Sciurus carolinensis) across 43 North American cities to test for parallel clines in melanism, a genetically based trait associated with thermoregulation and crypsis. We show the prevalence of melanism was positively associated with urbanization as measured by impervious cover. Urban–rural clines in melanism were strongest in the largest cities with extensive forest cover and weakest or absent in cities with warmer winter temperatures, where thermal selection likely limits the prevalence of melanism. Our results suggest that novel traits can evolve in a highly repeatable manner among urban areas, modified by factors intrinsic to individual cities, including their size, land cover, and climate.Urbanization is the dominant trend of global land use change. The replicated nature of environmental change associated with urbanization should drive parallel evolution, yet insight into the repeatability of evolutionary processes in urban areas has been limited by a lack of multi-city studies. Here we leverage community science data on coat color in > 60,000 eastern gray squirrels ( The dramatic environmental change associated with urbanization renders cities similar to one another in many abiotic and biotic characteristics4. For example, cities have extensive impervious cover , increased air temperature, and increased air, water, light, and noise pollution relative to their adjacent rural landscapes2.Urban land use is expanding at a rate faster than urban population growth5. Cities are thought to drive parallel evolution as urban populations adapt to similar selection pressures9. Cities are not homogenous, however, and variation in their characteristics likely mediates processes that facilitate or inhibit parallel evolution, generating variation in the shape of genotypic or phenotypic clines along urbanization gradients. Clines may be strongest in large cities where the environmental gradients driving divergent selection between urban and rural areas are steepest. Conversely, clines may be weak or nonexistent in small cities or cities with extensive greenspace connecting urban and rural populations via gene flow. Environmental conditions at large spatial scales—such as regional climate—may also modulate the evolution of urban–rural clines at a smaller scale. For example, directional selection for traits that reduce thermal stress at climate extremes may overwhelm selective pressures associated with local land use, weakening urban–rural clines10. Advances in our understanding of how landscape-scale differences among cities facilitate or constrain parallel evolution have been limited by a lack of studies conducted across multiple, replicate cities9. Addressing this knowledge gap will help clarify how urbanization, the megatrend of global land use change, will shape biodiversity.The convergence of environmental conditions in cities makes urban ecosystems an ideal focus for addressing longstanding questions in evolutionary biology about the degree to which populations exhibit parallel evolution, that is, when multiple populations occupying similar environments evolve comparable traitsSciurus carolinensis) in 43 cities in North America  and inherited in a simple Mendelian fashion at the melanocortin 1 receptor gene (Mc1R)12. Evidence suggests divergent selection associated with crypsis, predation risk, and road mortality favors the melanic morph in cities and the gray morph in rural woodlands13, which should cause a clinal decrease in melanism from urban to rural areas. However, urban–rural divergence in melanism has been documented in only two cities .Here we test for urban–rural clines as a signal of parallel evolution and examine how landscape features mediate the shape of clines. Our focus is on melanism in eastern gray squirrels  and drive variation in cline shape among cities . Gray squirrels can disperse long distances (> 10 km)15, but movements are limited by forest fragmentation16, so we expected clines to be weakest in small cities and cities with extensive forest cover where gene flow between urban and rural areas should be greatest. We included winter temperature in the model because melanic squirrels are known to have greater thermogenic capacity than the gray morph at cold temperature17. Clines may be weak in cities with cold winter temperature if thermal selection overwhelms the selection pressures on melanism associated with urbanization.We used a dataset on coat color from squirrel observations collected via the community science project P < 0.001; Figs. Mc1R12. Individuals with at least one copy of the deletion allele are melanic, so clines in melanism very likely occur at both phenotypic and genotypic levels.Our analyses revealed that a positive relationship between the prevalence of melanism and degree of urbanization was replicated across multiple cities in North America 24, including mechanisms involving correlated traits25. For example, melanic squirrels have less phylogenetically variable microbiomes than the gray morph in cities26. A more complete picture of the selective mechanisms driving urban–rural clines will require linking trait variation to fitness differences between squirrel color morphs in urban and rural areas.The presence of parallel clines in heritable traits is often considered evidence of adaptive evolution because gene flow should homogenize populations in the absence of natural selection23. Strong genetic drift via founder events, combined with restricted gene flow along the urbanization gradient, can generate single-locus clines27. Variation in the size of founding populations likely causes some variation in the prevalence of melanism among cities, and thus the strength of urban–rural clines. At an extreme, urban–rural clines are absent in cities where melanism was historically absent and melanic squirrels have never been introduced. However, two factors lead us to believe that urban founder events are not the driving force behind the observed urban–rural clines. First, single-locus clines generated by neutral evolutionary forces require strong isolation-by-distance (IBD)27. We suspect IBD between urban and rural areas exists but is limited in strength by high effective population size in cities , high capacity for long-distance dispersal15, and frequent translocation of squirrels29. Second, at northern latitudes where we observe most urban–rural clines, melanism was common in rural old growth forests prior to the 1800s32, including regions where it is now rare except in cities . Founder events in cities do not explain the paucity of melanics in rural areas relative to the past. We hypothesize that the dramatic shift from old growth to secondary forests following European settlement in North America35 changed the degree to which gray and melanic morphs are concealed from visual predators and hunters, favoring the gray morph in regrown forests13. Genomic analysis and field experiments are needed to clarify the role of natural selection versus nonadaptive processes in generating the observed urban–rural clines36.An alternative explanation for the urban–rural clines documented here is that melanic squirrels have been introduced by humans to some cities5. First, there was a strong negative relationship between melanism and winter temperature 39. Importantly, our results do not suggest urban–rural clines in melanism in gray squirrels are caused by the urban heat island effect, which should cause the opposite clinal pattern than that observed.Although clines in melanism tended to take the same form among cities, the strength of urban–rural clines varied, supporting the idea that repeated evolution is often inconsistentP < 0.001; impervious*forest cover interaction effect = 0.15, SE = 0.03, P < 0.001; Fig. 9. Selection associated with road mortality in urban areas may also be weak in small cities with limited traffic, which could explain the lower prevalence of melanism in smaller cities  contribute to variation in gene flow and selection along urbanization gradients within and among cities.Second, the positive relationship between urbanization and melanism was strongest in large cities with extensive forest cover , so we have no reason to believe the relative change in melanism along urbanization gradients is biased.Community science datasets in ecology and evolution are powerful for conducting studies at large spatial scales and with levels of replication that would otherwise be infeasible. However, one challenge of using community science datasets is the potential for sampling biases20, and it suggests cities can function as refuges for unique phenotypic traits42. Our study also provides insight into how landscape-scale differences among cities alter phenotypic clines along urbanization gradients. We show the degree of parallelism in urban–rural clines among cities is imperfect, with environmental heterogeneity among cities contributing to variation in the shape of clines. Greenspace availability in particular has been a focus in the ecological literature for understanding among-city variation in the response of species distributions to urbanization40—our results show that greenspace availability in the form of forest cover likely mediates evolutionary responses to urbanization as well. Additional multi-city studies will be essential for understanding how the degree of parallel evolution depends on factors intrinsic to individual cities, including those not examined here . Finally, our work underscores the value of leveraging community science data to conduct studies of urban evolution across multiple cities.Our work reinforces the idea that ecological homogenization among cities likely drives parallel evolutionSquirrelMapper (squirrelmapper.org). Starting in 2010, participants used a web platform to submit sightings of S. carolinensis, including the date of observation and color morph of each squirrel . Spatial coordinates for each observation were secured via Google Maps JavaScript API. A total of 5899 records were submitted to the original SquirrelMapper program from the native range of S. carolinensis in North America. Beginning in 2019, SquirrelMapper was integrated with iNaturalist, enabling users to submit georeferenced photos of each squirrel observation. All images of S. carolinensis submitted to iNaturalist through 21 December 2020 and classified as “research grade” were included for analysis . Data from the original SquirrelMapper program were merged with the iNaturalist dataset for analyses.Squirrel records were sourced from the community science program Zooniverse to crowdsource the classification of coat color of squirrels in images submitted to iNaturalist. Participants were asked to classify the number of squirrels observed in each image  and the coat color of squirrels present in each image . Each image was classified by a minimum of 10 participants . We retained images with a minimum agreement threshold of 80% for the number of squirrels observed. Of the 72,239 images (96%) that met the agreement threshold, 865 were classified as having no squirrel present , 70,258 had a single squirrel, and 1116 had two or more squirrels. Of the images