The olive is a fruit tree species with a century-old history of cultivation in the Mediterranean basin. In Apulia , the olive is of main social, cultural and economic importance, and represents a hallmark of the rural landscape. However, olive cultivation in this region is threatened by the recent spread of the olive quick decline syndrome (OQDS) disease, thus there is an urgent need to explore biodiversity and search for genetic sources of resistance. Herein, a genetic variation in Apulian olive germplasm was explored, as a first step to identify genotypes with enhanced bio-agronomic traits, including resistance to OQDS. A preselected set of nuclear microsatellite markers allowed the acquisition of genotypic profiles, and to define genetic relationships between Apulian germplasm and widespread cultivars. The analysis highlighted the broad genetic variation in Apulian accessions and the presence of different unique genetic profiles. The results of this study lay a foundation for the organization of new breeding programs for olive genetic improvement. Olea europea L. var. sativa Hoffm. e Lk.) is a fruit tree species with remarkable cultural and economic importance [http://www.fao.org/faostat/en/#home). In Italy, olive cultivation is mainly concentrated in the southern part, especially in the region of Apulia [The olive  approximately six thousand years ago . HistoriThe olive gene pool refers to thousands of cultivars, wild and feral forms. Italian germplasm includes more than 600 cultivars, even if many homonymies and synonymies are present, due to clonal selection, grafting and migration ,13.The study of olive genetic diversity is pivotal to guide the introgression of favourable allelic variants in future breeding programs, to assure food safety and protect product authenticity ,15,16,17Xylella fastidiosa subsp. pauca. The spread of the disease is favoured by the bacterial vector Philaenus spumarius [https://brevetti.cnr.it/InfoCatalogo.do?nsrif=547&dip=0).Olive cultivation is affected by climate change due to an increase of evapotranspiration and irrigation requirements and the occurrence of specific emergent pests and diseases ,20. Sincpumarius ,22. In tpumarius . The infpumarius . More repumarius . Fs-17 iMolecular markers are widely used in crop species, including olives, to dissect genetic diversity, characterize cultivars and identify synonymies and homonymies ,28,29,30In this study, the genetic variability of Apulian germplasm is explored, especially the one cultivated in Salento, as a first step for the identification of adaptation to environmental conditions and displaying enhanced bio-agronomic traits, including tolerance to diseases and pests. Synonymies and genetic relationships with Italian and international olive germplasm were established. The unknown accessions showing no OQDS symptoms and thus possibly tolerant to the disease were identified and characterized at the genetic level.The genetic variation among 218 olive cultivars was estimated using nine SSR markers. A total number (Na) of 189 alleles, ranging from 12 to 32 for the DCA05 and DCA18 loci, respectively, corresponding to an average of 21 alleles per locus, were detected . MoreoveThe calculation of the polymorphic information content (PIC) index, ranging from 0.69 to 0.92 for EMO90 and EMOL, respectively, highlighted good discriminating power for all the markers. The PIC values were high even when considering germplasm for each of the five Mediterranean countries considered in this study , althougThe genetic population structure was assessed through three different approaches, in order to validate results and define robust relationships among olive cultivars. a and SP1b include Algerian and Italian cultivars, respectively. The subgroup SP2a encompasses part of the Apulian germplasm and Tunisian cultivars, whereas the subgroup SP2b includes the remaining part of the Apulian germplasm and Syrian cultivars. At K = 7, the SP2 subgroups were further split. SP2a’ includes widespread Italian cultivars such as Taggiasca, Ogliarola Salentina and Cima di Mola, together with some Apulian accessions. SP2a’’ clusters three Italian cultivars  and all Tunisian cultivars, except for two falling in the admixed group (Hawaria and Zarrazi). The last three groups are (SP2b’) composed by Syrian cultivars , and two (SP2b’’ and SP2b’’’) including Apulian genotypes. Interestingly, one of the five unknown genotypes asymptomatic to the OQDS falls into SP2b’’’, together with two accessions of the resistant cultivar Fs-17, while the remaining four unknown asymptomatic genotypes are grouped in SP2a’, together with Leccino. Considering the mean qi, which is the estimated membership coefficient for each of five populations a priori defined based on geographical origin, a different genetic stratification (The application of the Bayesian clustering method implemented by the software STRUCTURE indicated that a number of subpopulations (K) of 7 best fits the data, immediately followed by K = 2 and K = 4 . At K = fication B was obsST distances among subpopulations identified by STRUCTURE at K = 7 highlighted great genetic differentiation between SP1b and SP2b’ (FST = 0.25), and between SP1b and SP2a’’’ (FST = 0.26) (ST distances were found between SP2b’’’ and SP1a’ (0.09) and between SP2b’’’ and SP2b’’ (0.10). Overall, SP2b’’’ presents the lowest pairwise FST distance, ranging from 0.09 (with SP2a’) to 0.18 (with SP1b)  . Conversth SP1b) .To validate clustering obtained by the STRUCTURE analysis, a discriminant analysis of principal components (DAPC) was performed. The application of the Bayesian information criterion (BIC) indicated a number of clusters (K) equal to 8 as the most probable for the data . A bar pThe dendrogram obtained by neighbor-joining clustering is substantially in agreement with the results of the STRUCTURE and DAPC analyses , and proThe synonymies were disclosed by hierarchical clustering and the Lynch and Ritland estimator (LRM) analysis . The LRMhttps://www.politicheagricole.it/flex/cm/pages/ServeBLOB.php/L/IT/IDPagina/10035). However, the lack of a certification system for propagating material has generated confusion among cultivars, with several cases of synonymy and homonymy [Understanding the basis of biodiversity is the first key step to identify genotypes that best fit the requirements of breeding programs. In Italy, a list of national olive varieties for marketing purposes has been prepared , while the accessions collected in Apulia occurred in two further groups, SP2a and SP2b, together with Tunisian and Syrian genotypes, respectively. At K = 7, the STRUCTURE subpopulations largely overlapped with clusters identified by DAPC analysis. In more detail, besides SP1a and SP1b, the others were further separated according to geographical origin. In addition, the accessions of Apulia were divided into three different groups . This result was also explored in view of hierarchical clustering, which offers a more detailed view of diversity between accessions, showing different levels of relationships and disclosing also synonymies. Three main groups were observed. The national olive germplasm separates from the remaining cultivars, which were divided in two main clades: The first includes Algerian, Syrian and the half of Apulia accessions, while the second one the Tunisian and the other half genotypes collected in Apulia.For K = 4, the STRUCTURE analysis assigned Algerian and widespread Italian cultivars to two separate subpopulations  only includes genotypes collected in Salento (except for Termine di Bitetto and Cascata) and shows the lowest differentiation.Our results indicate a high level of genetic variation of Apulian germplasm. Indeed, for K = 7, the STRUCTURE analysis assigned Apulian germplasm to three subpopulations. The subpopulation SP2enotypes . The gro SP2b’’’ , which iOverall, the genetic differentiation of the whole Apulian germplasm is lower than other populations B, indicaThis study highlights that Italian olive germplasm can be associated with different genetic clusters, while Algerian, Syrian and Tunisian cultivars mostly refer to single gene pools B. In addSurprisingly, the unknown genotypes collected in the area of Salento were shown to be in most cases genetically distinct from known cultivars, suggesting that they may be landraces selected by farmers on the basis of morpho-agronomic traits and adaptation to environmental conditions, or the result of hybridization among olive cultivars. This germplasm, in some cases, was shown to be asymptomatic to OQDS, and could represent a valuable source to investigate through new smart-breeding techniques , in ordefastidiosa (48/2018). These plants were assumed tolerant/resistant to the disease. Algerian germplasm (under code ALG) derived from the Institut Technique de l’Arboriculture Fruitière et de la Vigne . The Syrian accessions (under code SYR) were sampled in 2005 in the region of Aleppo by the General Commission for Scientific Agricultural Research (GCSAR), centre of Aleppo. The Tunisian genotypes (under code TUN) were collected from the experimental fields located at the Olive Tree Institute of Sfax . This study analyzed a panel of 218 olive accessions from Italy (149), Algeria (23), Syria (19) and Tunisia (27) . In moreThe genomic DNA was extracted from 70 mg of lyophilized young leaves, according to the protocol reported by di Rienzo et al. . The DNAo) and expected (He) heterozygosity and fixation index (F) [http://anu.edu.au./BoZo/GenAIEx) [The molecular marker diversity was investigated through different genetic indices, i.e. number of alleles (Na), observed (Hndex (F) , which wGenAIEx) . The polGenAIEx) , indicatGenAIEx) .Pairwise relatedness was also used to calculate the allelic similarity for codominant data using GenAlEx 6.501, by using the Lynch and Ritland estimator (LRM) . http://pritch. 258 bsd.uchicago.edu/structure.html) [ad hoc ∆K statistics [ST index for pairwise comparisons was calculated using Genalex v.6.5.The population genetic structure analysis was performed using three approaches. The first was the Bayesian model-based clustering method implemented by the software STRUCTURE v.2.3.4 (re.html) . To evalre.html) , which iatistics . The accThe discriminant analysis of principal components (DAPC) was used as complementary clustering methods to analyze genetic structure. The DAPC is a multivariate method that uses a non-hierarchical approach for defining genetic clusters. The DAPC was implemented in the adegenet package for the R statistical environment .http://darwin.cirad.fr), using bootstrapping with 1000 replicates to determine support for each node [Finally, the genetic structure and genetic relationships between individual cultivars were assessed using a weighted neighbor-joining method, using a dissimilarity matrix, through the software DARWIN v. 6.0.010 (ach node .


