Manihot esculenta Crantz) varieties is indispensable to genetic improvement efforts. In this study, we present a genetic analysis of 547 samples of cassava grown by 192 smallholder farmers, which were sampled at random within four districts in Uganda. We genotyped these samples at 287,952 single nucleotide polymorphisms using genotyping‐by‐sequencing and co‐analyzed them with 349 cassava samples from the national breeding program in Uganda. The samples collected from smallholders consisted of 86 genetically unique varieties, as assessed using a genetic distance‐based approach. Of these varieties, most were cultivated in only one district , and only three were cultivated across all districts. The genetic differentiation we observed among farming districts in Uganda (mean fixation index [FST] = .003) is similar to divergence observed within other countries. Despite the fact that none of the breeding lines were directly observed in farmer fields, genetic divergence between the populations was low (FST = .020). Interestingly, we detected the presence of introgressions from the wild relative M. glaziovii Müll. Arg. on chromosomes 1 and 4, which implies ancestry with cassava breeding lines. Given the apparently similar pool of alleles in the breeding germplasm, it is likely that breeders have the raw genetic material they require to match the farmer‐preferred trait combinations necessary for adoption. Our study highlights the importance of understanding the genetic makeup of cassava currently grown by smallholder farmers and relative to that of plant breeding germplasm.Understanding the genetic relationships among farmer‐preferred cassava ( GBSgenotyping‐by‐sequencingIBSidentity‐by‐stateIRBInstitutional Review BoardLDlinkage disequilibriumMAFminor allele frequencyNaCRRINational Crops Resources Research InstitutePCprincipal componentPCAprincipal component analysisSNPsingle nucleotide polymorphism1Manihot esculenta Crantz) is an important source of food to ∼800 million people globally  adequate genetic variation for the trait of interest is available, (b) the trait of interest is heritable, and (c) the trait can be efficiently and effectively assessed to enable selection decisions , Namulonge, Uganda.22.1Cassava mosaic virus and Cassava brown streak virus, respectively  in Uganda . These districts were selected because they are associated with high cassava production and consumption UBOS, . Also, t2.2ApeKI restriction enzyme following Hamblin and Rabbi (2 > 20), and loci with >80% missing data were removed  per district of sample origin. From this point onwards, we refer to this collection of representative samples as the set of unique varieties in each district.Based on the clustering results, we determined a threshold of Ward's distance that could separate biologically replicated samples into distinct clonal groups of genotypes. The chosen threshold was subsequently used in downstream analyses to declare which varieties were distinct. The selected threshold was applied to a distance matrix of a dataset including 547 samples from farmer‐varieties, 349 breeding lines, and 72 biological replicates using the –indep‐pairwise with a window size of 50, step size of 10, pairwise linkage disequilibrium (LD) r2 threshold of .3, and minor allele frequency (MAF) < .01. With the LD‐pruned dataset , we ran the ADMIXTURE program with the ancestral population number (K) varying from 1 to 18 to determine the optimal K based on the lowest program‐reported, fivefold cross‐validation error rate. The ADMIXTURE results for the optimal K value were compared with the IBS‐based set of genetically unique varieties. We used this to verify that putative identical varieties had approximately the same ancestry proportions.As a complement to our hierarchical clustering approach to identify genetically unique varieties from the 547 samples collected from farmer varieties, we ran the ADMIXTURE model  as implemented in vcftools  is represented by one sample per district in which that variety was found. Prior to FST computation, we removed SNPs with MAF < .01. We also used principal component analysis  to reduce patterns of genetic relatedness in our dataset to a few dimensions that could be visually examined. Before any PCA analysis, we filtered SNPs with MAF < .01 and also removed monomorphic SNPs. In order to observe trends in diversity across the genome, we used the vcftools function –window‐pi  or through its FTP  in a file named Iragaba GBS.vcf.gz.The imputed SNP genotypic data obtained from 968 samples used in this study are available on Cassavabase website 33.1In total, we successfully genotyped 547 leaf samples collected from different cassava plants grown by 192 smallholder farmers. Collectively, this translated to 156, 139, 137, and 115 samples that were sourced from farmers’ fields in Kibaale, Arua, Masindi, and Apac, respectively (Table n = 65) were only found in a single district: 30 in Kibaale, 19 in Masindi, 13 in Arua, and three in Apac. Of the remaining 21 unique varieties, only three were present in all four districts, six were present in at least three districts, and 12 were present in at least two districts . Similar to farmer‐reported variety names, we found an average of 2.3 genetically unique varieties cultivated per farmer in Apac, whereas an average of 2.6–3.3 distinct varieties were cultivated per farmer in the other three districts (Table n = 60); only 14 varieties were observed >10 times .A Ward's distance threshold of 0.075 clearly grouped biological replicates together