classified as having at least one squirrel, we retained 63,506 images that had a minimum agreement threshold of 80% for coat color of at least one squirrel in the image. We removed 5182 images that did not have location data available and 4035 images taken outside of North America where S. carolinensis is introduced. The final iNaturalist dataset consisted of 54,289 images, of which 54,215 were classified as having a single coat color present  and 74 had two color morphs present . A random sample of 500 images classified by the authors showed that the classification scheme we used with 80% agreement thresholds was 100% accurate for the number of squirrels observed in the image and 99.8% accurate for coat color, the only discrepancy being a single image classified as melanic that the authors classified as unclear. The final iNaturalist dataset was merged with squirrel records from the original SquirrelMapper project, yielding 60,262 squirrel records in total. Multiple coat colors observed at the same location were treated as separate records in this final dataset. Observations in the final dataset were made between 1980 and 2020 (median = 2019), with 97% of observations being reported since 2010. The 3% of observations prior to 2010 consisted of iNaturalist records and records by the authors and colleagues prior to SquirrelMapper.We used the participatory science portal S. carolinensis that had at least 100 squirrel observations in the city and surrounding rural area. We defined the spatial extent of each city’s footprint using the Urban Area National Shapefile Record Layout43 for the United States and the Population Center Boundary File44 for Canada. We calculated the distance from the centroid of each city footprint to its maximum extent in each of the four cardinal directions. We then buffered each urban area by 25% of the average distance from the city centroid to its extent 45 in R46, which helps minimize spatial sampling bias. If two cities with ≥ 100 squirrels had polygons that overlapped, we retained the city with a greater sample size or spatial spread of squirrel observations. The final dataset included 26,924 squirrels among 43 cities .We selected cities in the native range of 47. A 1-km buffer was chosen to encompass the typical home range size of S. carolinensis (< 5 ha) and long-distance movements typical of males during winter and summer mating seasons48. For each city and its surrounding rural area we measured forest cover and winter temperature. Forest cover was measured using the Global 2010 Tree Cover database (30-m resolution)49, and we used WorldClim (version 2.1) to measure winter temperature as mean temperature of the coldest quarter 50. We also measured the area of each city footprint as a metric of city size. All spatial analyses were conducted in R using the raster (v. 3.3–13)51 and exactextractr packages (v. 0.5.1)52.We measured percent impervious cover within 1 km around each squirrel observation using the Global Man-made Impervious Surface Dataset (30-m resolution)39. This ensured the slope term for impervious cover has a consistent interpretation among cities, allowing us to test for a general urban-to-rural cline in melanism. City size (log-transformed), forest cover, and winter temperature were included as fixed effects to test whether these variables predict variation in the prevalence of melanism among cities. Two-way interaction terms were included between each predictor and impervious cover to test how city characteristics affect the shape of urban-to-rural clines in melanism. We standardized each predictor so that each variable had mean = 0 and SD = 1, which aided in model convergence and allowed us to compare the effect size of each slope term53. We also included city as a random effect to account for the nested observations within each city and to estimate unexplained variance in the prevalence of melanism among cities (Supplementary Table lme4 package in R (v. 1.1-26)54. Two-sided Wald tests were conducted for each parameter in the model. Binned residual plots were generated using the arm package (v. 1.11-2)55 as recommended for models with a binary response variable56. Plots of binned residuals on fitted values and each predictor suggested adequate model fit and no substantial deviation from model assumptions.We fit a generalized linear mixed model with a binomial error distribution to test for effects of impervious cover, city size, winter temperature, and forest cover on the distribution of melanism. Melanism was the response variable and represented by a binary indicator, with 1 corresponding to melanic and 0 to other coat colors for each squirrel observation. Impervious cover within 1 km of each observation was included as a fixed effect to represent each city’s urbanization gradient. Because the range of impervious cover varied among cities, we rescaled impervious cover within each city to range from 0 (lowest impervious cover) to 1 (greatest impervious cover)57 to account for spatial autocorrelation in the residuals of the model. We extracted residuals from the global model and created a spatial autocovariate for the residuals using the spdep package (v. 1.1-7)58. We set the radius neighborhood to 31 km (the greatest nearest neighbor distance in the thinned dataset) and used an inverse distance weighting scheme with style “B” for symmetric weights59. Our global model was then refit with the spatial autocovariate term as an additional predictor, which reduced spatial autocorrelation in the residuals from a maximum of r = 0.11 within 1 km to r < 0.05  and buffer distances to measure impervious cover . These analyses showed the model results were not sensitive to spatial thinning distances (Supplementary Table Supplementary Information.


Setosphaeria turcica is a heterothallic fungus that is the causal agent of northern leaf blight (NLB), which is a devastating foliar disease of sorghum and maize. Despite of its adversary to crop production, little is known about the genetic diversity and population genetic structure of this pathogen from sorghum. In this study, we explored the utilization of single nucleotide polymorphism (SNP) molecular markers and three mating type-specific primers to analyze the genetic diversity, population genetic structure, and mating type distribution of 87 S. turcica isolates that had been collected in sorghum production areas from three provinces, including Henan, Shaanxi, and Shanxi in China. The populations are featured with moderate genetic diversity and relatively equal mating type distribution of MAT1-1 and MAT1-2. The genetic differentiation was significant (p < 0.05) among different populations except those from Henan and Shanxi provinces that showed particularly frequent gene flow between them. Neither the maxinum likelihood phylogenetic tree, nor principal coordinate analysis, nor genetic structure analysis was able to completely separate the three populations. The relatively low genetic distance and high genetic identification were also observed among the three populations. Nevertheless, the genetic variation within populations was the major source of variation as revealed by AMOVA analysis. The findings of this study have improved our current understanding about the genetic diversity, population genetic structure, and the distribution of mating type of S. turcica, which are useful for unraveling the epidemiology of NLB and developing effective disease management strategies. Sorghum bicolor) is an important crop with multiple uses for food, brewing, fodder, and biofuel applications in many countries in the world (Setosphaeria turcica (Luttr.) Exserohilum turcicum (Pass.) Sorghum  of S. turcica was first described in 1958 , random amplified polymorphic DNA (RAPD), simple sequence repeat (SSR), and sequence-related amplified polymorphism (SRAP). RAPD molecular markers were developed to analyze genetic diversity and population genetic structure of 264 S. turcica isolates from four different continents, which displayed the variability in the genetic diversity and mating type distribution of different regions and states  molecular marker is lacking. SNP is the third-generation molecular marker which is characteristic of high stability, large number, wide distribution, and the ability to detect the most frequent type of DNA variation , followed by 3% sodium hypochlorite for 2–3 min (min), and washed with sterile water for three times, prior to being transferred onto the potato dextrose agar (PDA) medium containing rifampicin (RFP) at 50 μg/ml and incubated at 25°C for 3–5 days (d). All the cultures were checked for conidiophore and conidium under microscopy to verify that isolates were S. turcica before being stored at 4°C until further use.Setosphaeria turcica isolates were cultured in liquid potato dextrose broth for 3 d at 25°C to collect mycelium, from which total DNA was extracted using a rapid blood genomic DNA isolation kit , following the manufacturer’s instruction. The quality and concentration of the obtained DNA was determined by using Qubit 2.0 fluorometer .S. turcica isolatesA total of 172,067 potential SNP loci were detected through the whole-genome resequencing of 15 ™ Thermal Cycler  with the following conditions: an initial denaturation at 98°C for 5 min, first 8 cycles of denaturation at 98°C for 30 s, annealing at 50°C for 30 s, elongation at 72°C for 30 s, and then 25 cycles of denaturing at 98°C for 30 s, annealing at 66°C for 30 s, elongation at 72°C for 30 s and a final extension at 72°C for 5 min, eventually held at 4°C. The PCR products were fractionated by using gel electrophoresis on 1% (w/v) agarose gels in TBE buffer , stained with ethidium bromide (EB) prior to visualization under UV light in order to verify the size of the desired PCR product. The PCR product was then isolated from the gel and recovered using AMPure XP magnetic beads . The second round of PCR reaction was performed using the first round of PCR product as a template to obtain a sequencing library with molecular tags. The second round of PCR reaction consisted of DNA (10 ng/μl) 2 μl, universal P7 primer (10 μm) 1 μl, universal P5 primer (10 μm) 1 μl, 2× PCR Ready Mix 15 μl. The condition of PCR reaction: an initial denaturation at 98°C for 3 min, then 5 cycles of denaturing at 94°C for 30 s, annealing at 55°C for 30 s, elongation at 72°C for 30 s and a final extension at 72°C for 5 min. When the second round of PCR reaction was completed, the PCR product was also purified and recovered using AMPure XP magnetic beads. Following the preparation of the DNA sequencing libraries, paired-end sequencing of the library was performed on the HiSeq XTen sequencers .A panel consisting of 77 target SNP sites was designed, and libraries were prepared by using two-step PCR. The PCR reaction consisted of 1 μl each of the forward and reverse amplicon PCR primers (10 μm), 15 μl kapa hifi 2 × PCR ready mix , 2 μl genomic DNA (10 ng/μl), and sterile water to 25 μl. PCR reaction was executed in a T100Q < 20) removed by PRINSEQ-lite (v 0.20.3). The remaining clean data were then mapped to the reference genome (GenBank assembly accession: GCA_013390295.1) by BWA (version 0.7.13-r1126) with default parameters. Samtools (version: 0.1.18) was used to calculate each genotype of the target locus. When the read frequency of a major allele was higher than 80%, it was described as homozygous, and those loci with missing data were discarded.Raw reads were filtered and having adaptor sequence removed by Cutadapt (v 1.2.1), and the low-quality bases from reads 3′ to 5′ (K) ranges from 1 to 10 with 10 runs at 10,000 burn-in followed by 100,000 Markov Chain Monte Carlo simulations. The optimal value of K was acquired by online tool Structure HarvesterThe SNP sites with high level of polymorphisms, but without missing data, were selected for further analysis. Arlequin 3.11  was usedp < 0.05 level.The mating type of 87 isolates was determined by using mating type-specific primers which were designed by S. turcica isolates  phylogenetic tree, population STRUCTURE, and principal coordinate analysis (PCoA) are complementary in this study. PCoA based on the paired genetic distance matrix also elucidated the population genetic structure. As shown in The genetic differentiation coefficient (Fst) was calculated to compare the three populations. Among Henan/Shaanxi, Henan/Shanxi, and Shaanxi/Shanxi, the Fst values were 0.1581, 0.0385, and 0.1208, respectively; the number of migrants per generation (Nm) values was 1.3253, 6.4885, and 1.8356, respectively . The FstNei’s genetic distance (Nei’s D) and genetic identity (Nei’s I) mainly analyzed the genetic relationship between populations, as tabulated in Analysis of molecular variance (AMOVA) revealed that the variation within and between the population accounted for approximately 89.23 and 10.78% of the total variation, respectively . Such a S. turcica isolates were investigated by using multiplex PCR analysis with mating type-specific primers (χ2 test (0.1 ≤ p ≤ 1.0). In the Henan population, 15 isolates were MAT1-1 and 15 were MAT1-2, representing a perfect ratio of 1:1. However, this was not the case in the Shaanxi and Shanxi populations, especially in Shanxi population, about 73% of isolates were identified as MAT1-2. Moreover, the isolates of S. turcica containing both MAT1-1 and MAT1-2 mating types were not found.The mating types of the 87  primers . As a reS. turcica isolates sampled from Henan, Shaanxi and Shanxi provinces in China were investigated by using SNP markers and mating type-specific primers. Population genetic diversity is prerequisite for evaluating the status of biological resources. Nei’s genetic diversity (h) and Shannon’s information index (I) are important parameters for measuring biological genetic diversity  between Henan and Shanxi population also illustrated the similarity of the two subgroups, further corroborating the above findings.The population genetic STRUCTURE and ML phylogenetic tree both separated the three populations into two groups, and the variations among the Henan, Shanxi, and Shaanxi populations is a manifestation that there was no particularly significant connection between genetic groups and the geographic origin of ocations . The samh Africa , and elsh Africa . Howeverh Africa . We specS. turcica isolates between Henan and Shanxi, indicating frequent gene exchange between these two populations. The value of Fst between Henan/Shaanxi (0.1573) and Shanxi/Shaanxi (0.1202) was greater than Henan/Shanxi (0.0385), demonstrating relatively moderate genetic divergence between these populations, which was lower than that previously reported by Gene flow (Nm) can reveal genetic infiltration between populations, reduce geographic differentiation and cause the homogenization of the genetic characteristics between populations ; Fst canS. turcica populations as revealed in this study. It is possible that there are limitations in the use of SNP molecular markers for exploring the genetic diversity and population genetic structure of plant pathogens, which mandates cautious interpretations of the findings and warrants further investigation and refinement of the molecular tools used in such study.By using type-specific PCR primers, we were able to identify two mating types, including MAT1-1 and MAT1-2, which are present in all the three different geographic populations. Although the distribution of the two mating types in three populations was not exactly equal, especially the Shanxi population had an obviously skewed distribution toward MAT1-2, but on the whole, the total ratio of the MAT1-1 and MAT1-2 was close to 1:1, indicating a positive association between the possibility of sexual reproduction among populations and genetic diversity . Such a S. turcica that affects sorghum growth and development, this study represents the first attempt to use SNP molecular markers to analyze genetic diversity and population genetic structure of 87 S. turcica isolates from Henan, Shaanxi and Shanxi, the three provinces in China. Our results revealed relatively equal mating type distribution, moderate genetic diversity, and genetic differentiation among the three populations. Although careful interpretation is required for the results, there is no doubt that it will enrich our knowledge about the genetic diversity, population genetic structure of S. turcica isolates and provide theoretical bases for the disease management of sorghum NLB in the relevant regions.There are relatively few studies about the pathogen of S. turcica for the first time. The S. turcica populations from three provinces in China are featured with moderate genetic diversity, high levels of gene flow among populations, and relatively equal mating type distribution of MAT1-1 and MAT1-2. The findings have improved our current understanding about the genetic diversity and population genetic structure of S. turcica in China.In this study, SNP molecular markers were developed to analyze the genetic diversity and population genetic structure of The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: NCBI BioProject–PRJNA798196.YH designed the study. LC and JD performed all the experiments. JD and LZ analyzed the data and drafted the manuscript. TL reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the grant U1804104 of the National Science Foundation of China, the grant CX(21)3022 of the Independent Innovation of Agricultural Sciences in Jiangsu Province and the grant BK20191236 of Jiangsu Science and Technology Development Program.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Prunus sibirica is an economically important tree species that occurs in arid and semi-arid regions of northern China. For this species, creation of a core collection is critical for future ecological and evolutionary studies, efficient economic utilization, and development and management of the broader collection of its germplasm resources. In this study, we sampled 158 accessions of P. sibirica from Russia and China using 30 pair of simple sequence repeat molecular markers and 30 different schemes to identify candidate core collections. The 30 schemes were based on combinations of two different sampling strategies, three genetic distances, and five different sample sizes of the complete germplasm resource. We determined the optimal core collection from among the 30 results based on maximization of genetic diversity among groups according to Number of observed alleles (Na), Number of effective alleles (Ne), Shannon’s information index (I), Polymorphic information content (PIC), Nei gene diversity (H) and compared to the initial collection of 158 accessions. We found that the optimal core collection resulted from preferred sampling at 25% with Nei & Li genetic distance these ratios of Na, Ne, I, PIC and H to the complete 158 germplasm resources were 73.0%, 113%, 102%, 100% and 103%, respectively, indicating that the core collection comprised a robust representation of genetic diversity in P. sibirica. The proposed core collection will be valuable for future molecular breeding of this species and management of its germplasm resources. Prunus sibirica) is a shrub or tree species of the genus Rosaceae family. P. sibirica is widely distributed across a wide range of geographical areas in the northern regions of China and Russia [P. sibirica can be used as a pioneer tree species for improving the environment in semi-arid and arid areas [P. sibirica and its promotion within the horticultural and silvicultural industries still face challenges. In particular, the cultivar that high-quality and high-yield remain scarce, but this can be overcome by plant breeding research, especially molecular breeding approaches, to expand the scale of cultivation of P. sibirica.Siberian apricot  molecular markers.P. sibirica were collected from China and Russia  and were used as the initial collection from which to establish a core collection, including the characteristics of high yield, late-flowering, late-maturing, sweet flesh, double kernels, sweet almond, frost resistance, drought resistance, extreme drought resistance, pink flower, fold flower, pink anther etc. .From April 2014 to September 2019, 158 accessions of her etc. . The 158Prunus sibirica, Prunus vulgaris, Prunus mandshurica. The surrounding woody plant resources are mainly Pinus tabuliformis, Robinia pseudoacacia, Juglans regia, Crataegus pinnatifida, Amygdalus Persica, Vitis vinifera, Salix suchowensis, etc.The region of Kazuo country belongs to the low hilly region, the elevation of 300~400 m, the climate of which is continental monsoon. The annual average temperature is 8.7°C, and the annual average precipitation is 491.5 mm. The average sunshine duration is 2807.8 h, and the average frost-free period is 144 d. The soil type of the experimental site is dominated by brown soil. The main woody plant resources include Fresh young leaves were collected from 1-year-old shoots on sun-facing sides of the 158 individual trees in mid-June and stored these in liquid nitrogen in the field and in a -80°C freezer in the lab prior to DNA extraction. DNA was extracted with DNAsesure Plant Kit (TIANGEN).P. sibirica in 2014, 600 SSR primers were designed [P. sibirica . Based on these preliminary screenings [PIC) >0.5.Based on the results of Reduced-Representation Genome Sequencing (RRGS) of designed  , the number of effective alleles (Ne), the Shannon’s information index (I), and Nei’s gene diversity (H) were calculated using POPGENE 32 software. Cervus 3.0 was applied to determine polymorphism information content (PIC). Cluster analysis was performed using an Un-weighted Pair Group Method of Arithmetic Average (UPGMA) by NTsys V2.10e software, based on three kinds of genetic distances evaluated by using the Simple Matching genetic similarity coefficient (SM), the Jaccard genetic similarity coefficient (JD) and the Nei & Li genetic similarity coefficient (ND) [The sizes of the amplified fragments for each locus were calculated by first comparing to a 100 bp DNA ladder (TIANGEN) in Imagelab 4.0 software and then with manual corrections based on the known size of the SSR repeat unit. Amplified fragments that differed by more than one repeat unit were identified as representing different alleles . SSR datent (ND) .Based on the cluster analysis, two sampling strategies  and three genetic distances  combined with stepwise clustering were used for construction of the core subsets. The (PS) strategy  meant the, I, PIC and H), the most optimal ones of core subsets were chosen. A t-test for means was performed to determine if there was a difference in the three parameters between the core collection and the initial collection, and reserved collection (the other accessions except for accessions of core collection). The representativeness of the core collection was also validated by principal coordinates analysis (PCOA) using NTSYS V2.10e, with the distribution of the initial collection and the core collection being plotted by the first principal component score and the second principal component score.By comparing 4 genetic parameters(Na), the effective number of alleles (Ne), Shannon’s information index (I), polymorphic information content (PIC), and Nei’s gene diversity (H) were 22, 8.194, 2.328, 0.847, 0.854, respectively. These parameters clearly indicated that the 158 P. sibirica germplasms had a high genetic diversity at the molecular level.The genetic diversity of the initial collection was analyzed based on 30 pairs of SSR primers . The rese, I, PIC and H of two sampling strategies were compared, to determine which sampling strategy could be used to construct the core collection  and coefficient of variability (CV) of Ne were 0.370 and 0.043, respectively, and the STDV and CV of I were 0.415 and 0.054, respectively  generated according to the PS strategy among three genetic distances  and their retention ratios, combined with the t-test and principal coordinate analysis (PCO), to verify and confirm the optimal core collection, and the effect is better. The retention ratio was the percentage of each genetic parameter of the core collection to each genetic parameter of the initial collection [Armeniaca vulgaris Lam. [Eucommia ulmoides [a were 73%, the core collection constructed in this study has better genetic parameters and meet the requirements of the core collection, indicating that the effect of retaining the genetic diversity of the initial collection was better.Representativeness is the most important property of core collection. Commonly used core collection genetic diversity evaluation parameters are: Number of alleles, Shannon diversity index and Nei’s gene diversity index. The number of alleles was considered the most relevant indicator ,51. Wangllection . In thisris Lam. , and Euculmoides , exist. ulmoides ,54. In tSiberian apricot breeding process hinge on the abundant germplasm resources, and understanding the characteristics of resources is the prerequisite for effective utilization of apricot germplasm resources. In the past 20 years, 158 Siberian apricot germplasm resources collected by our research group were characterized and evaluated. The germplasm resources of Siberian apricot were rich in characteristics, including high yield, frost resistance, drought resistance, bent branch, late-maturing, late-flowering, fold flower etc. Among those characteristics, 70.25% germplasms had the potential of high yield. Approximately 8% of the germplasm has the characteristics of late flowering or barren tolerance, which makes it adapt to a more complex ecological environment. A small amount of Siberian apricot also has attractive characteristics on branches, petals, fruits, and almond and germplasm with these characteristics could be very useful in food, ornamental and other aspects. All in all, Siberian apricot variation is abundant. We used these characteristics information to evaluate the core collection based on SSR, and the results showed that the core collection contains most of the characteristic types.P. sibirica from 13 geographical regions and could represent the genetic diversity of the complete germplasm collection. In the next step, we will focus on the investigation of phenotypic traits of core collection to provide more valuable information for the development and utilization of germplasm resources of Siberian apricot, which will lead to better utilization of germplasm for Siberian apricot breeding programs. In addition, the core collection is further modification by continuously adding new germplasm resources, which will give interesting insights about the representation of unknown Siberian apricot diversity.In this study, we tested combinations of sampling strategies, measures of genetic diversity, and sample reduction techniques to find the one most suited to this species using SSR markers data. Based on these tests, we established a core collection based on an allele preferred sampling strategy, the Nei & Li genetic distance, and 25% sampling from the complete resource. The new core collection comprises of 40 S1 Fig(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


For many authorized drugs, accumulating scientific evidence supports testing for predictive biomarkers to apply personalized therapy and support preventive measures regarding adverse drug reactions and treatment failure. Here, we review cytogenetic and biochemical genetic testing methods that are available to guide therapy with drugs centrally approved in the European Union (EU). We identified several methods and combinations of techniques registered in the Genetic Testing Registry (GTR), which can be used to guide therapy with drugs for which pharmacogenomic-related information is provided in the European public assessment reports. Although this registry provides information on genetic tests offered worldwide, we identified limitations regarding standard techniques applied in clinical practice and the information on test validity rarely provided in the according sections. The European Medicines Agency (EMA) advises an inclusion of pharmacogenomic information in drug labels where an impact on particular genomic subpopulations is recognizable and provides guidance on the identification and validation of genomic biomarkers . Thus, iFor some drugs, genetic testing for relevant biomarkers is recommended or even mandatory prior to prescription. Rapid developments of genetic testing techniques and advancements in cost reduction for testing material and services are constantly increasing the amount of accessible and affordable genetic tests for clinical diagnostics on the market. Thus, many different protocols are available for the determination of the same biomarker. Therefore, the EMA does not provide guidance for the selection of a particular genetic test method as companion diagnostic. In the EPARs, generally the utilization of a validated test for the according biomarker is stated ,4. This In the EU, the current Databank on Medical Devices (Eudamed2) is only used by national competent authorities and the European Commission with regard to market surveillance and is not publicly accessible. A new European database on medical devices (EUDAMED) will be established also to implement the new regulation (EU) 2017/746 on in vitro diagnostic medical devices, which will apply fully from 26. May 2022. The database is launched module wise starting by December 2020. EUDAMED will be in part accessible to the public and will increase transparency on medical devices available for the EU market including in vitro diagnostics like genetic tests ,6.In the United States of America (USA), an oversight of laboratories and offered genetic tests is provided by the Food and Drug Administration (FDA) and the Center for Medicare and Medicaid Services (CMS) respectively on federal level. Still, several regulatory gaps and ambiguities were reported . To incr® Discovery Profile for Solid Tumors encompasses a methodology of Immunohistochemistry, Fluorescence in situ hybridization and Next-Generation (NGS)/Massively parallel sequencing (MPS). However, according to the provider website only NGS is applied to test for EGFR mutations, which is not indicated and therefore misleading in the GTR entries . A l. A l8].  entries . Therefo® MC, Illumina HiScan™SQ system) classified as “Molecular Genetics” and for Trastuzumab and Trastuzumab emtansin response additionally Bi-directional Sanger Sequence Analysis  was registered in the GTR. For guided therapy with Crizotinib, also a combination of a cytogenetic and a molecular genetic technique was identified. Here, RT-PCR with gel analysis applied for chromosome breakage analysis was registered as a cytogenetic method to test for Crizotinib response in combination with RNA analysis by Allele-specific primer extension, a molecular genetic technique  and FISH with NGS (10.3%). Furthermore, for chromosome breakage studies, RT-PCR with gel analysis was registered in combination with Allele-specific primer extension (ASPE) for 8.6% of the analyzed drug-biomarker pairs. Here, always anaplastic lymphoma kinase (ALK) was analyzed as genetic biomarker by this combined approach. Thereby, FISH was identified as a single technique approach for this biomarker. Apart from Cystic Fibrosis, which is a rare disease, all of these drugs for which cytogenetic tests can be used to guide therapy are applied in cancer treatment.Although several cytogenetic methods were registered to test for biomarkers for which information on actionable pharmacogenomics are provided in an EPAR of the respective drug, these tests comprise a combined approach with several methods to test for a set of markers. However, the respective provider websites informed that different tests were used for certain markers and cytogenetic tests were not applied for the pharmacogenomic biomarkers of interest in the “Actionable PGx” listing. Therefore, no cytogenetic tests could be identified to test for the biomarkers relevant for the list of drugs for which annotations on actionable pharmacogenomics are provided in an EPAR of the respective drug .Giemsa (G)-banding is a cytogenetic method to visualize condensed chromosomes and to attain a visible karyotype using Giemsa stain. It is applied to identify and analyze individual chromosomes according to unique banding patterns for diagnostics of genetic diseases and cancers according to ploidy, chromosome abnormalities and rearrangements ,13,14.Skills and experience are crucial to analyze karyotypes which are frequently complex, especially if they are tumor related. This banding technique reliably gives the maximum level of about 550 bands per haplotype set (bphs) of band resolution for karyotyping . HoweverFluorescence in situ hybridization is a cytogenetic technique developed to detect target DNA of e.g., metaphase chromosomes or interphase nuclei and RNA with a high sensitivity and specificity in cells or tissues . It can Probe size and design, tissue type and laboratory validation affect sensitivity and specificity of this technique. Thus, it is labor-intensive, time-consuming, technically demanding and relatively costly. However, analytically valid test kits for the analysis of different clinically relevant conditions are commercially available and thus can be applied directly after tissue preparation . ChromogFor SISH, probes are labeled with dinitrophenol (DNP) and signal detection