recognition. It is very susceptible to fungal disease, and it is subjected to a defined management protocol in the field. Winemaking could influence pesticide residues through different mechanisms. This work investigated the influence on pesticide residues of the winemaking process at the industrial level of the wine Vernaccia di Oristano. Thirty-five samples of grapes, two musts, and two vines (both liquid fraction and pellets) were analyzed by using a validated multiresidue LC-MS/MS (Liquid Chromatography with tandem mass spectrometry) method. Data obtained showed the presence in grapes juice of 16 pesticides  with mandipropamid and mepanipyrim, the only ones with values higher than their MRL (maximum residue level). Pesticide residues decrease in must was related to the dilution effect due to mixing the grape samples. However, pellets analysis also confirmed the high affinity of pesticides for the suspended material (fenhexamid), whereas the increase in wine to a re-solubilization process from the lees during the fermentation step. The present paper highlighted the effectiveness of the technological process of winemaking to decrease pesticide residues compared to the raw material. Vernaccia di Oristano is a Sardinian  white grape cultivar used to produces different wines, which follow the tastes and aromas of Sherry, where almond blossoms with bitter tastes are present . It is aUncinula necator and Plasmopara viticola, which are responsible for the white disease and grape blight, respectively [The harvest is usually carried out from the second half of September until the first 10 days of October, and grapes must have a sugar content of at least 21–22° Babo. The vinification of Vernaccia is a typical white wine vinification with soft pressing; the obtained juice is sent to the barrel for fermentation. The decanting step (at least two) and the necessary clarifications are then carried out. During March following the harvest, the wine is transferred to chestnut or oak barrels with thin slats, left full for about 20% of their capacity. The biological refinement process follows this by the film-forming yeasts (flor); refinement continues with ageing in the oxidative phase. The major adversities of the cultivation of Vernaccia grapes are represented by the fungi ectively . These dLobesia botrana, Planococcus ficus, and Tropinota squalida [Moreover, fly attack by squalida  can be dsqualida . Howeversqualida ,8,9,10. squalida ,12.This paper reports a comprehensive study on pesticide residues contamination before and after the industrial processing of grapes collected in the field to produce Vernaccia wine. The analytical determination of pesticide residues was carried out using a validated UHPLC-MS/MS multiresidues method integrated with a modified QuEChERS extraction step .Trials were carried out in 300 ha located in the province of Oristano in Sardinia in August 2019. Thirty-five fields were selected from those that usually supplied grapes to the Industry. Raw grapes samples were harvested from each selected field with a potential alcohol content >15% and represented all field production. After that, samples were brought to the winery on two different days and processed at the industrial level. Grapes from fields 1 to 23 were merged in the fermentation vat V30, whereas grapes 24–35 were merged in the vat V31; each vat provided one wine W30 and W31. Briefly, the grapes were destemmed via a mechanical device that works like a cylindrical coin counter that rotates on its side; holes were big enough for grapes to fall through for collection, whereas the stems remain inside and are collected separately. Horizontal bladder presses allow a soft and gently pressing of grapes which keeps the aroma intact. Grape berries are loaded into the bladder, which inflates and presses against the wall of the tube. Over time, the pressure increases, and the juice is separated from skins and seeds and set aside. Fermentation is carried at 20 °C to protect fruit aromas. Ten random aliquots of 1 kg from each harvest were merged, destemmed and homogenized. After that, three grape juice samples (1L) were collected and used for pesticide analysis. Thirty-five samples of grape juice, two merged musts, and two wines were collected and transported to the laboratory. Field treatments followed the integrated production strategy set in Sardinia for Vernaccia cultivar. At the end of white wine fermentation, the temperature is dropped to a respectably chilly level, and the dead yeasts settle to the bottom of the tank. Specialized technicians supervised plant protection plans to use, among the authorized pesticides, those with the shortest pre-harvest interval, lowest toxicity, and minimum environmental persistence .2O2 was reagent grade , NH4HCO2 solution 5 M (0.315 g mL−1) . 4 g MgSO4, 4.1 g NaCl, 1 g C6H5Na3O7 * 2 H2O, 0.5 g C6H8Na2O8  and 150 mg PSA, 900 mg MgSO4  were used for the QuEChERS extraction step [Analytical standards (≥99.5% purity) were bought from Dr Ehrenstorfer  . Acetonin 5 M 0.3 g mL−1 (−1) in ACN. Working solutions were prepared daily by diluting the stock solutions with the eluent mixture (e.m.) at T = 0.MilliQ water from a Millipore purification system  was used with a conductivity below 18.2 MΩ. Stock standard solutions of pesticides were prepared at ~1000 mg Lg and 10 °C . The liquid phase was then separated from the pellet. 5 mL of sample and processed according to Corrias et al. (2020) [Samples of grape juice, must, and wine were collected directly from the industrial processing plant, brought to the laboratory and subjected to the analytical processing step. Twenty mL of each homogenized sample were put in a 50 mL test tube and centrifuged for 15 min at 3154× . (2020) . The pel. (2020)  (Table 1n = 18). Six blank control samples for each investigated matrix were spiked with a mixed multiresidue standard at 3 × LOQ and analyzed in one day for intraday repeatability , and two samples in six separate days (n = 36) were used for reproducibility (RSDwR). Each sample belonged to an independent experiment. The instrumental sequence was conducted according to SANTE indications [2) was above 0.990. The expanded measurement uncertainty (U), was calculated with a level of confidence of 95% using the equationsAn earlier validated analytical method was adapted to grapes, must and wine . For recications . The anaThe instrumental LOD and LOQ were calculated as three and ten times the signal to noise ratio (S/N) .The UHPLC-MS/MS method allowed to investigate 115 pesticide residues in grape juice, must and wine, of which 64 authorized in wine viticulture .2) ranging from 0.9955 to 1.0000 and RSD% max 2.08%. Linearity was above the condition set for method validation   data ranand wine . The insand wine .Thirty-five samples were collected from the fields representing 39,400 kg; the grape yield was 76%. Therefore, assuming that all pesticide residues in the grapes were transferred in the must, it should be reasonable to increase the concentration of pesticide residues by a factor of 1.3 in the wine.The analysis of the grape juice from fields 1–23 allowed the identification of 16 pesticides among the 115 searched with the above MRM method .The distribution among samples was uneven, with a frequency (F) of pesticide ranging from 1 to 23 in the worst case. Mandipropamid (23F) and fenhexamid (22F) were the most represented, followed by azinphos ethyl and mepanipyrim (16F), acephate, formetanate, pymetrozine, and carbendazim (13F), zoxamide, emamectine benzoate, and tau-fluvalinate (10F) .−1) and mepanipyrim , which showed values from 2.99 to 6.60 mg kg−1, and of 2.36 ± 1.15 mg kg−1, respectively.In all cases, the values found in grapes were below the limit of quantification (LOQ) except for mandipropamid, fenhexamid, mepanipyrim, iprodione, zoxamide, spinosad, and etofenprox. The pesticides accounting for a total residue >LOQ, showed values significantly lower than the MRL set for wine grapes, except for mandipropamid   were the most represented, followed by zoxamide, spinosad, emamectine benzoate, and tau-fluvalinate (5F), any residues of iprovalicarb and etofenprox was detected in this batch. Only eight pesticides showed residue values above the LOQ of the method but consistently below the MRL set for wine grapes. Mandipropamid showed three samples with values above the MRL (4.88–10.56 mg kg−1) .−1 ± RSD%) and zoxamide (0.02 ± 2.06 mg kg−1 ± RSD%) in V30 (−1 ± RSD%) zoxamide and emamectin benzoate (max 0.02 mg kg−1) in V31, were the only pesticide quantifiable  were lower than those found in the single grapes juice samples and below the instrumental LOQ. Mandipropamid (1.05 ± 0.61 mg kg) in V30 , and mantifiable . The dec−1 ± RSD% in the grapes, 0.34 ± 8.32 in the must and 0.02 ± 0.10 mg L−1 ± RSD% in the wine. The residues detected in the pellet showed high variability among the different matrices. Mandipropamid was below LOQ in the grapes pellet while showed increasing values passing from must to wine; a similar trend was detected for fenhexamid, zoxamide, spinosad, emamectin benzoate, and tau-fluvalinate in both wines and iprodione in V30-W30 , temperature (thermodegradation), plant activity (enzymatic degradation), and co-distillation during plant physiological respiration ,23. FoodAccording to EFSA (2018) , the pesAll pesticide showed a PF1 < 1 indicating a decrease of the residues during the processing, whereas they showed a PF2 > 1 confirming the hydroalcoholic media ability to extract pesticide from the lees .Among the 16 pesticides found in grape juice samples, must, and wine, eight were not allowed in wine grapes in the EU . HoweverThis paper reported the behavior of pesticide residues from field management of Vernaccia vineyards to produce white wine at the industrial level. The LC-MS/MS method was adapted to analyze 115 pesticide residues in grapes, must, and wine. The LOQ and LOD were suitable to quantify pesticide residues also in trace in the different matrices. Grapes showed 16 pesticides with values below the MRL; however, some samples showed values of mandipropamid 6 times higher than the MRL set for grapes and one time for mepanipyrim. The corresponding must showed a significant decrease of the residue, related to a dilution effect, while the wine showed a similar residue of the must, indicating no adsorbing effect by the lees. On the contrary, Fenhexamid confirmed its attitude to be adsorbed by the lees and the suspended particulate. Moreover, in the grapes, we also found eight active ingredients not allowed in the EU, below the LOD in the wine.The present paper highlighted the effectiveness of the technological process of winemaking to decrease pesticide residues compared to the raw material. Moreover, even if vineyards are not conduced in good agriculture practice (GAP), the technological process could reduce pesticide residues. Some pesticide can be resolubilized in the hydroalcoholic solution from the particulate, and their use should be avoided before harvest.