and distinctly separated the five known genotypes—UG110017 (NAROCASS 1), UG110004 (NASE 4), UG110014 (NASE 14), UG110015 (TME‐14), and UGL15228 (Lugigana)—from each other . After applying this threshold to the 547 experimental samples collected from farmers, we identified a total of 86 unique varieties. Most of these unique varieties . We observed that these 547 samples with the same proportion of ancestry were almost always identified to be in the same clonal group derived from the Ward's threshold  in Clonal Group 3 had ∼100% of their proportion derived from Ancestry 11. Similarly, all samples (n = 38) belonging to Clonal Group 355 were entirely derived from Ancestry 8 .To complement IBS results, we used ADMIXTURE analysis on the 547 samples at 3.2FST < .05 for all pairwise comparisons (Table −4) and lowest in Masindi (1.01 × 10−4)  Table .3.3Based on farmer‐reported naming, 156 unique varieties were reported . Variety names ‘Gwalanda’, ‘Bukalasa’, ‘Bao’, and ‘Longe’ were the most common, accounting for 22.5% of the samples collected . Overall, farmer‐reported variety names did not reliably correspond to genetically unique varieties as empirically revealed by SNP markers Figure . For exan = 12) clonal groups  variety names reported by farmers during the survey  between farmers’ varieties and breeding lines. The mean nucleotide diversity among breeding lines (1.08 × 10−4) was higher than that of farmer varieties (1.03 × 10−4) . We also conducted a PCA to visualize how farmers’ varieties related to breeding lines. In the genetic space described by the first four PCs, the farmer varieties are largely a subset of the breeding lines  Figure . The meaM. glaziovii introgressions in both the farmer and breeding lines, based on available introgression diagnostic markers  genetic differentiation among cassava varieties grown in different districts of Uganda, the varieties cultivated across different districts are often distinct genetically. Of the 547 samples collected from Ugandan farmers, there were 86 genetically unique varieties. Of these unique varieties, most of them were cultivated in only one single district , whereas only three were cultivated across all the four districts. However, these unique varieties are likely to be close relatives, given the observed levels of genetic differentiation between districts.Comparing genetic relationships among varieties adopted by farmers with those of breeding lines is important in developing new varieties that best meet the needs and preferences of the end users. This study revealed that despite the low (FST = .003) is similar to that observed between the two breeding programs based within Nigeria  and lower than observed levels of differentiation between, for example, East and West Africa . The implication of this result is that breeders should not solely rely on the farmer‐given variety name in variety identification studies. This is in agreement with previous studies, which have also reported a large discrepancy between genetically unique varieties and the variety names assigned by farmers  hybrid between an M. glaziovii and an M. esculenta parent. The passport data we collected from the farmer indicate that it had very bitter roots and leaves relative to other cassava varieties and that it was mainly used as a border row to deter thieves and animals from the main crop. This kind of information highlights the multiple functions of cassava varieties grown by farmers and the value of genetic surveys of farmer‐preferred varieties.Chromosomes 1 and 4 appeared notably more diverse among the breeding lines compared with farmer varieties Figure . Based ie et al.  revealede et al. . InteresOverall, findings from this study indicate that most smallholder farmers cultivated more than one type of variety, a result comparable with what was observed in Ghana (Rabbi et al., 5M. glaziovii introgressions on chromosomes 1 and 4 implies ancestry with cassava breeding lines. Given the apparently similar pool of alleles in the breeding germplasm, it is likely that breeders have the raw genetic material they require to match the farmer‐preferred trait combinations necessary for adoption. Our study highlights the importance of understanding the genetic makeup of cassava currently grown by smallholder farmers and relative to that of plant breeding germplasm.In this study, cassava leaf samples collected from 547 different cultivated plants grown by smallholder farmers within four districts in Uganda were genotyped with the major objective of understanding the genetic relationship among varieties grown by the farmers. We also explored the genetic relationship between these surveyed farmer varieties and breeding lines used at NaCRRI. We found that most farmers in Uganda grow two or three distinct cassava varieties and that each sampled district in Uganda contains several varieties not grown in other districts. The overall level of genetic differentiation between districts is relatively low, as is the divergence between farmer and breeding populations. Despite the fact that none of the breeding lines were directly observed in farmer fields, the presence of The authors declare that they have no conflict of interest.SUPPORTING MATERIALClick here for additional data file.SUPPORTING MATERIALClick here for additional data file.SUPPORTING MATERIALClick here for additional data file.SUPPORTING MATERIALClick here for additional data file.SUPPORTING MATERIALClick here for additional data file.SUPPORTING MATERIALClick here for additional data file.


