Elucidating the genetic variation and structure of Lemnos and Lesvos sheep is critical for maintaining local genetic diversity, ecosystem integrity and resilience of local food production of the two North Aegean islands. In the present study, we explored genetic diversity and differentiation as well as population structure of the Lemnos and Lesvos sheep. Furthermore, we sought to identify a small panel of markers with the highest discriminatory power to assign animals across islands. A total number of n = 424  ewes, sampled from n = 24 herds dispersed at different geographic regions on the two islands, were genotyped with the 50K SNP array. Mean observed heterozygosity was higher  in Lesvos than in Lemnos population (0.384 vs. 0.377) while inbreeding levels were higher in Lemnos than Lesvos herds (0.065 vs. 0.031). Results of principal components along with that of admixture analysis and estimated genetic distances revealed genetic clusters corresponding to Lesvos and Lemnos origin and the existence of infrastructure within islands that were associated with geographical isolation and genetic history of the studied populations. In particular, genetic analyses highlighted three geographically isolated herds in Lemnos that are located at mountainous areas of the island and are characterized as representatives of the local sheep by historic data and reports. Admixture analysis also showed a shared genetic background between Lemnos and Lesvos sheep attributable to past gene flow. Little overall genetic differentiation was detected between the two island sheep populations, while 150 discriminatory SNPs could accurately assign animals to their origin. Present results are comparable with those reported in the worldwide sheep breeds, suggesting geography related genetic patterns across and within islands and the existence of the local Lemnos sheep. Knowledge of the genetic variation within and between sheep populations is important for the identification of local genetic resources  to be maintained in animal genetics conservation efforts and the efficacy of applied selection programs. Preserving the genetic variability of livestock local genetic resources is important for ensuring food security under a changing climate and for sustaining productivity and resilience of agri-food chains. In Greece, sheep populations are reported to have been shaped by several factors such as geographical isolation, genetic drift, selection and crossbreeding . StudiesThe latter breed has been reported to cluster with other mountainous breeds in Greece . It is pThe Lesvos breed is a fat-tailed dairy sheep mainly located on the homonymous island of the North Aegean sea where about 260,000 purebred animals dispersed over 2,000 medium sized flocks  are being kept under a semi-intensive production system . Based on anecdotal reports, during the 1960s the local sheep population was extensively mated with the Lesvos breed. The Terra Lemnia project, aiming to evaluate the genetic resources of the island, conducted a survey among local livestock farmers regarding the characteristics of their farms. In this survey, 40 farmers were interviewed the majority of which characterized their sheep as crossbreds of the original local sheep mainly with Lesvos sheep and to a lesser extend with other Greek or foreign breeds. Nevertheless, pure-bred sheep belonging to the so called local Lemnos sheep were also reported at the mountainous Vigla region, at the northwestern part of the island. The survey data were supported by our own observations during visits to representative farms across the island, confirming high phenotypic variability within the Lemnos sheep population. This variability could be attributed either to extensive interbreeding or geographical isolation and local adaptation, with most distinctive that of the Vigla mountain (northwestern part of the island). In contrast to the Lesvos sheep breed that is largely purebred and relatively well characterized, the genetic makeup of the ‘modern’ Lemnos sheep remains largely unknown.Sheep breeding is an important activity for both islands that shapes their ecosystem and contributes greatly to their agricultural, cultural and culinary heritage. In particular, milk from local ewes is used to produce cheeses, some of which are granted protected denomination of origin status e.g. the Ladotyri (Lesvos PDO), Kalathaki (Lemnos PDO) and Melichloro (Lemnos).Driven from the importance of both sheep populations, in the present study we performed genetic analyses aiming at: (i) exploring the genetic makeup of Lemnos sheep and their relationship with the Lesvos breed, (ii) detecting population structure in the two populations and (iii) identifying a small panel of the most informative SNPs with high assignment power to predict island membership of animals. Current results are expected to shed more light in the evolutionary history of the two sheep populations and contribute to their better genetic management.All applicable ethical guidelines for the care and use of animals were followed and animal blood samples were collected by trained personnel under strict veterinary rules. All samples and data in our study were collected under the consent of the breeders. The protocol of the present study was approved by the Research Ethics and Ethics Committee of the Agricultural University of Athens (no 54/2020) according to the article 23 of law 4521/2018 of the Greek government.−4 and linkage disequilibrium (LD) r2 levels . Application of QC criteria at the marker level resulted in a total number of 39,885 autosomal SNPs retained for further analyses. QC was performed using the SNP & Variation Suite software (version 8.8.3).A total number of n = 433 sheep blood samples were collected from 24 herds of the two islands. All animals were genotyped with the Illumina Ovine 50K SNP array by Neogen Europe Ltd . First, the accuracy of SNP genotyping was assessed and following the manufacturer’s recommendations, genotypes presented with a GenCall score greater than 0.30 were considered called. We then performed quality control (QC) both at the animal level and marker level. At the animal level, n = 9 samples were excluded due to call rate lower than 0.95 resulting in a total number of 424 samples in 24 herds in Lemnos (n = 307 in 18 herds) and Lesvos (n = 117 in 6 herds). At the marker level, n = 3,762 markers of the 43,647 autosomal SNPs were excluded due to: call rate lower than 0.95, minor allele frequency (MAF) lower than 0.05, deviation from Hardy–Weinberg equilibrium (HWE) using a Fisher exact test p-value<10HE) and observed heterozygosity (HO) were estimated per island and per herd within islands using the ‘gl.basic.stats’ function from the dartR R package [Ho between islands as well as differences between He and Ho, within islands. This test was performed using SAS ver9.0 (2002).Measures of genetic diversity i.e. expected heterozygosity ( package . The Brofis) were calculated using PLINK 1.9 [fis is equivalent to Wright’s within-subpopulation fixation index FIS, with negative and positive values denoting heterozygote excess and deficit, respectively. Then, runs of homozygosity (ROH) per animal were detected using the following criteria: (i) the minimum number of SNPs included in a ROH was set at 25, (ii) the minimum length of a ROH was set at 1 Mb, (iii) one heterozygous genotype and no more than one missing SNP were allowed per ROH and (iv) the maximum distance between consecutive SNPs was set to 1 Mb. This analysis was performed using the ‘consecutiveRUNS.run’ function in the detectRUNS R package [fROH) were calculated using the ‘Froh_inbreeding’ function in the detectRUNS R package [fROH were calculated from the sum of ROH lengths, divided by the total length of the autosomal genome covered by markers (2610 Mb), as proposed in [fROH coefficients per animal were calculated using ROH of different length classes: (i) all ROH, (ii) 1 to 5 Mb, (iii) 5 to 20 Mb and (iv) >20 Mb. Finally, the degree of familial relationships i.e. shared ancestry between pairs of individuals sampled per herd was estimated using the P(IBD = 2) and P(IBD = 1) are the probabilities of two individuals carrying 2 or 1 SNP alleles identical by descent, respectively. Individual inbreeding levels were assessed using two measures. First, the individual inbreeding coefficients .Detection of genetic structure was assessed using three different approaches. First, the genomic relationship matrix (GRM) between all pairs of individuals using genotypes of the 39,885 SNPs was calculated in PLINK 1.9 [Next, Principal Component Analyses (PCA) of animals’ genotypes was conducted to detect genetic clusters between and within island populations. Each time, the first 10 PCs were calculated using the ‘—pca’ command in PLINK 1.9  and thenhttps://rajanil.github.io/fastStructure/). FastStructure results for each K cluster were graphically visualized using Distruct from CLUMPAK .FST) values between herds. Specifically, we used the ‘stamppFst’ function with a number of 1000 bootstraps and 0.95 confidence interval from the StAMPP [Genetic differentiation both between and within islands were explored via pairwise estimations of fixation index  on the constructed PCs was applied using normal-theory methods assuming unequal variances, prior probabilities proportional to sample sizes and the CrossValidate (CV) option to obtain CV error-rate estimates. LDA was performed using the DISCRIM procedure in SAS ver 9.0 (2002). When the CV classification is specified, PROC DISCRIM classifies each observation in the data set using the discriminant function computed from the other observations in the data set, excluding the observation being classified. Thus, every data point is reclassified as if it were a new unknown observation. This provides a more conservative accuracy assessment. A total number of 16 cases arising from combinations of different numbers of SNPs and constructed PCs were examined during LDA. Results of LDA are presented as misclassification error rates per island and on average.Prediction of group membership of sheep samples of the two islands using limited numbers of SNPs with highest discriminatory power, followed. To this end, first we selected limited numbers (25 to 150) of strongly differentiated SNPs between the two islands defined as markers with highest HO was higher in Lesvos than in Lemnos population (0.384 vs. 0.377)  while in the Lesvos sheep, Ho followed closely HW expectations .Based on estimates of inter-locus diversity, average . 0.377) . NeverthHO) along with fis, fROH and HO values varied more between Lemnos herds compared to Lesvos. Within Lemnos, HO widely ranged from 0.363 (LEMN2) to 0.393 (LEMN16) with many herds displaying HO values in the lower range (0.36–0.37). In contrast, average HO of the Lesvos herds was always in the upper range (0.38–0.39). Estimates of average herd fis ranged from -0.0125 (LEMN 16) to 0.066 (LEMN2) and ROH-based inbreeding coefficients (fROH) from 0.015 (LEMN16) to 0.063 (LEMN2). Furthermore, overall average fis estimates were higher in Lemnos (0.0284) than Lesvos (0.0066) and this was also the case for fROH . Estimates of shared ancestry , between HO and fROH  that was more apparent for the pair HO—fis  and less pronounced for pair Ho–rs = -0.483, p = 0.0168). Note the close relationship between fis and fROH estimates .Levels of genetic diversity  and most interestingly the formation of two off-diagonal clusters depicted as large sized red colored squares, one at the upper left formed by animals of three Lemnos herds  and another at the lower right with animals of the six Lesvos herds. Note that the three Lemnos herds that were represented as a separate group during k-means clustering are topographically isolated herds, located in the northwestern part of the island on the Vigla mountain and are considered representatives of the local Lemnos sheep.Two-dimensional plots of the first two PCs derived from PCA on the 424 genotypes and 39,885 markers across and within islands are shown in Detection of genetic clusters as ancestry proportions via AD is shown in Note that at K = 3 and 4 Lesvos genotypes were represented as an admixed population. At the optimal step (K = 5) i.e. lowest CV error (0.6020), there were four major clusters generated, formed by herd LEMN1 (green color), herds LEMN2 and LEMN3 (light blue color), herd LEMN6 (purple color) and the Lesvos breed-specific cluster (dark blue color).k-means clustering of the GRM heatmap and PCA confirming genetic distinctness of the three Lemnos herds and the Lesvos sheep. Nevertheless, AD disclosed an additional finding. Many of the Lemnos sheep were represented as admixed individuals with variable and occasionally sizeable degree of Lesvos sheep ancestry . FST estimates between herds that ranged from 0.014 (LESV4—LESV6) to 0.096 (LEMN2-LEMN4). Results of FST were also confirmed by estimates of Nei’s genetic distances between herds, with lowest distances (0.025) attained for herds LESV4-LESV6 and highest (0.085) for herds LEMN2-LEMN4  [Both sheep populations studied herein maintained sufficient genetic variation as inferred by 22–0.38)  sheep br22–0.38)  and comp22–0.38) .Of the two populations, Lemnos herds displayed less heterozygosity than expected under random mating. The most plausible cause for the heterozygote deficit is the mating of close relatives i.e. inbreeding . The linFST values in the range from 0.01 to 0.09), the Wahlund effect is an unlikely explanation for the observed negative relationship between inbreeding and HO. Furthermore, in contrast to the positive correlation between interlocus fst and fis that is theoretically