Leptoglossus zonatus, is widely distributed in the Western Hemisphere. In California, two lineages (strains) occur. One lineage is known from California, and the second is found in California and Brazil. Although this species has been in California since 1900, it has become a pest in almonds in the last decade. It is possible that a cryptic species or strain has been introduced. This study investigated the distribution of the two lineages (strains) of L. zonatus in the Western Hemisphere. Specimens from the Leptoglossus collection in the national insect collection in Mexico were used to extract DNA and sequence the mitochondrial DNA cytochrome oxidase I gene. These sequences were combined with others in Genbank from California and South America to determine the strain distributions. The first strain occurred in California and Mexico, while the second was widespread from California into South America. When all samples were combined, there was overall low genetic diversity. The small number of genetic types (haplotypes), the range expansion, and the economic pest status of L. zonatus in California, suggest this insect is a potentially invasive insect pest.The leaffooted plant bug, Leptoglossus zonatus  (Hemiptera: Coreidae) is polyphagous and widely distributed in the Western Hemisphere. Although it has been recorded in California since around 1900, it has become a more common pest in almonds in the last decade. Other studies have shown that an established insect can become a pest when a new genotype is introduced. This study investigated the distribution of two lineages (strains) of L. zonatus in the Western Hemisphere. Specimens from the Leptoglossus collection in the national insect collection in Mexico were used to extract DNA and sequence the mitochondrial DNA cytochrome oxidase I (mtDNA COI) gene, for use in population genetic and phylogenetic analyses. New sequences from Mexico, Central and South America were combined with those available in GenBank, from California and Brazil. Two lineages (strains) of L. zonatus were uncovered. One lineage occurs in California, Mexico and Ecuador. The second lineage is more widespread and found in California, Mexico, Guatemala, Nicaragua, Bolivia and Brazil. The haplotype number and diversity, and nucleotide diversity, were found for samples from California, Mexico, and Brazil, for the two lineages, and for all 118 sequences combined. All sequences combined produced five haplotypes, and a haplotype diversity of 0.54. California and Brazil had 3 haplotypes each, with one haplotype shared . Haplotype diversity in California and in Brazil were 0.526 and 0.505, respectively. A haplotype network found that one haplotype was most abundant and widespread. The small number of haplotypes, a range expansion, and economic pest status of L. zonatus in California, all contribute to this insect being a potentially invasive insect pest.The leaffooted plant bug,  Leptoglossus species (Guérin-Méneville) (Hemiptera: Coreidae), also known as leaffooted plant bugs, are endemic to the Western Hemisphere with at least 61 species recorded , in Mexico there were 15 collection sites from 12 sampling years with the widespread lineage predominant. It is possible that if larger sample numbers were available from these collection sites, higher haplotype diversity could have been detected. Another possibility is that the widespread lineage is replacing the more restricted lineage. This would need to be investigated with older museum specimens.One interesting finding is that the two L. neovexillatus was included due to its morphologically similar to L. zonatus; this species formed another lineage 13% divergent. This group consisted of two L. neovexillatus specimens from Bolivia and several GenBank sequences from insects identified as L. zonatus from Brazil. L. neovexillatus is recorded from Argentina, Bolivia, Brazil, Paraguay, Peru, and Uruguay [L. zonatus. The L. neovexillatus from Brazil appeared sufficiently morphologically similar enough to be mistakenly grouped with other L. zonatus from Brazil, yet genetically those specimens were quite genetically divergent. The L. zonatus group could benefit from a study of genetic distance among members to examine evolutionary relationship and to compare with existing taxonomic studies [ Uruguay . This sp studies ,21.L. zonatus in this study. While the precise region of origin and genetic diversity is not known for L. zonatus, the number of haplotypes found can be compared to those obtained for other Leptoglossus species. Leptoglossus clypealis Heidemann in California was genotyped from three sites within 100 km in the Central Valley of California. Among the 20 L. clypealis genotyped using mtDNA COI, there were 17 haplotypes with a haplotype diversity of 0.979, suggesting that California is a native region for this species [L. occidentalis Heidemann, has been introduced from North America into Europe. Prior to arriving in Italy, L. occidentalis slowly spread from the Western United States through the Midwest and into the Eastern United States [Genetic diversity can help determine where a species may be native, introduced, or invasive. For example, the number of haplotypes for a species can be compared between regions where it is native or suspected to be introduced. There was a low haplotype (5) and moderate genetic diversity for  species . Anotherd States ,41. Usind States .Halyomorpha halys Stal (Hemiptera: Pentatomidae), the brown marmorated stink bug (BMSB). The BMSB is native in China and Japan where more than 25 haplotypes were observed, while only five were observed in introduced areas including in the US [Lygus hesperus (Knight) from its endemic region of the Western US [When an insect is introduced on another continent, a range expansion can more easily be observed, such as with n the US ; in Chinn the US , as werestern US .Leptoglossus species occur in South America [L. zonatus genetic diversity from that region could uncover more haplotypes than found in this study, and potentially find a region where there is higher genetic and haplotype diversity for this species.Sample sizes genotyped from California, Mexico and Brazil, were sufficient to detect genetic variability yet low genetic and haplotype diversity was found . Some sa America ,2,21. AnL. zonatus may have been moved from its center of diversity inadvertently by humans, as is the case with other insects [L. zonatus has a wide distribution through much of this species’ known range, and the full distribution of the other lineage is unknown. To date, the five haplotypes uncovered are a relatively small number, and a higher number is expected in the insect’s center of origin. The introductions into Louisiana and Florida in the last few decades and the expansion into the northern Central Valley of California demonstrates the insect is expanding its range. Invasive species can be defined as those which are introduced, may transmit disease, cause economic damage, or outcompete native organisms [L. zonatus has become more abundant, expanded its range, and it can transmit plant pathogens, it is not known to kill trees directly. It does however cause economic damage in almonds, and is one of the most potentially damaging insect pests in almonds after navel orangeworm [L. zonatus as a plant pest may outweigh the benefits of this insect in California, but would need to be determined [ insects . One haprganisms . While Langeworm . Recent termined . There a