Stolothrissa tanganicae, using for the first time a genomic approach on this species. This is an important step towards knowing if the species should be managed separately or as a single stock. Population structure is important for fisheries management, yet understudied for many African freshwater species. We hypothesize that distinct stocks of S. tanganicae could be present due to the large size of the lake (isolation by distance), limnological variation (adaptive evolution), or past separation of the lake . On the other hand, high mobility of the species and lack of obvious migration barriers might have resulted in a homogenous population.Clupeid fisheries in Lake Tanganyika (East Africa) provide food for millions of people in one of the world’s poorest regions. Due to climate change and overfishing, the clupeid stocks of Lake Tanganyika are declining. We investigate the population structure of the Lake Tanganyika sprat S. tanganicae through a combination of mitochondrial genotyping  and RAD sequencing . Samples were collected at five locations along a north-south axis of Lake Tanganyika. The mtDNA data had low global FST and, visualised in a haplotype network, did not show phylogeographic structure. RAD sequencing yielded a panel of 3504 SNPs, with low genetic differentiation . PCoA, fineRADstructure and global FST suggest a near-panmictic population. Two distinct groups are apparent in these analyses , which do not correspond to sampling locations. Autocorrelation analysis showed a slight increase in genetic difference with increasing distance. No outlier loci were detected in the RADseq data.We performed a population genetic study on wild-caught Our results show at most very weak geographical structuring of the stock and do not provide evidence for genetic adaptation to historical or environmental differences over a north-south axis. Based on these results, we advise to manage the stock as one population, integrating one management strategy over the four riparian countries. These results are a first comprehensive study on the population structure of these important fisheries target species, and can guide fisheries management.The online version of this article (10.1186/s12862-018-1325-8) contains supplementary material, which is available to authorized users. Freshwater ecosystems support more species per unit area than any other ecosystem. Yet, they currently suffer from fast declines in species richness . The decThe sustainable exploitation of freshwater ecosystem services benefits from science-based management, based on sound biological knowledge of the system and its species. An important component of biological information is related to the structure of fish populations. The genetic structure of fish populations can be used to support the delineation of demographic units , 8, comm7 km3 of water [In tropical systems, the biological knowledge on fisheries target species is less advanced and information on the population structure is often lacking. Hence, the scope for science-based management is small. This also holds for the Great Lakes of East Africa, in spite of their ecological, economic and social significance. Lake Tanganyika (LT) is the oldest African Great Lake, in which unique and very diverse aquatic communities have evolved . It is sof water . The oxyof water . The preof water , 16. Theof water . Lake leof water .Stolothrissa tanganicae  and Limnothrissa miodon  provide 65% of the catch (by weight) [Lates stappersii , provides 30% of the catch [S. tanganicae serves as an important food source for L. miodon and L. stappersii [S. tanganicae dominates the catches of artisanal fishermen [L. stappersii [Stolothrissa tanganicae has a life style that is reminiscent of that of marine clupeids. It forms schools that differ in size and density throughout the day [Stolothrissa tanganicae spawns throughout the year, with peaks in February–May [S. tanganicae feeds on zooplankton, mainly the calanoid copepod Tropodiaptomus simplex [The fishery of LT plays an invaluable role in food security in one of the poorest regions in the world. Many people living near the lakeshore depend on artisanal fishing for their protein supply . The Lak weight) ; a percihe catch . Additioappersii . In the ishermen . In the appersii . Stoloth the day . The spe the day  and esca the day  and thei the day . Stolothuary–May  or Augusuary–May  in the Nuary–May , and posuary–May  in the Suary–May . FeedingSardinops sagax) [Clupea harengus) [Observations at landing sites have shown a decrease of clupeid catches in LT , 32. Hens sagax) , 38, andarengus) .S. tanganicae and L. miodon are scarce. In S. tanganicae, the only genetic study conducted so far suggested a single panmictic stock [L. miodon, no clear large-scale geographic structure could be identified [S. tanganicae; allozyme markers and mtDNA Restriction Fragment Length Polymorphism (RFLP) of the ND 5/6 gene in L. miodon), may lack the sensitivity to detect genetic structure in highly dispersive organisms. Recent developments in sequencing technologies, such as Restriction site Associated DNA markers (RAD sequencing) allow to infer population structure based on numerous single nucleotide polymorphisms (SNPs) [Previous attempts to reveal the population genetic structure of ic stock , while ientified . Howevers (SNPs) . The accs (SNPs) , 43. AltS. tanganicae along a north-south axis in LT. A heterogeneous population genetic structure is possible for three reasons. First, the distance between the northern and southern end of the lake is large, compared to the assumed migration distances of this species, so levels of mixing might decrease with distance (hypothesis of isolation by distance). Second, there are limnological differences between the North and the South of the lake, to which S. tanganicae might have distinct adaptations (hypothesis of adaptive evolution). Since the eggs slowly sink to a depth of 150 m in the South [In this study, we combine an analysis of mitochondrial DNA (mtDNA) haplotypes with a genomic analysis of nuclear DNA to assess population structure of he South , and neehe South  have affhe South .We selected five sampling sites along a latitudinal gradient covering the three subbasins of LT Fig. . Two sitc oxidase subunit I (COI) gene was amplified using the universal primer combination HCO2198 (5’-TAAACTTCAGGGTGACCAAAAAATCA-3′) and LCO1490 (5’-GGTCAACAAATCATAAAGATATTGG-3′) [2 (50 mM), 0.5 μL of dNTPs (10 mM), 1 μL of both primers (10 μM), 0.15 μL Platinum Taq polymerase (5 units/μL) and 18.1 μL of milli-Q water, totaling 25 μL. The PCR cycling profile consisted of 3 min at 94 °C, followed by 35 cycles at 94 °C for 45 s, 52 °C for 40 s, 72 °C for 90 s, 10 min at 72 °C and cooling to 4 °C. PCR products were purified by means of GFX purification columns , subjected to sequencing reactions using the BigDye v3.1 cycle sequencing kit  and sequenced using the LCO1490 primer, with an ABI Prism 3100 Genetic Analyzer (Applied Biosystems). Sequence quality was verified with Geneious v11 [ST and pairwise FST between sampling sites in the diveRsity package [The mitochondrial cytochrome TTGG-3′) . The PCRious v11  and MEGAious v11  by checkious v11  using thG-3′) [2  mM, 0.5  package  in R, usSbfI-HF . In silico digestion of the genome of the related Atlantic herring (Clupea harengus) [Six RAD libraries, each including 16 individually indexed specimens, were prepared according to the protocol described in Baird et al.  and Ettearengus)  revealedprocess_radtags module in Stacks v1.46 [clone_filter module and SNPs were called using the denovo_map pipeline, both implemented in Stacks. We screened a range of parameter combinations and selected a minimum coverage of ten reads per stack (m = 10) and a maximum number of five base pair differences between stacks within (M = 5) and between (n = 5) individuals. This parameter setting allowed us to retain a sufficient number of orthologues at a considerable depth. Individuals with insufficient raw reads (< 0.8 million), a high proportion of missing data (> 50%) and low depth (< 9.7) were removed. A final round of filtering was performed using VCFtools v0.1.14 [p-value < 0.01), a minimum allele frequency of less than 0.05, and more than 20% of missing data.Overall read quality was assessed using the FastQC software v0.11.5 . Raw seqks v1.46 , 58, whi v0.1.14  in orderST were calculated, for two geographic scales, lake-wide and between nearby locations. To make lake-wide comparisons, we pooled the two northern (Uvira + Uvira2) and the two southern  sampling sites. Pairwise FST was calculated across the sampling sites, with 95% confidence intervals based on 100 bootstrap iterations over loci.Genetic variation of each sample was assessed by expected and observed heterozygosity and allelic richness, using the diveRsity v1.9.90 package in R v3.4.1. Using the same package, estimates of global FPopulation structure was inspected with the R package ADEGENET v2.1.0  to perfoPopulation structure was assessed using an MCMC method to infer recent shared ancestry based on patterns of genomic similarity implemented in fineRADstructure , which iST values among loci, at the lake-wide scale and between all five of the locations, to identify possible candidates for outliers, using the diveRsity v1.9.90 package of R. Secondly, we performed a Bayesian outlier detection method in BayeScan v2.1 [ST effects [p-value by a genomic inflation factor, corresponding to the median of the square z-value divided by the median of the chi-square distribution [Putative signatures of natural selection were assessed using three different approaches to detect outlier loci. First, we assessed the distribution of the global Fcan v2.1  which in effects –69. For  effects . The numribution . To corrribution . LFMM anribution .ST value between sampling sites is 0.0026. Pairwise FST values (Table p < 0.01).The Median Joining Network based on mitochondrial COI fragments of a length of 643 bp, does not suggest separation either between the five sampling sites, or between the three subbasins Fig. . The gloDue to a low number of reads (< 0.8 million), a high percentage of missing reads (> 50%) and low depth (< 9.7), 12 individuals were discarded. Two individuals from the Mpulungu sampling site were very similar, indicating possible contamination. To solve this, one of these individuals was removed. This resulted in 83 retained individuals, at least 15 per sampling site, with the number of reads per specimen ranging from 0.9 to 3.7 million (average per specimen = 1.96 million). Filtering produced a final dataset containing 3504 SNPs distributed across these 83 individuals, with a mean depth per individual of 29.66 (minimum of 9.7 and maximum of 57.9) and a mean missing per individual of 12% (minimum of 0.007% and maximum of 48%). Detailed information on missing data per individual can be found in Additional file Observed and expected heterozygosity values were similar among the sampling sites, with expected heterozygosity ranging from 0.2088  to 0.2605 (Uvira) and observed heterozygosity ranging from 0.1920  to 0.2619 (Uvira) Table . AllelicST was relatively low, but significantly different from zero, with a FST value of 0.0068 (95% CI: 0.0057–0.0079) between sampling sites and a FST value of 0.0054 (95% CI: 0.0046–0.0066) between the northern, central and southern basin. Similarly, pairwise FST values between sampling sites are low, ranging from -0.0012 (95% CI: -0.002–0.0001)  to 0.0250 (95% CI: 0.0215–0.0281)  . A larger