expected as a result of the Wahlund effect [Aside from inbreeding, heterozygote deficits can also be interpreted as evidence of the Wahlund effect  that occd effect ,21, hereFinally, cryptic relatedness i.e. non-random sampling of members from a limited number of families may also cause deviations from Hardy-Weinberg proportions –24. Cryperds see  indicateportions –24 in cofis and fROH estimates delivered comparable results that largely coincide. A close relationship between the two measures (fis and fROH) has also been observed in other Greek breeds, e.g. Frizarta [fROH estimates present the best choice as they give a clearer insight on past and recent inbreeding history of animals [fROH estimates for the long ROH lengths measuring >20 Mb obtained herein that reflect recent inbreeding history, underscore the need for efficient genetic management of the sheep populations to avoid close relative matings, especially for the Lemnos sheep. Recent inbreeding has been identified as a major determinant of the current genetic diversity and effective population size of many Greek sheep breeds (e.g. Frizarta [As in other studies (e.g. ), currenFrizarta , or foreFrizarta . Of the  animals ,27. PartFrizarta ).FST values estimated herein indicate little genetic differentiation between Lesvos and Lemnos sheep populations. To further support this finding, AD results show that the genetic makeup of most Lemnos sheep examined here has largely been shaped by the Lesvos sheep. This genetic closeness of the two populations can most likely be attributed to past gene flow from Lesvos to Lemnos island as suggested by historic data, local reports and phenotypic appearance.Average Another interesting finding obtained here relates to the discriminatory power of differentiated markers in efforts to devise a tool for tracing the origin of local sheep and possibly their products. The idea here was first to identify limited numbers of strongly differentiated markers across islands, then to combine most of their genetic information into small number of variables (one or two PCs), and finally to derive a PC-based discriminatory criterion that could be used to accurately predict animals’ island membership. Present results have shown that the aforementioned approach could be practically implemented via development of a custom-made 150 SNP array that could be made available at an affordable price for large scale analyses. This SNP chip array could be used to discriminate Lesvos sheep from Lemnos sheep as long as no further gene flow occurs between the two islands. Although the discriminatory power of the 150 SNP panel under consideration was assessed using a conservative approach such as CV classification, its discriminatory performance further warrants to be tested using ‘true’ validation procedures by separating calibration and test data sets and a larger number of animals.Michailidou et al.  have alsGeographical isolation confounded with genetic origin as well as past interbreeding appeared to be key determinants of genetic structure patterns detected in the present study. The most important implication of the genetic patterns detected herein was the identification of three specific clusters within Lemnos that correspond to equal number of topographically isolated herds located at the rough terrain of the Vigla mountain. According to local farmers interviewed, animals of these herds are morphologically reminiscent of the local Lemnos sheep that historically prevailed across the island some decades ago. More recently, particularly in the lowland areas of Lemnos, the original local sheep were extensively interbred with other indigenous sheep from neighboring islands or other exotic breeds. The pure-bred Lemnos local sheep population was progressively restricted to the mountainous areas and declined in numbers, resulting in only about 800–900 animals remaining today mainly at the Vigla mountain. Due to their high adaptability and resilience, the owners of these remaining herds do not interbreed their animals with other indigenous or exotic sheep breeds. In contrast, interbreeding is still common practice in the lowland areas.Given the critical census size and the importance of these animals in terms of maintenance of local genetic diversity and ecosystem integrity, immediate conservation interventions should be undertaken as isolation, together with small population size could result in the decline and ultimately extinction of this population ,33.The use of genomic data information revealed that Lesvos and Lemnos sheep populations are clustered according to geographical location and/or genetic origin. Within Lemnos, genetic structure patterns were associated either with geographical isolation of local genetic resources or interbreeding with indigenous or exotic sheep breeds. Practical on-ground guidance and broader management actions should be undertaken to ensure the conservation of the ‘rediscovered’ Lemnos sheep.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


Saccharomyces cerevisiae is the main actor of wine fermentation but at present, still little is known about the factors impacting its distribution in the vineyards. In this study, 23 vineyards and 7 cellars were sampled over 2 consecutive years in the Bordeaux and Bergerac regions. The impact of geography and farming system and the relation between grape and vat populations were evaluated using a collection of 1374 S. cerevisiae merlot grape isolates and 289 vat isolates analyzed at 17 microsatellites loci. A very high genetic diversity of S. cerevisiae strains was obtained from grape samples, higher in conventional farming system than in organic one. The geographic appellation and the wine estate significantly impact the S. cerevisiae population structure, whereas the type of farming system has a weak global effect. When comparing cellar and vineyard populations, we evidenced the tight connection between the two compartments, based on the high proportion of grape isolates (25%) related to the commercial starters used in the cellar and on the estimation of bidirectional geneflows between the vineyard and the cellar compartments. The yeast species present on the grape berry are related to a fruit microflora  has not yet been clearly defined till now, with studies reporting controversial results concerning the positive impact or otherwinse of organic farming system on yeast diversity40. Around the wineries, commercial S. cerevisiae strains used in the alcoholic fermentation process have been found in grapes samples collected from vineyards within a 200-m radius of the winery buildings41. Integrating the endogenous population of the vineyard, the commercial strains would then appear to change the diversity and population structure of S. cerevisiae42. Yet the propagation of commercial starters and their persistence in the environment have been shown to be discontinuous and a non-persistent process43.Vines and cellars are two environments under tight human management, which might impact the global microbial community and influence yeast diversityS. cerevisiae diversity in the vineyards and in the winery, thus raising the question of the origin of wine yeast44. Strains involved in spontaneous fermentation originated partly from the vineyard and partly from winery23. Indeed, a large and diverse yeast population is present mainly in winery surfaces, including S. cerevisiae prior to harvesting, and represent a potential reservoir to inoculate the grape must during spontaneous fermentations46. Characterizing the links between the population from vineyards and that from ferments is thus important from an ecological point of view, but these links are still unclear so far.In addition, few research studies have worked on the possible relationships between the 48. These vineyards are planted today with five different red grape varieties: Cabernet Sauvignon, Cabernet Franc, Carmenère, Malbec, Petit Verdot, and Merlot, the latter representing more than 50% of the Bordeaux wine area.The Bordeaux area is one of the world’s most renowned winemaking regions. The first vineyards in Bordeaux were planted in Roman Times, with an expansion during the Middle Ages. The Graves region was the principal wine region, followed by the Entre-Deux-Mers and Saint EmilionS. cerevisiae isolates from 193 samples of Merlot grapes obtained across five regions in the Bordeaux and Bergerac areas, in organic or conventional farming system, and 289 S. cerevisiae isolates from 7 cellars were collected. The isolates were genotyped at 17 microsatellite loci. From the data analyses, we show how the human activity associated with the wine making process has shaped S. cerevisiae diversity in the vineyards of the Bordeaux region. We also show the significance of the exchange between cellars and vineyards populations.In the present study, 1374 2 indicated that more than the half of the glucose and fructose of the must had been fermented  and conventional (n = 14) wine estates, we collected 193 samples of grapes (134 in 2012 and 59 in 2013) among which 166 (107 in 2012 and 59 in 2013) initiated a fermentation , the Simpson index (D), its opposite (1 − D) and the Pielou evenness index , which gives more weight to common or dominant species, was smaller, indicating that the diversity of common and dominant isolates was alike. This is confirmed by the comparison of rarefaction curves for possible population diversity in both farming systems . The relationships of 100 isolates sharing at least 75% of their alleles with one commercial yeast starter are presented in 5 spanning trees , despite a similar allelic richness.Because the fraction of yeast starters used in the different regions may lead to a spurious increase in similarities between the regions, we removed all strains with profiles presenting more than 75% identity with industrial strains. This reduced the data set from 402 to 302 grape isolates, meaning that 1/4 of the grape isolates collected were closely related to commercial strains. Basic information on multilocus genotype per samples and heterozygosity are given as supplementary information , no significant correlation was observed between the Fst matrix distance and the geographic distance. We alternatively inferred gene flow between the different appellations, using a Bayesian coalescent approach implemented in the Migrate software, and using the method proposed by Sundqvist et al.S. cerevisiae, organic farming system have little impact on S. cerevisiae diversity.The AMOVA revealed an impact of the farming system on the population structure Table a. HoweveS. cerevisiae strains involved in the winemaking process, but the link between grape and cellar S. cerevisiae populations is still poorly characterized till now. Thus, we aimed to evaluate connections between grape and cellar metapopulations.Grapes are one source of the S. cerevisiae strains from 11 spontaneously fermenting musts in 7 cellars. Clusters gathering grape and vat isolates were highlighted in the dendrogram tree . Because it was performed on a large scale, with five wine-producing appellations, 25 wine estates including 2 farming systems over two consecutive years, this study is unique compared to other studies which only considered 2 wine estates for each farming system43.In this study, the diversity and population structure of S. cerevisiae strains, with more than 6000 unique genotypes. This region scale estimate is nearly four time higher than the estimate of 1700 inferred for the metapopulation sample of New Zealand vineyard29, which may be related to the recent arrival of the New Zealand wine yeast population from European wine yeasts54.Our Merlot grape variety sampling served to estimate that the Bordeaux and Bergerac region is expected to contain a much wider diversity of S. cerevisiae diversity and population structure: the geographic factor associated with the “appellation”, the wine-estate and organic versus conventional farming systems. The influence of pest management systems on vineyard-associated yeast biodiversity is a key issue for the wine industry in the context of sustainable agriculture but is still a controversial ecological topic. Some authors showed that the use of phytosanitary treatments in the vineyards could negatively impact the yeast population diversity56, especially that of S. cerevisiae yeast38. But other studies have reported higher S. cerevisiae strains diversity in conventional must fermentation in comparison to organic ones and have demonstrated that fungicides have no impact on yeast counts on grapes and during the alcoholic fermentation39. In our study, based on a large numbers of wine estates25, the global estimation of the number of genotypes from a rarefaction curve, indicates an approximately two folds increase in the number of S. cerevisiae grapes isolated from grapes in vineyards under a conventional farming system when compared to organic operations. Similar results were obtained recently from Spanish vineyards showing intermediate to low S. cerevisiae strain diversity for organic vineyards but higher levels for conventional practices40. A lower fungal diversity of the microbial community due to repeated fungicide applications in conventional farming could explain this higher diversity index for S. cerevisiae observed among conventional farming systems. We can hypothesize that a lower competition for nutrients could offer more ecological space to S. cerevisiae. In addition, a low differentiation was observed between the two farming systems which indicates that this factor is not a main driver of the S. cerevisiae population structure in the Bordeaux and Bergerac