ZEP1 gene, which encodes the central component of the meiotic synaptonemal complex (SC), in a hybrid rice using CRISPR/Cas9. The null mutants exhibited absolute male sterility but maintained nearly unaffected female fertility. By pollinating the zep1 null mutants with pollen from indica rice variety 93-11, we successfully conducted genetic analysis and found that genetic recombination frequency was greatly increased and genetic interference was completely eliminated in the absence of ZEP1. The findings provided direct evidence to support the controversial hypothesis that SC is involved in mediating interference. Additionally, the remained female fertility of the null mutants makes it possible to break linkage drag. Our study provides a potential approach to increase genetic diversity and fully eliminate genetic interference in rice breeding.Manipulation of the distribution and frequency of meiotic recombination events to increase genetic diversity and disrupting genetic interference are long-standing goals in crop breeding. However, attenuation of genetic interference is usually accompanied by a reduction in recombination frequency and subsequent loss of plant fertility. In the present study, we generated null mutants of the  Plant breeding aims to develop superior varieties to suit the needs of farmers and consumers . HoweverZIP1, which was identified in budding yeast G, the TF protein gene in Drosophila, causes the loss of all COs (syp-1 mutants in the roundworm Caenorhabditis elegans exhibit a severe reduction in COs (SYP-1 by RNA interference (RNAi) increases the CO frequency , maize (Zea mays), wheat (Triticum aestivum), and barley  led to reduced CO frequency  is a meiosis-specific structure involved in CO formation and chromosome segregation . SC asseng yeast . zip1 muappeared . Mutatio all COs . Unexpecn in COs , while krequency . The ZIPrequency . In conttype WT; .zyp1 have been generated in Arabidopsis thaliana, and cytological and genetic analysis reveals that ZYP1 is required for CO interference  inhibits the formation of other COs nearby on the same chromosome pair, imparts a remarkable level of regulation of the number and distribution of COs per chromosome in eukaryotes . Thereforference . Howeverjaponica male-sterile line, and the paternal C84, an indica-japonica intermediate-type line, as the acceptor and used the CRISPR/Cas9 technology to generate zep1 null mutants. By genetic analysis, we revealed that genetic interference is completely eliminated in the absence of ZEP1, indicating an important role of SC in mediating interference in rice. In addition, the way of generating zep1 null mutants with increased genetic recombination frequency and eliminated genetic interference is a promising approach to increase genetic diversity in rice breeding.In the present study, we selected the inter-subspecific hybrid rice variety Chunyou84 (CY84), an elite inter-subspecific hybrid rice from a cross between the maternal Chunjiang 16 A (16 A), a ZEP1 was constructed by the isocaudomer ligation method, as described in ZEP1 g++/ZEP1 g-- oligonucleotides  was assembled into the pC1300-Actin:Cas9 binary vector (digested with KpnI and BamHI) to obtain the vector pC1300-Actin:Cas9-sgRNAZEP1 for generation of zep1 null mutants.The CRISPR-Cas9 vector for complete knockout of leotides  were ligActin:Cas9-sgRNAZEP1 was introduced into Agrobacterium tumefaciens (Strain EHA105). The Agrobacterium was then transformed into calluses derived from CY84 to generate transgenic lines. Fifteen independent transgenic plants of pC1300-Actin:Cas9-sgRNAZEP1 were obtained and grown in summer in the transgenic paddy fields of the China National Rice Research Institute in Hangzhou, China.The binary vector pC1300-via the cetyltrimethylammonium bromide (CTAB) method. PCR was performed with KOD FX DNA polymerase  to amplify the fragments surrounding ZEP1 target site. Primers ZEP1 F and ZEP1 R are listed in Genomic DNA of transgenic plants was extracted from approximately 50mg fresh leaf tissue Panicles at meiosis stage were harvested and fixed in Carnoy’s solution  for more than 24h at room temperature. Microsporocytes were squashed on a slide with a needle and covered with a coverslip. Then, slides were frozen in liquid nitrogen, and the coverslips were removed rapidly with a blade. Chromosomes were stained with 4′,6-diamidino-2-phenylindole (DAPI) in an antifade solution  as described in . FluoresFresh young rice panicles were fixed in 4% (w/v) paraformaldehyde for 45min at room temperature. Anthers at proper meiosis stage were squashed with a needle in PBS solution and covered with a coverslip. After soaking in liquid nitrogen and removing the coverslip, slides were then incubated in a humid chamber at 37°C for 4h with anti-REC8 (Mouse) and anti-ZEP1 (rabbit) polyclonal antibodies . After three rounds of washing in PBS, Texas red-conjugated goat anti-mouse antibody and fluorescein isothiocyanate-conjugated sheep anti-rabbit antibody  were added to the slides. The chromosomes were counterstained with DAPI in an antifade solution . Fluorescence microscopy was performed as described above.Single nucleotide polymorphisms (SNPs) markers were developed on the basis of the whole-genome sequences of C84 and chunjiang16A . The priThe analysis of interference strength was described in zep1-KO plants were crossed with the indica variety 93-11 to generate the segregation population. The hybrid seeds were soaked in deionized water at 37°C in the dark for 2days and then transferred to a net floating on deionized water for further 5days. The seedlings were cultured in a half-strength Kimura B nutrient solution  and  pH 5.4; . The genZEP1 has a full length of 9.478-kbp, distributed in 21 exons and 20 introns and encodes a transverse filament protein containing 869 amino acids /CRISPR-associated protein 9 (Cas9) system to edit ZEP1 and chose the 19th to the 38th nucleotides in the first exon near the initiation codon as our target site  and five biallelic mutations  lined up on the equatorial plate at metaphase I , respectively  and zep1-KO lines with the indica rice variety 93-11 and then examined the genetic recombination events of WT×93-11 and zep1-KO × 93-11 F1 individuals via genotyping. To this end, we developed 10 nearly uniformly distributed single nucleotide polymorphism (SNP) markers along each of chromosomes 1 and 8  based on a previous method (zep1-KO mutants exhibited a decreased frequency of zero (8%), single (25%), and double (28%) COs but a higher frequency of triple (20%), quadruple (14%), quintuple (3%), sextuple (1%), and septuple (1%) COs  COs . Similarectively .zep1-KO and WT plants. The average number of COs per chromatid on chromosomes 1 and 8 in WT plants was 1.40 and 1.06, respectively, while that of zep1-KO mutants was significantly higher, with 2.33 and 1.57 COs per chromatid on chromosomes 1 and 8, respectively  equal to 1 is indicative of complete interference, while an I equal to zero is indicative of an absence of interference (I) in eight adjacent interval pairs of chromosome 1 ranged from 0.37 to 1.00, indicating the existence of strong genetic interference between neighboring COs. By contrast, the zep1-KO mutants showed reduced I for those interval pairs, with I values of −0.18 to 0.14  are assembled prior to SC and their proper position in the absence of SC formation demonstrated an aspect of interference that is independent of synapsis  can be found in the article/KW managed the project. CL, YC, YH, GD, XW, TS, JL, and MW performed the experiments. YH, QL, and CL analyzed the data. CL and YH wrote the manuscript. KW and ZC revised the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China (32025028 and U20A2030), Central Public-interest Scientific Institution Basal Research Fund (Y2020XK17), the Agricultural Science and Technology Innovation Program (CAAS-ZDRW202001), and China National Rice Research Institute Key Research and Development Project (CNRRI-2020-01).