sample size, as well as individuals of both species, are required to test the identity of the two separate groups. Individuals from the two groups have been found in all the sampling sites, so they do not alter the conclusions of the various analyses in this study.Some of our analyses suggested the existence of two separate groups, independent of geographical origin. This is apparent in the PCoA plot Fig. , where wentiated . AnotherStolothrissa tanganicae. At the start of this study, we hypothesized that population structure could arise due to isolation by distance, adaptive evolution, or the distinct history of the subbasins. We also considered the possibility of a homogeneous population because of large effective population sizes and high mobility of the species and the long period during which obvious migration barriers were absent. Our data did not show genetic differentiation between the different sampling locations over a north-south axis of the lake.It is worthwhile to speculate what may cause the weak geographical population genetic structure of S. tanganicae abundance and measures of chlorophyll a [S. tanganicae catches during phytoplankton blooms in the North [First, the data revealed a very weak pattern of isolation by distance, which was detected with the autocorrelation analyses. In 1970, Coulter stated, based on his observations in the northern and southern basins, that there were no reports of large clupeid migrations, and that there was no reason to assume there were any . Yet, asophyll a . Catch she North , 74 and he North  of the lS. tanganicae differ across the lake [We found no traces of local adaptation to different conditions in the North and the South of LT. The number of loci in this study may have been too low to detect genomic regions involved in adaptive processes. There are some limnological differences along a north-south axis that could trigger local adaptation. For instance, the timing of major spawning events in the lake , 25, 28,the lake . ExpandiS. tanganicae also differentiated into different populations in the isolated subbasins as a result of low water levels. This lack of observed differentiation could be due to the pelagic life style of the sprat, enabling dispersal throughout the lake, similar to the benthopelagic Lake Tanganyika’s giant cichlid (Boulengerochromis microlepis) [Bathybates species (B. fasciatus and B. leo) [Our results do not show signatures of a population that differentiated because of historical barriers, which would have caused greater differences in genotypes between our samples. At times of extreme low-stands of the water levels of LT, the lake would be divided into three separate lakes, according to the three subbasins. It is assumed that the differentiation in cichlids was triggered by this isolation –79. It irolepis)  and two  B. leo)  whose poS. tanganicae is that these populations could have passed through a bottleneck and quickly expanded again. This assumption is supported by a significant negative value of Tajima’s D statistic, showing that observed heterozygosity is lower than expected heterozygosity due to inbreeding. Clupeids are known to have highly fluctuating population sizes, with large declines in numbers and fast expansions [S. tanganicae excellently equipped for rapid population expansions [A possible explanation for the homogeneous structure found here for pansions , leadingpansions . Fishingpansions  with a spansions . Furtherpansions .S. tanganicae in this study, sardines worldwide, often assessed over greater geographic distances, show below-average levels of population differentiation in comparison to other marine fishes. This is generally explained by their pelagic lifestyle, limited proportion of the population that contributes to the next generation, overharvesting and population bottlenecks [Clupea harengus) from the North Sea [Just like tlenecks , 85, 86.tlenecks , for exatlenecks  or over tlenecks . In otheorth Sea  and the orth Sea .S. tanganicae over a north-south axis of LT, emphasises the need for integrated management of the entire stock. On the one hand, a single homogeneous stock might be easier to manage, since local extinctions can be countered by migrations from other populations. The adaptive potential and chance of survival of a metapopulation is bigger than that of an isolated subpopulation. On the other hand, managing such a homogeneous population has its own difficulties: Lake Tanganyika is bordered by four countries, each with its own legislation, law enforcement and economic reality. As the geographically unstructured sprat stocks do not correspond to international borders, each local management regime influences the stock available to the neighbouring countries. Our findings also underpin the importance of locating and protecting the spawning areas of S. tanganicae, since degradation of a spawning area could impact the stock in a wider area. Illegal fishing of clupeid fry in the spawning areas forms a huge burden on the stocks [S. tanganicae population. This information is vital to delineate spawning areas and source populations as protected areas.The weak genetic structure in e stocks , 93. It L. miodon and L. stappersii. They both have a more littoral lifestyle than S. tanganicae [L. stappersii has a very different life history than the clupeids: these predators are bigger and live longer, which might affect their population structuring. This type of research can be useful in many other systems. It can be expanded to pelagic fish of the other African Great Lakes, and beyond. There are many lake ecosystems where a small, fast growing, pelagic fish species forms the link between zooplankton and piscivorous animals, just like the clupeids of Lake Tanganyika. Many of these systems would benefit from having information about the population structure of their pelagic fisheries targets. In some of these lakes, for example Lake Victoria, the pelagic fishes are becoming more important in the ecosystem due to overfishing of the larger fish species.Future research on the pelagic species in Lake Tanganyika remains necessary to provide information for management and conservation. More information on migrations of these pelagic clupeids would be beneficial for more directed management. The availability of a reference genome would be a step towards interpretation of adaptive traits if outlier SNPs would be detected. It will also be vital towards discovering genomic signatures of overfishing. There is also a need to look at the population structure of the two other major fisheries target species in Lake Tanganyika, nganicae , hence tS. tanganicae in Lake Tanganyika. A near-panmictic population structure was detected over a north-south axis of the lake, with slightly increasing genetic distance over increasing geographical distance. This homogeneity in the stock of one of the major fisheries target species in LT underscores the need for integrated stock management between the four nations bordering Lake Tanganyika.Our study confirms previous findings on the population structure of Additional file 1:Sequencing quality and information on missing data per individual. Table shows the sampling site, individual ID, number of SNPs, number of missing SNPs, frequency of missing SNPs, mean read depth and number of raw reads per individual. (PDF 370 kb)Additional file 2:Density plot of DAPC. Densities of individuals on the first discriminant function of the DAPC shown in Fig. Additional file 3:ST of Stolothrissa tanganicae per SNP. Grouping by sampling site and subbasin. (PDF 5 kb)Frequency distribution of global FAdditional file 4:ST-Log10 posterior probability for two levels (sampling site and subbasin) for each of the three replicates . B. Q value for grouping according sampling site and subbasin with each of the three replicates . (PDF 169 kb)Outlier analysis based on BayeScan v2.1. A. FAdditional file 5:p-values, corrected with the genomic inflation factor. Made with the LEA package in R. (PDF 4 kb)Individual-based latent fixed mixed model (LFMM) analysis. Distribution of adjusted


Haemonchus contortus, are widespread pathogenic parasites of small ruminants. Studying their spatial genetic structure is as important as studying host genetic structure to fully understand host-parasite interactions and transmission patterns. For parasites having a simple life cycle , gene flow and spatial genetic structure are expected to strongly rely on the socio-spatial behavior of their hosts. Based on five microsatellite loci, we tested this hypothesis for H. contortus sampled in a wild Mediterranean mouflon population (Ovis gmelini musimon × Ovis sp.) in which species- and environment-related characteristics have been found to generate socio-spatial units. We nevertheless found that their parasites had no spatial genetic structure, suggesting that mouflon behavior was not enough to limit parasite dispersal in this study area and/or that other ecological and biological factors were involved in this process, for example other hosts, the parasite life cycle, or the study area history.Gastro-intestinal nematodes, especially  Parasitism has been shown to impact numerous host characteristics , and wiaries L. , 40). Thapreolus , Mediter Ovis sp , see als Ovis sp  for a res caffer , zebra E gazella ). Among  mouflon , 65, 82,nd sheep , Africanock see , 72), re, reBos tock (see ), scarceock  and andBos tock see , 64). Ho. HoBos tock , for which free-living infesting larvae remain close to the host feces [H. contortus.We aimed here to help answer this question by studying the spatial genetic structure of a ta 1 and , 57, 66)ta 1 and , Appendita 1 and ). Becausst feces , we expeH. contortus [H. contortus .Parasites were sampled from the abomasum of 85 Mediterranean mouflon harvested between September 2011 and February 2012 in the Caroux-Espinouse massif . Following supplier recommendations but adjusting volumes to the small size of samples, the lysis was performed in 100 μL of ACL buffer and 7 μL of proteinase K. Samples were incubated for 1 h at 55 °C under agitation (400 rpm). Purification and the two washing steps were performed using 150 μL of AB solution and 200 μL of buffer for each washing. Elution was done using 50 μL of EB buffer.Hcms25, Hcms27, Hcms33, Hcms36, Hcms40, Hcms22co3 and Hcms8a20, see [Supplementary Table 1) were amplified through polymerase chain reaction (PCR) in a final volume of 15 μL composed of QIAGEN Multiplex PCR kit Mastermix (ref. 206145), 40 nM of each primer, and 2 μL of DNA solution. PCR cycles consisted of 15 min of activation (95 °C), followed by 40 cycles of denaturation , annealing  and extension . Cycles were followed by a final extension step . PCR products were resolved on a capillary sequencer ABI 3730XL (Thermo Fisher Scientific) by the Genoscreen laboratory . The electropherograms obtained were analyzed using GENEMAPPER software (Applied Biosystems/Life Technologies) and read by two independent analysts to determine allele sizes for each individual and markers. This microsatellite panel is known to produce unambiguous genotypes and to be highly polymorphic, and has demonstrated its relevance in previous H. contortus genetic structure studies  was determined using R software (R core team 2016), and the hierfstat package [Genotyping errors were tracked using MICROCHECKER v.2.2.3 software . Using F package .H. contortus was first investigated using a sPCA  [n = 9999) to assess the significance of the local and global spatial structures [K varying from 