area.The principal goal of this study was to evaluate the factors that may explain vineyard-associated S. cerevisiae starters in the vineyard and its impact on autochthonous yeast diversity is another topic that has been investigated by several authors in different wine producing areas. Previous studies reported concordant results indicating an infrequent dissemination of commercial yeast in the vineyard surrounding the winery, and show that the dissemination is restricted to short distances (maximum distance of 100 m around the dissemination area and in a limited period of time)43. By comparing indigenous S. cerevisiae genotypes with a database of 79 commercial wine strains commonly used by the wine industry, Gayevskiy showed that only a few isolates shared one microsatellite allele with commercial starters, thus supporting the concept that a diverse natural population resides in New Zealand35. Our data including 23 vineyards and 7 cellars reported around 25% strains isolated from grapes with a close genetic relationship with the commercial starters, echoing those of Viel et al.57 in Italy. The distance separating the closest cellars and the sampling area ranged from 1 to 350 m, supporting the fact that commercial strains can be transferred to the vineyard at longer distances than previously reported, mixing with the endogenous grape strains population. Dispersal of commercial strains could be mediated by water run-off, macerated grape skins at dumping sites41, but also by drosophila33, or even by the air through CO2-extraction systems58. The clonal variations observed among grape isolates related to industrial starters could be an indication of a long-term dissemination of yeast starters in the environment. This hypothesis is reinforced by the fact that the use of industrial yeast to inoculate grape juice has been widespread in the Bordeaux wine producing area for over 40 years. Even though some of these starters have been isolated from the Bordeaux region (e.g. VL1 in 1987), the high diversity estimated from our sampling and the spanning trees centered on yeast starters make it likely that these clusters are derived from the starters and not from the local clones from which starters are derived. Cellars could contribute to the vineyard diversity enrichment by enologically relevant S. cerevisiae strains that were previously selected for their fermentative properties. However, the transition from nutrient-rich musts to nutritionally scarce natural environments has been shown to induce adaptive responses for the clonal variants that have diminished capacities related to winemaking in comparison with the reference strain42. It will be interesting to extend this study at the genomic and phenotypic level to the biological material provided by this work.The persistence of commercial 29. However, regional delineations of natural S. cerevisiae populations have been evidenced35. In this study, we aimed to test for geographic differences in S. cerevisiae populations at the appellation scale in the Bordeaux and Bergerac wine-producing region. A population-based analysis revealed differentiation between appellations, indicating a certain population structure. This pattern was not explained by geographic distance. Insects like bees, wasps and fruits flies, or even birds, could disseminate S. cerevisiae especially when the different regions are apart less than 100 km apart34 and could thus be responsible for the homogenization of S. cerevisiae within regions29. In the case of the Bordeaux wine-producing area, our results suggest higher migrations between Pessac Léognan, and Saint-Emilion which are consistent with low pairwise Fst between these two appellations. When comparing the net flux for each region, both Pessac-Léognan and Saint-Emilion appear to export migrants, whereas the Medoc appear to be a region importing strains from other appellations.The question of regional differentiation is still open for winemakers and wine microbiologists. It has been shown that within regions (encompassing a radius of 100 km) in New Zealand, there is no compelling evidence of genetic differentiation between managed niches and native ecosystem and within managed ecosystemsA critical feature of the relevance of yeast diversity for winemakers is the correspondence between cellar population and wine estates grape populations. Using a balanced sample, we show for the first time a low differentiation between cellars and grapes populations for the first time. We were able to show that the connectivity between the two groups arises from the flow of grape strains from cellar to vineyard, illustrated by the high frequency of related commercial related strains isolated from grape samples. This is a new indication of a long-term dissemination of yeast starters in the environment. The estimates of the internal and external migrations between cellars and vineyards attest to the importance of the flux of yeast cells from the vineyard entering the cellar, which has been suggested but never been properly estimated. The small differentiation also suggests few differences in the life styles between the cellars and the vineyards.S. cerevisiae within an historical wine making region. The geographic appellation and the wine estate significantly impact the S. cerevisiae population structure, whereas the type of farming system has a weak global effect. Our results do not give credits to the concept of clones isolated at high frequency and specific to a given appellation . However, at the appellation scale, the populations presented some structure suggesting the presence of region-specific populations. At a smaller scale, some wine estates presented specific populations, but their persistence should be further evaluated in the long-term.Overall, this study provides original results on the diversity and population structure of One main feature is the high inter-connection between vineyard and cellar population, making it an almost continuous ecosystem that does not have a single direction, from grapes to cellars, but also from cellars to grapes.Five wine producing areas in the Nouvelle-Aquitaine region of the south west of France were selected corresponding to 4 Bordeaux appellations: Medoc, Pessac Leognan, Entre-Deux-Mers, Saint Emilion and to one in Bergerac Fig.  and S2. Saccharomyces strains. Briefly, for each of the 193 fruit samples, the grapes were crushed, then macerated for 2 h with their skins and seeds. After addition of 50 mg/l of SO2, the extracted juice was fermented at 21 °C in small glass-reactors (500 ml). Fermentation progress was monitored through the amount of CO2 released by a daily weighing measurement of glass-reactor to assess the weight loss. For both samples, from grapes and fermented vats, sampling was performed when fermentation reached about 2/3 of the sugar consumption or had been stopped. 166 grape fermentations and 11 fermented vats were showed to reach 2/3 of sugar consumption and then were sampled. Fermented musts were plated at different dilutions  onto YPD  with 100 µg ml−1 of chloramphenicol and 150 µg ml−1 of biphenyl to delay bacterial and mold growth. At the optimal dilution, a maximum of 30 colonies were randomly collected after incubation (2 days at 26 °C) for a given sample, thus resulting in 3369 colonies. After two sub-cloning on YPD plates, each colony was stored in  glycerol at − 80 °C. For the fermenting must samples from vats, the same dilutions were made and a maximum of 40 randomly chosen colonies were collected after incubation (2 days at 26 °C) for a given dilution. After two sub-clonings on YPD plates, each colony was stored in  glycerol at − 80 °C.Yeast strains were isolated from the juice extracted from the grapes after enrichment to ensure the presence of 59,  a total of 15.5 μl multiplexed primers, 50 µl of QIAGEN Multiplex PCR kit Master Mix and 18.5 μl water MilliQ. The PCRs were run in a final volume of 12 μl containing 2 μl of cell suspension. The following PCR program was used in the routine: initial denaturation at 95 °C for 15 min followed by 35 cycles of 95 °C for 30 s, 57 °C for 2 min, 72 °C for 1 min and finally a final extension at 60 °C for 30 min. PCR products were sized on a capillary electrophoresis ABI3730 (APPLIED BIOSYSTEMS) using size standard 600LIZ (GENESCAN). Locus YLL049W providing non-reproducible amplification was removed for the subsequent structure and diversity analyses. Capillary electrophoresis runs were read using GENE MARKER  and the sizes of microsatellites amplicons were recorded to investigate the genetic relationships between strains. The presence of missing values was allowed up to 3 loci per individual and these were taken into account in the analyses in order to obtain a comprehensive picture of yeast diversity.Yeast colonies of all grapes and vats samples were cultivated on differential WL nutrient agar medium (2 days at 26 °C) which generated a specific coloration depending on their genus and 2 of each type of colonies were filed on FTA cards for DNA transfer. The PCR amplification of the ITS region with primers ITS1 and ITS467: the Shannon (H′) index that measure the diversity within a population and take into account both richness and evenness, the Simpson index (D) with its opposite Simpson’s index of diversity (1 − D) which gives more weight to common or dominant species, and the Pielou evenness index (J′). Estimation of population diversity by rarefaction of 10,000 individuals was repeated 10 times. H′ was determined with the following equation:We calculated three diversity indices using ESTIMATES V968 was used to remove from our dataset strains with similar profiles resulting from potential clonal expansion. Strains profile comparison with yeast starters was performed using the BIONUMERICS V5.1 software  with the categorical coefficients associated with the ward algorithm69 from the microsatellite data size. Strains sharing more than 75% of alleles at 17 loci with commercial strains yeast starters and no missing values were considered as related to these starters. They were retained for spanning tree drawing and removed from the different datasets. These spanning trees were drawn with the BIONUMERICS V5.1 software. Dendrograms were constructed using Bruvo’s distance50 as proposed by the POPPR 2.0270 and neighbor-joining clustering with ape 3.271 under the R environment v3.5.2 (R DEVELOPMENT CORE TEAM 2011). The Bruvo distance requires perfect microsatellite loci, which is almost the case for 17 out of 18 loci but not for locus C4 which is composed of two motifs (one locus over 17). The use of this distance provides a phylogenic signal closer to genome sequencing than that observed with Dc Chord distance72. As a consequence, we have retained this distance, despite potential violation of the initial model proposed by Bruvo et al.50.73 and inferred with MEGA6, all bootstraps lower than 25 were not shown in the trees.In order to assess the robustness of tree nodes, bootstrap resampling was performed by means of R and the pvclust 1.3-2 package51 which is the case for yeast. Five chains of 150,000 iterations with a burn-in of 5000 were run for K = 1 to K = 25. The most likely number of ancestry’s populations was selected choosing the lowest DIC (Deviance information criterion). Bar plots presenting the ancestry profile for each population were drawn from the InStruct output file using an R script. On each population, basic statistics were estimated with poppr v2.8.3 and the DiveRsity v1.9.90 package, and provided as averages between loci. AMOVA pas performed with the Pegas package v0.12 as proposed by the poppR R package. Pairwise Fst distance and significance tests, and the Mantel test evaluating the correlation between geographic distance and genetic divergence, were performed as implemented with GenAlex v6.576. Population geographic differentiation was also performed from the ancestry profile as implemented by the OBSTRUCT V1.0 software52.Population structure was evaluated using the Bayesian clustering method implemented by the software InStruct that considers the inbreeding is the main sexual mode of reproduction53 with the website divMigrate (https://popgen.shinyapps.io/divMigrate-online/), using the D distance77. Directional migration rates between the four appellations, Medoc (38 genotypes), Pessac Leognan (89 genotypes), Saint-Emilion (7 genotypes) and Entre deux-Mers (5 genotypes), were inferred using Migrate 4.2.14, assuming constant population size78. The dataset included only isolates from the 2012 sampling year, and the 4 loci  which contained several missing data were removed. Identical clones were discarded after GENECLONE analysis, and isolates related to LSA were removed from this dataset.Relative directional migration rates between the five appellations were estimated using the approach proposed by Sundqvist et al.https://doi.org/10.15454/GMRGPO).For grape and cellar population comparison, data sets containing a maximum of 20 individuals were built by random sampling using a custom R script, from the data of the 4 cellars (or grapes) from Pessac Leognan, Bergerac and Saint Emilion in order to avoid unbalanced sampling between cellars and between regions. Fst were estimated from the sampled population using a custom R script and Fst calculated using the Hierfstat v0.04-22 package under the R environment. This randomized dataset was then further used for the estimation of exchanges between cellars and grapes using Migrate 4.2.14. Given the small proportion of missing data, the 17 loci were used for the analysis. All these datasets are available on the Open Data portal of INRAE (Supplementary Information.


