Dispersal is generally difficult to directly observe. Instead, dispersal is often inferred from genetic markers and biophysical modelling where a correspondence indicates that dispersal routes and barriers explain a significant part of population genetic differentiation. Biophysical models are used for wind-driven dispersal in terrestrial environments and for propagules drifting with ocean currents in the sea. In the ocean, such seascape genetic or seascape genomic studies provide promising tools in applied sciences, as actions within management and conservation rely on an understanding of population structure, genetic diversity and presence of local adaptations, all dependent on dispersal within the metapopulation. Here, we surveyed 87 studies that combine population genetics and biophysical models of dispersal. Our aim was to understand if biophysical dispersal models can generally explain genetic differentiation. Our analysis shows that genetic differentiation and lack of genetic differentiation can often be explained by dispersal, but the realism of the biophysical model, as well as local geomorphology and species biology also play a role. The review supports the use of a combination of both methods, and we discuss our findings in terms of recommendations for future studies and pinpoint areas where further development is necessary, particularly on how to compare both approaches.This article is part of the theme issue ‘Species’ ranges in the face of changing environments (part I)’. Instead, species are often patchily distributed reflecting underlying habitats, barriers to dispersal and local extinctions. Species distributions are also shifting and being fragmented, caused by multiple human pressures leading to habitat loss . Local pet al. [et al. [et al. [Seascape genetics and seascape genomics meet at the interface between basic and applied sciences. The knowledge that comes out of such studies can provide important information for the management and protection of resources, ecosystems and species. For instance, Jahnke et al.  used a set al. . Similar [et al.  used sea [et al. . This st [et al.  by movin [et al. . Seascap [et al.  applied  [et al. . A cruci [et al.  and rela [et al. ,16. Cruc [et al. . Biophys [et al. . The add [et al. . Togethe [et al. . Clearly. 2We surveyed the seascape genetic and seascape genomic literature and searched for publications in February 2021 with the following keywords: ‘biophysical modelling, population genetics’, ‘dispersal, population genetics, marine’ and ‘seascape genomics’ on Web of Science and Google Scholar and retained all papers that used biophysical modelling as well as population genetic/genomic approaches. Some additional papers were added after broad reading of the literature by the authors. This resulted in 87 papers that were included in our database of publications that use biophysical and genetic approaches together. We note that those 87 papers that use both population genetic and biophysical approaches are a subset of the growing field of seascape genetics/genomics that also includes investigations into adaptation to heterogeneous local conditions and habitat availability . As sevemechanistic explanation to the observed IBD, as other processes than dispersal, particularly colonization and local adaptation, can lead to genetic differentiation along a spatial gradient, i.e. IBD [a).We then surveyed the 103 assessments further to understand under what conditions there is a good fit between genetics and biophysical modelling. This fit was assessed in a qualitative rather than a quantitative way, as (i) there is a wide heterogeneity of the methods used and parameters included in the different studies, so only a handful of papers each would be comparable enough for a formal meta-analysis; and (ii) it is challenging to define a null hypothesis. In some publications, isolation-by-distance  is considered a null-model to which the correlation coefficient for isolation-by-oceanography  is compared. These authors would argue that if IBO results in a significantly higher correlation this indicates that the biophysical model explains some added portion of the total variation in the genetic signal. We agree, but also argue that if IBO gives a similar correlation (but not higher) the biophysical model also offers a i.e. IBD ,21. In a (a)In terms of species biology, we assessed taxonomy, reproductive strategy and pelagic larval duration (PLD). Altogether the 87 papers assessed 90 different species, of which the majority were invertebrates . The taxThe majority of species are reproducing exclusively sexually (63%), but a considerable proportion (35%) of the assessed species are facultatively sexually reproducing, i.e. have sexual and clonal reproduction. This has important implications also for the potential fit to biophysical modelling, as generally only one of the reproduction modes disperses with ocean currents. For instance, in seagrasses, clonal reproduction is via rhizome extension, at the scale of centimetres per year, while fruits or seeds attached to shoots  can potentially be dispersed for hundreds of kilometres. The amount of sexual versus clonal reproduction at a specific site may therefore play a role for the geographical scale of dispersal and the fit with biophysical modelling. The situation is very similar for corals and some facultatively sexually reproducing algae, also included in this survey. In fact, the proportion of species that reproduce both sexually and clonally is similar in the IBO and ‘unclear’ categories (33% for IBO and 35% for unclear), but higher for IBD (42%). This could reflect the fact that in facultatively sexually reproducing species, biophysical dispersal modelling does not catch very local dispersal by clonal reproduction, and is therefore less realistic—at least if sexual reproduction rates are low. There were no strong patterns in regards to PLD among the three categories in assessment 1, but all six cases with a maximum tested PLD above 200 days were assessed as ‘unclear’.With respect to the type of fit between genetic and biophysical methods according to assessment 2 , species (b)The geomorphology of the seascape varies greatly depending on the land–sea contour and the bathymetry. Increased complexity of geomorphology may reduce water transport and biological dispersal, cause asymmetric gene flow, and promote genetic differentiation . We asseStrong asymmetry of biophysical dispersal, i.e. a situation where dispersal from A to B was much more likely than from B to A, seems to complicate assessments: the vast majority of ‘unclear’ cases show asymmetry in dispersal probability in biophysical models among the studied sites, with less asymmetry in IBD and IBO. (c)The first studies used gene markers, for instance CO1, but overall, the most common marker used were microsatellites (61%), with a median of 12 loci. Microsatellites also remain the method of choice in many current papers: of the reviewed papers that were published in 2020, 38% used microsatellites. However, the majority of studies (56%) from 2020 used single-nucleotide polymorphisms (SNPs), which only recently initiated seascape genomics. The first study that used biophysical modelling in combination with population genomic assessments using SNPs was published in 2016 . BetweenIn terms of the number of sampled sites included in each study, the median was 13, with a minimum of 2 and a maximum of 77 in yet another early study . The numIn terms of geographical scale, 11 different categories were defined ranging from fine-scale (1–10s of km) to large scale (10 000s of km). Most assessments sampled sites 10–1000s of km apart (32%), followed by 10–100s of km (21%) and 1–100s and 100–1000s of km apart . The distance among genetic sampling sites seemed to play a role in explaining the fit between genetic and biophysical modelling. Assessments with a good fit showed a higher proportion of lower maximum sampling distance at the scale of 100s of km, rather than 1000s of km. Some caution is therefore called for when the scale of genetic sampling is very large (greater than 1000 km), as such sampling scales often show genetic patterns well explained both by IBD and by modelled dispersal limitations (IBO). More importantly, large-scale genetic differentiation is likely reflecting processes in deeper time , and may (d)et al. [Biophysical modelling of dispersal generally consists of two components—an oceanographic circulation model hindcasting ocean current velocities, which are then used to drive a particle-tracking model that simulates the dispersal of biological propagules. All but one study  used thret al.  instead et al.  or ontoget al. , and eveet al. ). As botet al. . Resultset al. ,34. Whilet al. . Six of Most studies used a single structured grid 95%) with a few studies employing nested or unstructured grids. The horizontal resolution of the biophysical models ranged from 0.08 to 55 km (median: 6.5 km). Most studies used spatial resolutions of 3–10 km, but complex coasts, e.g. with archipelagos, require higher spatial resolution, often less than 1 km (e.g. ), which 5% with aMost studies (61%) correlated genetic patterns and biophysical modelling using estimates of single-generation dispersal events. We did not see that studies employing biophysical estimates of stepping-stone/multi-generation dispersal (39%) improved the fit between population genetic and biophysical predictions. Nevertheless, several publications that compared the fit of single versus multiple generation dispersal with spatial genetic structure found an improved fit of stepping-stone dispersal, particularly at the scale of 100s of km ,36. (e)Our analyses showed that there is no clear method to compare genetic patterns with biophysical predictions, and over 20 different approaches were identified in the reviewed literature. A quarter of the assessments also used two to three methods to assess the fit between genetic and biophysical modelling. We identified that most fits were assessed with Mantel tests , followed by ‘by eye’ (24%) and comparisons of network topologies (19%). Other important approaches are regressions, mostly linear regressions (14%), and the comparison of the geographical location of genetic and oceanographic barriers (10%).b), but several interesting features emerged. None of the ‘unclear’ cases used regressions. All assessments that used Moran's eigenvector maps (MEMs) and asymmetric eigenvector maps (AEMs) followed by redundancy analysis (RDA) [There was no clear relationship between the method used for assessments and the fit b, but seis (RDA)  were cla. 3This survey of the seascape genetics/genomics literature evidences that it is a rapidly expanding field that can improve the interpretation of spatial genetic patterns integral in many studies of evolutionary processes, species expansions and invasions, source–sink dynamics, and the management and conservation of the marine environment. As the main message, the present survey shows that many recent studies indeed find that predictions from biophysical models explain aspects of observed genetic patterns. This success of including biophysical modelling in seascape genetics is driven by the recent improvement of three-dimensional circulation models also applied to coastal environments and at scales relevant for biological dispersal. The oceanographic part of biophysical models continues to improve by including more physical processes, adding more detailed forcing data and being simulated with higher spatial and temporal resolution. For example, predictions of cross-shelf dispersal close to the coast is influenced by wave-induced