Ailurus fulgens) populations with respect to landscape connectivity in Kangchenjunga Landscape (KL), India. The study found about 1,309.54 km2 area suitable for red panda in KL—India, of which 62.21% area fell under the Protected Area network. We identified 24 unique individuals from 234 feces collected at nine microsatellite loci. The spatially explicit and non-explicit Bayesian clustering algorithms evident to exhibit population structuring and supported red panda populations to exist in meta-population frame work. In concurrence to the habitat suitability and landscape connectivity models, gene flow results supported a contemporary asymmetric movement of red panda by connecting KL—India in a crescent arc. We demonstrate the structural-operational connectivity of corridors in KL—India that facilitated red panda movement in the past. We also seek for cooperation in Nepal, Bhutan and China to aid in preparing for a comprehensive monitoring plan for the long-term conservation and management of red panda in trans-boundary landscapes.Wildlife management in rapid changing landscapes requires critical planning through cross cutting networks, and understanding of landscape features, often affected by the anthropogenic activities. The present study demonstrates fine-scale spatial patterns of genetic variation and contemporary gene flow of red panda ( Landscape connectivity demonstrates feasibility for wildlife to move through fragmented habitats and therefore maintaining corridors in fragmented landscapes are vital to ensure natural gene flow and the long-term survival of the species3. Further, heterogeneity and rapid changes imposed in the landscape often accelerate restriction in the species movement between suitable patches5. This restricted movement may lead to genetic consequences including disruption of gene flow, inflation of inbreeding and loss of rare alleles supporting local adaptation and genetic fitness7. This phenomenon may induce multifaceted challenges in small populations inhabiting in trans-boundary landscapes (TBL). Prioritizing species conservation across TBL is challenging due to differences in the national interest, policies, local communities, funds allocation and political will8.Habitat mapping and modelling corridors across species distribution are cardinal for prioritization of conservation strategies9. The red panda (Ailurus fulgens), a magnificent iconic species of this landscape, is endemic to temperate conifer and cool broadleaf forest with dense bamboo undergrowth of preferring altitude range 2,300 to 4,000 m of Central and Eastern Himalayan biotic province12. Red Panda was taxonomically classified to occur in two subspecies based on the morphology and distribution—Ailurus fulgens fulgens distributed in Nepal, India, Bhutan, Myanmar, and China (Tibet and western Yunnan province) and Ailurus fulgens styani, occurred from the Sichuan and Yunnan provinces of China and Nujiang river was believed to be a biogeographic barrier for the separation of two subspecies14. However, a recent study by Hu et al.15 demonstrated the presence of two phylogenetic species of red panda, i.e. the Himalayan red panda (Ailurus fulgens) and Chinese red panda (Ailurus styani), with high depth sequencing data and revealed that the Yalu Zangbu river might be the potential boundary for species divergence. Further, Himalayan red panda reported to be distributed in India, Nepal, Bhutan, northern Myanmar, and Tibet and western Yunnan Province of China, while the Chinese red panda distributed in the Yunnan and Sichuan provinces of China15. The anthropogenic factors including habitat loss, poaching for pelt, jhoom cultivation and conversion of forest to non-forest land use have caused rapid decline of red panda16. Nearly 50% of red panda habitat has been lost in the last three generations, bringing it as ‘Endangered’ in the Red list category12. In India, the red panda is protected under Schedule-I of the Wildlife (Protection) Act, 1972 and most of its populations reside in small and isolated protected areas (< 500 sq. km), thereby increasing high risk of local extirpation of red pandas due to genetic inbreeding and loss of heterozygosity19. Earlier studies available on red panda from KL—India have addressed population status, distribution, and abundance21, habitat preferences and diet composition23. However, with the emergence of landscape genetics, it is now feasible to explicitly quantify the effects of landscape features on the spatial patterns of genetic variation, population structure, gene flow, and adaptation26. Thus, population genetics integrated with landscape ecology and remote sensing data can be used to aid delineating shift, if any, in the identified corridors that maintain connectivity between habitat patches and facilitate biotic processes such as dispersal and gene flow30. In this view, the detailed population genetic assessment of red panda with respect to landscape connectivity and anthropogenic activities is imperative to prioritize the management strategies for ensuring long term population viability of red panda in Himalayas. The present study is aimed to address the fine-scale spatial patterns of genetic differentiation and gene flow among the habitat clusters supporting red panda population in KL—India.The Kangchenjunga Landscape (KL) is one of the six TBL in the Hindu Kush Himalayan region, sharing boundary among Nepal, India and BhutanForty-eight candidate maxent models were generated Table  and mode2 (12.78%), comprised of 1,097.26 km2 in Sikkim and 212.28 km2 in North West Bengal as suitable habitat for red panda with largest suitable habitats in KNP (493.37 km2) (Table The study covered 10 protected areas (PAs) that included Singalila National Park (SNP), Senchal Wildlife Sanctuary (SWLS), and Neora valley National Park (NVNP) located in Northern West Bengal and Barsey Rhododendron Sanctuary (BRS), Maenam Wildlife Sanctuary (MWLS), FambongLho