is performed adding an anti-DNP primary antibody. Furthermore, a secondary antibody conjugated to horseradish peroxidase, silver acetate, hydroquinone and hydrogen peroxide are added and initiate the reduction of silver ions to metallic silver in the nucleus, if the target sequence is present .CISH and SISH show a similar sensitivity ,25 and aAt present, FISH is the preferred cytogenetic method routinely applied e.g., for guiding targeted therapy to identify human epidermal growth factor receptor 2 (HER2) overexpression in breast cancer, human epidermal growth factor receptor 2 anaplastic lymphoma kinase (ALK) rearrangement in adenocarcinoma and BCR/ALB1 translocation in chronic myeloid leukemia  and can CGH was developed for the analysis of genomic alterations in cancer and allows the evaluation of genomic abnormalities such as mutations, deletions, duplications or amplifications. This cytogenetic technique has the advantage that it can be applied on stored DNA of a patient sample rather than metaphase chromosomes which are difficult to gain from tumor cells in the required amount and quality .The development of array-CGH (aCGH) provided the possibility for a genome-wide analysis with a high resolution . It is bHowever, balanced rearrangements or some mosaics cannot be identified by array based CGH and it cannot be used to determine the genomic position of inserted or deleted segments . aCGH ofFurthermore, in combined and single application Next-Generation (NGS)/Massively parallel sequencing was only registered as a test of the GTR category “Molecular Genetics”. However, NGS, which includes a variety of methods, is increasingly applied for cytogenetic analyses ,31. It aBiochemical genetic tests to analyze drug response could not be identified via screening of the GTR by drug. However, via a screening by the according biomarker, registered biochemical genetic tests  for abouImmunohistochemistry (IHC) is an essential application for the diagnosis of cancers and as companion diagnostic tool in cancer treatment. It requires the availability of tissue samples and can be performed on frozen or formalin-fixed paraffin-embedded (FFPE) tissue. IHC is also suitable for small amounts of tissue acquired in procedures such as core biopsies ,39.Thereby, monoclonal or polyclonal antibodies are incubated with the analyzed tissue to determine the distribution of a biomarker antigen of interest. For visualization, mainly enzymes or fluorescent dyes are used which are directly coupled to the primary antibody or to an appropriate secondary antibody but also radioactive elements, or colloidal gold can be applied. The signal is detected dependent on the method of visualization either under an ordinary or fluorescent microscope ,39.Diagnostic tests based on immunohistochemistry were identified by GTR screening for biomarkers such as Anaplastic Lymphoma Kinase (ALK), ROS and PD-L1. Although IHC is specified as a method of choice in several EPARs for HER2 overexpression, test for this biomarker were only identified in the GTR test categories “Cytogenetics” (FISH) and “Molecular genetics”.The biomarker Programmed cell death ligand 1 (PD-L1) is crucial for the selection of patients with e.g., advanced non-small-cell lung cancer (NSCLC) who would respond best to treatment with PD-L1 inhibitors. Unique immunohistochemical assays were developed for each inhibitor. However, several studies analyzing interlaboratory concordance found moderate to high agreement levels for various assays and laboratory developed tests . For detDue to technical artifacts and differences of sensitivity between differing antibodies and pretreatments of tissue, a lack of inter laboratory standardization of the IHC technique analyzing the positivity of HER2 and hormone receptor staining was observed. Furthermore, variability in interpretation by a pathologist occurs subjectively. Therefore, also methodological comparisons of kits that have been introduced (such as HercepTest) were observed with mixed results ,39. ConfEnzyme assays for lysosomal enzymes such as beta-glucocerebrosidase and alpha-galactosidase A are registered in the GTR and can be applied for guided treatment with Velaglucerase alfa in type 1 Gaucher disease (GD) and Migalastat in Fabry disease respectively. For both drugs, testing is required according to the respective EPARs. However, the EPARs of these drugs do not specify any testing technique ,44.Enzyme assays are applied to either identify a target enzyme or to determine the quantity of the enzyme in a sample ,46. TherEnzyme assays based on fluorometry are optical techniques measuring the emission of fluorescence on the breakdown of e.g., the most commonly used specific 4-methylumbelliferone synthetic substrate and release of fluorescent 4-MU, which is directly proportional to the activity of the enzyme in for example dried blood samples. The reaction is catalyzed by specific enzymes; therefore, dependent on the enzyme utilized, the assay protocol can vary ,48. HoweGBA mutation carriers compared to the fluorescence assay. Therefore, results by fluorescence assays require confirmation by genotyping [For measurement of enzyme activity, leukocytes, cultured fibroblasts or dried blood spots can be used ,56. A conotyping For analysis by tandem mass spectrometry, for each assay an internal standard is used to determine the enzyme activity. Molecular and atomic masses of enzyme products are analyzed via mass spectrometry by breaking each molecule into ionized fragments. The fragments are separated and analyzed according to their mass-to-charge ratio. MS/MS can also be used to quantify molecular species ,58. CoupIn the EU, mainly genetic tests are recommended or required for drugs authorized for cancer treatment. Therefore, despite the increasing utilization of molecular genetic techniques like qRT-PCR or next-generation sequencing and combined strategies, the diagnosis of chromosomal disorders by classical cytogenetic methods such as fluorescence in situ hybridization and biochemical genetic techniques such as immunohistochemistry remained a standard for guided therapy with several drugs uniformly approved in the European Union .Cytogenetic analyses provide the possibility to gain information on genetic abnormalities on single cell level . CurrentChromosome staining like Giemsa banding (G-banding) is an early method used since the 1970s for the detection of cytogenetic abnormalities and is still applied in clinical diagnostics. However, also molecular cytogenetic technologies that incorporate nucleic acid-based probes such as fluorescence, silver and chromogenic in situ hybridization (CISH and FISH), comparative genomic hybridization (CGH) and its respective further development to copy-number arrays for chromosomal microarray analysis (CMA), were developed for a higher resolution in the detection of chromosomal and genetic abnormalities. However, the utilization of targeted probes does not allow genome-wide analyses by in situ hybridization, and CMA cannot detect balanced rearrangements or determine the chromosomal position of inserted or deleted segments . Due to HER2 alterations like HER2 protein overexpression, HER2 gene amplification and HER2 gene mutations can be identified and assessed by several laboratory methods in Non-small cell lung cancer (NSCLC). Immunohistochemistry (IHC) is applied to test for HER2 positivity due to protein overexpression and genetic alterations are identified by next generation sequencing (NGS) and fluorescent in situ hybridization (FISH). However, a gold standard has not been established yet .GTR entries for tests including combinations of several techniques were often misleading as information on whether the relevant biomarker was targeted by all techniques was inconclusive. Therefore, additional information on the applied methodology on the provider websites is crucial for a first assessment of the respective tests. Due to such information, it was identified that for none of the biomarkers relevant for the listed drugs of the “Actionble PGx” fraction techniques of the category “Cytogenetics” were actually applied, although the according technique entries in the GTR are presented for several of these markers; however, techniques registered in a combination could not be assigned to a specific marker in the multigene approaches. Therefore, while GTR also presented several entries, for only one biomarker of the category “Biochemical Genetics”, a biochemical genetic technique was actually applied according to the provider websites. Here, it furthermore was a combined approach with a molecular genetic technique, as mainly these biomarkers (“Actionble PGx”) were analyzed by techniques categorized as “Molecular Genetics”. Thereby an emphasis on NGS was detected. Still, some websites, to which the user is directed by the Universal Resource Locator (URL) provided by the GTR, do not offer sufficient transparency on the applied techniques.A tendency of multigene testing using panels for certain disease conditions and combined approaches of several techniques was observed in a majority of offered test services. A clinical laboratory geneticist should therefore assess which method or combination of methods is appropriate also in accordance with the referral reason . TherebyAlthough EMA mainly does not provide guidance for the choice of companion diagnostic tests or techniques, according to drug label information provided by the respective EPAR, eligibility for treatment with Lapatinib, Trastuzumab and Trastuzumab emtansin is only warranted if HER2 overexpression status is determined by an IHC 3+ score, which can be achieved by applying immunohistochemistry to test the affected patient tissue ,65,66. CAccording to current literature, there are emerging technologies that have the potential for pharmacogenetic testing in clinical use and therefore may be commercially available in future. These are molecular genetic techniques such as custom-target sequencing and long-read sequencing approaches ,68. ThusInformation on suitable techniques for predictive biomarker testing is scarcely provided in the SmPCs in Europe, also with regard to new drugs for targeted therapy in oncology. Furthermore, developments concerning predictive biomarkers and testing techniques are quite fast, which provides a challenge. Therefore, a focus on standardization and validation of pharmacogenetic tests will be highly essential. Especially, since the importance of genetic tests as companion diagnostics is increasing with the available and harmonized information on pharmacogenetic and/or pharmacogenomic implications provided in drug labels. The demand for genetic testing guided treatment to increase the safety of drug therapy therefore could grow and may be accelerated by patients’ concern to receive optimal treatment. Therefore, prescribers will need facilitated access to reliable information on availability, validity and utility of genetic and genomic testing. Currently a comprehensive registry that meets the emerging needs for information on genetic tests that could be applied as companion diagnostics in the EU is not available.