water transport (Stokes drift), and this effect could be implemented into circulation models (e.g. ). More dThe biological part of biophysical modelling—where an individual-based particle-transport model represents the dispersal of propagules—still lacks many biological traits that may affect dispersal trajectories from release to settlement. Some of the reviewed biophysical models included information about habitat distribution constraining the domain for modelled particle release and settlement. If all or most of the habitat is included in the dispersal model it is possible to explore connectivity across multiple, stepping-stone dispersal events, which may be more representative of potential gene flow. More than one-third of the reviewed papers included projections of stepping-stone connectivity  and some studies concluded that this significantly improved the fit to spatial genetic metrics ,39,40. UFor population genetic/genomic assessments in a spatial seascape context, we identified that method development is necessary to calculate recent gene flow based on genomic datasets (see below). A very promising avenue is the inclusion of demographic models estimating how population genetic patterns develop through time while explicitly considering demographic parameters such as population sizes, migration rates, divergence times and perhaps oceanographic modelling results. Demographic modelling is a very powerful tool to understand (i) historical causes of differentiation and (ii) project the genetic structure of the metapopulation into the future that also includes modelled dispersal, particularly also range evolution within species or metapopulations. Only four of the 87 assessed papers used demographic modelling of the past ,29,41,42FST or similar pairwise genetic differentiation indices and biophysical connectivity metrics, generally dispersal probability. The most common genetic differentiation indices used are FST, GST and Jost's D, and while the choice of the genetic index may influence the estimated rate of gene flow, we refer to other excellent reviews on this topic [A critical step is to assess to what extent a biophysical model of dispersal explains a relevant component of observed genetic patterns. The reviewed papers include a range of comparisons and of statistical methods, and this diversity prevented a formal and unified meta-analysis approach. The most common approach is to test for correlations between pairwise is topic –47. Someis topic , Migrateis topic , divMigris topic  or usingis topic , is usedis topic ,29,52. Tis topic ). We belis topic . Neverthis topic , perhapsAn alternative way to the Mantel test that also accounts for non-independence of pairwise comparisons is by using a linear mixed model where each site is included as a random effect ,55, alth. 4 1.Better and more-widely available habitat mapping will improve the predictions from biophysical models particularly in multi-generation models, as has been shown for sessile priority species such as seagrasses  and cora 2.Biophysical models can also improve from a more complete inclusion of hydrodynamic processes, and from better knowledge of larval behaviour (and its interaction with hydrodynamics) ,33. 3.Method development regarding the inference of recent gene flow from whole-genome sequencing will facilitate the calculation of asymmetric migration rates from genomic datasets. 4.Advanced assignment methods based on whole-genome scans will improve the validation of biophysical modelling of current dispersal. 5.Increased uptake of genetic demographic modelling in seascape genetic/genomic studies, will provide additional assessable time layers (the deep past and/or the future) and will improve the applicability of seascape genetic/genomic studies to conservation management, particularly adaptive management . 6.For comparing genetic with biophysical approaches in the seascape context, the field needs to narrow down the methods which are used for comparisons between the two approaches. While the last 15 years were an extremely fruitful period for the development/application of over 20 methods, partly sparked by criticism of the Mantel test, this variability in methods makes different studies less comparable and meta-analyses challenging. Nevertheless, we also support the view of the majority of publications, that using more than one method is beneficial for assessments, for instance using two or more of the most commonly used approaches: Mantel test, network analyses and barrier analyses.This survey of the literature and the analysis on fit between genetic and biophysical methods highlighted several areas where future developments could further increase the power and application of seascape genetic studies that use biophysical models and population genetics.


African Yam Bean (AYB) is an understudied and underutilized tuberous legume of tropical West and Central African origin. In these geographical regions, both seeds and tubers of AYB are important components of people’s diets and a potential target as a nutritional security crop. The understanding of the genetic diversity among AYB accessions is thus an important component for both conservation and potential breeding programs.In this study, 93 AYB accessions were obtained from the International Institute of Tropical Agriculture (IITA) genebank and genotyped using 3722 SNP markers based on Restriction site-Associated DNA sequencing (RAD-Seq). Genetic data was analysed using multiple clustering methods for better understanding the distribution of genetic diversity across the population. Substantial genetic variability was observed in the present set of AYB accessions and different methodologies demonstrated that these accessions are divided into three to four main groups. The accessions were also analysed for important agronomic traits and successfully associated with their genetic clusters where great majority of accessions shared a similar phenotype.To our knowledge, this is the first study on predicting genotypic-phenotypic diversity relationship analysis in AYB. From a breeding perspective, we were able to identify specific diverse groups with precise phenotype such as seed or both seed and tuber yield purpose accessions. These results provide novel and important insights to support the utilization of this germplasm in AYB breeding programs.The online version contains supplementary material available at 10.1186/s12870-021-03302-0. Sphenostylis stenocarpa Hochst. ex. A. Rich. Harms, Fabaceae) is an understudied and underutilized tuberous legume of tropical Africa  during the season of 2020.A total of 93 AYB accessions were collected from the Genetic Resources Center (GRC), International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria. The accessions used in present AYB panel originated from diverse sources which were collected and conserved in IITA gene bank along the last 20 years  and subsequently ground in a spex™Sample Prep 2000 Geno/Grinder . Genomic DNA was extracted from the ground leaves of each accession using commercial QIAGEN DNeasy Plant Mini Kits according to the manufacturer’s protocol . The DNA purity and quantity in each sample were checked on the NanoDrop spectrophotometer (ND-1000)  and subsequently, confirmed on a 1% agarose gel stained with SYBR green . African yam bean DNA library was constructed using the double digest RAD protocol  which rehttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA389330.A total of 393,414,882 reads was generated from the sequencing of the 93 AYB accessions panel using the Illumina RADseq RAD markers. Of the 43,061 RAD tags identified by the Tassel’s UNEAK Pipeline, only the RAD tag pairs differentiated by one SNP were depicted. The raw file was converted into Powermarker V3.25  format fhttp://darwin.cirad.fr) [The genetic diversity analysis of the 93 AYB accessions was performed using 3722 SNP RAD markers. The key measures of genetic diversity, including the genetic diversity (GD), polymorphism information content (PIC), heterozygocity (He) and minor allele frequency (MAF) were computed using the PowerMarker version 3.2.5 software . The priirad.fr)  using 30Different complementary methods, ADMIXTURE model incorporated in STRUCTURE , discrimfind.clusters function of the adegenet package [To cross-check the results obtained by STRUCTURE, DAPC analysis was performed using the  package  in R sta package . The tot package .Population relationships were studied by imputing the full clean set of data into DARwin software  using neAnalysis of molecular variance (AMOVA) was performed on DAPC population groups using GenAlEX software .Additional file 1. List of the 93 used AYB lines and phenotypic related traits.Additional file 2. Genetic distance matrix.Additional file 3. Structure results at K = 2, K = 3 and K = 4.Additional file 4. DAPC analysis results.Additional file 5. STRUCTURE vs DAPC genotypic-phenotypic traits.Additional file 6: Figure S1. PCoA results; Figure S2. PCoA at K = 3 using 80% threshold.Additional file 7. STRUCTURE results at K = 4. (PPTX 330 kb)


Quercus) are major components of temperate forest ecosystems in the Northern Hemisphere where they form intermediate or climax communities. Sessile oak (Quercus petraea) forests represent the climax vegetation in eastern Germany and western Poland. Here, sessile oak forms pure stands or occurs intermixed with Scots Pine (Pinus sylvestris). A large body of research is available on gene flow, reproduction dynamics, and genetic structure in fragmented landscapes and mixed populations. At the same time, our knowledge regarding large, contiguous, and monospecific populations is considerably less well developed. Our study is an attempt to further develop our understanding of the reproduction ecology of sessile oak as an ecologically and economically important forest tree by analyzing mating patterns and genetic structure within adult trees and seedlings originating from one or two reproduction events in an extensive, naturally regenerating sessile oak forest. We detected positive spatial genetic structure up to 30 meters between adult trees and up to 40 meters between seedlings. Seed dispersal distances averaged 8.4 meters. Pollen dispersal distances averaged 22.6 meters. In both cases, the largest proportion of the dispersal occurred over short distances. Dispersal over longer distances was more common for pollen but also appeared regularly for seeds. The reproductive success of individual trees was highly skewed. Only 41 percent of all adult trees produced any offspring while the majority did not participate in reproduction. Among those trees that contributed to the analyzed seedling sample, 80 percent contributed 1–3 gametes. Only 20 percent of all parent trees contributed four or more gametes. However, these relatively few most fertile trees contributed 51 percent of all gametes within the seedling sample. Vitality and growth differed significantly between reproducing and nonreproducing adult trees with reproducing trees being more vital and vigorous than nonreproducing individuals. Our study demonstrates that extensive, apparently homogenous oak forests are far from uniform on the genetic level. On the contrary, they form highly complex mosaics of remarkably small local neighborhoods. This counterbalances the levelling effect of long‐distance dispersal and may increase the species’ adaptive potential. Incorporating these dynamics in the management, conservation, and restoration of oak forests can support the conservation of forest genetic diversity and assist those forests in coping with environmental change.Oaks ( Extensive, apparently homogenous oak