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Mycobacterium avium subsp. paratuberculosis (MAP) infection by a newly standardized Mce-truncated ELISA (Mt-ELISA) kit for the detection of antibodies against MAP. Four diagnostic applied tests were evaluated including Indigenous plate-ELISA (IP-ELISA), Mt-ELISA, fecal Polymerase Chain Reaction (f-PCR) and fecal culture (FC).This study aimed to screen the sera of goats and sheep from flocks suspected of Assuming the absence of a gold standard, latent-class models in a Bayesian framework were used to estimate the diagnostic accuracy of the four tests for MAP.Mt-ELISA had higher Sensitivity (Se) in sheep : 0.43–0.95), while IP-ELISA recorded the highest Se in goats as 0.83 . The f-PCR Se estimate slightly differed between species , while the Se of FC was similar between species . The specificity estimates for all tests were high, close to unity, and similar between species.Overall, the results showed that the Mt-ELISA method can be used for MAP detection in small ruminants’ flocks.The online version contains supplementary material available at 10.1186/s12917-022-03141-7. Mycobacterium avium subsp. paratuberculosis (MAP) , while the same holds for goats .Using multiple detection tests, 28 (22%) goats and sheep samples were positive for MAP infection cumulatively in the two antigen tests. Using the two antibody detection tests, 36 (28.4%) and 46 (36.2%) serum samples were positive in Mt-ELISA and IP-ELISA, respectively. However, two tests together detected 55 (43.3%) samples positive.The posterior medians and 95% probability intervals (95% PIs) for all parameters of interest for both species are summarized in Table The Sp estimates for all tests were high, close to unity, and similar between species. Specifically, the IP-ELISA Sp estimates were 0.92 (0.85–0.97) in sheep and 0.95 (0.9–0.98) in goats and, the Mt-ELISA Sp estimates were.96 (0.91–0.98) in sheep and, 0.95 (0.9–0.98) in goats. The f-PCR and FC tests recorded slightly higher Sp estimates [f-PCR: 0.98 (0.94–1) in sheep and, 0.98 (0.93–1) in goats; FC: 0.98 (0.95–1) in sheep and, 0.99 (0.96–1) in goats.As indicated by the posterior medians and 95% PIs for the covariance terms conditional dependence existed between IP-ELISA and Mt-ELISA and between f-PCR and FC.The results of uniform, uninformative prior distributions were unstable, even after restricting the parameter space; thus, they were not reported here. The results unrestricting the parameter space for the covariance terms are displayed in Supplementary Table Johne’s disease (JD) inflicts huge economic losses to animal productivity and revealed the potential risk to human populations, as infected animals could shed huge quantities of causative agents in their feces, colostrum, and milk , 36. SevStudies are few on the diagnosis of JD prevailing in sheep and goat flocks and herds. Hendrick et al. (2005) reported moderate agreement between milk and serum ELISA, but milk ELISA results had a higher agreement with culture than serum ELISA . Also, APillai and Jayarao (2002) showed high Se of direct PCR with a detection limit of 10–100 CFU/ml pertaining to the use of large amounts of milk sample . In our Of the 127 animals screened, 18 (14.2%) were positive by FC and 49 (38.5%) were sero-positive with Mt-ELISA. Twelve out of 18 FC-positive animals (70%) could be detected by Mt-ELISA and only six out of 18 FC-positive animals (30.0%) could not be detected by Mt-ELISA. Also, Mt-ELISA has rated some of the animals as positive, whereas FC missed these animals for several reasons. These results may be attributed to the significant loss of viable MAP during HPC decontamination, the presence of low shedder animals or the disease status of each animal tested. Of the 49 sero-positives animals, only 12 (33.3%) were culture-positive. Lower detection in culture is due to lower Se of FC. Whereas due to higher Se, Mt and IP-ELISA showed though higher but true prevalence of MAP in the herds. Similarly Muskens et al. (2003), also showed that only low percentage of sero-positive animals (64 (17.3%) of 371) were confirmed as MAP shedders by FC . In the Sps of the applied methods to account for the sparsity of the observed data .This study was approved by the Animal Ethics Committee (AECs) of School of Veterinary Medicine, Shiraz University (permit: 94GCU6M163973) and all the animal experiments were performed with our institutional guidelines and regulations (dated 20 September 2013) and ARRIVE guidelines for reporting animal research as much as possible , fecal PCR (f-PCR), Mce-truncated-ELISA (Mt-ELISA) and IP-ELISA was performed in all sampled animals.Fecal samples (4–6 pellets) were collected by gloved hands directly from the rectum and blood samples (~ 5 ml) were collected from the jugular veins of each animal. Feces and blood samples were transported on ice directly to the bacteriology laboratory of the School of Veterinary Medicine of the Shiraz University, Iran. In the laboratory, fecal samples were processed freshly for MAP culture and f-PCR technique and blood samples are allowed to clot in tubes and sera were collected by centrifugation and were used for ELISA testing. All DNA samples and sera stored at − 70 °C prior to testing.900-PCR was performed to confirm the MAP colonies.FC was performed using Herrold’s egg yolk (HEY) agar as per Singh et al., 2007a . Approxi900 element, an insertion sequence unique to MAP as per Millar et al. (1996) [Taq Polymerase (5 U/μl), MgCl2 (25.0 mM), dNTPs (2.0 mM), Buffer 10X). PCR reactions were performed  and included an initial activation step at 95 °C for 10 min followed by 36 cycles of a 94 °C for 30 s, 58 °C for 20 s and 72 °C for 30 s. Fecal samples giving positive amplification for 413 bp product were considered as positive for MAP bacilli.MAP genomic DNA from feces was extracted by chemical lysis followed by chloroform; Isoamyl alcohol purification. Purity and concentration of isolated DNA were determined Spectro-photometrically. The isolated DNA from each fecal sample was subjected to fecal-PCR (f-PCR) using MAP specific primers (P90/P91) targeting the IS. (1996) . BrieflyMt-ELISA was performed to quantify titer of the antibody in serum samples according to previously described by Hemati et al. . BrieflyIP-ELISA kit, based on MAP semi-purified protoplasmic antigen (sPPA) prepared from the characterized strain (S 5) ‘Indian Bison Type’ bio-type of MAP of goat origin, was used as the manufacturer’s instructions  . BrieflyIn this study the STARD-BLCM reporting guidelines on the design, conduct and results of diagnostic accuracy studies that use BLCMs were adhered . The STAIn a Bayesian Latent Class analysis, the true infection status is considered latent i.e., hidden and depends on the condition/biomarker that the tests under evaluation target. The two ELISAs  measure the host’s immune response . On the other hand, both f-PCR and FC detect the infection agent itself or its products/debris. Therefore, “infection” means presence and persistence of MAP long enough to produce a detectable humoral immune response and sufficient shedding in feces. This definition of “infection” is similar with the one described in Nielsen et al. (2002) .Se and the Sp of each test remain constant across all (sub) populations (b) all tests are conditionally independent of each other given the disease status and (c) a distinct difference between the (sub) populations’ true prevalence exists [In the absence of a gold standard, latent-class models  in a Baye exists ). Our see exists . Therefoe exists . On the e exists . Furthere exists . Thus, aBayesian inference allows the estimation of the posterior distribution, which is the product of the prior information and the likelihood.y are the counts of various test combinations, Q = {1,2} the dichotomized test result , n the population size and p the probability of observing each test combination, with the covariance terms introduced to adjust for the conditional dependence between IP-ELISA & Mt-ELISA and f-PCR & FC.The model was constructed based on the assumption that the various test combinations follow the multinomial distribution. That is,Ses, four Sps, four covariance terms and one pi), while the degrees of freedom offered by the data are fifteen (15). Therefore, identifiability criteria are met and a uniform, uninformative Beta prior distribution Be  can be adopted for all parameters of interest. However, informative prior distributions for the Sps of the applied methods were used, to account for the sparsity of the observed data (i.e. zero cell observations – see Table The number of parameters to be estimated are thirteen 13) for each species  prior distributions, to assess the influence of prior information. Further, to validate the conditional dependence assumption between IP-ELISA & Mt-ELISA and f-PCR & FC the parameter space was unrestricted for the covariance terms, to monitor if negative values are recorded. Model selection was based on the DIC (Deviance Information Criterion) dialog box in OpenBUGS environment. The model with the smallest DIC is the model that best fits the data i.e., the model that would best predict a replicate dataset of the same structure as the currently observed .Additional file 1: Supplementary Table 1. Posterior medians and 95% probability intervals (PrIs) for the Sensitivity (Se) and the Specificity (Sp) of IP-ELISA, Mt-ELISA, f-PCR, Culture.