1 to 10, with 1,000,000 iterations, a thinning of 100, and a burn-in of 1000. All analyses involving R packages were conducted with R 3.3.2 (R core team 2016).The spatial population genetic structure of oftware) , 48, 60.ructures , using tHcms8a20, Supplementary Data 2) failed to amplify and was thus not included in the dataset. MICROCHECKER indicated higher than 0.05 null allele frequencies for the loci Hcms22Co3, Hcms27 and Hcms40 , suggesting overall deviation from Hardy–Weinberg equilibrium likely attributed to the high null allele frequencies observed for some loci [Of the seven loci selected for genotyping, one . In addome loci .p = 0.97) or local (p = 0.32) spatial genetic structures when considering the first positive and negative axes (Supplementary Data 3). Accordingly, in the 10 independent GENELAND runs, the maximum posterior density was obtained for K = 1 (Supplementary Data 3), indicating an absence of spatial genetic structure in the study area.The sPCA revealed no significant global ulations . SeveralH. contortus having an environmental phase in their life cycle might be maximized even if their hosts are only slightly mobile, because this allows for parasite exchanges without a need for the hosts to encounter one another, but just to share feeding areas. In addition, male mouflon perform reproductive excursions during the rutting period  and, evhed data  and act hed data , 79 due hed data ). Finallhed data , Supporthed data ). Taken H. contortus is a generalist parasite of small ruminants [Capreolus capreolus) inhabit our study area. Although roe deer are present at much lower densities than those for Mediterranean mouflon [H. contortus gene flow by having different socio-spatial behaviors such as territoriality [H. contortus might thus also have been present before the mouflon introduction, and the current spatial genetic structure of H. contortus could result from the historical and contemporary interactions between these three different hosts’ spatial structures and behaviors.Second, uminants , and roe mouflon , they maoriality , attractoriality , or markoriality , linked oriality . In addioriality , 17, 18,H. contortus worldwide and in the present study, in which the number of alleles by locus is even higher than the number observed in other populations . Nus e.g., , 46, 68), 68H. cos e.g., , 73). Th. ThH. cos  present in the study area to determine whether the panmictic population we detected in Mediterranean mouflon also extends to other sympatric species. Such a comparison would help us to understand how parasites spread in the Caroux-Espinouse massif and give supplementary information to wildlife managers when defining management and conservation planning.Contrary to our expectations, we did not detect any spatial genetic structure in l factor . It alsol factor ) might bs . In thes (e.g., , which hs (e.g., , 67, the


Rare genetic disorders are currently in the spotlight due to the elevated numberof different conditions and significant total number of affected patients. Thestudy of these disorders is extremely helpful for the elucidation ofphysiological processes related with complex disorders. Isolated populations areinstrumental for the study of genetic disorders, considering their homogeneityand high proportion of affected patients in a small geographic area. Thesefavorable conditions lead to the creation of a new discipline, known as“population medical genetics”, which integrates medical genetics, populationgenetics, epidemiological genetics and community genetics. In order to developpractical activities in this new discipline, the National Institute ofPopulation Medical Genetics (INaGeMP) was created in 2008 in Brazil. INaGeMP hasdeveloped several tools and funded numerous research activities. In this review,we highlight three successful projects developed in the first 10 years ofINaGeMP activities (2008-2018): a newborn screening pilot study for MPS VI inNortheast Brazil, the study of Machado-Joseph disease in Brazilian families withAzorian ancestry, and the high twinning rate in a small town in southern Brazil.The results of these projects in terms of scientific output and contributions tothe affected communities highlight the success and importance of INaGeMP. The interest on rare genetic disorders has been increasing exponentially due toseveral factors: new genetic disorders are still being described; severalindividuals and families are affected by rare disorders requiring attention andpublic policies from governments; and genetic disorders are also a good model forelucidation of physiological processes, thus providing knowledge applicable tonon-genetic disorders.Population medical genetics (PMG) is the area of medical genetics that aims at thestudy and medical care of the population, and not of the individual or the family,as is the normal practice in clinical or medical genetics . Based oThis Institute, works on the interface of Medical Genetics, Population Genetics,Epidemiology Genetics and Community Genetics, serving as a driver for activitiesfocused on PMG.Throughout the last decade (2008-2018), this initiative became a reference in theinvestigation of rare genetic disorders on population isolates with the creation ofseveral tools for the study of genetic disorders and congenital anomalies, such asCENISO  and CELAISO (Latin American Statistics ofIsolates). Additionally, INaGeMP supported initiatives related to PMG, such as theCongenital Malformations Atlas (http://en.atlaseclamc.org/), theosteochondrodysplasias website (http://ocd.med.br/), the Latin AmericanCollaborative Study on Congenital Malformations – ECLAMC (http://www.eclamc.org/),and the MPS Brazil Network (www.mps.ufrgs.br), among other projects.INaGeMP is a state-of-the-art initiative that succeeded not only to provide benefitsto local communities, but it also contributed to increase the knowledge on this areaproviding helpful information to the scientific community, governmental, andnongovernmental organizations. The headquarter is located at the Hospital deClínicas de Porto Alegre (HCPA), with several associated institutions being part ofthe network: Universidade Federal do Rio de Janeiro (UFRJ), Fundação Oswaldo Cruz(FIOCRUZ), Universidade Federal do Rio Grande do Sul (UFRGS), Universidade Federaldo Pará (UFPA), and Centro de Educación Médica e Investigaciones Clínicas NorbertoQuirno .In addition, many collaborating institutions participate of INaGeMP, running specificprojects. This review will focus on three of these projects: Newborn screening forMucopolysaccharidosis VI in a community in the Northeast of Brazil; Machado-Josephdisease in Brazilian families with Azorian ancestry in the South of Brazil; hightwinning rate in a small town close to the Argentinian border, in the very South ofBrazil.The incidence of MPS VI varies from 1:43,2361 to 1:1,505,461 births . The biochemicalassay in dried blood spots (DBS) described by In due course, INaGeMP worked to develop a test for the common mutation found in allpatients in the region [p.His178Leu]. The analysis was developed with real-time PCRusing custom designed TaqMan™ probes, at the Medical Genetics Service (MGS) ofHospital de Clínicas de Porto Alegre (HCPA). Samples from newborns from Monte Santowere collected and shipped to Porto Alegre by the Association of Parents and Friendsof Exceptional (APAE) of Salvador, BA, from January 2011 to August 2017 .Until now, DBS of 3,903 newborns have been analyzed and 67 (1.72%) were identified asheterozygotes for p.His178Leu. Although no homozygote patients have been identifiedyet, the number of heterozygotes enabled us to estimate a prevalence ofapproximately 1:16,000 live births. If confirmed, this frequency would be evenhigher than phenylketonuria (PKU), a disease included in the public newbornscreening program in all states of Brazil. The identification of carriers allowedthe identification of families at risk, enabling genetic counseling, carrierdetection and prenatal diagnosis.ATXN3, molecular diagnosis enabled to define MJD as the most commonspinocerebellar ataxia (SCA) worldwide (Machado Joseph disease (MJD) is a progressive and disabling autosomal dominantspinocerebellar ataxia that affects gait, speech, swallowing, and limb coordination.Since the first descriptions in 1972, MJD has been related to the people whomigrated from the Azorean islands. After the discovery of the causal mutation, atranslated CAG repeat expansion (CAGexp) in a gene now calledorldwide .Researchers associated to INaGeMP have already been doing research on MJD since thenineties, when the first MJD families from Rio Grande do Sul were identified  and Parkinsonism were also described in MJD  is asmall town in the South of Brazil with approximately 6,000 inhabitants. Our firstvisits to the community date back to 1994, when an investigator supported by thecommunity visited and interviewed local families, reconstructed pedigrees andcollected blood for future studies. The first conclusion was that the birth of twinswas indeed increased in CG, estimated as 10% for the period 1990-1994, whereas forBrazil in general it was 1.8% , geologists , a historian (familyhistories), a social scientist  and a facilitator(to establish the contact and the logistic with the families and the field team),all working under the coordination of Ursula Matte  and Lavinia Schuler-Faccini (HCPA/INaGeMP). Both thesocial scientist and the facilitator were from CG municipality.TP53 genewas higher in mothers of twins compared to mothers of singletons, suggesting apossible effect of this gene in the biology of twinning .To better explore the hypothesis of the founder effect, we located geographically allresidences where twins were born in CG, and also collected the surnames of allresidents in CG as surrogates to estimate inbreeding. Our data supported thehypothesis of relative isolation of some communities in CG, particularly in LSP,where the twinning rate was higher, with low population dispersion and highinbreeding indexes , aswell as in the project’s second phase (from 2009 on). We limited ourselves oncommenting published results only, and any request that involved contacting thecommunity should be authorized directly by them. Even if sometimes we felt thatcooperating with the media was positive for the community as a whole, we made itclear the decision was theirs. It was important that not only we had this attitude,but that this was perceived and understood by the community.A consistent body of evidence strengthens the participation of genetic factors in theetiology of twin births, such as ethnic differences in twinning rates andintrafamilial predisposition for twinning , INaGeMP became an example of a successfulinitiative, supported with Brazilian public funding, that focuses on the study ofclusters of genetic diseases and translates the results to practical health relatedactions in the affected communities, together with important contributions toscience and creation of several tools .The impact and importance of our work was recognized by comments in top rankedjournals, such as Lancet .In summary, we developed an organization that became a template for other centerswishing to provide prevention, diagnosis, management, and research on geneticdisorders, as well as centers aiming to work with local communities to address localneeds. The expansion of this model to a higher number of communities, not only inBrazil but also in other Latin American and African countries was set as a goal forINaGeMP in the coming years.