forests are far from uniform on the genetic level. On the contrary, they form highly complex mosaics of remarkably small local neighborhoods. This counterbalances the levelling effect of long‐distance dispersal and may increase the species’ adaptive potential. Incorporating these dynamics into the management, conservation, and restoration of oak forests can support the conservation of forest genetic diversity and assist those forests in coping with environmental change. So far‐reaching is the ecological importance of the genus, that oaks were called “the most important woody genus in the Northern Hemisphere”  liebl.) forests represent the climax vegetation in eastern Germany and western Poland . Despite a long tradition of silvicultural use, extensive stands considered to represent the natural state of this vegetation community can still be found in the region  seed and pollen dispersal distances, (2) distribution of individual reproductive success, and (3) spatial genetic structure and genetic diversity within and across both generations.Our study is an attempt to further develop our knowledge of the reproduction ecology of sessile oak as an ecologically and economically important forest tree and provide insights that may assist in the management and regeneration of production forests as well as in the restoration of degraded populations. In order to achieve this, we studied reproduction and dispersal patterns in 22.1Q. petraea, locally in association with Scots pine, naturally form the climax vegetation  roth (Poaceae) and Vaccinium myrtillus L. (Ericaceae). Browsing pressure by deer is high and oak seedlings can be found mostly in fenced areas, where herbivore access is prevented. As the forest is not managed for timber production at our study site, regeneration sites suitable for long‐term establishment of seedlings are therefore restricted to crown openings caused by windthrow and other natural causes. As no such sites are present in our particular study plot, there is no subcanopy vegetation of older oak saplings and the forest is strictly divided into the canopy layer formed by adult trees and the understory formed by grasses, blueberry, and young oak seedlings. The adult tree and seedling cohorts therefore represent two discreet generations without overlap. Plants within the seedling generation uniformly measure about 40 cm in height and most likely originate from only one or two reproduction events. Long‐term establishment of the light‐demanding seedlings is not possible in the absence of suitable regeneration sites caused by crown openings and no larger and obviously older saplings are present within the study site.The understory vegetation is characterized by To address our questions, we used an already established long‐term monitoring plot with an extension of 100 x 100 meters  on both the population and the full‐sibling level  and effective number of alleles (AE), private alleles (AP), observed (HO) and expected (HE) heterozygosity, and effective population size (NE). Except for effective population size, all calculations were performed using GenAlEx 6.503 , derived by Wright  of the marker set was 1.4 × 10‐22. The PID of full siblings was 2.1 × 10‐8. This resolution allows unambiguous identification of individual genotypes. Identical genotypes can therefore safely be assigned to accidental duplicate sampling and do not indicate insufficient genotyping resolution.The overall probability of identity  which reached 8 to 27 alleles per locus and exceeded the number of alleles in any single generation.The genetic homogeneity of the two generations was further illustrated by the results of the AMOVA: Only 0.2 percent of the genetic diversity was partitioned among the two generations. As expected in a large, naturally regenerating population, the majority of the genetic diversity (93.8 percent) was partitioned within both generations. Still, a comparatively large proportion of 6.0 percent was partitioned within individuals.E amounted to about 71 percent of the census size in the adult generation and about 72 percent in the seedling generation.The estimated effective population size was comparable for all statistical models applied  as well as between the two generations Table . NE amou3.2Significant spatial genetic (SGS) structure was detected within the adult generation as well as the seedling generation Figure . PositivFST=0.001, DEST=0.003, p > .05). There was pronounced genetic differentiation in both estimators between most of the seedling sampling areas (Appendix FST averaged 0.05 between sampling areas (range 0.00–0.12) and DEST averaged 0.12 (range 0.00–0.27). To test for significant differentiation, p‐values were Bonferroni‐corrected for multiple testing. The genetic differentiation between seedling sampling areas was significant in 87.3 percent of all cases for FST and in 89.7 percent for DEST after correction (p < .002).Differentiation between the adult and seedling generation was very low and nonsignificant  contained 1–3 members. Only 19 percent of all families included more than three members. However, due to their larger sizes, these families represented a total of 40 percent of all individuals in the adult generation. Appendix Within the seedling generation, both parents could be identified within the sampled adult generation for 24 percent of all seedlings. No case of selfing was observed. For 60 percent of all seedlings, only one parent could be identified within the stand. For 16 percent of the seedlings, no parent could be identified within the stand.Seed and pollen dispersal distances are shown in Figure The reproductive success of individual trees was highly skewed Figure . Of the p = 6.5 × 10‐7). The effect size after Cohen  to 10 percent in class 4 . No tree in class 5  participated in reproduction.Reproducing and nonreproducing trees differed strongly in their social class Figure . The meaer Cohen  was d = 4NE between generations indicate changes in the underlying mating system, gene flow, selection, genetic drift, or inbreeding rate  oak in Europe.The occurrence and accumulation as well as the mendelian transmission of intraindividual somatic mutations in long‐lived forest trees have recently been described for European oaks ; Formal analysis (lead); Investigation (lead); Methodology ; Project administration (lead); Resources ; Validation ; Visualization (lead); Writing‐original draft (lead); Writing‐review & editing . Heike Liesebach: Conceptualization ; Formal analysis (supporting); Funding acquisition (lead); Methodology ; Resources ; Supervision (lead); Validation ; Writing‐review & editing .Appendix S1‐S5Click here for additional data file.


Vigna subterranea (L.) Verdc.], an indigenous African legume crop can contribute immensely to food security. This miracle crop is used as food and for the enhancement of soil fertility in South Africa. Knowledge on the genetic diversity and structure among the Bambara groundnut landraces can pave the way for the effective use and cultivation of this crop in southern Africa, especially South Africa. The aim of this study was to assess the genetic diversity and structure among Bambara groundnut landraces collected across South Africa and compared to a limited number of accessions from southern Africa using SSR markers. Seventy-eight Bambara groundnut accessions were genotyped using 19 Bambara specific SSR markers. SSR loci explored in this study, were all polymorphic. A total of 127 alleles were detected with a mean of 6.7 alleles per locus. Allele diversity and frequency among genotypes varied from 0.21 to 0.85 with an average of 0.62 per locus. Genetic variation as described by the analysis of molecular variance indicated higher genetic diversity (92%) within landraces than between (8%) different landraces. Population structure analysis showed that three subpopulations existed, and most of the South African accessions were restricted to one subpopulation, indicating that Bambara landraces has the ability to form unique haplotypes in different environments. Information harnessed in this study is helpful for further use in breeding programs for crop improvement.With its drought tolerant and protein-rich properties, Bambara groundnut [ Bambara groundnut [Vigna subterranea (L.) Verdc.], an indigenous legume crop also known as a poor man’s crop, is cultivated at low levels across the African continent, especially Sub-Saharan Africa. This protein-rich (18–26%), drought tolerant, under-utilized legume species has several medicinal benefits and is rated as the third most produced and consumed crop after groundnuts and cowpea in semi-arid Africa6, with Nigeria as the largest producer in Africa8. In some African countries, like Zimbabwe, Bambara is preferred to maize, groundnuts and cowpea9. Most of the Bambara groundnuts produced, is maintained as local populations and grown as landraces3. These landraces are preserved and maintained in gene/germplasm banks for their agricultural significance10. Nigeria has the highest number of accessions with Zambia and Zimbabwe ranking 2nd and 3rd. South Africa has the lowest number of accessions as the introduction of this crop has been very recent11.With an ever changing climate and increasing drought reports across the world, agriculture is suffering, as current crops struggle to adapt in the new conditions, impacting food security. Several African countries’ economies based on agriculture, struggle to keep afloat as staple crops battle to adapt, risking production and food supply11. The growth in South African production areas for this crop are restricted by various limitations which include good stable varieties. The potential of this ‘miracle’ crop has not yet been explored to its full extent in South Africa, while Shegro et al.6 suggested that information on genetic variability among available local accessions (landraces) of Bambara groundnut can contribute to an improvement program in this country. Screening these Bambara landraces and the assessment of the genetic diversity of this poorly understood minor legume can provide breeders with genetic resources to assist with crop improvement including, yield, biotic and abiotic stress tolerance as well as the adaptability of the crop to various environments, which can contribute to the enhancement and maintenance of food security12.In South Africa, the crop is used as food as well as for the enhancement of soil fertility due its ability to fix nitrogen in the soil, with production limited to Mpumalanga and KwaZulu-Natal13 with a few reports on the use of molecular resources using isozyme markers14, random amplified polymorphic DNA (RAPD) markers15, amplified fragment length polymorphism (AFLP) markers17, and diversity arrays technique (DArT) markers5. Molecular markers have significant potential to explore genetic diversity as they are stable and detectable in all plant tissues and not affected by environmental effects18. SSR markers are described as such molecular markers and can be used to enhance the improvement of the ability and precision of classical plant breeding. SSR markers have been successfully employed to effectively reveal and interpret the genetic diversity found between Bambara groundnut landraces23.Research on the genetic diversity of Bambara groundnut landraces mostly rely on phenotypic descriptors25. Somta et al.23 suggested that the exploitation of the genetic diversity of Bambara groundnut landraces, will increase its potential as food, feed and cultivation in diverse environments. Bambara groundnut landraces from major growing centres, including southern, western, eastern and central Africa, as well as Asian regions