Wildlife Sanctuary (FLWLS), Pangolakha Wildlife Sanctuary (PWLS), Kangchenjunga National Park (KNP), Shingba Rhododendron Sanctuary (SRS), and Kyongnosla Alpine Sanctuary (KAS) located in Sikkim State of India. Our results predicted a total of 1,309.54 kmThe ensemble approach by combining both the genetic divergence and environmental conductance depicted the landscape connectivity and its functionality to support movement corridors of red panda in KL—India. The predicted genetic divergence among the identified clusters was in coherence to the habitat suitability model Fig. b,c. The IS) —6 ± 0.58, observed heterozygosity (HO)—0.493 ± 0.06 and expected heterozygosity (HE) 0.664 ± 0.027  ranged from 0 to 0.294 while we did not observe any significant amount of False allele (FA) Table . Four loS) Table . Among 2The STRUCTURE analysis detected three clusters based on the mean ln P (k) and Delta K Fig. , Fig. S3ST based gene flow revealed SNP and NVNP populations were highly differentiated (FST 0.352) and NVNP and ES did not qualify to be separate populations (FST − 0.052) . Though Fst value was not statistically significant but observed signal was an indication of the red panda population to exist in metapopulation framework. Pairwise F052) Fig .The contemporary migration assessed by the BAYESASS resulted to favour asymmetric movement of red panda from West to East in KL—India Fig. . We dete32 which must have deteriorated connecting corridors in this landscape. For habitat suitability, predictors such as precipitation, temperature and vegetation with high weightage appeared reasonably correct as vegetation cover, and climatic factors are directly linked to the species diet, survival and reproductive necessities. A few earlier studies addressed the species distribution modelling (SDM) of Red panda in Himalaya35. These studies considered different variables like elevation, slope, aspect and distance to water etc. for predicting red panda habitat. These variables may facilitate in old growth forest dominated by Betula utilis, Rhododendron spp. and Abies spp. with dense bamboo cover in the understory and high densities of fallen logs and tree stumps at ground level. Further, the growth of bamboo understory in the temperate forests is highly influenced by the rainfall and temperature36 and red pandas as being the canopy-dwelling species prefer temperate forests in the central and eastern Himalayas with dense bamboo undergrowth. Therefore, rainfall, temperature and vegetation cover played a significant role in predicting red panda habitat.The red panda population in KL has experienced massive habitat loss and fragmentation in the past11 estimated 1,700 km2 potential habitat of red panda in Sikkim and 200 km2 in North West Bengal by undertaking exploratory field surveys based on one time efforts. Ziegler37 estimated 684 km2 suitable forest for red panda in Sikkim with limited efforts confining to PWLS only. However, Ghose and Dutta38 estimated 1,200 km2 suitable forest for red panda in Sikkim and 250 km2 in North West Bengal. These authors collected primary data by undertaking field surveys through transect and trail monitoring39 from Sikkim and North West Bengal and also recorded indirect evidences such as droppings, browsing marks, nest sites, pugmarks, skins or pelts, etc. Ghose and Dutta38 also collected secondary data through questionnaires and public interviews in the villages around the potential red panda habitats. Our field surveys and efforts were relatively comparable to Ghose and Dutta38 and we obtained an estimated suitable habitat 1,097.26 km2 in Sikkim and 212.28 km2 in North West Bengal, which represented a relative decline the habitat suitability in last ten years when compare to Ghose and Dutta38. In northern region, red panda habitat was relatively fragmented and circuitscape also supported lack of horizontal connectivity in KL—India. However, the identified corridor favoured red panda movement from West to East in KL India, forming a crescent arc , when compared to other red panda populations i.e. HE = 0.71940 and HE = 0.77241. The observed low genetic diversity of the Himalayan red panda might be due to historical bottlenecks15. The observed loss of suitable habitat by the emerged anthropogenic activities in last few decades might have disrupted the contiguous gene flow and confined red panda in isolated patches, also a contributing factor for inbreeding and loss of heterozygosity in KL—India.Red panda population of KL—India being a trans-boundary population, must be carrying ancestral genetic attributes shared with Nepal and Bhutan populations. The genetic assessment suggested a relatively low genetic diversity in red panda population of KL—India (HST = 0.352). These populations earlier reported to exist in relatively high density . Individuals from these populations showed asymmetric gene flow and contributed migration to Sikkim population from either-side. The WS population was rationally distinct from SNP (FST 0.142), however ES and NVNP, to a large extent represent a single population based on the multiple evidences, e.g. a negative FST − 0.052 and a high migration rate from one another population  is fascinating to evaluate fine scale population genetic structure and investigating loci under natural selection facilitating populations to adapt in the changing climatic conditions50. However, to check the immediate effects of landscape features on the genetic variability and population contiguity, the assessment of wild populations using microsatellites is still most cost effective and widely applied way to genetic monitor of free ranging populations51. Further, consolidating landscape connectivity through mapping corridors, validating movement through genetics and expanding natural PAs is fundamental to make red panda conservation a long-term success across the distribution range. We also seek for the possible collaboration in