Cryptolaemus montrouzieri, we used a reduced‐representation sequencing method to analyze the genome‐wide differentiation of the samples from two native and five introduced locations. Our analyses based on a total of 53,032 single nucleotide polymorphism loci showed that beetles from the introduced locations in Asia and Europe exhibited significant reductions in genetic diversity and high differentiation compared with the samples from the native Australian range. Each introduced population belonged to a unique genetic cluster, while the beetles from two native locations were much more similar. These genomic patterns were also detected when the dataset was pruned for genomic outlier loci , suggesting that random genetic drift was the main force shaping the genetic diversity and population structure of this biological control agent. Our results provide a genome‐wide characterization of polymorphisms in a biological control agent and reveal genomic differences that were influenced by the introduction history. These differences might complicate assessments of the efficiency of biological control and the invasion potential of this species but also indicate the feasibility of selective breeding.Biological control is the main purpose of intentionally introducing non‐native invertebrate species. The evolutionary changes that occur in the populations of the introduced biological control agents may determine the agent's efficiency and the environmental safety. Here, to explore the pattern and extent of potential genomic changes in the worldwide introduced predatory ladybird beetle The intentional introduction of invertebrate species is generally used as a form of biological control , is native to Australia and is a predator specialist of mealybug  markers, we found high and significant genetic differentiation between native and introduced populations  and bioinformatic tools have spurred the development of genome‐wide genetic markers for studying the ecology and evolution of nonmodel organisms , dNTPs, and adapter primers. The PCR products were purified using Agencourt AMPure XP  and subjected to electrophoresis through a 2% agarose gel. Fragments in the size range of 250–350 bp were excised and purified using a QIAquick Gel Extraction Kit . The samples with indexes were allocated to a single lane and subjected to 150 bp paired‐end sequencing on the HiSeq 2500 platform  at Biomarker Technologies Corporation (China). Real‐time monitoring was performed for each cycle during sequencing. The ratio of high‐quality reads exhibiting Q30  and the guanine‐cytosine (GC) content of the raw reads were calculated for quality control.Approximately 500 ng of genomic DNA from each sample was processed for reduced‐representation genome sequencing using the specific‐locus amplified fragment sequencing (SLAF‐seq) technique  higher than 0.75  for each SNP were calculated using PLINK, and those loci that significantly deviated from HWE at more than three studied locations were excluded. The filtered SNPs were mapped to the annotated transcriptome of the species  iterations. The q‐values for each locus in AH and ANH were subsequently calculated from the p‐value with p.adjust in R to monitor the false discovery rates (FDRs). Those loci with q‐values below 0.05 were considered FST outliers. Some simulation studies have suggested that potential error rates must be evaluated by interpreting the results with a variety of methods . Genetic diversity and population structure were analyzed based on the total loci, the neutral loci, and the high FST outlier loci.To detect the contribution of different loci to the diversity of this biological control agent, the total SNPs were further separated into three datasets, which included the high and low HO), and expected heterozygosity (HE). Comparisons of these parameters between native and introduced locations were performed using t tests in SPSS 21 .We employed Arlequin to calculate genetic diversity parameters at each of the studied locations, including the percentage of polymorphic loci (Poly%), nucleotide diversity (Pi), observed  the reconstruction of phylogenetic trees, (b) the estimation of ancestry proportions, and (c) an analysis of molecular variation (AMOVA). Neighbor‐joining (NJ) trees were reconstructed in MEGA6 . We explained the genetic structure by obtaining the lowest cross‐validation error from K = 2 to 10. Then, AMOVA was performed in Arlequin with 10,000 permutations to further test for the population structure identified by clustering with ADMIXTURE.We calculated overall and pairwise 33.1A total of 504.25 million reads were obtained from the raw sequencing data. The sequencing quality of each sample is shown in Supporting Information Table HO, a single SNP per tag, and HWE , MAF, WE Table .3.2FST outliers , acetyl‐CoA acyltransferase , glycine‐tRNA ligase , and the calcium‐activated potassium channel slowpoke .The allele frequencies of the high s Figure a. A totae Figure b, some o3.3FST outlier loci). The results based on the datasets containing all loci and the neutral loci only were similar, while the results based on the high FST outlier loci were quite different. Significant reductions in the number of polymorphic loci and nucleotide diversity were found when the introduced populations were compared to the native populations . There was no significant difference in HO and HE values between the native and introduced locations.The genetic diversity parameters of each studied location are shown in Table C. montrouzieri samples was very similar between the dataset with all loci and only the neutral loci. In the dataset with all loci, we detected high differentiation among global populations . The pairwise FST values among the studied locations are shown in Table FST = 0.017, p < 0.001), while all introduced populations showed much higher differentiation compared to the native populations . All of the introduced populations were also highly differentiated from each other . The NJ tree clustered individuals largely by population of origin, placing individuals from the native Australian range  in the middle of separate branches leading to the introduced populations SA, GT, and TP as well as the common branch to SY and SZ  was higher and significant for K = 6 structuring . Our genome‐wide description of polymorphisms in the ladybird C. montrouzieri demonstrates ample genetic variation and thus suggests that such approaches might be promising avenues for further improvements of the performance of this biological control agent.Second, the extent of the detected genetic changes might indicate the feasibility of selective breeding for improvement of traits of interest .All raw sequences are available in the NCBI SRA databases . The filtered 53,032 SNPs used in this study are available at Dryad ( Click here for additional data file. Click here for additional data file.


AlphaMate is a flexible program that optimizes selection, maintenance of genetic diversity and mate allocation in breeding programs. It can be used in animal and cross- and self-pollinating plant populations. These populations can be subject to selective breeding or conservation management. The problem is formulated as a multi-objective optimization of a valid mating plan that is solved with an evolutionary algorithm. A valid mating plan is defined by a combination of mating constraints  that are gender specific or generic. The optimization can maximize genetic gain, minimize group coancestry, minimize inbreeding of individual matings, or maximize genetic gain for a given increase in group coancestry or inbreeding. Users provide a list of candidate individuals with associated gender and selection criteria information (if applicable) and coancestry matrix. Selection criteria and coancestry matrix can be based on pedigree or genome-wide markers. Additional individual or mating specific information can be included to enrich optimization objectives. An example of rapid recurrent genomic selection in wheat demonstrates how AlphaMate can double the efficiency of converting genetic diversity into genetic gain compared to truncation selection. Another example demonstrates the use of genome editing to expand the gain-diversity frontier.http://www.AlphaGenes.roslin.ed.ac.uk/AlphaMate.Executable versions of AlphaMate for Windows, Mac and Linux platforms are available at  This paper describes the AlphaMate program that optimizes selection, maintenance of genetic diversity and mate allocation in breeding programs. Breeding programs aim to achieve defined targets over the course of a time horizon. Some programs select individuals to improve future performance, while other programs try to maintain the current state or even save a population from extinction. In all cases optimal management of genetic diversity within the bounds of practical constraints is crucial to sustainably support the current and yet unknown future targets. For example, breeding programs that select for improved performance must balance short and long-term genetic gain by avoiding excessive use of elite individuals. While elite individuals increase the mean of next generations, their excessive use also significantly reduces the amount of genetic diversity. This reduction limits the potential for long-term improvement. Breeding programs that focus solely on maintenance of diversity must also ensure that individuals contribute in a somewhat balanced manner. Therefore, breeding programs must balance individuals’ contributions to future generations to ensure long-term viability.The optimal contribution theory formulates balancing selection and maintenance of genetic diversity as optimization of individuals’ contributions to the next generation under constrained rate of group coancestry; see Existing programs that implement the above described approaches are often applicable to specific applications and are not generically applicable to both animal and plant populations or do not accommodate application of modern biotechnologies such as genome editing. The aim of this work is to present a flexible program AlphaMate that covers all these use cases. We describe its methodology and show its application in two examples (i) maximizing efficiency of converting genetic diversity into genetic gain in a rapid recurrent genomic selection program for wheat and (ii) expanding the gain-diversity frontier with genome editing.AlphaMate by default jointly optimizes contributions and mate allocations. The goal of this optimization is to find a valid mating plan that delivers desired targets. This is achieved with an evolutionary optimization of a single objective or multiple objectives simultaneously.A valid mating plan is defined by a combination of mating constraints: (i) the number of matings, (ii) the maximal number of parents, (iii) the minimal, equal, or maximal number of contributions per parent, or (iv) allowance for selfing.The desired targets formulate optimization objectives, such as: (i) maximize genetic gain, (ii) minimize group coancestry amongst contributors, (iii) minimize expected inbreeding of individual matings, (iv) maximize genetic gain with constrained group coancestry or inbreeding, or (v) as (i) or (iv) but with the ability to genome edit a fixed set of contributors.Optimization is performed with an evolutionary algorithm based on differential evolution  with modOptimization works with mating plans, which we encode as proposed by The basic files are the coancestry matrix, selection criteria and gender information for candidates. The coancestry matrix and selection criteria can be based on pedigree or genomic data. Additionally, further individual or mating specific information can be provided to enrich optimization objectives.Mating constraints can be gender specific or generic to accommodate different reproductive systems. A user can specify all the mating constraints or a subset of them depending on the optimization objectives and biologic or logistic reasons.Desired targets define the optimization objectives. For ease of use, we allow for various forms of some targets, e.g. constraint on the loss of genetic diversity can be specified with the targeted value of coancestry, rate of coancestry, percentage of the minimum possible coancestry, or trigonometric degrees between genetic gain and group coancestry .Optimization controls specify weights to combine multiple targets into a single objective function or to penalize invalid mating plans, and parameters of evolutionary algorithm such as the number of iterations, convergence criteria, etc.AlphaMate was written in object oriented Fortran 95 as a standalone program and compiled versions are available for Windows, Linux, and Mac platforms. A single specification file controls the program. In this file, a user specifies: (i) input files, (ii) mating constraints, (iii) desired targets and (iv) optimization controls. Below we describe these groups of specifications, while the full list is available in the AlphaMate manual.The AlphaMate output consists of: (i) input data summary, (ii) list of contributors with associated data, (iii) optimized mating plan, (iv) optimization log and (v) the seed value for random number generator to enable reproducibility. A utility R script is provided to plot the Pareto frontier and the optimization paths.We demonstrate the use of AlphaMate with two examples. The first example optimizes conversion of genetic diversity into genetic gain based on a subset of the results from a previous study we undertook to model the benefit of rapid recurrent genomic selection in wheat  .Conflict of Interest: none declared.