have been characterized to determine the level of genetic diversity within and between landrace germplasm23.The genetic structure of any crop reflects the evolutionary history and the evolutionary potential of such a crop. Understanding of the genetic structure of a crop population assists in the evaluation and improvement of such a cropThe aim of this study was to assess the genetic diversity and structure among Bambara groundnut landraces collected across South Africa and other regions in southern Africa using SSR markers for the cultivation and improvement of Bambara groundnut.22.Seventy-eight Bambara groundnut accessions Table  from Sou20. DNA quantity and quality were estimated from a 0.8% (w/v) agarose gel with electrophoresis at 80 V in UNTAN  buffer. DNA was visualized with ethidium bromide staining under UV light. The concentration of the DNA samples was determined by using a UV spectrophotometer and measuring absorbance at A260 and A280. Samples were diluted to 20 ng/μl for SSR analysis.Total genomic DNA of each accession was extracted from young leaves using the GenElute Plant Genomic DNA kit (Sigma Aldrich) according to manufacturer’s instructionsEach PCR amplification reaction contained 40 ng DNA, 1 × KAPATaq ReadyMix DNA polymerase, 50 ng each of the forward and reverse primer (Integrated DNA technologies) and 0.1 mg/ml Bovine serum albumin (BSA) in a total reaction volume of 10 μl. The optimized cycling conditions for the primers used were: 94 °C for 2 min, 35 cycles of 94 °C for 1 min, 45–60 °C (depending on the primer) for 1 min and 72 °C for 30 s followed by a final extension of 72 °C for 5 min and a 10 °C hold. Polymerase chain reaction products for all markers were separated on a 5% (w/v) non-denaturing polyacrylamide gel using the GelScan 3000 Real-Time DNA Fragment Analysis system with software version 8.00.01 . The alleles were scored as present (1) or absent (0) based on the size of the amplified product using a 25 bp DNA ladder .26. PIC evaluates polymorphism of a marker by characterizing the efficiency of each primer for detecting polymorphic loci27. A PIC of > 0.5 indicates high diversity, a PIC < 0.25 low diversity and a PIC between 0.25 and 0.5 intermediate diversity28. As part of the assessment of the genetic diversity among the Bambara groundnut landraces an analysis of molecular variance (AMOVA) was done using GenAlex 6.51b230. A rooted, unweighted neighbor-joining (NJ) dendrogram with 30,000 bootstrap repetitions was constructed with DARwin 6.0.1931. Genetic similarities between landraces were compared by using the Jaccard similarity index32. Cluster analysis information is sensitive to closely related individuals, therefore the constructed dendrogram illustrates the relationship among the Bambara groundnut landraces based on SSR allele variation. The Bayesian clustering method implemented in the STRUCTURE 2.3.4 software33 was used to infer clusters of the landraces. Analysis from the STRUCTURE software was used to estimate the ‘true’ number of populations (K) without prior knowledge of the population35. The K value was first calculated using a burn-in length of 104 and 104 Monte Carlo Markov Chain (MCMC) repetitions using 10 iterations for K1 to K10 using optimum K from CLUMPAK36. The dataset was then re-analyzed using the best calculated K value with a burn-in length of 105 and 105 MCMC replicates to determine the final population groupings.Scored data were used to construct a binary data matrix for statistical analysis. Different statistical programs were used for genetic diversity analyses. Total allele number (Na) and average number of alleles at each loci were calculated manually. Allelic polymorphic information content (PIC) was calculated from the binary data of the 19 SSR markers using iMEC: Online Marker Efficiency Calculator developed by Amiryousefi et al.19 who also used some of the SSR markers employed in this study. Polymorphic information content (PIC) value is an indication of allele diversity and frequency among genotypes. The PIC values varied significantly among the different markers used in this study. The values ranged from 0.21 for marker PR-7 to 0.85 for marker mBam2co80  Table  was due Three clear and distinct clusters were observed from the rooted cluster analysis from the different origins using DARwin 6.0.19 software , Botswana (4), Namibia (3), Swaziland (2), Malawi (1) and two landraces (ND_2 and ND_10) from unknown origin (ND). This cluster also contained the most South African and Botswana landraces and none of the Zimbabwean landraces. This cluster contain 39 landrace accessions of which the seed colour indicated to be predominantly dark brown (38%), followed by cream white (33%), light brown (26%) and only 3% black seeds. The landrace from unknown origin (ND_2) in sub-cluster 1 of cluster I showed no relation with any of the landraces from the known or unknown origins. The landrace from unknown origin (ND_10) however, showed 73% similarity with one of the SA landraces (SA_30). Even though these accessions share a high genetic similarity, they differ based on seed colour where the ND_10 accession have dark brown seeds and the SA_30 accession have cream white seeds. Genetic variation of 6% similarity between a SA landrace (SA_24) and a cluster of 3 other SA landraces  was observed in one of the sub-clusters of Cluster I. The seed colour of accessions in this sub-cluster, are predominantly dark brown with only one accession (SA_54) with cream white seeds with dark blotches.Cluster II consisted of landraces from SA (15), Botswana (3), Namibia (3), Swaziland (3), Zimbabwe (2), Singapore (1), Madagascar (1) and three landraces from unknown origin (ND). This cluster was divided into two sub-clusters. This is the only cluster that included Zimbabwean landraces. More accessions from cluster II indicated to have cream white seeds (39%) followed by accessions with light brown (32%) and dark brown (29%) seeds. This cluster have no accessions with black seeds. Sub-cluster two from cluster II contained mostly SA landraces and one from Zimbabwe. Seed colour within this sub-cluster is observed to be mostly variations of brown, while only one accession had cream white seeds. The landraces from unknown origin (ND_7 and 8) in cluster II showed 90% and 82% similarity with a SA (SA_2) and Botswana (BOT_1) landrace, respectively. The high genetic similarity between ND_7 and SA_2 again shows that there is no correlation between seed colour and the genetic similarity of different landraces. The landrace from unknown origin (ND_5), showed 67% similarity with one of the landraces from Zimbabwe. Furthermore, the genetic variation between landraces varied between 87% similarity between a landrace from the South African germplasm collection (SA_7) and a landrace from Swaziland (SWAZ 4). In this case however, both accessions had dark brown seeds.Cluster III is the cluster with the lowest number of landraces (8). These included landraces from SA, Botswana, Namibia and two landraces from unknown origin (ND). This small cluster was divided into two sub-clusters. Seed colour for this cluster indicated to be predominantly light brown (38%), followed by equal contributions to cream white (25%) and black (25%) and only 12% dark brown seeds. The landrace from unknown origin (ND_1) in sub-cluster 1 of cluster III showed significant genetic similarity as but differed in seed colour with landraces from Namibia and Botswana. This also describes the relationship of a landrace from unknown origin (ND_4) in sub-cluster 2, with landraces from SA and Botswana. No clones (100% similarity) were observed in any of the main clusters.K = 3 as identified by Evanno et al.’s34 ad hoc ΔK method. The 78 landraces divided into three populations (K = 3)  Fig. , which i= 3 Fig.  identifiPopulation 1 consisted mainly of the genetic contribution of Swaziland landraces (60%) and landraces of unknown origin 57%)  was found common to all of the populations, with a much lower percentage (8%) of genetic variation limited to one or more populations, suggesting significant gene flow among the landraces from the different origins, across large geographical distances.Most of genetic diversity investigations documented high levels of genetic diversity among Bambara groundnut landraces sampled across large geographic regions37 where higher levels of genetic diversity within groups rather than among groups were reported. However, the genetic diversity observed for Bambara groundnut landraces depends on the techniques used and the number of individuals evaluated.Some of the earlier studies from outside South Africa found low genetic diversity within groups and a higher diversity among groups. Most of the literature agrees that Bambara groundnut landraces show high levels of genetic diversity among rather than within groups. This study is the first in South Africa and confirms previous studies38 and highly inbred, suggesting a limited amount of gene flow and therefore low levels of genetic diversity between groups. The adaptability of this self-pollinating crop to various environments promotes the genetic diversity observed within the groups. Three subpopulations were observed, where most of the South African accessions were restricted to one subpopulation. South Africa has the lowest accession number of Bambara landraces, and the production areas are very limited. This can explain the restriction of the landraces to one populations. This can also mean that the South African landraces possesses unique alleles and has the potential to be explored more intensely than what was done during this study.Bambara groundnuts are self-pollinators, described as predominantly homozygous39, as was observed for the 78 Bambara groundnut landraces used in this study. The observed clustering patterns are quite different from the results reported by previous researchers as the landraces included in this study did not cluster based on origin or seed colour. The statistical analysis methods used in this study all concluded that the three identified populations have more genetic diversity within than among the groups, because of the integration of landraces from the diverse origins.The genetic structure of a population can be influence by several factors that range from gene flow (migration) and population size to the ability of the population to adapt to changes in the environment. These influential factors makes it difficult to correlate the genetic structure of a population to the geographical distribution of individuals involvedUnderstanding the genetic variability of Bambara landraces in South Africa will increase the efficiency of future crop improvement programs in the country and will encourage the expansion of Bambara production areas. Consequently, this could potentially contribute to increase farmer, especially small-scale producer’s income, better food security, especially in regions of water scarcity and improve the quality of local diets within the region. The genetic diversity of Bambara groundnut evaluated in this study is relatively higher within than among the landraces. This is confirmed by the self-pollinating characteristics of the crop, as well as the slow gene flow identified between the diverse origins. The seed colour recorded for the landraces, differ significantly and could not be correlated with the genetic clusters. Future recommendations would include the addition of more landraces and a more intense study on the morphological traits, because the knowledge of the genetic diversity and population structure of Bambara groundnut landraces can contribute to the cultivation and improvement of this crop.