We have identified a 31.7 Mbp interval on the short arm of wheat chromosome 4D that is involved in increased Type II susceptibility to Fusarium head blight. Fusarium head blight (FHB) causes significant grain yield and quality reductions in wheat and barley. Most wheat varieties are incapable of preventing FHB spread through the rachis, but disease is typically limited to individually infected spikelets in barley. We point-inoculated wheat lines possessing barley chromosome introgressions to test whether FHB resistance could be observed in a wheat genetic background. The most striking differential was between 4H(4D) substitution and 4H addition lines. The 4H addition line was similarly susceptible to the wheat parent, but the 4H(4D) substitution line was highly resistant, which suggests that there is an FHB susceptibility factor on wheat chromosome 4D. Point inoculation of Chinese Spring 4D ditelosomic lines demonstrated that removing 4DS results in high FHB resistance. We genotyped four Chinese Spring 4DS terminal deletion lines to better characterize the deletions in each line. FHB phenotyping indicated that lines del4DS-2 and del4DS-4, containing smaller deletions, were susceptible and had retained the susceptibility factor. Lines del4DS-3 and del4DS-1 contain larger deletions and were both significantly more resistant, and hence had presumably lost the susceptibility factor. Combining the genotyping and phenotyping results allowed us to refine the susceptibility factor to a 31.7 Mbp interval on 4DS. Triticum aestivum) and barley (Hordeum vulgare). In wheat, the primary symptom is the premature bleaching of spikelets that progressively spreads through the head. Infected spikelets produce shrivelled and chalky grain, which can have a significant negative impact on yield. Furthermore, mycotoxins accumulate in infected grain, which are harmful to humans and animal consumers. The most important mycotoxin is deoxynivalenol (DON) which acts as a virulence factor in wheat by promoting the spread of the fungus  is an economically important fungal disease of various cereal crop species, in particular wheat (O-glucoside (D3G)  and to the spread of infection through the rachis (Type II) were first proposed by Wheat and barley differ noticeably in Type II resistance. Wheat typically possesses some degree of Type II susceptibility whilst, in contrast, barley is generally highly resistant to fungal spread through the rachis .Fhb1, the most widely deployed FHB resistance QTL, remains controversial, there is evidence that Fhb1 may be considered a disrupted susceptibility factor  and DT(4DS) lack 4DS and 4DL, respectively. Four homozygous Chinese Spring terminal deletion (CSTD) lines of 4DS, described by http://plants.ensembl.org/Triticum_aestivum/Info/Index). Gene names and the physical positions reported correspond to the IWGSC RefSeq v1.1 wheat genome assembly , whilst avoiding amplification of wheat orthologues . PCRs were prepared using Taq Mastermix (Qiagen) and amplified using the following PCR settings: 94 °C 3 min; 35 cycles of 94 °C 30 s, 58 °C 30 s, 72 °C 1 min; and 72 °C 10 min. PCR products were separated on a 0.8% (w/v) agarose gel.F. graminearum (UK1) or F. culmorum (Fu42) were used in disease experiments. Production of inoculum was carried out as described previously in 6 spores ml−1, was injected into a spikelet approximately central on the wheat head. The spread of disease symptoms was scored at 2–4 d intervals between 7 and 21 days post-inoculation (dpi). Polytunnel experiments were organized in a randomized complete block design with four replicates each containing four or five plants per line. For the glasshouse experiment, at least 16 plants per lines were randomized and individual inoculated heads were considered as replicates.Highly virulent DON-producing isolates of Disease data were analysed using a linear mixed model (REML) in Genstat software (v18.1) to assess the variation attributable to line (fixed), inoculation date (fixed), the interaction between line and inoculation date (fixed), and replicate (random), where factors were significant in the model. Data from which residuals were not normally distributed or where residuals did not appear independent of fitted values were log10 transformed, which was sufficient in correcting for these assumptions. Predicted mean and SE values were calculated for lines included in the REML. Pairwise comparisons were made between the wild-type wheat parent/genetic background and the other genotypes tested in each experiment using Fisher’s protected least significant difference. All predicted values generated from transformed data were back-transformed to the original scale for presentation.–1 amended with 0.01% (v/v) Tween-20] was applied to the two outer florets of each cut spikelet, between the palea and lemma. To increase the humidity at the site of DON application, treated wheat heads were bagged. At 48 h post-application, the DON application was repeated, and heads were bagged again. Hence, each treated wheat head received a total application of 0.8 mg of DON. After a further 48 h, crossing bags were removed from the DON-treated heads. The severity of bleaching for each treated wheat head was scored, out of 10, daily between 5 d and 9 d post-application (from the first application). A score of zero was given when no evidence of DON damage was present and a score of 10 was recorded when the spike was completely bleached above the point of DON application. Scores between 1 and 9 were used to record the progressive yellowing and bleaching of the DON-treated wheat heads, which occurred relatively uniformly above the point of DON application in the case of Chinese Spring (JXB online). After the experiment, DON-treated and untreated heads from each plant were harvested. From each plant with a DON-treated head, a comparable untreated head (with similar spikelet number and head length) was selected for grain weight analysis. Grain number and grain weight data were collected from DON-treated and comparable untreated heads from each plant, to observe any difference in the effect of DON on grain filling.DON was purified to >98% at IFA-Tulln, as described by DON bleaching data and associated grain data were analysed using a REML. In all cases of both DON bleaching data and grain data, transformation of data was not necessary, as residuals appeared to be normally distributed and independent of fitted values based on visual checking. For bleaching data, line was included as a fixed term and replicate as a random term in the model. For DON grain data, the REML model was constructed using line, treatment (DON-treated or untreated heads), and the interaction between line and treatment as fixed terms, and replicate as a random term.FHB point inoculation experiments of the wheat–barley material were conducted twice and are described as experiment 1  and expeP <0.001 in both experiments). Whilst the 3HS.3BL centric fusion line (3HS.3BL centric) was more highly susceptible in experiment 1 (P <0.001), the line showed similar disease to Mv9kr1 in experiment 2 (P=0.566). The addition of chromosomes 1HS (1HS add) and 7H (7H add), in addition to the 5HS-7DS.7DL wheat–barley translocation (5H-7D trans) and the 2DS.2DL-1HS translocation line (2D-1H trans), all showed highly significant increases in FHB resistance compared with Mv9kr1 . The 3H addition (3H add) was inconsistent between the two experiments. In experiment 1, the 3H addition was significantly more susceptible to FHB than Mv9kr1 (P=0.004) whilst, in experiment 2, it was significantly more resistant (P<0.001).The addition of barley chromosomes 2H (2H add) and 6HS (6HS add) appeared to have no effect on FHB resistance in either experiment. Disease symptoms in these lines were not statistically significantly different from those of Mv9kr1. The 6BS.6BL-4HL translocation (6B-4H trans) was significantly more susceptible than Mv9kr1 (P<0.001 in both instances). In contrast to this, the addition of barley 4H (4H add) showed similar disease levels to Mv9kr1 in experiment 1  substitution, in which disease was almost entirely restricted to the inoculated spikelet in both experiments , although the line was still significantly more susceptible than Betzes (P=0.042). The 5HS addition line was also statistically significantly more resistant compared with Chinese Spring (P=0.039). A marker targeting the barley UGT gene, HORVU5Hr1G047150, confirmed that this gene was present in Betzes and the 5HL addition line, but was absent in the 5HS addition line (The 5HL addition line exhibited significantly increased FHB resistance when compared with Chinese Spring (ion line . ConsistP<0.001).The contrast in the effect of adding 4H or substituting 4D with 4H indicated that the presence of 4D may be responsible for a significant proportion of the susceptibility of both Mv9kr1 and Chinese Spring. To test this possibility, Chinese Spring and two DT lines: DT(4DL) and DT(4DS), missing 4DS and 4DL, respectively, were tested in three independent FHB point inoculation experiments. Data are presented here from a 2013 experiment conducted in a glasshouse, but the results were