For this study, skin samples were analyzed from 77 individual stranded dugongs collected in Thai waters from 1994–2019 using six microsatellite markers to assess the genetic diversity and population structure. Dugongs in the Andaman Sea had higher genetic variation than those in the Gulf of Thailand. Populations in Trang, Satun and some areas of Krabi had highest diversity compared to other regions of Thailand. The analysis of Bayesian genetic clustering showed that dugongs in Thailand consist of five genetic groups. Furthermore, dugongs in the middle and lower Andaman Sea presented the greatest gene flow compared to other regions. Based on calculation of inbreeding coefficients, dugong populations in the Sea of Thailand are experiencing some levels of inbreeding, and so may warrant special protections. Results of this study provide important information on genetic diversity and genetic population structuring of dugongs in Thailand and for understanding the genetic status of dugongs that can lead to improved management and conservation of this endangered species.Dugong dugon) is an endangered species of marine mammals, so knowledge of genetic diversity of these populations is important for conservation planning within different habitats. In this study, six microsatellite markers were used to assess the genetic diversity and population structure of 77 dugongs from skin samples of stranded animals collected from 1994–2019 (69 from Andaman Sea and 8 from the Gulf of Thailand). Our results found that dugongs in the Andaman Sea had higher genetic variation than those in the Gulf of Thailand. Populations in Trang, Satun, and some areas of Krabi had highest diversity compared to other regions of Thailand. Bayesian genetic clustering analysis revealed that dugongs in Thailand consist of five genetic groups. Moreover, dugongs in the middle and lower Andaman Sea presented the greatest gene flow compared to other regions. However, based on calculation of inbreeding coefficients , dugong populations in the Sea of Thailand are experiencing some levels of inbreeding, and so may warrant special protections. These results provide important information for understanding the genetic status of dugongs that can lead to improved management and conservation of this endangered species.The dugong ( Dugong dugon), order Sirenia, family Dugongidae, is the only herbivorous marine mammal in the world, with a diet consisting of seagrass [The dugong  and Natural Resources , and in In Thailand, dugongs were once common along both coasts of the Andaman Sea and Gulf of Thailand, but have since declined severely . ApproxiExamining population structure is an important aspect of evolutionary genetics and such studies are of vital importance for conservation and management. In the past decades, researchers have studied the genetic diversity of several Sirenia in diverse habitats using different techniques. For example, Seddon et al.  used micSamples (skin tissue) from 77 deceased dugongs  that were stranded between 1994–2019  were proThe samples were processed using DNA extraction kits according to manufacturer’s instructions  at the Faculty of Veterinary Medicine, Chiang Mai University , and the2)4SO4, 50 mM Tris-HCl, 1.75 mM MgCl2, and 0.01% TritonTM X-100), 0.2 µM dNTP , 0.2 µM microsatellite primer , 1 U Ta3 primer  by REDSA3 primer . A fragm3 primer .http://km.dmcr.go.th (accessed on 7 August 2020)), which identified discrete clusters of dugongs from aerial surveys of living dugongs in the seas of Thailand. Microsatellite allele frequencies were calculated by a simple counting scheme. The genetic diversity indexes [p-value , observed number of alleles (Na), effective number of alleles (Ne), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), F-statistics, and Nei’s genetic distance were analyzed by GENALEX program version 6.5 [Microsatellite comparisons were made among five location zones : upper G indexes ,41 inclusion 6.5 . These vsion 6.5 . The gension 6.5 .A total of 77 samples were analyzed by distance and model-based clustering methods to reveal population affinity and structure. Pairwise linearized genetic distance based on the difference in allele frequencies (Fst) was computed by GENALEX program version 6.5 . The proThe ML-RELATE program  was usedAllele frequency distributions and statistical parameters of forensic interest for each population are provided in The population structure consisted of five genetic clusters (ΔK = 5), depicted as orange, pink, blue, red and yellow . In bothThe PCA of microsatellite loci of the five zones plotted on two axes cumulatively explained 35.41% of the variation . The PCA plot revealed that the five zones were not clearly differentiated from each other a. The saTwo genetically related groups of dugongs were identified: those from the Gulf of Thailand; and those from Andaman Sea . Those ip < 0.05) deficiencies in heterozygotes were identified. The mean allele difference between the populations (Fst) was 0.138 and the mean number of migrants (Nm) was 2.944 , and the highest between Zones 1 and 2 in the Gulf of Thailand (0.55) .From this study, dugongs from the Andaman Sea were shown to have higher genetic diversity than those in the Gulf of Thailand, similar to data from previous inter-simple sequence repeat (ISSR)  and micrWe found that dugongs in the Gulf of Thailand and Andaman Sea have different population structures. Because there is no movement between the Gulf of Thailand and Andaman Sea , and theA study from Bushell (2013)  reportedThis study had some limitations. First, the number of samples was not equal across the zones due to differences in stranding rates and sample availability. Second, there were no samples available between 2003–2008 because they were used for other research projects. Third, success was accomplished using only six microsatellite markers. From our study, the MP value was 0.000000226, meaning that over 10 million dugongs had the same genetic makeup as two dugongs; however, the dugong population in Thai seas is considerably less than 10 million animals. Thus, six microsatellites markers were enough to evaluate the genetic diversity of these dugong populations.This study found that dugongs throughout the Thai seas have a similar genetic structure, although with different proportions. In the Gulf of Thailand, two dominant genetic structures were identified, while in the Andaman Sea, all five genetic structures were found in similar proportions. Moreover, the gene flow was similar between zones on the same coasts, with the greatest flow found between the middle and lower Andaman regions. In addition, our data found that dugong populations are exhibiting signs of inbreeding. The information from this study revealed the genetic status of dugongs in Thailand, which should be useful in developing management and conservation guidelines for the species in the long-term. At present, captive breeding has not been successful in dugongs, thus limiting its effectiveness as a conservation strategy for the species. Therefore, it is important to conserve extant wild populations through legal regulations to stop the continuous decline in both the Andaman Sea and the Gulf of Thailand.


Calibrachoa heterophylla to infer the influence of abiotic landscape features on the level of gene flow in this coastal species of the South Atlantic Coastal Plain.Historical and ecological processes shape patterns of genetic diversity in plant species. Colonization to new environments and geographical landscape features determine, amongst other factors, genetic diversity within- and differentiation between-populations. We analyse the genetic diversity and population structure of C. heterophylla populations located on early-deposited coastal plain regions show higher genetic diversity than those closer to the sea. The genetic differentiation follows a pattern of isolation-by-distance. Landscape features, such as water bodies and wind corridors, and geographical distances equally explain the observed genetic differentiation, whereas the precipitation seasonality exhibits a strong signal for isolation-by-environment in marginal populations. The estimated levels of gene flow suggest that marginal populations had restricted immigration rates enhancing differentiation.The C. heterophylla. Gene flow is mainly restricted to nearby populations and facilitated by wind fields, albeit without any apparent influence of large water bodies. Furthermore, differential rainfall regimes in marginal populations seem to promote genetic differentiation.Topographical features related to coastal plain deposition history influence population differentiation in The online version contains supplementary material available at 10.1186/s12862-021-01916-4. Coastal areas in South America constitute distinct landscapes with unique abiotic and biotic compositions. Many geomorphological, climate, oceanographic features, and colonization events from the surrounding biomes shape the ecosystems of these areas –6. ThereThe species’ geographical distribution and genetic (nucleotide) diversity result from historical and contemporary processes acting together with ecological factors –19. ColoCalibrachoa heterophylla is a perennial nightshade shrub growing in dunes and sandy grasslands predominantly along the South Atlantic Coastal Plain (SACP). Previously phylogeographical assessment based on plastid markers supports that the species likely originated and subsequently diversified into four intraspecific lineages between 1 and 0.85 Mya , and the numbers in parentheses show the standard deviation. Bold values indicate the diagonal (intra-population estimates), and red values indicate the highest migration estimates .Additional file 2:Migrate-n detailed output for the best-supported model showing parameter estimation values and convergence statistics.