Epidermolysis bullosa (EB) is a hereditary heterogeneous group of mechanobullous disorders caused by mutations in several structural skin proteins observed in both humans and animals. In this work, we report the incidence and the genetic trend of the junctional epidermolysis bullosa (JEB), a major type of EB, in the Italian German Shorthaired Pointer (GSPs) population in a 10 years span.LAMA3 gene.In this study, we monitored the genetic trend of JEB in the Italian population of the GSPs from 2009 to 2018 in 750 animals. The studied mutation was the insertion (4818+207 ins 6.5 kb) of repetitive satellite DNA within intron 35 of the Allele frequencies showed a reduction of the mutated (C) allele during the years, with the only exception of 2017, when 13 dogs were diagnosed as carrier for the genetic pathology. A regression logistic analysis was performed, including sex, coat colour and their interaction. Our results showed that there was a statistically significant association with coat colour.The simplicity and the low cost of the analysis for the detection of this pathology suggests that a deeper identification of carrier dogs will allow better breeding strategies and management, leading to a rapid JEB eradication. Epidermolysis bullosa (EB) is a hereditary heterogeneous group of mechanobullous disorders caused by mutations in several structural skin proteins observed in both humans and animals.LAMA3 gene. The insertion causes an abnormal mRNA transcript with an insertion of 227 nucleotides in position 4818 at the junction between exon 35 and 36 of the LAMA3 gene. This 227 bp sequence carries a non‐sense (TAA) codon 33 bp downstream of the insertion site. Considering that conventional treatments are ineffective for homozygous dogs and that heterozygous animals are symptomless, the genetic identification of carrier animals is important.JEB, specifically, encompasses all of the subtypes of EB that have mechanical fragility ansd blistering in which tissue separation occurs within the lamina lucida of the basement membrane zone at the dermal–epidermal junction. The defective adhesion of the epithelium results from mutations affecting either the extracellular matrix protein laminin 5, its cellular receptor integrin a6b4, or collagen type XVII, a transmembrane component of the hemidesmosomes.In humans, EBs are estimated to affect one in 17,000 live births, with 500,000 cases estimated worldwide,In this work, we reported the incidence and the genetic trend of the pathology in the Italian population in a 10 years span. Furthermore, mechanisms of pigmentation are extremely complex, and many cofactors and interactions are still unknown.In this study, we monitored the genetic trend of JEB in the Italian population of the GSPs in a period of 10 years (2009–2018).Samples processing and analysis were carried out by Vetogene SRL, Milano , one of the official reference labs for the Italian Kennel Club ENCI (FCI associate), who collected all the samples through the years.All samples were sent for the genetic test analysis by veterinarians who certified the identification of the sample through microchip control. All the owners gave an informed consent for research purpose to the Vetogene laboratory.1/n] − 1[(Present/Past)The average growth rate on the entire population of GSPs in Italy has been calculated according to the formula:n” is the number of years among the two registrations.where “Present” is the number of registrations of the last recorded year, “Past” is the number of registrations of the first years and “The experimental sample was composed of 750 animals, in which 398 were male and 352 were female. We also collected information about coat colours, considering as phenotypic pattern “solid” when the animals were totally black or brown and “roan” when showing the peculiar mixture of white and pigmented hairs. Coat colour patterns were assigned according to official self‐declarations of breeders storing the samples of the dogs in the Vetogene laboratory. Among male dogs, 217 were considered “roan” and 180 “solid”; among female dogs, 233 were classified as “roan” and 112 as “solid”. For eight tested dogs, it was not possible to collect any coat colour/pattern information.Since 2008, in collaboration with the Italian Kurzaar Kennel Club (KCI), Vetogene has been able to perform a diagnostic test for JEB in this breed within the country.LAMA3 gene, developed by Polli and others,Genomic DNA has been extracted either from peripheral blood  or FTA cards , according to the manufacturer's instructions. An allele‐specific PCR method for molecular detection of the causative mutation in the To evaluate if there was any difference among the genotypes observed in the first 5‐year period (from 2009 to 2013) and in the second 5‐year period (from 2014 to 2019), we applied a chi‐square test with Yates's correction for continuity.The prevalence of the genetic pathology was estimated as a percentage by dividing the number of wild‐type and carrier dogs by the total number of dogs analysed.LAMA3 gene: N: null ; C: carrier .Here is the classification of the alleles in the A regression logistic model has been created to analyse the associations between genotype and coat colour:ijk = genotype at the (NN or NC), Sexi = male or female; Coatj = the colour of the coat: “solid” (black or brown) or “roan”; and eijk = random residual effect.where yStatistical analysis was performed using the software JMP of SAS .The German Shorthaired Pointer is a medium to large sized breed of dog developed in the 19th century in Germany for hunting. In Italy, this breed is well known and widely reared. Table Inherited JEB has been reported in this breed, exhibiting generalized skin blistering. For an informative and representative scope, Figure LAMA3 gene insertion with an allele‐specific PCR, for 750 GSPs from 2009 to 2018. The number of genetic tests per year, after reaching a maximum peak in 2014 (107 provided tests), decreased in the next 4 years with a minimum of 41 tests provided in 2018 , so that the number of carrier subjects observed from 2014 to 2018 is significantly lower if compared to the one observed in the first five years.In Figure Overall, the mutant allele showed a frequency of almost 6% in the population, with 97 carrier subjects (12.9% of the entire tested cohort). Having available data and pedigree information of the cohort analysed, we investigated if there was any relationship among the mutated allele and the sex of the dogs or their coat colour. A regression logistic analysis has been performed, including sex, coat colour and their interaction, to investigate any possible correlation with the observed genotype and the factors in the model. According to our results, there was no significant interaction between gender and coat colour and no effect of gender of the dogs, while there was a significant effect of coat colour.In this paper, we showed the genetic trend of a serious genetic pathology, the JEB, in the GSP population in Italy. Considering the total number of GSPs in a span of 10 years in Italy  and the total number of tests provided (750), a 2.8% of the population has been surveyed. The size of the Italian population of GSPs in the 10 years period showed a negative average growth rate of −3.09%, as shown in Table According to the genotypes detected, data highlighted a percentage of the C (mutated) allele ≤0.10 on every year. Regarding the genetic trend over time, if we consider that in 2009 there were more than two out of 10 tested subjects with the mutant allele, in 2018 there were about two out of 100 tested subjects with the heterozygous genotype (NC).The encouraging trend of the reduction of the C allele is confirmed in the first nine months of the 2019, when only one dog out of the 60 tested has been diagnosed as a carrier (data not shown).p‐value = 0.035). In particular, 16.10% of carrier dogs showed a solid coat colour, while 10.89% had a roan pattern. The percentages are referred to the number of carriers on the total number of solid and roan dogs, respectively (47 NC on 292 solid dog and for 49 NC on 450 roan). It is interesting to notice that the association of the epidermiolysis bullosa and the mottled pigmentation is knownhttp://www.genecards.org, Human Phenotype Ontology).The regression logistic analysis, including sex, coat colour and their interaction showed a significant effect of coat colour on the genotypes detected. Dogs with a solid coat colour (whether brown or black) showed more carriers than those with a roan coat  in Italy is probably underestimated in this study. In the authors’ experience, affected dogs are usually euthanized without being tested because the footpad lesions make them almost unable to walk (as shown in Figure In conclusion, the simplicity and the low cost of the analysis for the detection of this pathology suggests that a wider population screening and identification of carrier dogs will allow better breeding strategies and management, leading to a rapid JEB eradication. This simple genetic analysis can efficiently increase the welfare of the GSP in Italian population.The statistical evidence of the higher observation number of NC genotype in solid coat colour dogs must be carefully evaluated in a wider cohort, keeping in mind that the objective of a judicious selection is to keep the genetic pool as wide as possible. Genomic analyses of the population could add useful information to explain the evidence found. The identification of healthy carrier dogs is the only way to enable control of the presence of JEB in the GSP breed by applying an appropriate breeding strategy.The authors declare no conflict of interest.