Nepal, Bhutan and China to aid in preparing a comprehensive monitoring plan for red panda conservation in TBL and to evaluate the ongoing conservation efforts across the political boundaries.Further, studying wildlife using genome-wide markers , Senchal Wildlife Sanctuary (SWLS), and Neora valley National Park (NVNP) located in North West Bengal and Barsey Rhododendron Sanctuary (BRS), Maenam Wildlife Sanctuary (MWLS), FambongLho Wildlife Sanctuary (FLWLS), Pangolakha Wildlife Sanctuary (PWLS), Kanchenjunga National Park (KNP), Shingba Rhododendron Sanctuary (SRS), and Kyongnosla Alpine Sanctuary (KAS) located in Sikkim State of India. Together these PAs, although most of them are fairly small in size, contribute approximately 3,112.21 km2 area . The habitat types ranging from tropical, subtropical, warm temperate, cool temperate, subalpine, and alpine forest types20, Buckland et al.39 and Joshi et al.54. Further, it was not logistically feasible to cover the entire area systematically due to the rugged terrain, unpredictable weather and poor resources availability. Hence, representative sampling was carried out in KL—India that represented 109 spatially distinct locations, 12 locations from camera traps, seven direct sightings 12 presence locations from reliable interviews and 78 distinct sites where we collected 234 faecal samples of red panda. For model building, we used 56 spatially independent locations after correlation testing among the location following Kramer-Schadt55. Informed consent was obtained from all participants who responded to the questionnaire interviews and all methods including human participation in the study was in accordance to the relevant guidelines and regulations of Zoological Survey of India (ZSI), Kolkata. All experimental protocols and methods were approved by the Research and Academic committee (RAC) of ZSI, Kolkata that has a ethics committee/institutional review board. All field surveys and sampling were conducted with taking necessary permits issued by the Forest Department of West Bengal and Sikkim.Firstly, we stratified the study area based on the forest types, topography to cover all the logistically possible habitat patches in KL—India. Secondly, we adopted a landscape approach, instead of prioritizing PAs for sampling and we aimed to cover maximum reported habitats of red panda in KL—India. Thirdly, for recording the species presence, we adopted a three-pronged approach i.e. transects/trail surveys, questionnaire surveys, and also the remote camera traps following Pradhan et al.56 while, any significant change in climatic isotherm to a species having special requirements might result in local extirpation or shifting the species to other ranges47. Therefore, it is imperative to use variables for SDM which define the likely habitat of a species based on the field observation. Considering these facts, we selected 26 variables out of which 19 variables represented climatic isotherm, four-topography and three variables represented land covers. These variables were also related to the habitat selection of red panda57. The selected variables represented the present environmental conditions and habitat covariates to facilitate red panda distribution patterns in KL—India. The biotic predictors (19) were obtained from the WorldClim data base at 30 arc second scale58; https://www.worldclim.org/ (Table https://earthexplorer.usgs.gov/). The forest canopy was classified into four categories viz., very dense, moderately dense, open forest and scrubland59. Moreover, considering the fact that the species is a canopy dweller11 the Canopy height data was obtained from Oak Ridge National Laboratory (https://webmap.ornl.gov/ogc) for understanding the influence on the species habitat suitability. Since, different variables like climate, landscape, topography and anthropogenic influences used in present study were compiled from various resources present in different resolution scales. Hence, to bring uniformity in the selected data, we re-sampled all raster on the same resolution scale (1 km resolution).Species are sensitive to habitats as well as the climatic isothermsg/ Table  and Bioc62. Bias file was prepared in ENMeval; R package63 following the methodology suggested by Dudik et al.64. We omitted highly correlated variables with a threshold of 0.866. Although we selected initially 26 variables but final model was predicted using 19 variables which passed on the multicollinearity test67 and other related variables with a threshold of 0.8 were excluded to get rid of the over fit model  values were used to determine the best fit model with the lowest values in ENMeval. The resultant habitat was classified into suitable, with probabilities 0.584–1 and unsuitable below 0.584 based on the 10-percentile training presence thresholds following Radosavljevic and Anderson70  that compensates for the shortcomings of kappa while keeping all of its advantages following Allouche et al.72 We generated a raster surface of genetic divergence using Genetic landscape GIS Toolbox Ver. 10.173 by generating the inverse distance weighted interpolation within the study area boundary74. To evaluate the operational-structural connectivity by predicted movement corridors of red panda in KL—India, we used an ensemble approach by combining both the genetic divergence and environmental conductance (SDM output) in the final circuit model following Mateo-Sánchez et al.75 and Roffler et al.76 in Circuitscape 4.077.We employed maximum entropy algorithm for species distribution modelling (MaxEnt 3.4.1), using presence data onlyel Table 66,68. Fo70 Table . The Jac78 and CRP357, CRP385, CRP381, CRP367, CRP409, CRP240 and CRP260 from Yang79 were amplified into two multiplex PCRs. Forward primer of all nine microsatellite loci was fluorescently labelled at 5′ with one of the four dyes, FAM, VIC, NED and PET  and false allele (FA) error rates were quantified using PEDANT