As a consequence of precision medicine initiatives, genomic technologies have rapidly spread around the world, raising questions about genetic privacy and the ethics of data sharing. Previous scholarship in bioethics and science and technology studies has made clear that different nations have varying expectations about trust, transparency, and public reason in relation to emerging technologies and their governance. The key aims of this article are to assess genetic literacy, perceptions of genetic testing, privacy concerns, and governing norms amongst the Singapore population by collecting surveys.This study investigated genetic literacy and broad public attitudes toward genetic tests in Singapore with an online public survey (n = 560). To assess potential changes in attitudes following receipt of results from a genetic test, we also surveyed undergraduate students who underwent a genetic screen as part of a university class before and after they received their test results (n = 25).Public participants showed broad support for the use of genetic tests; scored an average of 48.9% in genetic literacy; and expressed privacy concerns over data sharing and a desire for control over their genetic data. After taking a genetic test and receiving genetic test results, students reported less fear of genetic tests while other attitudes did not change significantly.These findings highlight the potential of genetic education and active engagement with genetic testing to increase support and participation in genomic projects, PM, and biobanking initiatives; and they suggest that data privacy protections could potentially reduce discrimination by giving participants control over who can access their data. More specifically, these findings and the dataset we provide may be helpful in formulating culturally sensitive education programs and regulations concerning genomic technologies and data privacy.The online version contains supplementary material available at 10.1186/s12910-022-00744-5. In recent years, genome technologies have rapidly fallen in cost and massively increased in speed, leading to their global expansion and yielding unprecedented amounts of sensitive genomic information –3. The eSeveral studies have highlighted the importance of genetic literacy with regard to public attitudes toward participation in genomics research. A recent study of Qatari citizens , for exaSo far, however, there are few studies of Southeast Asians’ views regarding genetic testing. Two recent focus group discussion (FGD)-based studies , 22 of SThus far, however, Singapore’s lay public attitudes and knowledge of genetics and genetic privacy have not been assessed. The key aims of this article are to assess genetic literacy, perceptions of genetic testing, privacy concerns, and governing norms amongst the Singapore population by collecting surveys. The secondary aims are to identify how these categories vary across demographic lines and to determine possible changes in attitude after receiving results of a genetic test. In doing so, we aim to yield a data set that could facilitate the formation of tailored bioethical guidelines for genetic testing and personal data protection.This study consists of two surveys. The first survey addressed attitudes and knowledge amongst the general public of Singapore aged 21 years or older. Participants were required to be Singapore citizens, PRs, or long-term residents. The second survey addressed potential changes in attitude following experience with genetic testing and participants were university students enrolled in a biology module on genetics and evolution at Nanyang Technological University (NTU). As part of the educational component of the module, students were offered a personal genetic test kit and participation was voluntary. Students who participated in the genetic test were invited to partake in a survey on attitudes and beliefs before and after they received the results of the genetic test. Prior informed consent for participation in both surveys was obtained from the respondents and both surveys were conducted online in Singapore in line with the project’s approval by NTU-Institutional Review Board (NTU- IRB-2020-07-056).A literature review was performed for research papers that assessed attitudes and perceptions of the general public toward genetic testing and genome projects, targeting standard databases using keywords: “attitudes”, “perception”, “concerns”, “public”, “society”, “genetic testing”, “biodata sharing”, “genomics” and “genomic data.” Thirteen articles published from 2009 to 2019 were selected –30 from Pre-test and post-test surveys were created using Microsoft Forms with 48 and 45 questions respectively, using the same scoring scale as the public survey described above  and female 42.1%), in the range of 21–39 (49.2%), 40–59 (42.1%) and above 60 (8.5%) years . Participants with annual household income greater than SGD 150,000 scored more questions correctly (55.5% correct) than the lower-income  groups . Participants who studied genetics as part of biology education scored higher in genetic literacy (56.3%) than the participants who did not study genetics . Participants with lower educational attainment (less than Bachelor’s degree) scored lower in literacy (44.7%) than more highly educated groups . Within our sample (n = 560), 45 participants had previously been diagnosed with a genetic disease. This group had higher literacy (50.7%) than literacy in non-diagnosed participants . The eight participants that reported being previously diagnosed with COVID-19 also scored higher (62.5%) for genetic literacy than non-COVID participants .Literacy questions show significant differences in mean score across various categories of demographic groups, such as age and income. Participants younger than 40 years of age scored higher in genetic literacy (51.2%) when compared with those aged 60 and older  that genetic testing can improve the quality of life and health. While 34.3% of the participants claimed confidence in understanding the relevant information about results from a genetic test, 65.7% were neutral. 45.9% of participants agreed that genetic testing does more good than harm while 13% disagreed. 47.1% disagreed that genetic testing is tampering with the will of God although 38.7% agreed .− 2 to + 2 indicating a mean positive perception. Participants who had earlier been diagnosed with a genetic disease held less positive perceptions of genetic testing, with a score of 0.61 compared to the non-diagnosed participants  (See Table p < 0.01 t-test). The participants in the highest income group  scored higher for positive perceptions .The mean score for perceptions towards genetic testing across all questions was derived to be 0.75 (SD 0.95) in a range of 51.4% of participants felt that by donating their samples to a genetic database they would be helping future generations and 54.1% felt that taking part in genetic databases could lead to better medical treatments. Participants broadly approved the use of genetic testing to know their genetic risk for a wide range of medical conditions: 79.6% approved of the use of genetic testing for optimizing medical treatment and a majority approved of the use of genetic testing (76.6% agreed) to determine the likelihood of future diseases, with the greatest interest in kidney diseases, diabetes, heart disease, Alzheimer’s and rare diseases.p < 0.001 t-test).Overall, participants expressed concerns over privacy issues with 77.5% agreeing they would worry if their genetic information were shared, with 6.8% disagreeing and 15.7% neutral. Most (81.6%) wanted to know if their health information would subsequently be shared with insurance companies if they opt for genetic testing, and 64.4% of participants agreed that they would worry that research they did not consent to would be done. In terms of legal protections against racial discrimination in the context of genomics, a majority disagreed (63.9%) that if new scientific findings show ethnic differences in COVID-19 susceptibility this information should be kept secret. For genetic data privacy concerns, the mean score derived from the relevant questions was 0.63  of participants agreed that researchers who conduct the biobank project should control and take ethical and legal responsibility for such projects.Regarding benefit-sharing from large-scale genetics research, the data were unclear in delineating views. 61.7% of participants agreed that the people who provided their genetic samples should benefit from large-scale genetics research while 60% agreed that the researchers who conducted the research project or their institution also should benefit from such projects. Most participants (85.7%) expressed interest in knowing who runs the genetic database if they take a genetic test.25 students volunteered for the genetic testing and completed both the pre-test and post-test surveys, 84.0% of whom were ethnically Chinese and 64.0% of whom were female and all of whom were aged 18–29 years.p = 0.01 paired t-test). Also, a significantly higher proportion of participants discussed their genetic test with their families after receiving the test results .Most survey items did not show a significant difference in participant responses after undertaking the genetic test. For the survey item “The idea of a genetic test frightens me,” however, a significantly higher proportion of participants disagreed after undertaking the test (Until now, studies of public attitudes toward genetics and data sharing have focused on the issues of informed consent , privacyIn terms of genetic literacy in Singapore, we report an average genetic literacy score that may indicate a need for more public education and engagement programs on genetics and participation in PM projects. This level of literacy is similar to survey findings recently reported from Qatar , where 5We followed a previous Singapore FGD-based study’s call to assess “governing norms and values”  and our Another way attitudes could be shaped is through active participation in genetic testing. By demonstrating a reduction in fear of genetic tests amongst university students after they received their test results, we invite further studies to consider whether hands-on education, outreach interventions, and the consequent familiarity with genetic testing, may improve participation rates in genomic projects and PM initiatives. Indeed, our public survey findings suggest that genetics education is associated with increased support and positive perception of genomics research and genetic testing. Moreover, participants previously diagnosed with a genetic disease reported less data privacy concern, suggesting that exposure to genetic testing may reduce concerns about genetic testing and its consequences.Indeed, some scholars suggested genetic discrimination is a possibility in Singapore in the absence of genetic non-discrimination regulations . Thus faAs our student survey sample of 25 may not capture other significant changes in attitudes following genetic testing, we suggest further work incorporate a control survey to assess changes in attitude as a consequence of genetics education alone. Moreover, the cohort studied is one high in genetic literacy and subjects participated voluntarily and may not be representative of the wider population. Our preliminary findings also raise the question of potential unintended discriminatory effects and potential social disharmony if public education about ethnic and religious differences in relation to genomics is not addressed.In conclusion, we show broad support for the use of genetic tests in Singapore, with moderate genetic literacy in the general population, and with broad privacy concerns regarding data sharing and governance. However, after taking a genetic test and receiving genetic test results, participants reported significantly less fear of genetic tests. Together these findings highlight the potential of genetic education and active engagement with genetic testing to increase support and participation in genomic projects, PM, and biobanking initiatives; and they suggest that data privacy protections could potentially reduce discrimination by giving participants control over who can access their data. The value of this dataset lies in its cultural specificity and relevance to Singapore for the development of future PM research projects and clinical initiatives. Beyond the scholarly significance of our findings for the social study of genomic technologies in Singapore, however, our findings may also be of interest to biotech companies, clinicians, and community leaders around the world interested in developing culturally tailored bioethical protocols for genomic technologies.Additional file 1. Microsoft Excel file with raw public survey and student survey data.