Directional selection in a population yields reduced genetic variance due to the Bulmer effect. While this effect has been thoroughly investigated in mammals, it is poorly studied in social insects with biological peculiarities such as haplo-diploidy or the collective expression of traits. In addition to the natural adaptation to climate change, parasites, and pesticides, honeybees increasingly experience artificial selection pressure through modern breeding programs. Besides selection, many honeybee breeding schemes introduce controlled mating. We investigated which individual effects selection and controlled mating have on genetic variance. We derived formulas to describe short-term changes of genetic variance in honeybee populations and conducted computer simulations to confirm them. Thereby, we found that the changes in genetic variance depend on whether the variance is measured between queens (inheritance criterion), worker groups (selection criterion), or both (performance criterion). All three criteria showed reduced genetic variance under selection. In the selection and performance criteria, our formulas and simulations showed an increased genetic variance through controlled mating. This newly described effect counterbalanced and occasionally outweighed the Bulmer effect. It could not be observed in the inheritance criterion. A good understanding of the different notions of genetic variance in honeybees, therefore, appears crucial to interpreting population parameters correctly. Apis mellifera), additional genetic changes are to be expected due to artificial selection, since great efforts have been undertaken to establish modern breeding programs for honeybees in Europe , the variance of breeding values within one generation g of the population is precisely the additive genetic variance z is inherited from its sire s(z) and dam d(z) via the formulaδs(z) and δd(z) are independent of us(z) and ud(z) and one another, and each has variance g are independent random samples of the population, whence their variances are again δs(z) and δd(z) remain unchanged, the sires and dams in generation g are no longer a representative sample of the population and their variance will differ. If the genetic variance within the selected sires and dams in generation g are g + 1 will beκs and κd are usually positive, Eq. (κj (=κs or κd) can be quantified asij denotes the sex-specific selection intensity, xj the standardized truncation point, and ρj the correlation between estimated and true breeding values of sires and dams, respectively (Dekkers ij(ij − xj) is commonly abbreviated by kj  and direct (worker group) effects  ensure that only drones with favorable genetic features are involved in the mating process, and (b) provide pedigree information on the paternal side and therefore lead to more accurate breeding value estimations. In honeybee pedigrees, mating stations correspond to sires in pedigrees of other farm animals. Thus, mating stations are also referred to as Simulation studies have shown that breeding schemes with controlled mating are superior to those without  her dam queen and by D(q) her sire drone. Likewise, let Q(d) be the dam queen of a drone d. Finally, for a worker group w, let Q(w) be the dam queen and let Q(w) mated with.  has variance d, we have:δQ(d) has variance w:When it comes to the inheritance of breeding values in honeybees, three different pathways have to be distinguished, depending on whether the recipient is a queen, a drone, or a worker group  may may11) muQ(q)∣q is queen)” and “var(uD(q)∣q is queen)” may be replaced by “var(uq∣q is queen)” and “var(ud∣d is drone)”, respectively, and the covariance term vanishes. Likewise, all queens have the same chance to become the dam of a drone, whence the term “var(uQ(d)∣d is drone)” in Eq. (uq∣q is queen)”.We first consider the case that no selection is employed and mating always occurs uncontrolledly. In this case, dam queens and sire drones of queens are independent random samples of the entire queen and drone population, respectively, whence in Eq. , the ter” in Eq.  is identVA:Combining the thus modified Eqs.  and 10)10) yieldy to Eq. , how theLike in the inheritance to queens, the covariance terms also vanish in the inheritance to worker groups (Eq. ). Since A. m. siciliana), which are endangered by introgression of other subspecies and where the effective population size is too small to allow for directional selection, it may be a viable conservation strategy 14) remaiDi(w) ≠ Dj(w) involved in a controlled mating, we may assume a probability p that they descend from the same drone producing queen and probability 1 − p that their dams are different queens on the same mating station. Usually, this probability p is assumed to depend only on the number of drones producing queens on a mating station and potentially the number nD of drones that mate with a queen (Brascamp and Bijma Di(w) ≠ Dj(w):aDPQ denotes the average relationship between drone producing queens on a mating station. For shorter notation, we will denote this covariance term byBut when it comes to the variance structure of worker groups, differences appear in the term p and aDPQ depend on assumptions on the composition of drone producing queens on a mating station, the distribution of offspring to drones and queens, and the history of the breeding program  breeding value estimation  = Q(q) the joint variance structureFor a queen uq among the next generation of queens isTruncation selection with intensity s in Eq. , \docume, by Eq.  and usinEquation  correspoIn standard theory on the Bulmer effect, the reduction of genetic variance in the population is inferred from the variance among the selected parents (cf. Eq. ). For hoThe consequences of selection for the genetic variances among drones and worker groups are omitted in this manuscript but can be worked out following similar lines as those specified in Appendix In order to assess and judge the status of a honeybee population regarding genetic variance in practice, it is necessary to condense the different variances of direct and maternal effects for the members of the three castes into one notion of total additive genetic variance. In practical applications, focus mostly is not laid on individual queens or worker groups; instead, colonies consisting of a queen and her offspring workers are seen as the relevant entities. For these, different combinations of direct and maternal breeding values are considered to be of interest. The sum of direct and maternal breeding values of a queen, The theory developed above implies that the introduction of controlled mating increases the variance in the performance and selection criteria (because these consider worker groups), but leaves the IC unaffected. The introduction of selection decreases the genetic variance of a honeybee population in all three criteria . In the following, we confirm and quantify these theoretical findings with the help of a computer simulation study.tcont, mating took place on isolated mating stations. From year tsel on, the reproducing queens were selected after a BLUP breeding value estimation like previously simulated in , (b) first selection, then controlled mating , and (c) simultaneous start of controlled mating and BLUP selection (tcont = tsel = 10).We used the program BeeSim , ns of the 3-year-old queens were chosen to serve as dams of the drone producing queens of one mating station each  determined by the worker groups but had little effect on the IC which is measured only in the queens. Meanwhile, the introduction of selection caused a reduction of variance in all criteria see Fig. . A similrmd = −0.35, there was an increase between 13.4 and 15.2%; for the trait with strong correlation, rmd = −0.71, the effect was significantly stronger: between 26.4 and 31.2%. Finally, the SC showed similar behavior in both traits. If controlled mating was introduced before selection, it caused an increase of genetic variance of between 24.7 and 27.4%; if it was introduced after selection the increase of variance was only between 20.3 and 24.4%. In the breeding schemes which introduced controlled mating after selection, the increase of genetic variance in the SC was slightly higher, when the negative correlation between maternal and direct effects was strong, rmd = −0.71. There was no clear indication of an effect of the number of dam queens on the intensity of the variance increase , it caused a decrease of variance in the population in all variance criteria  of between 8.8 and 21.2% , the reduction of variance appeared less severe, between 3.2 and 10.4%  resulted in a stronger decrease of variance within the population. When the selection was introduced after controlled mating, the Bulmer effect showed most strongly in the SC, followed by IC and PC. In the case of tcont > tsel, the opposite order could be observed: the Bulmer effect affected the PC strongest and the SC weakest. The number of mating stations, i.e., pseudo sires, ns, had only a minor effect on the results and there was no clear tendency indicating whether a higher number of pseudo sires caused a stronger or weaker Bulmer effect (data not shown). Furthermore, in the case of selection preceding controlled mating, we observed a significant reduction of genetic variance in the IC from year 15 to year 16 by between 3.2 and 7.6% which was mainly caused by a reduced variance of direct breeding values of queens  resulted in an immediate reduction of genetic variance in the IC of 6.9–11.3% (rmd = −0.35), and of 3.1–6.2% (rmd = −0.71), respectively. Two years after the introduction of the breeding scheme, a second drop in genetic variance occurred with a reduction of 6.6–8.6% (rmd = −0.35), and of 3.9–7.2% (rmd = −0.71), respectively  and 26.2% (rmd = −0.71), respectively. Subsequently, however, the level of genetic