Caenorhabditis elegans have long been known to harbor genetic diversity greater than the rest of the worldwide population, but this observation was supported by only a small number of wild strains. To better characterize the niche and genetic diversity of Hawaiian C. elegans and other Caenorhabditis species, we sampled different substrates and niches across the Hawaiian islands. We identified hundreds of new Caenorhabditis strains from known species and a new species, Caenorhabditis oiwi. Hawaiian C. elegans are found in cooler climates at high elevations but are not associated with any specific substrate, as compared to other Caenorhabditis species. Surprisingly, admixture analysis revealed evidence of shared ancestry between some Hawaiian and non-Hawaiian C. elegans strains. We suggest that the deep diversity we observed in Hawaii might represent patterns of ancestral genetic diversity in the C. elegans species before human influence.Hawaiian isolates of the nematode species  Caenorhabditis elegans has been central to many important discoveries in the fields of developmental, cellular, and molecular biology. The vast majority of these insights came from the study of a single laboratory-adapted strain collected in Bristol, England known as N2 (C. elegans strains and enabled the study of genetic diversity and ecology of the species (C. elegans genetic variation showed that patterns of single-nucleotide variant (SNV) diversity were shared among most wild strains, with the exception of a Hawaiian strain, CB4856, which has distinct and high levels of variation relative to other strains  were PCR-positive and belonged to 13 distinct taxa. Among all isolates, we identified five Caenorhabditis species at different frequencies across the 2263 samples: C. briggsae (4.2%), C. elegans (1.7%), C. tropicalis (0.57%), C. kamaaina (0.088%), and a new species C. oiwi (0.53%) , and C. oiwi was enriched on Oahu relative to Kauai, Maui, and the Big Island .In August 2017, we collected a total of 2263 samples across five Hawaiian islands and ascertained the presence of nematodes in each sample . We isol (0.53%) . We formbarcodes  and on bbarcodes . C. brighe world . We founCaenorhabditis nematodes were isolated more frequently from flower substrates (40 of 202 collections) than any other substrate category  (Caenorhabditis nematodes were enriched on fruit (33 of 327 collections) relative to leaf litter substrates (76 of 1493 collections)  but not other substrate classes  and C. tropicalis  but not for C. elegans , which exhibited no substrate enrichment   . When wep<0.026) . We also classes . These fd fruits . We obserichment . Interesections) .C. briggsae, C. tropicalis, and C. oiwi on flowers might indicate that this substrate class has a higher nutrient quality for these species. If this hypothesis is correct, we might expect to see a greater incidence of proliferating populations on flower substrates than other substrates. However, we saw no observable association between large population size (approximate number of nematodes on collection plate) and substrate class for C. briggsae , C. tropicalis , nor C. oiwi , which suggests that other factors might drive the observed flower enrichment or that we are limited by the small sample size. Taken together, these data suggest that the Caenorhabditis species we isolated do not exhibit substrate specificity, despite flower-substrate preferences of C. briggsae, C. tropicalis, and C. oiwi, which is different from some other species in the genus that demonstrate substrate specificity  (The enrichment of opinata) .Caenorhabditis species , although these two environmental parameters were correlated with elevation (C. elegans (19.4°C), C. tropicalis (26.0°C), and C. briggsae (23.7°C) positive collections are close to the optimal growth temperatures for these species in the laboratory setting  or substrate (<10 cm2) scales. To sample at the local scale, we collected samples from 20 gridsects . Within these 26 isotypes, we identified approximately 1.54 million single nucleotide variants (SNVs) that passed our filtering strategy   . We alsoisotypes .C. elegans genetic diversity  as well as near the city of Münster, Germany , which suggests that balancing selection might maintain diversity at these loci in both samples  exists in these regions. Nevertheless, the variant sites within these discrete peaks in π and Tajima’s D are unlikely the result of sequencing errors because they are identified across multiple isotypes . Our previous analysis showed that 70–90% of isotypes contain reduced levels of diversity across several megabases (Mb) on chromosomes I, IV, V, and X  allowed us to perform detailed analyses of Hawaiian genetic diversity. Consistent with what is known about iversity , we obseiversity . By cont Germany . This re Germany . This tr Germany . The gen Germany . This pa Germany . Interes samples . This hymples  or assigned to populations that contain non-Hawaiian isotypes (e.g. K = 12 and 13) . This analysis identified at least 11 genetic groups for this set of analyses . Supportanalyses . Furtheranalyses . For Ks  and 13) . These d and 13) .The majority of isotypes assigned to the seven non-Hawaiian populations exhibit a high degree of admixture with one another, indicating that these populations are not well differentiated. By contrast, isotypes assigned to three of the four Hawaiian populations showed little admixture with other populations . We refei.e., C→Hawaiian Invaded) suggested that the evidence of gene flow is not caused by incomplete assortment of ancestral alleles  , and 11 of 18 Hawaiian isotypes showed admixture with various non-Hawaiian populations, particularly the non-Hawaiian population C . By contch tips) . TreeMixch tips) . The G pch tips) . AlthougC. elegans isotypes  and stable temperatures throughout the year. The Hawaiian climate is particularly less variable than many temperate regions where C. elegans populations are known to exhibit seasonal population expansions and contractions  as well as evolutionary processes such as natural selection, recombination, and mutation. To further untangle the evolutionary history and origin of this species, additional samples from natural areas around the globe and in particular the Pacific region will be required.The higher genetic diversity sampled from the Hawaii Islands could have at least two possible explanations other than residual ancestral diversity present since the transition to selfing. First, the higher genetic diversity in the Hawaiian Islands might be driven by population demography on the Islands. It is possible that Hawaii harbors larger, more temporally stable effective population sizes than other regions of the world that have been sampled. Under a neutral model, populations with a larger effective population size are expected to have a greater number of neutral polymorphisms . These lractions . FurtherC. elegans. In particular, we asked whether haplotype sharing among Hawaiian and non-Hawaiian isotypes could be explained by emigration of nematodes from Hawaii (out of Hawaii) or immigration of nematodes to Hawaii (invasion of Hawaii).Our sampling efforts within the Hawaiian Islands allowed us to explore how gene flow into and out of the Hawaiian Islands contributes to the patterns of genetic diversity we observe within the current global sample of C. elegans , containing 1% agar and 0.7% agarose to prevent animals from burrowing  is also deposited at the Caenorhabditis Genetics Center (Appendix 1).Nematodes were reared at 20°C using urrowing . In totaanalysis . The typC. elegans had been collected previously , its moisture content using a handheld pin-type wood moisture meter MD912 , and the ambient temperature and humidity near the sample using a combined thermometer and hygrometer device GM1362 . These measurements were entered into the appropriate fields in the application  . The remaining 274 samples were shipped overnight from Hawaii to Northwestern University in collection bags, and the nematodes were isolated approximately 172 hr after sample collection . For each collection plate, up to seven gravid nematodes were isolated by transferring them individually to pre-labeled 3.5 cm NGMA isolation plates seeded with OP50 bacteria. We refer to these isolation plates as ‘S-plates’ in the Fulcrum application we called 'Nematode isolation' . At the awaiiMS) .2, 0.9% IGEPAL, 0.9% Tween 20, 0.02% gelatin with proteinase K added to a final concentration of 0.4 mg/ml) then frozen at −80°C for up to 12 hr. If isolated nematodes were not found on the isolation plates prior to genotyping, they were categorized as ‘Not genotyped’. If the isolation plate only contained dead or sterile nematodes, we attempted to lyse five to ten carcasses or sterile individuals. The lysed material was thawed on ice, and 1 µl was loaded directly into 40 µl reactions with primers spanning a portion of the ITS2 region  between the 5.8S and 28S rDNA genes with forward primer oECA305 (GCTGCGTTATTTACCACGAATTGCARAC) and reverse primer oECA202 (GCGGTATTTGCTACTACCAYYAMGATCTGC) (TACAATGGAAGGCAGCAGGC) and RHAB1868R (CCTCTGACTTTCGTTCTTGATTAA), which amplify about 500 bp of 18S rDNA of Rhabditid nematodes  database using the BLAST algorithm. Isolates with sequences that aligned best to genera other than Caenorhabditis were only classified to the genus level.The isolated nematodes were stored at 20°C for approximately 14 days  but were not passaged during this time to avoid multiple generations of proliferation. For initial genotyping, whenever possible five to ten nematodes were lysed in 8 µl of lysis solution  . The ITSematodes . The RhaC. elegans, had an unexpectedly high number of mismatches in the center of the read, or did not match any known sequences because of poor sequence quality, we performed another independent lysis and PCR using high-quality Taq polymerase  to confirm our original results. For this confirmation, we used the forward primer oECA305 and the reverse primer oECA306 (CACTTTCAAGCAACCCGAC) to sequence the confirmation ITS2 amplicon in both directions. The sequence chromatograms were then quality trimmed by eye with Unipro UGENE software (version 1.27.0) and compared to known nematode species in the NCBI sequence database using the BLAST algorithm. We used the consensus alignment of the forward and reverse reads to confirm our original results. For C. elegans, five of the 100 strains perished before we could confirm their identity. We also confirmed that several strains that best