From this dataset, the phylogenetic relationships and biogeographic history of O. rufipogon and O. sativa were reconstructed. Our results revealed two major maternal lineages across the two species, which further diverged into nine well supported genetic clusters. Among them, the Or-wj-I/II/III and Or-wi-I/II genetic clusters were shared with cultivated (percentage for each cluster ranging 54.9%∼99.3%) and wild rice accessions. Molecular dating, phylogeographic analyses and reconstruction of population historical dynamics indicated an earlier origin of the Or-wj-I/II genetic clusters from East Asian with at least two population expansions, and later origins of other genetic clusters from multiple regions with one or more population expansions. These results supported a single origin of japonica rice (mainly in Or-wj-I/II) and multiple origins of indica rice  for the history of rice domestication. The massive plastomic data set presented here provides an important resource for understanding the history and evolution of rice domestication as well as a genomic resources for use in future breeding and conservation efforts.The plastid is an essential organelle in autotrophic plant cells, descending from free-living cyanobacteria and acquired by early eukaryotic cells through endosymbiosis roughly one billion years ago. It contained a streamlined genome (plastome) that is uniparentally inherited and non-recombinant, which makes it an ideal tool for resolving the origin and diversity of plant species and populations. In the present study, a large dataset was amassed by  Oryza sativa L.) is one of the most important given that it provides roughly 20% of the calories consumed by humans worldwide  have been frequently studied using a wide range of analytical approaches mainly from the fields of genetics and archeology. Yet debates regarding the exact origin and timing of domestication persist (sh4) proposing that a desirable set of alleles occurred once, was selected for and that subsequent introgression with wild gene pools (while maintaining strong selection for the domestication genes) accounts for the current high diversity found in domesticated rice (Oryza rufipogon) in multiple locations were the source of different domesticated lineages  and that the multi-origin sourcing accounts for most of the current diversity in domesticated rice rather than amalgamative introgression  Is the current diversity of domesticated rice derived from a single or multiple independent lineages? and (2) whether a common geographic origin or multiple geographic origins existed for the lineages? We employed numerous analytical approaches from population genetics and phylogeography to test these hypotheses. The results from these analyses were interpreted in light of previous work on rice history such that an updated model based on matrilineal diversity could be presented for this essential food crop. Our results from this large dataset of complete plastid genomes for wild and domesticated rice resolves distinct patterns in the evolution and domestication of different rice lineages. As with similar studies on the history and diversity of crop species the results herein will be directly applicable to crop improvement as well as in situ and ex situ conservation efforts.In the present study, we constructed a large plastome data set containing 1445 complete O. rufipogon and O. sativa accessions were obtained from the EMBL database uploaded from previous studies , aus, aromatic, and intermediate based on the identification included in the uploaded information (Oryza species were downloaded from the NCBI database (Whole genome sequencing (WGS) data for  studies . A total Oceania . The samormation . Nineteedatabase .Oryza (de novo assembly of complete plastid genomes were conducted using SPAdes (de novo assembly pipeline: (1) The problem of excessive variation introduced by using highly variable reference sets was reduced by using a narrower set of reference plastomes only from Oryza and (2) the F 12 option in SAMtools was used to bait target reads of plastomes from the WGS dataset. The obtained plastid contigs were further visualized and scaffolded to complete circular genomes in Bandage  were calculated for each group of haplotypes based on plastomic SNVs by using DnaSP v6.0  by using Admixture software , with 5 aSP v6.0  and MEGAaSP v6.0 . The varaSP v6.0 . The priaSP v6.0 . NucleotaSP v6.0 .1To perform the phylogenetic analysis, complete sequences of plastid genomes were aligned in MAFFT  and manu2 were employed: the divergence between O. rufipogon and Oryza punctata [5.7 mya 95% highest posterior density (HPD): 2.6–8.9], the crown age of O. rufipogon (2.32 mya HPD: 2–2.93), and the divergence time between O. rufipogon and Oryza glaberrima and its wild relative Oryza barthii (estimated at c. 0.83 mya HPD: 0.51–1.11). Following the suggestion of For molecular dating, a set of representative haplotypes from major clades in the phylogenetic tree (consistent with the genetic clusters) was used for analyses. The smaller data set was used in order to simplify analyses and avoid zero-length branches from including repeat or highly similar haplotypes . A relax3 was used to assess appropriate burn-in and the adequate effective sample size values (>200). A burn-in of 25% was applied, and the maximum clade credibility (MCC) tree with the mean ages and 95% HPD intervals on nodes was produced in TreeAnnotator v1.8.4 (part of the BEAST package) and visualized in FigTree v.1.4.2.4The Markov Chain Monte Carlo procedure was run for 100 million generations, with sampling every 10,000 generations. Tracer v.1.7.1Oryza populations using the MCMC tree from BEAST. Three biogeographical models were compared using a ML framework: Dispersal-Extinction-Cladogenesis (DEC) model  model , dispersC) model , and BayC) model , with eaC) model  and DEC C) model  in RASP C) model . MaxEnt C) model . Nineteemodel  was completed from 1135 O. sativa and 295 O. rufipogon accessions and 15 in other rice species with AA genomes in Oryza  with an average length of 20,802.7 bp and an average GC content of 44.3%, separated by a large single copy region (LSC) with an average length of 80,575.7 bp and an average GC content of 37.1%, and a small single copy region (LSC) with an average length of 12,346.2 bp and an average GC content of 33.3%. The highest GC content was observed in the IRs, followed by the LSC and SSC, suggesting that different functions , and 2 O. rufipogon-wild-and-cultivated-type clusters (Or-wj and Or-wi). The cultivated accessions were separated into Or-wj and Or-wi with multiple well-supported genetic clusters within each. Three O. rufipogon-wild-to-japonica (Or-wj) genetic clusters were resolved. The genetic clusters Or-wj-I and Or-wj-II mainly consisted of japonica cultivated varieties with some other cultivars, while Or-wj-III contained most of the aromatic rice cultivars that have often been considered as a related ecotype of japonica rice (O. rufipogon-wild-to-indica (Or-wi) genetic clusters were resolved and referred to as Or-wi-I and Or-wi-II. The Or-wi-I genetic cluster consisted of mainly indica rice cultivars and the aus rice ecotype, while the Or-wi-II genetic cluster contained indica cultivated rice and a high percentage (45.1%) of wild rice accessions  resolved in genetic clusters Oryza-I (O-I), Oryza-II (O-II), and Oryza-unclassified (O-unc) with the BB genome species O. punctata (Op), used as the outgroup  genetic clusters (Or-w groups) showed higher haplotype diversity (as a function of accessions per haplotype and multi-step branching within each genetic cluster) with 81 haplotypes in total and 1–11 accessions per haplotype. While cultivated rice genetic clusters (Or-wi and Or-wj groups) had 149 haplotypes in total with 1–301 accessions per haplotype. The nine most common haplotypes (>21 accessions) contained 77.3% of all the accessions in the cultivated rice genetic clusters. The cultivated clusters showed very low nucleotide diversity (π) with a range from 0.00047 to 0.0015 which is lower than wild genetic clusters ranging from 0.0035 to 0.014  to assess divergence between different clusters, the Or-wj genetic clusters all showed relatively large population differentiation to Or-wi genetic clusters with a range of Fst from 0.946 to 0.979 at a significance level of p < 0.01. These values are larger than the range of Fst between either the Or-wj genetic clusters and Or-w-I  or between Or-wi genetic clusters and Or-w-II . Additionally, the Or-wj genetic clusters showed the least genetic distance from Or-w-I (K = 24.3–26.3) while Or-wi genetic clusters were the least distant from Or-w-II . The variational autoencoder estimation (VAE), principal component analysis (PCA), and MDS plot were conducted to further investigate the genetic relationships of all the accessions based on plastomic SNVs. From all multidimensional individual based analyses, the accessions in Or-wj and Or-wi genetic clusters were grouped together in a similar manner to the other analyses and resolved closest to Or-w-I and Or-w-II, respectively  or Or-wi (4.2) genetic clusters . When usectively . All of O. rufipogon and O. sativa diverged from a clade containing O. barthii and O. glaberrima (an African species of cultivated rice) approximately 1.6 mya. This indicated that the wild progenitors of the two cultivated rice species may have originated from a common ancestral lineage. The O. rufipogon and O. sativa clade diverged into two major lineages 1.31 mya and subsequently split up into multiple genetic clusters before 0.86 mya. The Or-wj genetic clusters have a stem age of 0.87 mya and further diverged into two mainly cultivated genetic clusters Or-wj-I (99.3% were cultivated accessions) and Or-wj-II (95.6%) and a wild-cultivated cluster Or-wj-III (61.7%) 0.43 mya  possesses a number of special characteristics such as lack of recombination, conserved gene order and content, and differential rates of mutation . These cportance . Additioffolding . As suchde novo assemblies of well over 1000 plastid genomes, we developed an accurate and effective pipeline for auto-assembly of rice plastid genomes from high throughout WGS data. Several strategies were employed to obtain the optimal result of complete high-quality plastid genomes. The strategies included: (1) using only 102 plastomes in the Oryza genus as reference, instead of using a collection of dozens of distantly related species for reference which is the common practice; (2) applying the F 12 option when using SAMtools software to bait target reads of plastomes from the WGS dataset which resulted in improved baiting efficiency of plastomic reads from the WGS dataset and reduced non-target reads. Assembly parameters were also optimized according to the methods previously reported in In the present study, to conduct large scale indica and japonica, originated from different ancestral wild rice populations, with gene flows observed from japonica to indica as well as two relatively small ecotypes, aus and aromatic rice. The plastome data in this study provides more strong evidence for multiple origins of Asian cultivated rice and further resolution of the maternal relationships between its different subspecies/groups (japonica (61.9%) type of genetic cluster Or-wj-II contained a large proportion of indica accessions (26.3%); more than half (58.2%) of aromatic rice accessions shared a common maternal lineage in the genetic cluster Or-wj-III, in which 31.6% accessions were indica or aus rice  of rice cultivars have been described since the beginning of documentation of rice cultivation . Distincjaponica (90.8%) and aromatic (86.6%) cultivars  were present in the Or-wj genetic clusters. While the indica (67.0%) and aus (63.5%) cultivars  were mainly present in the Or-wi genetic clusters. From this it appears that japonica and aromatic cultivars are more closely associated with the Or-wj genetic clusters than the indica and aus cultivars are associated with the Or-wi genetic clusters. Such a pattern might be the result of biased maternal introgression from the Or-wj genetic clusters into indicia and aus cultivars. However, the inferred introgression of plastomes from the Or-wj genetic cluster into indica and aus varieties do not appear to have resulted in morphological changes that made them unrecognizable as indicia or aus cultivars. That said the influence of the Or-wj plastomes on the indica and aus traits might be minimal or they could be expressed in traits typically unrelated to cultivar recognition such as changes to photosynthetic rate.In our analysis of population structure using plastomic data most of the indica and aus cultivars raise several questions. For instance, is this pattern of asymmetrical introgression evidence of the transfer of the sh4 and other domestication genes from gene pools containing the desired traits to gene pools without the desired traits as proposed in indica and aus cultivars? Do Or-wj plastomes provide desired traits not found in Or-wi plastomes to indica and aus cultivars? Obviously further work is needed to better understand how plastids are involved in the domestication process generally and in rice specifically. With datasets such as those presented here these questions are now possible to investigate more thoroughly. The outcome of such studies could be directly applicable in the development of new rice cultivars through plastomic marker assisted breeding and editing of key plastid genes. In addition, geographic locations with high plastomic haplotype diversity can be identified and prioritized for conservation of valuable wild rice germplasm such that they can be preserved for use in future rice breeding efforts.The higher rate of inferred plastomic introgression from the Or-wj into the https://www.ebi.ac.uk/ena, PRJEB43684 and https://ngdc.cncb.ac.cn/, PRJCA006880.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: WH, ZW, DJ, and LT conceived and designed the study. WH, CC, KX, JW, and PZ collected the public data and performed the data analysis. WH, CC, and KX wrote the manuscript. ZW, LT, and DJ revised the manuscript. All authors have read and agreed to the published version of the manuscript and reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Caenorhabditis Intervention Testing Program (CITP) was founded on the principle that compounds with positive effects across a genetically diverse test-set should have an increased probability of engaging conserved biochemical pathways with mammalian translational potential. To fulfill its mandate, the CITP uses a genetic diversity panel of Caenorhabditis strains for assaying longevity effects of candidate compounds. The panel comprises 22 strains from three different species, collected globally, to achieve inter-population genetic diversity. The three represented species, C. elegans, C. briggsae, and C. tropicalis, are all sequential hermaphrodites, which simplifies experimental procedures while maximizing intra-population homogeneity. Here, we present estimates of the genetic diversity encapsulated by the constituent strains in the panel based on their most recently published and publicly available whole-genome sequences, as well as two newly generated genomic data sets. We observed average genome-wide nucleotide diversity (π) within the C. elegans (1.2e-3), C. briggsae (7.5e-3), and C. tropicalis strains (2.6e-3) greater than estimates for human populations, and comparable to that found in mouse populations. Our analysis supports the assumption that the CITP screening panel encompasses broad genetic diversity, suggesting that lifespan-extending chemicals with efficacy across the panel should be enriched for interventions that function on conserved processes that are shared across genetic backgrounds. While the diversity panel was established by the CITP for studying longevity interventions, the panel may prove useful for the broader research community when seeking broadly efficacious interventions for any phenotype with potential genetic background effects.The  Caenorhabditis elegans. The reproductive style of C. elegans makes it particularly easy to generate and maintain large populations of genetically identical individuals. In fact, the control over genetic variability helped make C. elegans the first multi-cellular organism to have its entire genome sequenced (C. elegans Sequencing Consortium 1998). Despite the success garnered using genetically homogeneous populations, it has become increasingly apparent that many of the phenotypes of interest for study are dependent on genetic background . Examples of these background-influenced phenotypes range from α-synuclein toxicity , to behavioral responses to temperature , dietary influence on lifespan and reproduction , and pharmacological efficacy . The dependence on genetic background suggests that attempts to identify core biological systems and functionality could benefit from assaying across genetic diversity to identify genetic background-independent phenotypes. One way to achieve this is to use a panel of populations with intra-population homogeneity and inter-population diversity.Model organisms have been fruitful tools for elucidating core biological principles. The power of model organism study, in part, is due to the ability to grow large populations with known genetic makeup. One of the most widely adopted genetic models is the hermaphroditic nematode Caenorhabditis Intervention Testing Program (CITP) was designed to identify anti-aging and longevity-promoting compound interventions effective in a genetically diverse set of Caenorhabditis nematode populations . To date, 55 chemical compounds have been tested for reproducible, genetic background-independent effects on longevity . The genetic diversity panel used by the CITP is composed of 22 strains from three hermaphroditic species, which facilitates maintenance of intra-population homogeneity. While the full panel is composed of 22 stains, a smaller core sub-panel of nine strains , C. briggsae , and C. tropicalis  is used in initial compound effect characterization . Here, we present estimates of the genetic diversity encapsulated by the constituent strains of the CITP panel, and core sub-panel, based on their most recently published and publicly available whole-genome sequences.With the importance of genetic background effects in mind, the C. elegans is typically isolated from cooler ecological niches than C. tropicalis , while C. briggsae is