version 1.0 involving 10,000 search steps for enumeration of per allele error rates81. To avoid ambiguity in ascertaining unique genotypes, we limited the number of loci used based on their high success rate (> 90%), presence of no or minimum genotyping errors and exhibiting an informative PID value . The locus wise and cumulative probability of identity for unrelated individuals (PID) and siblings (PID sibs) were calculated following identity analysis module in GenAlEx version 682.We genotyped each sample four times to minimize genotyping errors and a heterozygote was ascertained only if there were different alleles in at least three independent attempts. In addition, we also followed an independent allele scoring method where two different researchers perform allele scoring individually and only the consensus genotypes were used for further analysis. The genotyping errors arising due to null allele and the presence of stutters, scoring errors were assessed using MICRO CHECKER 2.2.282. For the Hardy–Weinberg equilibrium (HWE) test, we followed the probability test approach using the program GENEPOP version 4.2.183. Wright’s inbreeding coefficient (FIS) was estimated following Weir & Cockerham84 using GENEPOP83. Linkage disequilibrium (LD) was tested using GENEPOP83 to determine the extent of distortion from independent segregation of loci following 10,000 dememorizations, 500 batches and 10,000 iterations per batch after Bonferroni correction85.The genetic diversity estimates were accounted by calculating the number of allele (Na), observed (Ho) and expected (He) heterozygosity using GENALEX 686 to estimate the proportions of the total genetic variation, arose from, within and between populations using AMOVA. Among different Bayesian clustering methods, individuals were assigned exclusively on the basis of their multi-locus genotypes (e.g. STRUCTURE), and also using the both, multi-locus genotypes and geo-referenced information (e.g. GENELAND). Non-Bayesian multivariate ordination analyses, i.e. discriminant analysis of principle components (DAPC) and spatial principle component analysis (sPCA) were also used, to compare population assignment with the Bayesian clustering outputs87 since they were not based on any model assumptions.We attempted three different clustering methods to capture the most possible population genetic structure of red panda in KL—India i.e. Fst based Analysis of Molecular Variance (AMOVA), explicit Bayesian and non-Bayesian clustering algorithms. We used Arlequin 3.5.2.188 was used to determine the number of genetic clusters (K) following 20 iterations  with NOPRIOR with admixed and correlated allele frequencies. We considered there were K populations (1 to 10), with repeating each analysis for 10 times at each K value. The most probable cluster was calculated via estimating the distribution of Delta K89 using STRUCTURE HARVESTER v.0.6890. GENELAND v 4.0.391 was run through an extension of R v.3.0.1 with the correlated allele frequency and spatial uncertainty model. We allowed K to vary between 1 and 10 following 20 independent runs, each with 100,000 iterations, and a thinning of 1,000. The inferred spatial clusters were georeferenced in ArcGIS 10.6. Two non-Bayesian clustering methods i.e., DAPC and sPCA, were run to assign the possible clusters in Adegenet v1.3.4 package of R92.In Bayesian analysis, STRUCTURE 2.3.4 software93 among the different sub-populations of red panda in KL—India using Arlequin v 3.5.286. The contemporary and asymmetric migration rate in last two generations were estimated using BayesAss 1.343. We used 9 × 106 iterations, with a burn-in of 106 iterations, 1,000 number of permutations and a sampling frequency of 2000 to ensure that the model's starting parameters were sufficiently randomized. We also estimated the first-generation migrants between all pairs of subpopulations of red panda in KL—India using GENECLASS 2.094, an approach described by Paetkau95 for likelihood computation (Lhome/Lmax), with 1,000 simulations at an assignment threshold  of 0.01 and 0.05.We assessed Fst based gene flow in last 150–200 yearsSupplementary Information


Brachypodium distachyon (Brachypodium) is a non-domesticated model grass species that can be used to test if variation in genetic sequence or methylation are linked to environmental differences. To assess this, we collected seeds from 12 sites within five climatically distinct regions of Turkey. Seeds from each region were grown under standardized growth conditions in the UK to preserve methylated sequence variation. At six weeks following germination, leaves were sampled and assessed for genomic and DNA methylation variation. In a follow-up experiment, phenomic approaches were used to describe plant growth and drought responses. Genome sequencing and population structure analysis suggested three ancestral clusters across the Mediterranean, two of which were geographically separated in Turkey into coastal and central subpopulations. Phenotypic analyses showed that the coastal subpopulation tended to exhibit relatively delayed flowering and the central, increased drought tolerance as indicated by reduced yellowing. Genome-wide methylation analyses in GpC, CHG and CHH contexts also showed variation which aligned with the separation into coastal and central subpopulations. The climate niche modelling of both subpopulations showed a significant influence from the “Precipitation in the Driest Quarter” on the central subpopulation and “Temperature of the Coldest Month” on the coastal subpopulation. Our work demonstrates genetic diversity and variation in DNA methylation in Turkish accessions of Brachypodium that may be associated with climate variables and the molecular basis of which will feature in ongoing analyses. Brachypodium distachyon (hereafter Brachypodium) is a well-established grass model species, to be found mostly in countries bordering the Mediterranean. With its