aligned to C. kamaaina shared a large number of mismatches in the center of the ITS2 amplicon, suggesting they belonged to a new species. For these strains, we performed reciprocal mating tests with C. kamaaina to infer the new species by the biological species concept (Caenorhabditis species (Appendix 1).For every isolate where the BLAST results either aligned to  concept . None ofE. coli into a 15 ml conical tube by washing with 10 mL of M9. We then used gravity to settle animals on the bottom of the conical tube, removed the supernatant, and added 10 mL of fresh M9. We repeated this wash method three times over the course of one hour to serially dilute the E. coli in the M9 and allow the animals time to purge ingested E. coli. Genomic DNA was isolated from 100 to 300 µl nematode pellets using the Blood and Tissue DNA isolation kit cat# 69506  following established protocols .To ensure reproducible data analysis, all genomic analyses were performed using pipelines generated in the Nextflow workflow management system framework . Each Netrimmomatic-nf, which uses trimmomatic (v0.36)  (Raw sequencing reads were trimmed using  (v0.36)  to remov strains . The con (v.1.9)  variant  (v.1.9) .alignment-nf with BWA (v0.7.17-r1188)    to align7-r1188) . Next, w (v.1.9) . The wi-red VCFs . After v . The preC. elegans isotypes using RAxML-ng with the GTR DNA substitution model and maximum likelihood estimation to find the parameter values that maximize the phylogenetic likelihood function, and thus provide the best explanation for the observed data  using PLINK (v1.9)  R package  . Prior tK (v1.9)  with the package . We chos package . Further package . We perf package .minalleles = 0.01, ibdtrim = 0, r2max = 0.8. IBD segments were then used to infer haplotype structure among isotypes as described previously (We determined identity-by-descent (IBD) of strains using IBDSeq  run on teviously . After hC. elegans-positive collections on Hawaii and detected five distinct geographic clusters, each of which contain collections that are within 20 meters of one another. The largest of these clusters comprised 18 collections in the Kalopa State Recreation Area on the Big Island of Hawaii. This cluster contained 11 collections from gridsect-3 and seven additional collections within 20 meters from the edge of the gridsect. The other four geographic clusters contain four or fewer collections each. We used the average values of environmental parameters from geographically clustered collections to avoid biasing our results by local oversampling. We applied this strategy to the comparison of environmental parameters between the Hawaiian admixture populations and used the Kruskal-Wallis test to detect differences (α = 0.05).We calculated the pairwise distances among all ftp://ftp.ncdc.noaa.gov/pub/data/noaa/) managed by the National Oceanic and Atmospheric Administration (NOAA), and the National Climatic Data Center (NCDC). The data were processed using the noaa-nf pipeline available at the GitHub repository https://github.com/AndersenLab/noaa-nf (https://github.com/elifesciences-publications/noaa-nf).Climate data were downloaded from the Integrated Surface Data (ISD) FTP server . These Hawaiian samples also lack, for the most part, the large-scale selective sweeps that dominate the genomes of other C. elegans.The authors report a large collection of Decision letter after peer review:Caenorhabditis elegans reveals high genetic diversity and admixture with global populations" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Diethard Tautz as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Asher Cutter (Reviewer #2).Thank you for submitting your article "Deep sampling of Hawaiian The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Essential revisions:Drawing from reviewer 1 we all agreed that the paper should acknowledge the current limitations of sampling more, i.e. it may be that the Pacific region at large shows a high diversity and there is nothing special about Hawaii.The reviewers' full reviews are given below. The points they raise are reasonable, and I think can be easily be addressed mostly just with some rewriting.Reviewer #1:Caenorhabditis nematodes in the Hawaiian islands (>2000 samples). The authors isolate five Caenorhabditis species from about 150 samples: C. briggsae, C. elegans, C. tropicalis, C. oiwi (a new species described here) and C. kamaaina. C. elegans is found at cooler temperatures and higher altitude than the others. The authors sequence the genomes of all C. elegans (their isolates and prior ones from Hawaii) and analyze the single-nucleotide variant diversity. The molecular diversity of C. elegans is three-fold higher in the Hawaii set than in the present set coming from the rest of the world. The within-Hawaiian population structure is altitudinal. Admixture analysis suggests that there is some gene flow between low-altitude Hawaiian populations and the rest of the world. The authors discuss Hawaii as a center of diversity and potential origin of the species.This manuscript reports on a large collection effort of The results are important and considerably extend the known diversity of this model organism, as well as shedding light on its evolutionary history. I see no major flaw.A few suggestions to improve the manuscript:1) It may be that the Pacific region at large shows a high diversity and there is nothing special about Hawaii.C. elegans are divergent from the global population” and in the Discussion. The Discussion is centered on the center of origin of C. elegans and the supposed ancestral niche. Although the Pacific region is mentioned, it would be good to more systematically indicate that one cannot distinguish at the moment whether this diversity is particular to the Hawaiian islands. That C. inopinata was found in South Japan could be mentioned.The present sampling is on the Hawaiian islands. Prior sampling is mostly in North America and Europe, and little in the Pacific region, which introduces a bias. This should be made clearer, for example in the second paragraph on subsection “Hawaiian C. elegans are divergent from the global population”: note that CeNDR shows more strains with the corresponding "Hawaiian-like" isotypes (BRC20231 from Taiwan (?), JU1171 from Chile, thus from the Pacific region). In line with this, it could be noted in the penultimate paragraph of the Results section that the admixed isotype was QG556 (and QG558), from California, also on the Pacific rim.Second paragraph of subsection “Hawaiian 2) Could it be that the relatively high diversity is due to a lack of selective sweeps reducing the diversity (due to the high linkage disequilibrium) in the Hawaii set because of geographic isolation? Diversity may have been higher elsewhere before and lost perhaps due to human activity and movement.3) Gene flow between "Hawaii/Pacific" and the rest of the world.I do not understand why there is emphasis on the gene flow with the C group and little is made (i) of the gene flow to the G group that is shown in Figure 5—figure supplement 2 and Figure 7—figure supplement 1; and (ii) of the admixture with the “HI Low” of some non-Hawaiian strains (including the G group). It seems that both “HI Low” and “HI Invaded” are partially admixed.4) The category of “fruits, nuts or vegetation”, opposed to “flower” is odd. First, nuts are fruits. Second, flower is vegetation. Please at least change the name and perhaps reconsider the categories.5) I could not find the correspondence between the samples, the strains, the species, the isotypes. The supplementary table with the sampling detail lacks the strain and species names in each sample.C. elegans. However, some other points could be discussed, for example the use of these data and strains as resource, e.g. for GWAS, the sampling design, the other species, etc.6) The Discussion is currently centered on the center of origin of Reviewer #2:C. elegans, and Caenorhabditis more generally. They focus their study on ~100 samples isolated and whole-genome sequenced from the Hawaiian islands, and integrate this outstanding dataset with ~175 C. elegans wild isolate strains from around the world that the authors genome-sequenced previously. Their data collection also incorporates an extremely streamlined protocol for associating geographic, ecological and environmental data with the isolates, which they use to demonstrate species differentiation by elevation/temperature (in the process discovering and naming a new species) as well as some genetic differentiation within C. elegans by island and elevation. They corroborate some previously established patterns of polymorphism across the genome and, more novel, discover a trove of diversity for those isolates from Hawaii. The data and analysis pipeline are publicly available. This abundance of polymorphism in Hawaii exceeds the sum total of molecular variation for all other strains found around the world and hints at the evolutionary and ecological origins of C. elegans as a species potentially deriving from cool, high-elevation habitats in or around the Pacific within the last 10My.Crombie et al. present the collection of and population genomic analysis for the largest such effort for C. elegans research community, especially for researchers interested in its ecology and evolutionary history, but also those interested in using natural allelic variants to understand molecular mechanisms. My criticisms are mostly to do with wording, specifically around the meaning and consistency of use of "population."The clearly presented analysis, despite comprising an impressively large sample of genomes, is relatively simple. These results will be valuable for the C. elegans are divergent from the global population” and elsewhere: use of the word "population" to refer to the Hawaiian vs. non-Hawaiian samples seems a bit inconsistent/misleading, as the additional analyses show that they each comprise multiple genetic clusters  and the non-Hawaiian sample in particular is geographically global, and so may confuse readers. I would be more comfortable with "population sample" or "sample of strains" or just "sample." This issue appears again in paragraph two of the Discussion after much effort went into analyzing distinct genetic groups within the Hawaii sample that are not entirely defined by geography. In subsection “The ancestral niche of C. elegans might be similar to the Hawaiian niche”, it seems the use of "populations" might better be replaced by "localities." Please comb