variance in these criteria decreased and thus undermined the initial gain  showed different behavior upon the introduction of breeding schemes, which entails the question of which criterion is the most relevant for honeybee breeding.heritable variance in the presence of indirect genetic effects in other agricultural species  have more indirect effects on breeding success. The PC influences the phenotypic variance of a population. An increase of genetic variance in the PC without an increase of variance in the IC does not increase the evolutionary potential of a population. However, in a study on microbia, van Boxtel et al.  showed tOverall, the simulation results confirmed the theoretical predictions, i.e., that the introduction of selection decreases the genetic variance within the population in all criteria , while the introduction of controlled mating results in an increase of genetic variance in those criteria which account for worker groups (PC and SC) but does not influence the variance structure of the queens in the population (IC). In the context of honeybees, possible consequences of the Bulmer effect have been mentioned by Moritz , but onlBoth theory and simulation studies agreed that the introduction of controlled mating increases the genetic variance in the PC and SC but not in the IC. In the situation where controlled mating is imposed on unselected populations, theory and simulations for the SC also confirm each other quantitatively, with the realized increase of genetic variance being only slightly lower than the 31.8% we derived in the theory section.cw between the selection index and the true breeding values and thus to a further reduction of genetic variance among the dams of future generations according to Eq. . Equation 24) descrSupplementary Figure S1


Similarly, vivid visual imagery tended to co-occur with vivid auditory imagery. Nevertheless, the aphantasic group included one individual with typical auditory imagery; and the group lacking auditory imagery (N = 29) included one individual with typical visual imagery. Hence, weak visual and auditory imagery can dissociate, albeit with low apparent incidence. Auditory representations and auditory imagery are thought to play a key role in a wide range of psychological domains, including working memory and memory rehearsal, prospective cognition, thinking, reading, planning, problem-solving, self-regulation, and music. Therefore, self-reports describing an absence of auditory imagery raise a host of important questions concerning the role of phenomenal auditory imagery in these domains. Because there is currently no English word denoting an absence of auditory imagery, we propose a new term, anauralia, for referring to this, and offer suggestions for further research.Auditory and visual imagery were studied in a sample of 128 participants, including 34 self-reported aphantasics. Auditory imagery  and visual imagery  were strongly associated, Spearman's  I just don't have an inner voice that speaks to me or which I can listen to talking”—Kendle (I silently think and silently read (with no auditory ‘voice’)”—Faw ”—Faw , p. 46; —Watkins , p. 44; ”—Kendle , p. 38. Two recent survey studies have furnished data on the extent to which individuals described as aphantasic, experience imagery in other sensory modalities. Dawes et al.  used theIn addition to evidence from survey research, some personal accounts of aphantasic individuals describe a mental life that includes imagery in other sensory modalities, including auditory imagery and an “inner voice” Kendle, . As noter = 0.62). However, it is worth noting that in this study, participants' scores on a modified version of the Vividness of Visual Imagery Questionnaire (VVIQ-M),  being 18–29 years. Ages of the remaining participants were 30–49 years (N = 30), 50–69 years (N = 13) and over 70 years (N = 1).Potential participants were recruited through several avenues. Firstly, information concerning the project, together with invitations to participate were circulated among three Facebook groups entitled: “Aphantasia!,” “Aphantasia Support Group,” and “Aphantasia  Awareness Group.” Each Facebook group advertised itself as a social group for individuals with self-proclaimed low visual mental imagery. Secondly, information about the project, together with an invitation to participate was circulated digitally to undergraduate psychology classes, and via physical advertising on notice-boards at the University of Auckland. Thirdly, project information and an invitation to participate was circulated to social media contacts of one of the researchers (RH). 197 individuals responded to these invitations by commencing the survey; 128 participants  completed all survey items successfully. All participants were over 18 years, with the majority , descriptive statistics for visual and auditory imagery vividness were: Mean VVIQ-M = 4.23, 95% CI (with bootstrapping) = 3.90–4.58, SD = 2.09; Mean BAIS-V = 4.05, 95% CI (with bootstrapping) = 3.73–4.38, SD = 1.92. Raw data can be found in the Across the entire sample , with 82% also being categorised as anauralic. Similarly, the anauralic group reported very weak visual imagery , with 97% also being categorised as aphantasic. As one would expect, given these observations, the association between aphantasia and anauralia was highly reliable, χ2 = 48.37, df = 1, p < 0.001.27 participants were categorised as hyperphantasic; and 18 were categorised as hyperauralic (mean VVIQ-M / BAIS-V score ≥ 6). Once again, there was substantial overlap between these classifications. As a group, the hyperphantasic individuals also experienced strong auditory imagery , with 56% also being categorised as hyperauralic. Similarly, the hyperauralic group reported strong visual imagery , with 83% also being categorised as hyperphantasic. As one would expect, given these observations, the association between hyperphantasia and hyperauralia was highly reliable, χ1/34 aphantasic individuals reported average auditory imagery (Mean BAIS-V = 5.0); and 1/29 anauralic individuals reported average visual imagery (Mean VVIQ-M = 5.75). Hence, it appears that auditory imagery can dissociate to some extent from visual imagery in aphantasia; and visual imagery can dissociate to some extent from auditory imagery in anauralia, albeit with low incidence in each case. On the other hand, strong dissociations were not observed. In the current sample, no aphantasic participants were hyperauralic; and no anauralic participants were hyperphantasic.N = 26) with scores representing extremely weak or absent visual imagery (mean VVIQ-M < 1.2) and extremely weak (or absent) auditory imagery (mean BAIS-V < 1.2) are represented by the large bubble in the lower left of this figure. Notably, there were no fewer than 24 participants with mean VVIQ-M = 1.00 and mean BAIS-V scores = 1.00. That is, these participants indicated the extreme value “1—no image present at all” for all 16 VVIQ-M items, and all 14 BAIS-V items. Data points representing the one individual with average auditory imagery in the context of aphantasia, and the individual with average visual imagery in the context of anauralia, are apparent in the upper-left and lower right portions of A bubble plot illustrating the relationship between rated vividness of visual (VVIQ-M) and auditory (BAIS-V) imagery is shown in rho, was used to estimate the association between visual and auditory imagery. This association was strong: Spearman's rho = 0.832, 95% CI (with bootstrapping) = 0.743–0.897, p < 0.001.Because our recruitment strategy targeted aphantasia interest groups, the distribution of VVIQ scores was non-normal, with a clear mode at the lower extreme. The distribution of BAIS-V scores was also non-normal, with a similar mode at the lower extreme of the distribution. Accordingly, a non-parametric statistic, Spearman's rho =0.83). A large majority of self-reported aphantasics were also anauralic, and vice-versa; and most hyperphantasics were also hyperauralic, and vice-versa. A salient feature of the data is the number of participants who reported a complete or near-complete absence of auditory imagery, meriting inclusion in the anauralia category. When recruiting participants for this study we sought to include those with weak imagery, but of necessity this involved targeting individuals with weak visual imagery, via aphantasia online interest groups. Because anauralia is a new term, online interest groups concerned specifically with weak or absent auditory imagery do not exist. Nevertheless, a substantial number of our participants reported a complete or near-complete absence of auditory imagery: 29 were categorised as anauralic, of whom 25 indicated the extreme minimum option  for all 14 BAIS-V items. In the literature to date, investigations of aphantasia have, perhaps unsurprisingly, emphasised associations of visual imagery and its absence with other aspects of psychological functioning. For example, aphantasia has been linked with poor autobiographical memory (AM) and face recognition problems; and aphantasics appear more likely to follow scientific and technical occupations  can be found in the article/The studies involving human participants were reviewed and approved by University of Auckland Human Participants Ethics Committee. The patients/participants provided their written informed consent to participate in this study.anauralia. All authors contributed to the article and approved the submitted version.The initial conception and design of the study was provided by AL. RH contributed to the design and selected the questionnaire measures. The data were collected by RH. RH and AL analysed the data. Both authors contributed to writing the manuscript, with AL taking the primary role. AL coined the term 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.