The purpose of the current study was to examine the cortical correlates of imagery depending on instructional modality (guided vs. self-produced) using various sports-related scripts. According to the expert-performance approach, we took an idiosyncratic perspective analyzing the mental imagery of an experienced two-time Olympic athlete to verify whether different instructional modalities of imagery  and different scripts  could differently involve brain activity. The subject listened to each previously recorded script taken from two existing questionnaires concerning imagery ability in sport and then was asked to imagine the scene for a minute. During the task, brain waves were monitored using EEG (32-channel g. Nautilus). Our findings indicate that guided imagery might induce higher high alpha and SMR , whereas self-produced imagery might facilitate higher low alpha . Results are discussed in light of the neural efficiency hypothesis as a marker of optimal performance and transient hypofrontality as a marker of flow state. Practical mental training recommendations are presented. Imagery is a multisensorial mental representation of the image of actions (or objects and situations), without an actual experience and without appropriate sensory input  activity- which is in accordance with the Studies on superior human performance reported low alpha event-related desynchronization (ERD) in elite athletes, for example in expert golfers when the stroke was correct compared to a missed one  that involve scripts to follow whilst focusing and directing participants’ imagination. In medicine guided imagery serves to empower patients to actively participate in their recovery or control their emotional reactions  using various scripts related to training and competition involving all sensory systems, emotions, and feelings. Mental imagery implemented as a part of sports training should: (a) activate all senses . Written consent was obtained from the athlete to participate in the study. Moreover, written informed consent was obtained from the participant for the publication of any potentially identifiable images or data included in this article.Two questionnaires to assess the imagery ability in sport-related situations and assessing multiple dimensions of imagery but also general tendency to use imagery were implemented.The Imagination in Sport Questionnaire  is a questionnaire developed by Watt and Morris  that conFitness activity, Start in a high-level championship, Successful Competition, Training Session, Your Home Venue  associated with the sports situations. After 60 s of imagining each of the four generic sports scenes, they provide self-report ratings of 48 imagery ability items (12 for each imagined scene), one reflecting each dimension, sense modality, and emotion. At the end of the imagery interval, they are requested to rate each scenario by placing a cross (X) on a 100 mm visual analog scale for each item. Respondents can obtain from 0 to 4,800 points as a total score and from 0 to 400 points for each subscale.Respondents are asked to imagine five scenes: The sailor achieved higher scores than those achieved by athletes from the national level from the Polish sample; for means and SD see Budnik-Przybylska et al.  in the mFitness Activity. Imagine yourself doing an activity to improve your fitness for your sport. Get a clear picture of what you are doing, where you are, and who you are with. Take notice of what you can see around you, the sounds you hear, and the feel of any muscles moving. Do you get the sensation of any smells or tastes? Can you feel the equipment and surfaces you are using? Do you get an emotional feeling from this activity? Now you have 60 s to create and experience your image of the scene.Successful Competition. Imagine you are competing in a specific event or match for your sport. Imagine that you are at the very end of the competition and the result is going to be close. You pull out a sensational move, shot, or effort to win the competition. Take notice of what you can see around you, the sounds you hear, and the feel of any muscles moving. Do you get the sensation of any smells or tastes? Can you feel the equipment and surfaces you are using? Do you get an emotional feeling from this activity? Now you have 60 s to create and experience your image of the scene.The raw electroencephalographic data were recorded using the 32-channels (incl. ground and reference). Nautilus wearable EEG headset with 24-bit ADC accuracy at 250 Hz sampling rate. The instantaneous values in the time domain have been stored for each electrode in the double-precision floating-point format, and then saved in the MATLAB data file. The accompanying data consisted of the recorded markers using a push-button, indicating the beginning of each scene in the time domain. For convenience, that original dataset was then converted into separate CSV files, storing raw data for different scenes according to the markers.Fitness Activity, Start in a high-level championship (precompetitive routine), Successful Competition, Slow Start, Training Session, Your Home Venue. Each scenario was first listened to and then imagined with the eyes closed in both conditions.The sport psychologist (first author), who used to work with the athlete, recorded six scripts that were linked to the questionnaires previously described, which were part of the training routine of the sailor. The athlete listened to the recorded scripts (the recorded script lasted from 33 to 47 s), then had a 2-s break for washing out the mind and finally was asked to imagine the scene for a minute. There were the following six scenes in each condition: During these two imagery modalities (listened-guided imagery and imagined-self-produced imagery), the athlete’s brain activity  was monitored by the EEG. Between listened-guided imagery and imagined-self-produced imagery of each specific scene, there was a short break (2-s). However, between each of the blocks of scripts there was a longer break to allow the athlete to change his position and take a rest. Additionally, the participant was asked to fill in the imagery questionnaires—The SIAM and the ISQ—to verify his imagery ability.Studying imagery, there were different analyses of the EEG data that could have been implemented. For instance, following previous studies  were divided into six situations according to the collected EEG markers and timings of considered situations. Such prepared raw EEG signals were visually inspected to identify improper channels exposing non-physiological behavior, outliers, and unidentified peaks, as well as zero or constant value regions. No bad channels were detected. The next step of the chosen preprocessing procedure was to apply Butterworth’s FIR filter  current potential on the electrode, N—number of adjacent electrodes. The new signal is calculated by subtracting the current potential from the averaged potential from adjacent electrodes. In this article, we have calculated the average potential of appropriate nearest neighbors. We also removed the mean values of all channels in order to obtain the stationarity of the EEG signals. Finally, we applied Independent Component Analysis (ICA), implemented in the MNE-Python package  of a real-valued array by means of an efficient algorithm called the Fast Fourier Transform (FFT), as implemented in the Python scipy.fft package using Kaiser windowing  and 10 regions of interest (ROIs): frontal right—{Fp2, F4} and left—{Fp1, F3}; central right—{C2, C4, FC2} and left—{C1, C3, FC1}; temporal right—{T8, C6, FC6} and left—{T7, C5, FC5}; central parietal right—{CP2, CP6} and left—{CP1, CP5}; and finally parieto occipital right—{P4, PO8} and left—{P3, PO7}. Therefore, we also calculated the mean power of each ROI and hemisphere for all situations.A Wilcoxon signed ranks test was employed to determine significant differences between the two types of instruction in low and high alpha as well as in SMR, according to each script, hemispheres, and regions.The head maps of low alpha, high alpha, and SMR during the two imagery instructional modalities (guided vs. self-produced) and the six scripts are displayed in A Wilcoxon-signed-ranks-test indicated that low alpha in self-produced imagery was significantly higher than in guided imagery in almost all scenes (except from successful competition—but the direction was the same). Adversely, high alpha and SMR were significantly higher in guided than in self-produced imagery . Detailed statistics are presented in M guided imagery = 0.59, SD = 0.19; M self-produced = 0.32; SD = 0.08; Z = 2.20; p < 0.05). Detailed statistics are presented in A Wilcoxon-signed-ranks-test indicated that SMR in guided imagery was significantly higher than in self-produced imagery in both hemispheres and all regions . A similar tendency was observed in the right hemisphere for high alpha in the following brain regions: frontal, central, and temporal. As concerns, the left hemisphere, high alpha in guided imagery compared to self-produced imagery was significantly higher in the frontal, temporal, and central parietal regions. However, low alpha in self-produced imagery was significantly higher in the frontal and temporal brain regions in the left hemisphere. Although the main focus was on the differences between left and right hemispheres, we also observed that guided imagery was higher compared to self-produced in the SMR in the CPz  during various scenes in a 32-year-old two-times male Olympic-sailor. We observed that in accordance with the hypothesis H1a low alpha was significantly higher in self-produced imagery as compared to guided imagery in almost all imagined scripts. The achieved results are in line with the previous studies concerning elite athletes, in which experts have generally presented an increased oscillation of the low alpha associated with a lower energy expenditure according to the neural and psychomotor efficiency  instructional modality induced more low-alpha in the frontal and temporal left hemisphere, which meant that the right hemisphere was more “active”. We also observed higher power of low alpha in the temporal regions of the left hemisphere which confirmed the reduced verbal analytic processing in flow  instructional modality activated more frontal and temporal regions in both hemispheres, central-right regions, and centro-parietal regions in the left hemispheres . We also observed a lower power of low alpha in the both frontal regions and in the temporal left region, which confirmed our hypothesis H2b and H2c. These results are also consistent with the notion that various regions of the brain are highly intercorrelated  and one domain can influence another  using various sport-related scripts. Such a methodological approach enabled us to confirm that instructional imagery modality facilitates cortical activity which can be an expression of attentional focus and mental effort during imagery. Thus, our results confirmed that imagery mental training might still be performed and developed to achieve a particular mental state, even when considering elite-level athletes. Moreover, the obtained results not only confirm but also elaborate on the transient hypofrontality and neural efficiency hypotheses.https://mostwiedzy.pl/en/open-research-data-series/niema,202103272042247055361-0/catalog.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: the data that support the findings of this study are available from the corresponding author upon reasonable request and are openly available in MOST Wiedzy at The studies involving human participants were reviewed and approved by University of Gdańsk Ethics committee . The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the participant for the publication of any potentially identifiable images or data included in this article.DB-P designed the study of the project, cooperated with the participant, contributed to the acquisition, analysis, interpretation of data as well as wrote the original draft of the manuscript. AK, PJ, ŁD, and PS contributed to the analysis, interpretation of data, and concept and design of the project. MK contributed to the analysis, interpretation of data, drafting of the manuscript, and concept and design of the project. JP contributed to the acquisition and analysis. MŁ and SF contributed to the concept and design of the project, review, and editing of the manuscript. MB contributed to the analysis, interpretation of data, drafting of the manuscript, concept and design of the project, and supervising EEG data analysis and interpretation. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.