replicated in a 2013 experiment under controlled conditions and in a polytunnel experiment conducted in 2016. Chinese Spring and DT(4DS), missing 4DL, showed very similar disease symptoms to each other . In contP=0.222) (P<0.001)  and DT(4DS). Chinese Spring was moderately susceptible to DON, with an average bleaching score of 3.39 . DT(4DS)P=0.222) . On the P<0.001) .Grain was harvested and dissected from DON-treated and untreated heads to assess any difference in grain weight. These data mirrored the bleaching data. Chinese Spring and DT(4DS) showed similar reductions in grain weight when comparing DON-treated and untreated heads  . AverageThese data suggest that DON is not implicated in the function of the susceptibility factor. However, there does appear to be an independent DON resistance factor also on 4DS.Experiments using 4D DT lines strongly suggest that the FHB susceptibility attributed to chromosome 4D is isolated to the short arm (4DS). Genotyping was performed on four Chinese Spring lines with terminal deletions on 4DS to verify the deletions present and more precisely position the deletion breakpoint in each line relative to the physical map. Markers were designed that can reliably detect genes on 4D and their homoeologues on 4A and 4B. The ability to detect and distinguish all three homoeologues provides two internal positive controls for each marker when identifying deletions of any particular homoeologue. Up to five markers, tagged using different fluorophores , were multiplexed into a single sample for efficiency, using markers designed to produce PCR product sizes sufficiently different for each gene target and its respective homoeologues when resolved using capillary electrophoresis .Genotyping was successful in identifying genes and their respective physical positions, flanking the deletion breakpoint in all four 4DS CSTD lines . A markeP=0.796) and del4DS-4 (P=0.278) showed similar disease levels to that of euploid Chinese Spring (P<0.001 for both lines)  were point-inoculated in a polytunnel experiment in 2017 . Chinesee Spring . Lines dh lines) .This information was used to infer that the susceptibility factor was present in the two deletion lines carrying the smaller deletions (del4DS-2 and del4DS-4) but was lost in the two lines containing the larger deletions (del4DS-3 and del4DS-1). Hence, the FHB susceptibility factor appears to reside between the deletion breakpoints of del4DS-4 and del4DS-3—a 31.73 Mbp interval .P=0.019). However, del4DS-4  del4DS-3 , and del4DS-1  all showed statistically significantly higher susceptibility to DON bleaching symptoms than Chinese Spring .Previous studies have shown that barley is able to detoxify DON through glucosylation by the UGT HvUGT13248  and the short arm of chromosome 1H (1HS add), or the translocation of 1H to 2D (2D-1H trans), significantly increased Type II FHB resistance in the winter wheat variety Mv9kr1. Despite enhanced FHB resistance from the 7H addition to Mv9kr1, the addition of neither 7HS nor 7HL had an effect in the Asian spring wheat cultivar Chinese Spring. 1H addition lines were not available in the Chinese Spring–Betzes addition set, so this could not be compared between populations. These findings suggest that barley contains genes conferring Type II resistance that are lacking in one or both wheat varieties. The addition of barley chromosomes 5H and perhaps 1H and 7H is likely to offer the best opportunity of enhancing FHB resistance, when considering the use of wheat–barley introgressions.We confirmed that the presence of the short arm of chromosome 4D was increasing Type II FHB susceptibility in three independent experiments. The loss of 4DS [line DT(4DL)] resulted in a high level of FHB resistance, whilst the loss of 4DL [line DT(4DS)] resulted in little change compared with Chinese Spring. We performed FHB disease experiments on the four genotyped 4DS CSTD lines. This clearly demonstrated that the lines with the two smaller deletions (del4DS-2 and del4DS-4) retained the susceptibility factor and showed a similar phenotype to euploid Chinese Spring, while the two lines containing the larger deletions (del4DS-3 and del4DS-1) showed significantly increased FHB resistance and hence the susceptibility factor has presumably been lost. As del4DS-4 exhibited wild-type FHB susceptibility but del4DS-3 was comparatively more resistant, the cause must be situated between the deletion breakpoints of these two lines, restricting the susceptibility factor to a 31.7 Mbp interval containing 266 high confidence genes . AlternaIt may be considered surprising that an FHB susceptibility factor with such a powerful effect has not been detected before now. However, we hypothesize that the FHB susceptibility factor is highly conserved among wheat cultivars. The susceptibility factor exists both in the Hungarian winter wheat cultivar Martonvasari 9 and in the Asian spring wheat variety Chinese Spring. Preliminary experiments of a γ-irradiated Paragon line containing a deletion of the entire 31.7 Mbp FHB susceptibility interval indicates that this line possesses potent Type II resistance and hence confirms that the susceptibility factor is also present in the UK spring cultivar Paragon (data not shown). If there was sufficient allelic variation at the locus, the effect of the susceptibility factor is likely to have been detected as an FHB QTL in existing mapping populations. In the absence of such reports, we predict that the FHB susceptibility factor is fixed in both spring and winter wheats.Reduced height-D1 (Rht-D1) is located on the short arm of chromosome 4D. The mutant alleles Rht-D1b and homoeologous Rht-B1b (on chromosome 4B) result in reduced gibberellin sensitivity, causing decreased stem elongation and hence reduced plant height. These semi-dwarfing alleles were widely deployed during the Green Revolution and resulted in greatly increased crop yields  . Rht-D1 is at 18.78 Mbp on chromosome 4D, meaning that Rht-D1 is distal to the FHB susceptibility interval identified in this study , which did not show altered FHB symptoms. Furthermore, no obvious sign of altered plant height was observed in any of the Chinese Spring 4D DT or terminal deletion lines (data not shown). For these reasons, Rht-D1 is unlikely to be responsible for the altered FHB susceptibility observed in the 4DS deletion lines.p yields . Howevery to FHB . Both Chis study . Rht-D1 We also used 4D CSTD lines in a DON application experiment. All four lines showed increased susceptibility to DON-induced bleaching compared with Chinese Spring. The effect on grain weight was less pronounced compared with the 4D DT lines. However, both del4DS-2 and del4DS-4 showed greater reductions in grain weight compared with Chinese Spring. These data suggest that DON resistance is associated with the region distal to 51.6 Mbp on 4DS in Chinese Spring. Further experiments are necessary to confirm whether the DON resistance is present in additional wheat varieties.Fhb1 QTL, have been inconsistent and controversial (TaHRC) within the Fhb1 locus was sufficient in identifying varieties carrying Fhb1. Fhb1 possesses enhanced resistance due to the loss of function of TaHRC and that the wild-type allele is hence functioning as a susceptibility factor. TaHRC gene was responsible for Fhb1 resistance. However, in conflict with the findings of Fhb1 being a loss-of-function susceptibility factor. It remains possible that more than one gene is responsible for FHB resistance conferred by Fhb1.Genetic resistance to fungal diseases is critical to the protection of food crops such as wheat. The search for and incorporation of resistance factors is common practice in cereal breeding. However, identifying novel sources of resistance to FHB is challenging and time consuming. FHB resistance is quantitative, highly polygenic, and often environmentally labile. Few large-effect FHB QTLs have been identified. Attempts to clone the gene underlying the best-known source of FHB resistance, the mildew resistance locus o (Mlo) is one of the earliest and best characterized examples of how disruption of a susceptibility factor could be exploited to improve disease resistance; in this case, to powdery mildew caused by the biotrophic fungus Blumeria graminis f. sp. hordei , triggering a hypersensitive response that results in necrosis that benefits the fungus .R genes, usually nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes, are typically used by plants to detect and respond to attack by biotrophic fungi. However, necrotrophic pathogens have evolved methods of exploiting such plant defences to aid infection. Fusarium graminearum leads a hemibiotrophic lifestyle whereby the hyphal front remains surrounded by living tissue but cell death is triggered soon after colonization .Fig. S2. Agarose gel image following PCR of Chinese Spring–Betzes 5HS and 5HL addition lines using primers targeting the barley UDP-glucosyltransferase gene HORVU5Hr1G047150.Fig. S3. Example outputs of five multiplexed homoeologue non-specific markers used to genotype 4DS CSTD lines.eraa226_suppl_Supplementary_Figures_and_TablesClick here for additional data file.