found in niches that range from cool to warm . The wide species distributions enabled the CITP to assemble a panel of strains isolated worldwide, with representatives from most continents , to project genotypes of the strains on two latent dimensions , which is consistent with previous estimates for those species . Caenorhabditis species is higher than that within human populations  and comparable to that found in mouse populations . Our analysis supports the assumption that the CITP screening panel encompasses broad genetic diversity, suggesting that lifespan-extending chemicals with efficacy across the panel should be enriched for interventions that function on conserved processes that are shared across genetic backgrounds. While the diversity panel was established by the CITP for studying longevity interventions, the panel may prove useful for the broader research community when seeking broadly efficacious interventions for any phenotype with potential genetic background effects.When establishing the genetic diversity panel for compound screening, the CITP sought strains that represented both broad geographic and genetic diversity. The three species represented in the panel are themselves globally distributed, but with ecological restrictions. For example, ntinents . We nextmensions . We thenTo generate estimates of the genetic variability among the strains in the CITP diversity panel we collected publicly available genomic data for 18 of the 22 CITP strains (see Reagents below). Because comparable Illumina-based sequencing was unavailable for N2-PD1073 and ED3092, two strains present in the core-subpanel of nine strains, we generated whole genome data for these two strains using standard protocols. In brief, we used proteinase K to digest whole worms and isolated total genomic DNA using the Genomic DNA Clean & Concentrator kit (Zymo Research). Genomic libraries were then generated using the Nextera XT DNA Library Preparation Kit . Sequencing was carried out on the Illumina NovaSeq 6000 platform by the Genomics and Cell Characterization Core Facility (GC3F) at the University of Oregon. We then used the two new genomic datasets, along with the 18 available in the NCBI database, to calculate genetic diversity estimates using our software pipeline (described below).Caenorhabditis Genetics Center (CGC), the C. elegans wild-isolates should be ordered from the Caenorhabditis elegans Natural Diversity Resource (CeNDR) . Strains unavailable from those sources  can be obtained directly from the CITP upon request. Relevant to the unavailability of PD1073 at the CGC, PD1073 is a close relative of PD1074 which is distributed by the CGC as a wild type reference strain. PD1073, PD1074, and PD1075 are three subclones of the N2-derivative strain VC2010 that were generated in the process of assembling a new N2 reference (“VC2010-1.0”) genome . All strains in the genetic diversity panel and the associated SRA read accession IDs used in this study are listed below:While most of the strains in the CITP diversity panel can be obtained from the C. elegans: strain CB4856 , ED3040 , JU1088 , JU1652 , JU775 , MY16 , QX1211 ;C. briggsae: AF16 (SRR2002620), HK104 , JU1348 (SRR1793004), JU726 (SRR1792964), NIC20 (SRR1793012), QR25 (SRR1793006);C. tropicalis: JU1373 , JU1630 (SRR12623130), NIC58 , QG131 (SRR12623047), QG834 (SRR12623046).C. briggsae strain JU1264 and C. tropicalis NIC122.No genomic data is publicly available for C. elegans N2-PD1073 and C. briggsae ED3092 strains generated in this study were deposited to the SRA database (https://www.ncbi.nlm.nih.gov/sra) under the project accession ID PRJNA773598.Raw reads for C. elegans N2-PD1073, MY16, and JU775; C. briggsae AF16, ED3092, and HK104; C. tropicalis JU1373, JU1630, and QG834.The core CITP strains are Softwareet al. 2016), and filtered and trimmed reads with Skewer v.0.2.2 . We mapped filtered reads to the C. elegans, C. briggsae,and C. tropicalis genomes obtained from the WormBase database (https://wormbase.org//) version WS280  by BWA-MEM v.0.7.17 (Li 2013) and SAMtools v.1.5 , and deduplicated with the Picard tools v.2.17.6 (Broad Institute). We called and filtered variants using GATK v.3.7  software and BEDtools v.2.25 , and estimated the nucleotide diversity using VCFtools v.0.1.15 , masking repetitive regions, indels, complex variants, and regions with too low or high coverage. We projected genotypes of the CITP strains on two latent dimensions using popVAE , and visualized them in R v.3.5 (R Core team 2018) using packages ggplot2 (Wickham 2016) and ggrepel.We estimated genetic diversity of the CITP strains using whole-genomic data for 20 out of 22 CITP strains from the panel. Reads from the SRA database were downloaded with SRA-toolkit (the SRA Toolkit Development Team). We evaluated the read quality with FastQC v.0.11.5 (Andrews 2010) and MultiQC v.1.3  and are available under the MIT license. The global map was generated in MATLAB R2021b  using the MATLAB Mapping Toolbox. The location coordinates used for the strains are included in the GitHub repository. Figure readability was improved by moving panel relative positions, updating color coding, and improving text aesthetics using Adobe Illustrator 2022 in a manner consistent with image integrity standards.


SUDs) presents a major opportunity for mouse genetics. Inbred mouse strains exhibit substantial and heritable differences in their responses to drugs of abuse (DOA) and in many of the behaviors associated with susceptibility to SUD. Therefore, genetic discoveries emerging from analysis of murine genetic models can provide critically needed insight into the neurobiological effects of DOA, and they can reveal how genetic factors affect susceptibility drug addiction. There are already indications, emerging from our prior analyses of murine genetic models of responses related to SUDs that mouse genetic models of SUD can provide actionable information, which can lead to new approaches for alleviating SUDs. Lastly, we consider the features of murine genetic models that enable causative genetic factors to be successfully identified; and the methodologies that facilitate genetic discovery.The tremendous public health problem created by substance use disorders ( We believe that the relationship between murine models and human diseases  resembles that between a small Cessna airplane and a large 787 jet plane. You can learn most of what you need to know about the fundamental principles of aviation by studying the Cessna, but this will not enable you to pilot the 787. The 787 has many more capabilities, much more complex and computer-controlled systems, and multiple redundancies that are essential for its function than are contained within a Cessna. Nevertheless, you wouldn't be able to pilot a 787 without knowing the aviation principals that are learned by studying the Cessna. Similarly, studying the mouse has revealed the basic principles underlying many areas of human physiology and pathobiology. Within the neurobiology realm, many of the mechanisms and circuits utilized for learning, memory, cognition, and the effects that drugs have on these processes have been uncovered through analysis of mouse models. However, since laboratory mice function within a very limited behavioral domain and lack some of the neural pathways that regulate human behavior, many of the complex factors mediating human psychiatric diseases cannot be understood by analyzing rodent models. The aviation analogy is quite appropriate for SUDs. Rodent models are ideal for understanding DOA neurobiology and for providing information about how drug seeking behaviors are generated; but they provide a very poor substrate for investigating the impact of that socioeconomic and psychosocial factors have on triggering relapse. This is an important limitation since human drug addiction proceeds through a three-stage cycle whose intensity increases over time, and each stage results from DOA-induced changes in brain circuits –3. The fHere, we examine what we have learned from our prior analyses of murine genetic models of responses related to SUD. First, we discuss a murine genetic model of a drug-induced toxicity to indicate the different types of genetic factors that can be identified. We then we review the genetic factors identified from our prior analyses of murine genetic models of opiate responses. Lastly, we consider the features of murine models that enable causative genetic factors to be successfully identified; and the methodologies that can facilitate genetic discovery.