through the manuscript for use of "population" to ensure consistency in what is meant, and what the analysis shows.Subsection “Hawaiian C. elegans population structure on Hawaii”: I think this conclusion may be too strong. The admixture analysis does provide evidence for 11+ genetically-distinguishable groups, though whether these necessarily correspond to (geographic) ancestral populations is tricky to infer. This challenge is especially true given the highly selfing reproduction of C. elegans that violates assumptions for what populations mean in admixture, so some caution is needed in interpretation. I'd be more comfortable with wording along the lines of, "This analysis identified at least 11 genetic groups for this sample of C. elegans, each of which we refer to as a population for the purposes of further analysis."Subsection “ Essential revisions:Drawing from reviewer 1 we all agreed that the paper should acknowledge the current limitations of sampling more, i.e. it may be that the Pacific region at large shows a high diversity and there is nothing special about Hawaii.C. elegans are divergent from most strains sampled across the globe” and “C. elegans population structure”) and the Discussion (“The origins of C. elegans”) we have added content to address the sampling biases in Hawaii and Europe and more systematically indicate that we cannot know whether this diversity is specific to the Hawaiian Islands.Yes, we completely agree about this concern. In the Results  It may be that the Pacific region at large shows a high diversity and there is nothing special about Hawaii.The present sampling is on the Hawaiian islands. Prior sampling is mostly in North America and Europe, and little in the Pacific region, which introduces a bias. This should be made clearer, for example in the second paragraph on subsection “Hawaiian C. elegans are divergent from the global population” and in the Discussion. The Discussion is centered on the center of origin of C. elegans and the supposed ancestral niche. Although the Pacific region is mentioned, it would be good to more systematically indicate that one cannot distinguish at the moment whether this diversity is particular to the Hawaiian islands. That C. inopinata was found in South Japan could be mentioned.Thank you for pointing out this limitation. In the Results and the Discussion we have added content to address the sampling biases in Hawaii and Europe and more systematically indicate that we cannot know whether this diversity is specific to the Hawaiian Islands.Second paragraph of subsection “Hawaiian C. elegans are divergent from the global population”: note that CeNDR shows more strains with the corresponding "Hawaiian-like" isotypes (BRC20231 from Taiwan (?), JU1171 from Chile, thus from the Pacific region). In line with this, it could be noted in the penultimate paragraph of the Results section that the admixed isotype was QG556 (and QG558), from California, also on the Pacific rim.C. elegans population structure” we added this detail about QG556 and a new figure  that more clearly shows admixture and geographical proximity between the Hawaii Invaded and non-Hawaiian C populations.These are excellent points. We have reworked the manuscript to highlight the Pacific region isolations for the MY23 isotype. In paragraph three of subsection “2) Could it be that the relatively high diversity is due to a lack of selective sweeps reducing the diversity (due to the high linkage disequilibrium) in the Hawaii set because of geographic isolation? Diversity may have been higher elsewhere before and lost perhaps due to human activity and movement.C. elegans”).We added this possible explanation for the high genetic diversity found among the Hawaiian isotypes (subsection “The origins of 3) Gene flow between "Hawaii/Pacific" and the rest of the world.I do not understand why there is emphasis on the gene flow with the C group and little is made (i) of the gene flow to the G group that is shown in Figure 5—figure supplement 2 and Figure 7—figure supplement 1; and (ii) of the admixture with the “HI Low” of some non-Hawaiian strains (including the G group). It seems that both “HI Low” and “HI Invaded” are partially admixed.C. elegans population structure”) to reflect the evidence of gene flow from a population related to the Hawaiian Invaded and Hawaiian Low populations to the non-Hawaiian G population. We have also added to the Results  regarding the haplotype sharing between the Hawaiian Low and non-Hawaiian populations. Furthermore, we specifically mention that the Hawaii Low population contains some admixture with non-Hawaiian isotypes and shows some evidence of globally swept haplotypes .Thank you for this valuable feedback. We have added text to the Results (subsection “4) The category of “fruits, nuts or vegetation”, opposed to “flower” is odd. First, nuts are fruits. Second, flower is vegetation. Please at least change the name and perhaps reconsider the categories.We agree that the substrate category names in the original manuscript were potentially confusing. We have changed the names and reorganized the samples into new categories. After changing the names and categories, we reran all statistical calculations involving substrate categories.1) We removed the eight “vegetation” samples from the original “fruits, nuts or vegetation” category and recategorized them into a new “vegetation” category.2) We recategorized all the samples originally called “other” into the new vegetation category because these samples all consisted of various forms of vegetation that were each infrequently sampled.3) We changed the name of the original “Fruit/nut/veg” category to “Fruit” because nuts are fruits, as pointed out.C. elegans,including fruits, flowers, stems, leaf litter, fungus, compost, wood, live arthropods, and molluscs . We chose to group these substrates into six major categories: leaf litter, fruit, flower, fungus, invertebrate, and vegetation. The vegetation category contains all substrates that do not fall into any of the other five categories. We chose to distinguish the leaf litter, fruit, and flower substrates from the vegetation category because we suspect these substrates provide distinct microhabitats and each substrate was sampled extensively.”We now include our logic for choosing the six major substrate classes in the Materials and methods section: “On these hikes, we opportunistically sampled substrates known to harbor Importantly, while we were updating the categories, we discovered 19 samples that had been inadvertently filtered from our data set prior to plotting Figure 2 and running the statistics involving substrate categories in the original manuscript. These samples have been added back into the data set for plotting and analysis. No inferences from our statistical tests changed because of the reorganization or the reintroduction of the 19 missing samples.5) I could not find the correspondence between the samples, the strains, the species, the isotypes. The supplementary table with the sampling detail lacks the strain and species names in each sample.The sample, strain, species, and isotype names are all included in Source data 1. The column names for each are; c_label, strain, species_id, and isotype. We have updated the text to reference Source data 1 (full collection data) specifically, rather than just Supplementary file 1 (table of collection categories by island), which might have been confusing.6) The Discussion is currently centered on the center of origin of C. elegans. However, some other points could be discussed, for example the use of these data and strains as resource, e.g. for GWAS, the sampling design, the other species, etc.We agree that the utility of these data in GWA should be addressed in the discussion. We added text within the first paragraph of the Discussion to highlight both the utility and potential disadvantages of these data with respect to GWA (First paragraph).Reviewer #2:[…] My criticisms are mostly to do with wording, specifically around the meaning and consistency of use of "population."Subsection “Hawaiian C. elegans are divergent from the global population” and elsewhere: use of the word "population" to refer to the Hawaiian vs. non-Hawaiian samples seems a bit inconsistent/misleading, as the additional analyses show that they each comprise multiple genetic clusters  and the non-Hawaiian sample in particular is geographically global, and so may confuse readers. I would be more comfortable with "population sample" or "sample of strains" or just "sample." This issue appears again in paragraph two of the Discussion after much effort went into analyzing distinct genetic groups within the Hawaii sample that are not entirely defined by geography. In subsection “The ancestral niche of C. elegans might be similar to the Hawaiian niche”, it seems the use of "populations" might better be replaced by "localities." Please comb through the manuscript for use of "population" to ensure consistency in what is meant, and what the analysis shows.Thank you for this helpful criticism. We revised use of the word “population” throughout the manuscript to avoid confusion and ensure consistency with the results of our analyses. We no longer use the word “population” to refer to the Hawaiian and non-Hawaiian isotype samples; we now refer to them as “samples”. The term “Hawaiian population” has been removed. We also removed the use of the word “populations”. We changed this to “Hawaiian sampling locations”.Subsection “C. elegans population structure on Hawaii”: I think this conclusion may be too strong. The admixture analysis does provide evidence for 11+ genetically-distinguishable groups, though whether these necessarily correspond to (geographic) ancestral populations is tricky to infer. This challenge is especially true given the highly selfing reproduction of C. elegans that violates assumptions for what populations mean in admixture, so some caution is needed in interpretation. I'd be more comfortable with wording along the lines of, "This analysis identified at least 11 genetic groups for this sample of C. elegans, each of which we refer to as a population for the purposes of further analysis."C. elegans population structure”) to include the suggested text. Throughout the manuscript, we are consistent with our usage of the term “population” to refer to genetically distinct groups identified by ADMIXTURE.We thank the reviewer for this helpful comment. We have updated the admixture Results section (subsection “