relatively small (~270 Mb) [Arabidopsis thaliana (hereafter Arabidopsis) [~270 Mb)  nuclear idopsis) . The modidopsis)  and flowidopsis) . Brachypidopsis) , salt an+) and Spanish (S+) accessions and identified 3,933,264 high-confidence SNPs. Phenotypically, the populations could be split between Extremely Delayed Flowering (EDF+) phenotype, which was most common in the T+ populations, and those where flowering was more rapid [Although ecologically important, until recently, the geographical variation of Brachypodium has been relatively poorly characterized . Genome-re rapid . This geRecently, increasing attention has focused on the possible contribution of DNA methylation, as a component of the epigenomic variation in response to environmental adaptation. DNA methylation forms distinct patterns on cytosines; 5′C-phosphate-G3′ (CpG), CHG, and CHH contexts (where H is any nucleotide except for G)  which toLer cross, phenotypic differences, e.g., flowering time, were linked to patterns of DNA methylation [Arabidopsis has proven to be especially useful in examining epigenetic variation related to the environment. A study of 150 Swedish Arabidopsis accessions demonstrated considerable epigenomic variation, particularly around transposable elements (TEs), when Arabidopsis was grown at 10 °C or 16 °C . When thhylation . Other ahylation . In Brachylation . A recenhylation . Considehylation .In this current study, we adopted a different strategy to assess the variation in genomic sequences, DNA methylation and phenotypes in Brachypodium. Thus, we established a new bespoke collection of 55 Brachypodium accessions from Turkey, a center of Brachypodium diversification . The samTo establish if genomic variation can be linked to climatic variables, a new collection of Brachypodium accessions was required where seed sampling reflected the different prevailing conditions. Köppen climate classifications can be used to divide Turkey into seven different climatic environments . These w+ and the EDF+ previously defined by Gordon et al. [+. Surprisingly, accessions 2_20_16, 2_14_15 and 2_15_20 belonged to the S+ cluster, which otherwise comprises only accessions from Spain and France  and Slovenia (ABR9) . From thn et al.   also exhibited methylomic variation which placed them in the same coastal subpopulations. Further, in all contexts, the S+ accessions 2_20_16, 2_14_15 and 2_15_20 in our collection had distinctive features of their methylome compared to the other Turkish accessions. Therefore, genetic and methylomic variation in the Turkish accessions were closely aligned.We next assessed how the methylome could also vary across the Turkish regions that were sampled. DNA extracted from sample Tn = 8) from each accession were sown and at two weeks after sowing were vernalized at 4 °C for six weeks. After two weeks of growth at 22 °C (n = 4) plants of each accession were exposed to drought targeting 15% soil water content (SWC) over a period of 12 days. We had previously shown that this level of SWC was sufficient to impose drought stress on a diversity collection of Brachypodium accessions [n = 4) control plants were watered as normal. RGB images were obtained for plants and assessed for height and area as estimated from side view images (p < 0.05) by drought treatment (p = 0.92) were observed between the different regions or the previously defined population groups.Computerized image analysis approaches were employed to assess phenotypic variation in the sampling sites. Seeds   was significantly less in plants from region 1a and 1c as compared to plants from other regions (< 0.001) C. In our< 0.001) D. This apulation . Too manp = 0.01) and ‘I’ (p = 0.01). Phenotypic, genetic and methylation analyses indicated that vernalization and drought tolerance could differentiate between the coastal and central subpopulation. Given this, we tested a series of environmental variables (“Bioclim”) to see if they aligned with the distribution patterns of the subpopulations. This analysis was based on the derivation of Maxent  climate Laguncularia racemosa) epigenetic patterns can emerge in the absence of major genetic variation and these could be maintained for at least 20 years [In this study we used Brachypodium to examine how genetic and methylation variation could reflect climatic variation across Turkey. The natural selection of particular alleles is the foundation of evolutionary thinking, but the potential selective role of epigenetic features also needs to be considered ,30. Ther20 years . Other p20 years ,32. Ther20 years ,33 or wh20 years . It is a20 years ,21.+ and T+ [+ cluster discussed above). In the other regions, 2, 3 and 4 the coastal (belonging to the EDF+ cluster) subpopulation was predominant. A recent study described the derivation of a new inbred population from Turkey to assess SNP and methylome variation . This wo+ and T+  or Subsp+ and T+ . CrucialTo preserve the methylome, seeds gathered from Turkish regions were assessed without undergoing generations of plant growth and meiosis in captivity. We did not use the inbred Turkish lines that are available e.g.,  as epige+ phenotype which appeared to predominate in our coastal subpopulation although there was a significant proportion showing the rapid flowering type. The major differentiation between the two subpopulations could reflect the impact of allopatric separation by a barrier such as a mountain range. Thus, the coastal vs central subpopulation could have arisen from the highland Anatolian plateau which roughly corresponds to the location of the central subpopulation, reducing gene flow. This could be reinforced through localized adaptation to such as stress driving increased tolerance. Equally, reduced gene flow can also arise from a shift in flowering times which would influence the frequency of cross-pollination . To inve+ phenotype. Conversely, the distribution pattern of the central subpopulation was