Background: Genetic testing in clinical trials introduces several ethical and logistical issues to discuss with potential participants when taking informed consent. The aim of this study was to explore the attitudes of healthy volunteers in phase 1 studies to the topics of genetic security, genetic privacy and incidental genetic findings.Methods: Healthy volunteers presenting for screening appointments at a phase 1 clinical trial unit  took an anonymous paper survey about genetic testing.Results: There were 275 respondents to the survey. The mean age was 27 years (range 18-73); 54% were male and 53% were of North/Western European ethnicity. Just over half the healthy volunteers thought genetic security (56%) and genetic privacy (57%) were “important” or “very important”. However, the security of their genetic information was ranked less important than other personal information, including mobile phone number, internet browser search history and email address. Two-thirds of respondents would trade genetic privacy for re-identifiability if information relevant to their health were discovered by genetic testing. Healthy volunteers favoured the return of incidental genetic findings (90% indicated this was “important” or “very important”). A level of risk (10 to 90%) for developing a serious medical condition that would “trigger” the return of incidental genetic findings to participants was not identified.Conclusions: Healthy volunteers screening for phase 1 clinical trials have mixed views about the importance of genetic security and genetic privacy, but they strongly favour the return of incidental genetic findings that could affect their health. These issues should be discussed with potential participants during informed consent for phase 1 clinical trials with genetic testing. Increasingly, genetic information is collected from healthy volunteers during phase 1 clinical studies to identify factors that inform later stages of drug development, including dosing strategies, patient selection, and the potential role of biomarkers to monitor drug responses.Genetic testing during clinical drug trials can determine whether genetics influences how patients respond to drug therapy. so there is potential for misappropriation, release and misuse during this time. Genetic privacy is the protection of a person’s genetic information so they cannot be identified without knowledge and consent. Incidental genetic findings are results unrelated to the initial reason for genetic testing. These results may have implications for the current and future health and wellbeing of participants e.g., if genetic results influence susceptibility to disease or raise questions about ancestry or parentage. In Australia, the National Health and Medical Research Council (NHMRC) has recommendations on how to address genetic topics in clinical drug trials. For example, a separate participant information sheet and consent form (PICF) for the genetic component of studies should be used to discuss the nature of the genetic testing and its consequences for participants.Genetic testing in clinical trials introduces several ethical and logistical issues for participation, including concerns about genetic security and genetic privacy and how investigators should proceed with incidental genetic findings. Genetic security is the secure storage of genetic data and material. Clinical trial facilities and pharmaceutical companies must store these data for at least 15 years, but sometimes express concerns regarding genetic privacy and security, fearing disclosure of genetic information and widespread data sharing and misuse. There are no comparable data on these genetic topics for healthy volunteers. Given that healthy volunteers and patients are motivated differently for clinical trial participation, for example, healthy volunteers primarily focus on financial remuneration whilst patients do not, differences in attitudes towards genetic testing between the groups may be expected. Thus, the aim of this study was to explore the attitudes of healthy volunteers in phase 1 studies to the issues of genetic security, genetic privacy and the return of incidental genetic findings.The attitudes of patients and the public to genetic testing in clinical trials have been explored in a very limited number of previous studies. These two groups generally favour the return of incidental genetic findings,Potential participants being screened for studies at a clinical trial facility  were questioned about their attitudes to genetic testing using a paper survey (May to September 2019). No formal sample size was set, with as many potential participants approached during the study period as possible. The survey was de-identified and no personal information was collected apart from demographics. No potential biases were considered to influence participation and therefore the inclusion criteria were kept simple: participants were eligible if they were presenting for a healthy volunteer clinical drug study, were ≥18 years of age, and had sufficient English to allow informed consent and to answer the survey. There were no further inclusion or exclusion criteria. Informed consent was obtained by the lead author (S.L.) and participants completed the survey independently in the waiting room. The Human Research Ethics Committee at the University of Adelaide approved the study (H-2019-085).st survey) involving Q1-6 and then an updated version (2nd survey) that included three additional questions (Q7-9) (see extended data). Several question formats were used. A 5-point Likert scale included the options “very unimportant”, “unimportant”, “neutral”, “important” or “very important”. Other questions used a rank scale method , and in the case of ties, a mean rank was ascribed to the responses. Polar responses (yes/no) were used for statements regarding incidental genetic findings. The final question format was a visual analogue scale to investigate attitudes towards genetic privacy. The demographic data collected were age, ethnicity (2nd version of the survey only), level of education, and the number of clinical trials undertaken previously at CMAX Clinical Research. The definitions of ethnicity were those used by the Australian Standard of Classification of Ethnic and Cultural Groups.Surveys were written by the authors to investigate attitudes towards genetic security, genetic privacy and incidental genetic findings. Questions were written in non-technical language to maximise readability and understanding. No formal validation or piloting of the survey was conducted prior to enrolment of the first participant. There were two iterations of the survey: an initial version . Some responses were dichotomised as “favoured” (comprising the responses “important” and “very important”) or “not favoured” . After analysis of the first 190 surveys, an amended survey was created which included three new questions. These questions created scenarios to discern the motivations for some responses. A sample size of 300 was calculated to result in a 95% confidence interval of 5-10% for a range of proportions between 30%-70% for each question. Potential statistically significant relationships between responses and demographics were examined. Kruskal-Wallis tests were performed for any relationships with age, number of previous CMAX studies, ethnicity, and education. Mann-Whitney U-tests were performed for any relationships with gender. Statistical significance was set at p < 0.05.Data from paper surveys were transcribed electronically and analysed usingJust over half the participants had a favourable attitude towards the importance of genetic security (56%) . The favMore than half the participants had a favourable attitude towards the importance of genetic privacy (57%) . No demoParticipants strongly preferred the return of incidental genetic findings (90% favoured) and wanted this information included in PICFs (87% favoured)  Figure. Both quThis is the first study to report the attitudes of healthy volunteers to genetic testing in phase 1 clinical trials. These attitudes are important to understand given the rise of genetic testing in early drug development and the unique ethical and practical aspects of testing healthy volunteers who typically have few interactions with healthcare providers. Here, the focus was the genetic testing issues previously explored in limited studies with patients and the public – genetic security, genetic privacy, and the return of incidental genetic findings.There was relative indifference towards genetic security, with healthy volunteers ranking the importance of this topic lower than for other personal information. Indeed, mobile phone number and email address are provided to the clinical trial site during screening, so for genetic security to rank lower than these two pieces of information emphasises the relative indifference to the topic. This is similar to limited data in patients, with only 35% of potential biobank participants and just one patient in a focus group of 15 with epilepsy expressing concerns about genetic security. Additionally, a survey assessing the views of 57 patients to pharmacogenomic testing showed that approximately 40% expressed concerns about privacy. When asked whether they would trade privacy for re-identifiability in the future, respondents to our survey overwhelmingly favoured re-identifiability of their genetic information. Taken together, these findings support clinical research governance that de-identifies genetic information to maintain privacy, but with mechanisms in place to re-identify that information if asked by clinical trial participants.Regarding genetic privacy, healthy volunteers also held mixed opinions, with just over half considering it “important” or “very important”. This is comparable to a survey of 4659 US adults from the public, in which 44% would protect genetic test results, whereas the others were happy to share their genetic data with researchers. Studies of the public indicate the same (4961 participants), with “treatability” being an important consideration and with 98% in favour of findings being returned for “a serious disease that is life threatening but could be prevented”. Together with the results in healthy volunteers, these data suggest that everyone should be asked about their wishes for the return of incidental genetic findings when being screened for clinical trials.The healthy volunteers wanted incidental genetic findings returned to them, and this applied to a range of hypothetical clinical scenarios with varying degrees of disease risk. Older participants and those with more clinical trial experience accepted higher disease risk before triggering the return of such information. Exactly why these two groups indicated higher disease risk is unclear, but it may be simply related to life experience and greater acceptance of the unknown. The majority of oncology patients in a previous study also favoured the return of incidental findings, even if they constituted “bad news”, such as high risk of developing an untreatable cancer.There are several limitations of the study. First, it was conducted at one site, so care is required in extrapolating the results to other phase I clinical units with significantly different volunteer demographics. Second, the survey was offered opportunistically to all people in the waiting room, independent of the type of clinical trial screening. It is possible that attitudes to genetic testing are different between healthy volunteers being screened for clinical trials with genetic testing and those being screened for clinical trials without genetic testing. Third, the study did not assess baseline “genetic literacy”, so misunderstanding of questions is possible, particularly the conceptually more difficult questions related to disease risks. Lastly, the study was not designed to capture the nuances of genetic testing because the topic is too broad and complex to be condensed into a short survey e.g., the specifics of testing.In conclusion, healthy volunteers who are screening for phase 1 clinical trials have mixed views about the importance of genetic security and genetic privacy, but they strongly favour the return of incidental genetic findings that could affect their health. These issues should be discussed with potential participants during informed consent for phase 1 clinical trials with genetic testing.https://doi.org/10.6084/m9.figshare.14204507.v1.Figshare: Attitudes to Genetic Testing in Phase 1_Survey Data.https://doi.org/10.6084/m9.figshare.14204516.v1.Figshare: Untitled Attitudes to Genetic Testing in Phase 1_Amalgamated Survey Data.https://doi.org/10.6084/m9.figshare.14182898.v1.Figshare: Attitudes to genetic testing in phase 1 survey questions.-A supplementary figure showing all the survey questions used in the study.This project contains the following extended data:Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of the Healthy participants were selected to complete an unpiloted and non-validated questionnaire which investigated attitudes to genetic information security, privacy and the return of incidental genetic findings. Ethics permission was provided and informed consent was noted. This is a descriptive study and the data is presented appropriately. The Lickert scale data is converted into a form which can be statistically compared. The findings are clearly summarised. There is a conclusion based on the data that the return of incidental genetic findings should be addressed. The limitations of the study are stated.A sample size of 300 participants had been calculated, however the final number reported on was 275. Could the authors please clarify this point?Ethnicity is addressed but seems not to have much of a role in the answering of the research questions. In the abstract the reader is left questioning the other ethnicities after only North/Western European ethnicity is mentioned. In Table 1 the ethnicity of only 84 participants appears to have been reported. I would suggest that the authors state that ethnicity, for this study, was not addressed.  This study raises further questions for future studies. Queries:Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Healthcare research with a special interest in rare and congenital disorders.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors address important aspects of genetic testing, namely participant concerns regarding data security, privacy and incidental findings. As most participants were found to favour being informed of incidental findings, this should influence future consent options for study participants. The number of studies involving genetic testing is increasing, with this fundamental component of the ethics process critically important. The article is well written, with the study, survey design, and analysis very clearly and appropriately described. The literature cites the current national guidelines and standards, in addition to relevant publications in the manuscript focusing on questions addressed in this field. All data was made available ensuring reproducibility. The conclusions draw from the data are adequately supported.Minor changes: Page 4 - The sentence under Demographic stating, “There were 275 respondents that completed both surveys…” could be misunderstood as some respondents completed two surveys. This could be altered to “There was a total of 275 respondents that completed the first (189) or second (85) iteration of the survey”.  There appears to be an inconsistency with the previous paragraph under ‘Data Analysis’ that says after analysis of the first 190 surveys, a second iteration was created. Were 190 survey of the first iteration started and only 189 completed?Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Genetics, neuroscience, cell biology, major depressive disorder and pharmacogenomics.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.