Acropora cervicornis, which local managers and conservation organizations view as a potential source population for coral restoration projects. The aim of this study was to determine the genetic diversity of colonies across three banks within the protected area. We identified low genetic diversity (FST = 0.02) across the three banks, and genetic similarity of colonies ranged from 91.3 to 95.8% between the banks. Clonality rates were approximately 30% across the three banks, however, each genotype identified was unique to each bank. Despite the low genetic diversity, subtle genetic differences within and among banks were demonstrated, and these dense thickets were shown not to be comprised of a single or a few genotypes. The presence of multiple genotypes suggests A. cervicornis colonies from these banks could be used to maintain and enhance genetic diversity in restoration projects. Management of hope spots, such as Cordelia Bank, and the incorporation of genetic information into restoration projects to ensure genetic diversity within out-planted populations, will be critical in the ongoing challenge of conserving and preserving coral reefs.Amongst the global decline of coral reefs, hope spots such as Cordelia Bank in Honduras, have been identified. This site contains dense, remnant thickets of the endangered species  Coral reefs are reaching a tipping point, with phase shifts from coral to algal dominance becoming increasingly prevalent7, and potentially irreversible. As the biological and physical structure of coral reefs change, ecosystem service provision and the resilience of these systems to future stresses is reduced8. The loss of ecosystem services is of concern for coastal populations who rely on them, both directly, e.g., for fisheries9, and indirectly, e.g., for storm protection10. To abate phase shifts and conserve coral reef biodiversity, urgent management is required at both global and local scales.Coral reefs are under severe threat from global climate change. Particular issues include increases in sea surface temperature11. Of significance during this period was the loss of approximately 80% of Caribbean Acroporid corals, which was triggered by an outbreak of white band disease in combination with multiple climatic events, including hurricanes12. During the intervening decades, there has been little to no recovery of these populations, and both Acropora palmata  and A. cervicornis  have been listed as critically endangered by the International Union for Conservation of Nature14. However, remnant Acroporid populations have been documented throughout the Caribbean, e.g. in Mexico and Belize15, Honduras16, Guadeloupe17, U.S. Virgin Islands, St. Vincent and the Grenadines, Bonaire and Curacao18.Within the Caribbean, average coral cover declined from 34.8% in 1970 to 16.3% in 201217. Asexual or clonal reproduction strategies are associated with maintaining and preserving existing genetic diversity during periods of population decline and poor recruitment from sexual reproduction, a particular concern in fragmented and remnant populations19. Critically, remnant populations have the potential to become sexually extinct after prolonged periods of clonal growth, if recruitment of sexually reproduced individuals from other populations is low20. Which may be attributed to the Allee effect, as fertilization success in broadcast spawning corals, such as Acroporids, is density dependent21. Caribbean Acroporid populations are generally considered to be dominated by clones, and thus non-sexual reproduction, however, there are exceptions to this; high levels of genetic diversity have been observed in populations of A. palmata in Mexico, Belize15, and the Eastern Caribbean22, and A. cervicornis populations along the Florida Reef Tract23. Higher levels of genetic diversity suggest a greater prevalence of sexual reproduction, and within the Eastern Caribbean this has been considered to be related to habitat characteristics22. Sexual reproduction has the potential to promote genetic diversity and, therefore, the ability to respond to environmental change within a species, increasing resilience in the face of environmental stresses24, and may enhance species diversity at the community level25. Further, areas with high genetic diversity have been associated with higher coral cover26. Within A. cervicornis clumping of ramets, or clonal genotypes, has been observed across the reef scape27. This clumping suggests low genetic diversity at the micro-scale and increased genetic diversity at the macro-scale, therefore greater allelic diversity is observed in larger populations.Low genetic diversity and high clonal frequency can be common within Acroporid populations28. There is concern that recovery by natural processes may not be sufficient, e.g., if coral settlement is inhibited by algae29, interventions such as anthropogenic restoration may also be required30. In light of this, initiatives are focusing on remnant populations as potential seed populations31, at least at the local scale.Whilst the presence of remnant populations of threatened species is a cause for hope, there is a realization that coral reefs are unlikely to return to past configurations in terms of community assemblage. Therefore, the challenge for both the scientific and management communities is to maintain ecosystem function in these critical systemsA. palmata and A. cervicornis have been observed in Honduras16, and Guadeloupe17 and corals from these populations have the potential to seed the recovery of Caribbean Acroporid populations31. The Cordelia Bank Site of Special Importance to Wildlife is one such area. The reef system, located in the Honduran Caribbean, was identified to contain extensive A. cervicornis colonies16  was officially declared a Site of Special Importance for Wildlife in 2012, by the Honduran governmentA. cervicornis, as identified by Riegl et al.36. Sampling was not undertaken on Little Cay due to weather constraints. In-water observations were first conducted to confirm the suitability of sampling areas and ensure that the selected locations had close to 100% A. cervicornis coral cover. For each bank, 100 5 m × 5 m sampling cells were initially established across a 50 m × 50 m grid. Due to inclement weather and the risk of causing damage to the reef, the sampling grid was modified on the shallowest banks: Big Cay to 50 m x 25 m and on Cordelia Shoal to 50 m × 30 m. This provided a combined area of 5250m2, representing over 8% of the total estimated cover of A. cervicornis cover across the three banks.Sampling was conducted in April 2014 on three of the four banks: Big Cay; Cordelia Shoal and Smith Bank, based on the presence of high densities of The sampling grid was laid out on the reef using four 50 m measuring tapes, to demark the sampling area. Three additional measuring tapes were used to make horizontal internal lines at 5 m intervals, to create two adjacent rows. Flagging tape placed at 5 m intervals along the measuring tapes was used to demark individual sampling cells of 5 m x 5 m. Once sampling was completed for these two rows, measuring tapes were moved further up the reef to create two subsequent rows and repeated until the sampling was complete. Sampling started at the deepest part of the reef, working up to the shallows.A. cervicornis colony within each of the sampling cells. Cuttings were placed into individually labelled bags containing seawater, taken ashore and then transferred to 100% ethanol and frozen for storage prior to genetic analyses. Sampled colonies were chosen if: (1) they were the dominant colony within the grid that had a basal attachment, and had not been sampled in a previous grid; and (2) they had a minimum branch length of 17 cm, to ensure they were sexually mature38. If the dominant colony had been sampled previously, the next largest colony in the grid was sampled instead. Sampling only mature colonies was a specific strategy to detect the full genetic composition of the potential reproductive stock of A. cervicornis within the protected area. Each sample was geo-referenced, with GPS coordinates recorded by a snorkeler at the surface, and depth recorded to 0.1 m accuracy using a Matrix dive computer . A total of 205 samples were collected and successfully genotyped from across three offshore banks, Big Cay n = 50, Cordelia Shoal n = 57, Smith bank n = 98  at 56 °C and 600 rpm for 4 h. Once digestion was completed, DNA extractions followed the Qiagen DNeasy Blood and Tissue protocol. DNA concentration was calculated using a BioTek Epoch Microplate Spectrophotometer , and where necessary, DNA was concentrated to ensure that 20 ng of DNA was used in each subsequent amplification reaction.A. cervicornis colonies were genotyped using fourteen polymorphic microsatellite loci: 0166, 0181, 0182, 0192 & 020739 and 0513, 0585, 1195, 1490, 2637, 5047, 6212, 9253 & 000740. Polymerase chain reactions were carried out on BIO-RAD T100™ Thermal Cyclers , with an initial denaturation step at 95 °C for 5 min followed by 35 cycles of 95 °C for 20 s, 51–55 °C for 20 s, 72 °C for 30 s, and a final extension of 30 min at 72 °C, with the exception of 0007. This marker required an initial denaturation step at 95 °C for 5 min followed by 31 cycles of 95 °C for 15 s, 55 °C for 15 s, 72 °C for 30 s, and a final extension of 30 min at 72 °C. Genotyping was performed using an ABI 3730xl automatic DNA analyzer . An internal size standard  was used for accurate sizing. Electropherograms were analyzed using GeneMapper v.5.0 and alleles were subsequently binned with the R-package Msatallele version 1.0241. Genotyped colonies with more than 20% missing data (missing data from three or more loci) were removed from subsequent analyses. The locus 0192 did not genotype evenly across samples and therefore was removed from the analysis. All of the laboratory and computer work was conducted in and with the support of the Laboratories of Analytical Biology facilities of the Smithsonian’s National Museum of Natural History .Individual 42, a distance matrix was calculated using a stepwise mutation model, where missing data was not counted, the threshold was set at zero, and clonal structure was tested using a stepwise mutation model of the corrected Nei’s diversity index statistic with the randomize alleles over individual colonies of all three banks, using 999 permutations. These outputs were cross-checked in GenAlEx 6.543, which allows for the inclusion of colonies with missing data, using the matching function where all data is considered as a single population and alleles are codominant. Through this step, an additional three colonies were identified as clones and assigned to corresponding ramets. Where clones were corroborated, a single representative of the ramet was used in further analysis. Summary data of each locus  were calculated for each population, and pairwise FST and Nei unbiased genetic identity tests were conducted in GenAlEx 6.543.Clones were identified as genetically identical to another individual, and these individuals were then assigned to a ramet, using a two-step process. Firstly in GenoDiveA. cervicornis colonies was analyzed using the software STRUCTURE44, using an admixture model with allele correlation. The Burn-in period length was set at 100,000, and the number of Markov chain Monte Carlo replications after Burn-in was set at 100,000. We ran the model with K values of 1 through 10, and with 10 permutations for each K value. To identify the optimal K, the model outputs were analyzed in STRUCTURE HARVESTER45, with the highest delta K value used to identify the optimal K value. Mantel tests were conducted to test for correlations between genetic distance and geographic distance, and genetic distance and depth, and a partial Mantel test to test for partial correlations among all three, these analyses were conducted using the vegan package46. Additional Chi-squared analyses of clonal diversity across the three banks were conducted in R Studio version 1.2.133537.Population structure of p = 0.846). Across all alleles, the number of ramets varied among banks, as did the mean number of colonies per ramet, and neither was significant , nor was there an interaction between the number of ramets and the number of colonies per ramet, per bank  , varying from FST = − 0.032 to 0.102 across the individual banks , no relationship was observed between genetic distance and depth  or between genetic distance and a combination of geographic distance and depth . Population structure analyses highlight the similarities in the genetic structure of A. cervicornis colonies within and across the sampling locations, with individual colonies having both K clusters well represented and no individual colony fully assigned to either cluster  differs from how Acropora reefs are generally considered