(Abcb5) that caused susceptible strains to have higher brain haloperidol levels; and a genetic association study in a haloperidol-treated human cohort identified human ABCB5 alleles as susceptibility determinants for HIT  that impaired the biosynthesis of a protective metabolite (cysteamine)  CNS toxicity illustrates the potential outcomes that could emerge when evaluating murine genetic models of SUD because drug addiction (in many ways) is a toxicity caused by DOAs. Although haloperidol is an effective anti-psychotic agent, it causes a treatment-limiting side effect in most treated subjects, which is very debilitating Parkinsonian-like extrapyramidal symptoms. When we began our studies of haloperidol induced toxicity (HIT), genetic susceptibility factors for this toxicity were completely unknown. Therefore, we analyzed a murine genetic model of HIT where the inbred strains exhibited very large and reproducible differences in susceptibility to HIT. Our analysis revealed that susceptibility was quantitatively determined by two distinct genetic loci: one encoded a pharmacokinetic factor and the other a pharmacodynamic factor. The pharmacokinetic factor was allelic variation within a murine ABC-drug efflux transporter Abcb that cauteamine) . While dAbcb that cauteamine) . Thus, aLike haloperidol, murine opiate response models hold great promise for genetic discovery. The inbred strains exhibit very large and heritable differences in their responses to opiates, which include the development of opioid analgesia, tolerance, dependence, and hyperalgesia –10. We pInbred strains also exhibit substantial and heritable differences in their cocaine responses, which include the extent of cocaine-induced locomotor activation , 29, cocP-glycoprotein transporter (Abcb1b) contributed to inter-strain differences in this opiate response  as a genetic locus contributing to the inter-strain response difference. This response was markedly diminished in Adrb2 knockout mice and was reversed by administration of a commonly used Adrb2 antagonist, which suggested a novel strategy for reducing OIH (Netrin-1 receptor (Dcc) (multi-PDZ-domain protein (Mpdz that encodes MUPP1)  was analyzed, we discovered that genetic variation within the response . Analysicing OIH . We alsoor (Dcc)  and mults MUPP1)  also condcc encodes a receptor for an axonal guidance protein (netrin-1) that plays a role in synaptic plasticity in the adult brain  reduced NPOW [and opioid-induced hyperalgesia (OIH)] in a dose-dependent fashion; and ondansetron co-administration with morphine impaired the mCPP response, which indicated that ondansetron eliminated the reinforcing effects of morphine (p = 0.03) in the NPOW in the volunteers, and it decreased all 11 of the measured manifestations of opiate withdrawal. Since the ondansetron effect observed in mice translated to humans, it is likely of fundamental importance. In a separate study  also prevented NPOW symptoms in normal human subjects and that a pretreatment that combined palonosetron with a commonly used antihistamine (hydroxyzine) caused a 95% reduction (p = 0.014) in withdrawal manifestations. The effect of the combination pretreatment was significant even when compared with that of palonosetron alone (p = 0.012)  response after 4 days of morphine administration in 18 inbred strains . Allelicresponse . Consistmorphine . Thus, ote study , we demo= 0.012) . These rneonates , we are neonates , 62. Thi> 15) of inbred strains. When a small number of strains are evaluated, the actual extent of the phenotypic variation present in the mouse population is under-estimated  methods are used for their analysis. (i) The DOA response must be measured across a large number  Sistimated , 68; thestimated  and Collstimated  panels , can be analyzed to facilitate identification of causative genetic factors.While many different methods can be used to analyze GWAS data obtained from inbred stains, we have successfully used haplotype based computational genetic mapping (HBCGM) to identify murine genetic factors underlying 22 biomedical traits , 75–90. hijack” the neural circuits used for learning and memory  to GP.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Mantidactylus (Brygoomantis) bellyi, across the diverse vegetation types of Montagne d’Ambre National Park (MANP), Madagascar. Genetic diversity was assessed with seven newly developed microsatellite markers as well as mitochondrial DNA sequences and concordance with patterns of ecological, morphological, and bioacoustic divergence evaluated. We found M. bellyi lacking mitochondrial differentiation within MANP, while microsatellite datasets partitioned them into three highly differentiated, geographically separated subpopulations (with indications for up to five subpopulations). The molecular grouping–primarily clustering individuals by geographic proximity–was coincident with differences in mean depth and width of waters, suggesting a possible role of fluvial characteristics in genetic exchange in this stream-breeding species. Genetic clustering not consistent with differences in call properties, except for dominant call frequencies under the two-subpopulations model. Morphological divergence was mostly consistent with the genetic clustering; subpopulations strongly differed by their snout-vent length, with individuals from high-elevation subpopulations smaller than those from populations below 1000 m above sea level. These results exemplify how mountains and environmental conditions might primarily shape genetic and morphological divergence in frog populations, without strongly affecting their calls.In the processes that give rise to new species, changes first occur at the population level. But with the continuous nature of the divergence process, change in biological properties delimiting the shift from “individuals of divergent populations” towards “individuals of distinct species”, as well as abiotic factors driving the change, remain largely ambivalent. Here we study diversification processes at the population level in a semi-aquatic frog,  Delimitation of species boundaries is a controversial topic in biodiversity research. To alleviate ambiguity, species limits are currently preferentially assessed through integrative approaches where molecular analyses play a critical role , 2. In aTightly linked to species delimitation is the study of the process of species formation. It is generally assumed that allopatric processes, i.e., the inhibition of gene flow due to geographical barriers, is the prevalent mechanism by which new species arise . On the As a promising model region for studying species diversification , 15, MadStudies on population genetics of Malagasy fauna have generally been focused on animals with high dispersal abilities like lemurs , 26, batMantidactylus where taxonomic revisions were made difficult by the low-intensity, irregular calls of several species, polymorphism, noticeable intraspecific divergence despite morphological similarities, and existence of synonymies and uncertainties in their original descriptions ). bellyi . Thus, t R 3.6.1 . Normalit2 3.2.1 , cowplott2 3.2.1 , tidyvert2 3.2.1 , dplyr 0t2 3.2.1 , and RCot2 3.2.1  R packagMorphological measurements were available for 329 adults . As mentioned before, eleven morphological characters were taken from each individual. We created three datasets that were separately analyzed: (i) measurements of males and females pooled in one dataset, (ii) measurements of males only, and (iii) measurements of females only. All analyses were performed in R 3.6.1 . After t16S rRNA between specimens from MANP and other occurrence localities of Mantidactylus bellyi, including the Ankarana Massif, and Montagne des Français. This low genetic divergence primarily confirms them as conspecific frogs, excluding the existence of multiple cryptic species hidden under the name Mantidactylus bellyi on MANP, as observed in the Mantidactylus ambreensis species complex  Spectrogram, B] Oscillogram, C] Zoomed view of the oscillogram D] Call spectrum for visualizing the dominant frequency. S3 Table gives the summary table of the minimum, the maximum, the mean values ± standard deviation of the males of M. bellyi call parameters, and the number of analyzed calls for the six different sites, represented as Min–Max .S1 Fig gives the graphic representations of the advertisement call of (PDF)Click here for additional data file.S3 FileMantidactylus bellyi. Repeat count are from the initial library. Percentage of missing data, numbers and length ranges of alleles refer to the entire dataset. Length range (inferred bp) includes primers and linker. S2 Table gives the inference of the population structure of M. bellyi based on Bayesian analysis for K = 1 to K = 7 for four models. Ad = Admixture model, AdLOCPRIOR = Admixture model with LOCPRIOR information, NoAd = No admixture model, NoAdLOCPRIOR = No admixture model with LOCPRIOR information. K = number of assumed subpopulations; Reps = number of MCMC iterations; ΔK = ad hoc statistic based on the change in the log probability data between successive K values. The most likely K is highlighted in bold. S2 Fig gives the genetic cluster assignment of the sampled populations of M. bellyi in Montagne d’Ambre. A–E] Individual assignments of M. bellyi to genetic clusters as inferred by STRUCTURE from a data set of seven microsatellites. A–C] The clustering scenarios of assumed subpopulations K = 4–6 were depicted for the models showing highest ΔK, i.e. A] Admixture model with LOCPRIOR information for K = 4, B] Admixture model for K = 5, and C] Admixture model for K = 6. D–E] The clustering scenarios were depicted for no admixture models with LOCPRIOR information of assumed subpopulations D] K = 5, and E] K = 6.S1 Table gives the list of forward (Fwd) and reverse (Rev) primers for seven newly established microsatellite markers used for (PDF)Click here for additional data file.