and what has been observed in other populations, e.g. Florida (Ng/N = 0.33), Belize (Ng/N = 0.39)47 and Guadeloupe (Ng/N = 0.01)17. The systematic sampling methodology used in this study, which ensured that multiple sexually mature colonies were sampled, can maximize the genetic diversity observed. This may have contributed to the lower prevalence of clonality than that observed in other studies. However, similar to this study, high frequencies of unique genotypes and low clonality have been observed in A. cervicornis populations, e.g., the Bahamas (Ng/N = 0.64), Turks and Caicos (Ng/N = 0.65), and Panama (Ng/N = 0.66). The high frequency of distinct, but similar, genotypes within and across the three banks of Cordelia Bank Site of Special Importance to Wildlife provide a small, but potentially significant, reservoir of genetic diversity. Whilst genetic diversity may be low within, and across A. cervicornis populations, significant, but weak, genetic differences driven by geographic distance were observed. This research did not address the drivers of genetic differentiation, and therefore we can only postulate that the genetic differentiation observed is a result of natural selection or a founder effect. These subtle genetic differentiations could be key in allowing individual colonies to adapt to future stressors, and therefore it is critical that this diversity is protected and conserved, this will become more important if these reefs, and others, are not restored through sexual recruitment.The extensive thickets of 48. Spawning activity within Acroporid populations has been observed in the Florida Keys, Panama and Belize, July through October49, and spawning in Belizean A. palmata has been observed most years from 2010–2019 . Monitoring for spawning activity of A. cervicornis in Cordelia Bank Site of Special Importance to Wildlife was last conducted during the full moons of June, July and August 2013. No spawning was observed in A. cervicornis colonies during this period, however, spawning of Orbicella annularis and O. faveolata was observed during the August full moon . Spawning in multiple A. palmata colonies in Tela Bay, Honduras, was observed during the same August 2013 full moon . These observations suggest that environmental cues to trigger spawning are present in the region. Whilst the potential for natural recovery exists, even where spawning has regularly been observed, the overall cover of Acroporids has remained low50. It is therefore likely that further intervention is required to assist in the recovery of Caribbean Acroporid populations.Maintaining this genetic diversity will be crucial if sexual reproduction events are triggered in the future; such events provide the opportunity to increase genetic diversity within populations52 and within the Caribbean over 150 projects in more than 20 countries have been implemented53. Coral gardening, a preferred technique in the Caribbean, inherently limits genetic diversity as the technique focuses on the growing and out-planting of clones54. Despite genetics being an important factor that complements traditional restoration ecology methodologies55, and ensures ecological and evolutionary processes are incorporated into the restoration process56. Genetic diversity provides colonies with the potential to respond to changing environmental conditions, and where no genetic variation exists, responses are limited to phenotypic plasticity to deal with these stressors. During restoration there is the potential for the loss or reduction of fitness in the restored population, driven by founder effects, genetic swamping and inbreeding or outbreeding depression32. Greater attention needs to be given to genetic diversity when restoring systems57, especially when projects are dominated by a single species, such as coral gardening of A. cervicornis, the genetic diversity represents the primary biodiversity of the habitat. Genetic composition of out-planted colonies is one of many important criteria that should be considered within a best practices approach to restoration58.Restoration is becoming an increasingly popular tool for conservation and management of marine habitats59. In the specific case of Cordelia Bank, fishing and recreational activities have been excluded from highly sensitive areas, however, urban runoff and untreated effluents from Coxen Hole, and the proximity of two major cruise ship docks and an international airport, represent potential major threats60. If coral reefs are to have sufficient resilience to climate change and continue to provide critical ecosystem services to the coastal communities that depend on these resources, the drivers of their decline must be reduced. Whilst management cannot prevent the damaging effects of major disturbances, it can provide protection to reefs that have the greatest potential to be resilient and contribute to recovery through natural processes61. Natural regeneration promotes more complex and resilient systems than active restoration62, therefore restoration should be considered as one of a multitude of management tools in the conservation of coral reefs.Understanding the drivers of existing genetic structure and environmental conditions will be important in the successful management and conservation of these populations, and of restoration projects that use colonies from these populations. If a restoration project using colonies from Cordelia Bank Site of Special Importance to Wildlife is to be implemented, then the genetic diversity across the banks observed in this study should be considered. Selectivity of colonies during the restoration process can ensure representation of a range of genotypes maximizing the potential for evolutionary adaptation of corals within a restored area. There is an important caveat that underlies this potential and the future of the corals within Cordelia Bank, the Caribbean, and globally. Understanding and reversing the ultimate localized drivers of reef decline  must be part of comprehensive local and regional management strategies. The coral populations of the Mesoamerican barrier reef system, which encompasses Cordelia Bank, are under pressure from ocean acidification, hurricanes, pollution and fishing, and at high risk from mass bleaching over the next decades, and the ecosystem has been categorized as critically endangered by the IUCN


Obsessive-compulsive symptoms (OCS) in the population have been linked to obsessive-compulsive disorder (OCD) in genetic and epidemiological studies. Insulin signaling has been implicated in OCD. We extend previous work by assessing genetic overlap between OCD, population-based OCS, and central nervous system (CNS) and peripheral insulin signaling. We conducted genome-wide association studies (GWASs) in the population-based Philadelphia Neurodevelopmental Cohort  of the total OCS score and six OCS factors from an exploratory factor analysis of 22 questions. Subsequently, we performed polygenic risk score (PRS)-based analysis to assess shared genetic etiologies between clinical OCD (using GWAS data from the Psychiatric Genomics Consortium), the total OCS score and OCS factors. We then performed gene-set analyses with a set of OCD-linked genes centered around CNS insulin-regulated synaptic function and PRS-based analyses for five peripheral insulin signaling-related traits. For validation purposes, we explored data from the independent Spit for Science population cohort . In the PNC, we found a significant shared genetic etiology between OCD and ‘guilty taboo thoughts’. In the Spit for Science cohort, we additionally observed genetic sharing between ‘symmetry/counting/ordering’ and ‘contamination/cleaning’. The CNS insulin-linked gene-set also associated with ‘symmetry/counting/ordering’ in the PNC. Further, we identified genetic sharing between peripheral insulin signaling-related traits: type 2 diabetes with ‘aggressive taboo thoughts’, and levels of fasting insulin and 2 h glucose with OCD. In conclusion, OCD, OCS in the population and insulin-related traits share genetic risk factors, indicating a common etiological mechanism underlying somatic and psychiatric disorders. OCD affects 2–3% of the world’s population3. OCD is moderately heritable, with approximately 40% of the phenotypic variance explained by genetic factors, and a higher genetic heritability has been reported in childhood onset OCD6. The genetic architecture of OCD is complex, with multiple genetic variants of small effect size contributing to its etiology. This has hampered the identification and replication of genetic susceptibility factors. A meta-analysis of hypothesis-driven candidate gene association studies has implicated serotoninergic and catecholaminergic genes in OCD, while studies focusing on glutamatergic and neurotrophic genes have shown inconsistent results7. Neither of the two independent genome-wide association studies (GWASs) of OCD9 nor a subsequent meta-analysis 10—including children, adolescents and adults—yielded genome-wide significant findings, likely due to lack of power. However, the meta-analysis demonstrated that the polygenic signal from either sample predicted OCD status in the other sample, indicating the polygenic nature of the disorder10.Obsessive-compulsive disorder (OCD) is a heterogeneous psychiatric condition characterized by persistent, intrusive thoughts and urges (obsessions) and repetitive, intentional behaviours (compulsions)11. However, factor analyses of OCD symptoms have consistently identified specific OCD symptom clusters or dimensions, with the most reliable including contamination/cleaning, doubt/checking, symmetry/ordering, and unacceptable/taboo thoughts14.The diagnosis of OCD is based solely on clinical symptoms, and no genetic or biological markers are available with sufficient specificity and accuracy to be clinically actionable17. Indeed, 21 to 38 % of individuals in the population endorse obsessions and/or compulsions, although only a small minority (2–3 %) meet the DSM-5 criteria for a clinical OCD diagnosis19. OCS are also heritable, with their heritability ranging from 30 to 77%21. In addition to contributing to overall OCS, genetic factors contribute to specific OCS dimensions, including contamination/cleaning24 and checking/ordering25. Genetic overlap between clinical OCD and OCS in the population is suggested by the fact that polygenic risk scores (PRS) based on OCD GWAS data significantly predict OCS in two population-based samples of 6,931 and 3,982 individuals, respectively26. Moreover, a very recent analysis found that compulsive symptoms in the general population overlap with the genetic liability for clinical OCD27, and incorporating compulsive symptom GWAS data in a meta-analysis with OCD GWAS data yielded new findings in gene-based and gene enrichment analyses. Therefore, genetic studies of OCS in the general population could aid in the identification of susceptibility loci for clinical OCD and provide insight in specific symptom domains affected by individual genetic risk factors.Obsessive-compulsive symptoms (OCS) are also present in the general population28. Support for a role of dysregulated insulin signaling in OCD and OCS comes from studies showing increased OCS in men with type 1 diabetes29 and from a study indicating that OCD patients have a higher risk of developing type 2 diabetes (T2D)30. Furthermore, OCS were found to be positively correlated with blood levels of glycosylated hemoglobin (HbA1c), a diagnostic measure of T2D31, and OCD patients had markedly higher levels of fasting glucose (a characteristic of T2D)32.In a study aiming to identify molecular mechanisms underlying OCD, we earlier performed integration of the top-ranked results from the existing GWASs with genes implicated in OCD through other evidence. This resulted in a ‘molecular landscape’ that suggested the involvement of genes regulating postsynaptic dendritic spine formation and function through central nervous system (CNS) insulin-dependent signallingIn this paper, we aimed to assess the presence and extent of genetic overlap between OCD, OCS in the population, and insulin signaling, using the largest available data sets. Specifically, we parsed phenotypic heterogeneity using an exploratory factor analysis of OCS measured in a population cohort of children and adolescents. Subsequently, using PRS-based analyses, we investigated the presence of shared genetic etiologies between OCD and the total and factorized OCS. We then assessed genetic sharing between OCD, OCS, and insulin-related traits. In addition to assessing shared genetic etiologies, we tested for potential overlapping biology using gene-set analyses. Lastly, we extended our findings in an independent population cohort of children and adolescents.37, which includes 8,719 children and adolescents aged 8–21 years with neurobehavioral phenotypes and genome-wide genotyping data. Participants in the PNC provided written consent for genomic studies when they presented to the Children’s Hospital of Philadelphia health care network. OCS were assessed with GO-ASSESS, a computerized version of the Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADs)38. For the current study, we selected 22 GO-ASSESS questions that corresponded to the diagnostic criteria for OCD First, using SPSS 23 , we determined the internal consistency (Cronbach’s α) of the 22 questions that constitute the total OCS scores in the 650 PNC participants. We then conducted a factor analysis of the scores on the 22 questions using Promax rotation to determine the number of factors that, when combined, explains the largest portion of the observed variance in the total OCS score. Specifically, we considered scree plots, eigenvalues >1 and the cumulative variance explained when selecting the number of factors and assigned questions to factors based on the highest absolute loading value.P < 1 × 10−6) (details in the Supplementary Methods). The imputation protocol used MaCH39 for haplotype phasing and minimac40 for imputation. Imputed SNPs with low imputation quality score (r2<0.8) and low MAF (<0.05) were removed. If the total OCS score or the scores for the OCS factors fell within the limits of a normal distribution , we used a continuous trait design for the genome-wide association analysis. Otherwise, we used a pseudo case-control design, in which all individuals with a score of 0 for a factor were defined as ‘controls’ and compared against the ‘pseudo cases’, i.e., all individuals with a score of 1 or more for that factor. GWASs were carried out with mach2qtl39 using the total OCS score and the scores for those factors that showed sufficient variation as phenotypes, with age, gender, and the four principal components from MDS included as covariates. GWASs were performed separately for each genotyping platform and combined in an inverse-variance-weighted meta-analysis using METAL41, accounting for genomic inflation.Quality control filtering was applied to the genetic data to remove non-European individuals, single nucleotide polymorphisms (SNPs) with low minor allele frequency (MAF) (<0.05), poor genotype call rate (<95%), and deviations from Hardy-Weinberg equilibrium (10 (data provided through the Psychiatric Genomics Consortium (PGC) for 2,688 OCD cases and 7,037 controls) as the ‘base’ sample for polygenic risk score (PRS)-based analyses in PRSice42. The summary statistics from the GWASs of the different OCS in the PNC were used as the ‘target’ samples for the PRS-based analyses. For details see Supplementary Methods.First, we determined the level of shared genetic etiology between diagnosed OCD and OCS in the population. For this, we used the summary statistics from the meta-analysis of the two published GWASs of OCD42, as described above. As base samples, we used summary statistics data from GWASs of the following peripheral insulin signaling-related traits: type 2 diabetes (T2D) and the blood levels of four T2D markers: HbA1c, fasting insulin, fasting glucose and glucose 2 h after an oral glucose challenge (2hGlu) (details in Supplementary Methods). As target samples of the PRS-based analyses, we used the summary statistics from the OCD GWAS meta-analysis and the GWASs of the total OCS score and the scores for the OCS factors in the PNC. Multiple comparisons correction for all tests performed  was done using the Benjamini-Hochberg false discovery rate (FDR) method. With this method, we aggregated the calculated p-values of the shared genetic etiology analyses44, which is similar to the approaches used in earlier studies working with multiple phenotypes and PRSice46.To determine the level of genetic sharing between five peripheral insulin signaling-related traits and OCD as well as OCS, we conducted PRS-based analyses in PRSice28 (see above). In this paper by van de Vondervoort et al., the OCD landscape was built based on proteins that have been implicated in the disease through different types of genetic evidence. Firstly, proteins were included if their corresponding genes have been implicated in OCD through SNPs from the published GWASs that are associated at P < 1.00E-04 and are located within the gene and/or 100 kb of flanking downstream and upstream sequences. In addition, genes/proteins were included that have been implicated in other ways in OCD etiology. After critical evaluation of the literature, only genes/proteins that have received support through findings from (genetic) animal studies, gene mutations, and/or two or more independent candidate gene association studies  and/or mRNA/protein expression studies, were included. Our selection resulted in a set of 51 autosomal genes for subsequent analyses. Using the GWAS results of the total OCS score and the scores for the OCS factors, competitive gene-set analyses were then performed using the Multimarker Analysis of GenoMic Annotation (MAGMA) software47, see Supplementary Methods. P-values were considered significant if they exceeded a Bonferroni-corrected threshold accounting for the number of phenotypes tested  = 7.14E-03). For significant gene-set associations, we looked at the individual gene-wide P-values and applied Bonferroni correction (P < 0.05/51 genes in the gene-set = 9.80E-04).We first compiled a set of genes encoding proteins from our molecular landscape of OCD48. OCS were measured using the Toronto Obsessive-Compulsive Scale (TOCS), a validated 21-item parent-or self-report questionnaire15. TOCS items are scored from −3 (far less often than others of the same age) to +3 (far more often than others of the same age). We first assessed which TOCS questions could be grouped into OCS factors similar to those calculated based on the PNC data. Only two OCS factors (being ‘symmetry/counting/ordering’ and ‘contamination/cleaning’) were similar and therefore could be used for validation purposes, see Supplementary Table 49 and GWAS details in the Supplementary Methods. Using summary statistics of the GWASs of the two TOCS OCS factors, we examined the shared genetic etiology between OCD and the TOCS OCS factors, and between the five peripheral insulin signaling-related traits and the TOCS OCS factors. As described above for the PNC data, multiple comparisons correction was done using the FDR method for all tests performed in the Spit for Science cohort. Gene-set analyses between the set of 51 genes from the OCD landscape and the two TOCS OCS factors were also performed.In order to validate and possibly expand our findings, we performed PRS-based and gene-set analyses using data from GWASs of OCS in an independent population sample: the ‘Spit for Science’ project, which includes 16,718 children and adolescents aged 6–17 years recruited from a local science museumα = 0.69). Supplementary Fig. The internal consistency between the scores on the 22 OCS questions from the PNC was satisfactory    and for HbA1c and fasting glucose levels, we did not find evidence of genetic sharing. Lastly, we observed genetic sharing between 2 h glucose levels and OCD    revealed a significant association with ‘symmetry/counting/ordering’ P = .08E-03  and ‘contamination/cleaningTOCS’ .Two OCS factors were similar between the PNC and Spit for Science cohort, i.e., ‘symmetry/counting/ordering’ and ‘contamination/cleaning’   found genetic sharing between OCD and the OCS factor ‘guilty taboo thoughts’. In the larger Spit for Science cohort , we expanded our results by showing genetic sharing between OCD and ‘symmetry/counting/ordering’ as well as ‘contamination/cleaning’. Our findings are in keeping with the literature suggesting  genetic overlap between OCD and population-based OCS27. Since OCD is genetically correlated with other psychiatric disorders , future studies investigating OCS as (a) shared trait(s) between disorders could help address underlying biological mechanisms of comorbidity.In this study, we extended previous work by assessing genetic etiologies between OCD, OCS in the population, and CNS and peripheral insulin signaling. While previous studies51. Interestingly, SSRIs are particularly effective for treating harm-related obsessions, which are a part of ‘aggressive taboo thoughts’52. This is in line with our finding of genetic sharing between T2D and ‘aggressive taboo thoughts’. In addition, a recent study demonstrated that bilateral deep brain stimulation (DBS), a safe and effective treatment option for pharmaco-resistant OCD, not only reduced OCD symptoms but also decreased fasting insulin levels in the blood of both OCD patients with T2D and non-diabetic OCD patients53. Moreover, insulin in the CNS - either entering from the periphery by crossing the blood brain barrier54 or synthesized in the CNS55—has important non-metabolic functions, including modulating synaptic plasticity56 and learning and memory58.OCD and OCS have been linked to altered CNS and peripheral insulin signaling. When testing for potential overlapping biology, we found significant association between a set of 51 autosomal OCD genes centered around CNS insulin-regulated synaptic function and ‘symmetry/counting/ordering’. As for peripheral insulin signaling, we found genetic sharing between T2D and—based on the PNC data—‘aggressive taboo thoughts’, and—in the Spit for Science cohort—between T2D and ‘contamination/cleaning’. For two out of the four T2D blood markers (blood levels of fasting insulin and 2hGlu), we also identified a shared genetic etiology with OCD. These findings provide support for ‘dysregulated’ peripheral insulin signaling as a biological process contributing to both OCD and population-based OCS. Further evidence for a role of  peripheral insulin signaling in OCD etiology is suggested by the fact that selective serotonin reuptake inhibitors (SSRIs), the first-line pharmacological treatment for OCD, positively affect diabetic parameters when used to treat depressive symptoms in T2D 59.Although it is not clear yet what the relative contributions are of dysregulated peripheral and CNS insulin signaling to OCD and OCS, we recently demonstrated that compulsivity observed in Tallyho (TH) mice, a rodent model of T2D, is potentially linked to disturbances in insulin signaling. TH mice both displayed compulsive behaviour and increased glucose levels in their dorsomedial striatum, which could be due to decreased action of peripheral and/or CNS insulin, and the glucose levels correlated with compulsivity60. A limitation of the current study is the small sample size of the GWASs and limited power to discover new single genetic variant associations. However, this sample size was large enough to provide proof of concept for genetic sharing between OCD, OCS in the population, and insulin signaling. A second limitation we faced was that the questions in the discovery and validation cohorts were not exactly the same, which may partly explain the lack of validation. Another limitation may be that the proportions of the variance in the target phenotypes being explained by the base phenotypes are quite small. However, these ‘variances explained’ are in fact similar to or higher than those found in similar analyses, e.g., the PRS derived from a GWAS of OCD explained (only) 0.20% of the variance in OCS in a population sample20. Moreover, as the variance explained is dependent on the size of the ‘base sample’ for the generation of the PRS61, the observed variances explained with the still relatively small meta-GWAS of OCD as base sample may be underestimated.The current results should be viewed in light of some strengths and limitations. A strength is that we used quantitative symptom scores collected through questionnaires in the general population, which has enabled us to generate OCS phenotypes that we could then perform GWASs on. Using samples selected from the community may also reduce selection bias, which can occur when patient samples are analysed In conclusion, we identified a shared genetic etiology between OCD, OCS in the population, and both CNS and peripheral insulin signaling. Our results imply that altered insulin signaling is not only relevant for somatic disorders but is also involved in the etiology of psychiatric disorders and related symptoms in the population, especially OCD and OCS. Further studies are needed to disentangle the contributions of peripheral and CNS insulin production and signaling to these disorders and symptoms.Supplementary MethodsSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Figure 1ASupplementary Figure 1BSupplementary Figure 2Supplementary Figure 3ASupplementary Figure 3BSupplementary Figure 3CSupplementary Figure 3DSupplementary Figure 3ESupplementary Figure 4ASupplementary Figure 4BSupplementary Figure 5ASupplementary Figure 5BSupplementary Figure 5C