best explained by the “precipitation during driest quarter” Bioclim which would explain its greater degree of drought tolerance. These twin features could be major drivers of the marked genomic differences between the subpopulations. This stated the influence of flowering time as a barrier to gene flow in Brachypodium could be limited given its almost cleistogamous behavior which also results in a high degree of gene homozygosity [Flowering time in Brachypodium is strongly associated with vernalization period  and drouzygosity . Thus, tMore detailed assessments are in progress; however, this current study highlights the importance of sampling strategies based on prevailing environmental conditions in order to better reveal differences between wild populations.0) collection was transferred to Aberystwyth University, UK and three seeds from each accession were germinated under controlled environmental conditions . After six weeks the first three leaves were collected from each accession and frozen in liquid N2 prior to DNA extraction. Single seed inflorescences were sampled from 55 Brachypodium accessions from five distinct Turkish environments. The sites are described in https://phytozome.jgi.doe.gov/). After removing duplications with Sambamba (v.0.6.8) [Brachypodium distachyon synonymous positions in the genome (v.3.1), LD-pruned and filtered for minor allele frequency of 0.05. This generated a final data set of 5021 SNPs. To assess the genetic structure in 55 accessions, a phylogenetic tree was derived using HDClusters in the SNPrelate package (v.1.22.0) [https://www.ncbi.nlm.nih.gov/sra/PRJNA605320.Genomic DNA was isolated from 10–15 mg of leaf tissue using the cetyl trimethylammonium bromide (CTAB) method . Sequencv.0.6.8) , SNPs we.1.22.0) . Populathttp://www.bioinformatics.babraham.ac.uk/projects/trim_galore/, accessed 10 September 2020). Trimmed reads were mapped to the B. distachyon v.3.1 chloroplast and genomic reference sequences  were used. Seeds of the collected accessions were germinated in pots with 50 g of 4:1 Levington F2: grit sand. After two weeks, seedlings were vernalized for a further six weeks, and then the eight-week-old plants were transferred into the plant screening system . The NPPC allows computer regulated watering of each individual plant and watering was withdrawn from four replicates from each genotype to achieve 15% soil water content (SWC) by seven days. This level of SWC was maintained for 12 days; the end of the experiment, the remaining replicates continued to be watered to 75% SWC. Images were captured at 12 days after watering was first restricted using a single-lens reflex camera Nikon D60  with an 18–55 mm lens. For uniform processing results and further analysis, images were processed to generate 24-bit RGB color images where each channel had 256 class color levels. Images were segmented from background in RGB color space. Plant growth parameters and color pixel data were extracted as plant height, top view and side view projection area and color information were extracted from the processed images using C++  and Open Source Computer Vision Library  [To provide sufficient plants for the phenotyping experiments, seeds of second generation   with theDefining variation amongst populations is important to determining likely evolutionary pressures shaping natural selection. In this study, we use the well-established model grass—Brachypodium—to define variation in Turkish populations. Crucially, our sampling strategy was biased towards representing the major climatic regions of Turkey. Variation was characterized at the genetic and methylome levels, to reflect two of the levels at which selection could act. Both genetic and methylome variation suggested two subpopulations which we designated as coastal and central. Phenotypic assessment suggested the subpopulations exhibited, respectively, a preponderance of differential flowering and drought tolerance phenotypes. This aligned with Bioclim models which suggested that late flowering was linked to the cold month—i.e., vernalization—and drought with relative precipitation in the driest month. Therefore, we provide evidence of climate being associated with genetic and methylome variation. Although Turkey has been extensively sampled by others, our environment bias sampling approach has provided important insights into potential drivers of Brachypodium evolution.


As applications of genomic sequencing have expanded, offering genetic counseling support to all patients is arguably no longer practical. Additionally, whether individuals desire and value genetic counseling services for genomic screening is unclear. We offered elective genetic counseling to 5110 individuals prior to undergoing sequencing and 2310 participants who received neutral results to assess demand. A total of 0.2% of the study participants accessed genetic counseling services prior to sequencing, and 0.3% reached out after receiving neutral results. We later conducted 50 interviews with participants to understand why they did not access these services. Many interviewees did not recall the availability of genetic counseling and were unfamiliar with the profession. Interviewees described not needing counseling before sequencing because they understood the study and felt that they could cope with any result. Counseling was considered equally unnecessary after learning neutral results. Although the participants had questions about their results, they did not feel that speaking with a genetic counselor would be helpful. Genomic screening efforts that employ opt-in models of genetic counseling may need to clarify the potential value of genetic counseling support from the outset and feature genetic counseling services more prominently in program materials. The rapid expansion of genomic sequencing has required adaptations to traditional models of in-person genetic counseling to allow genetic specialists to focus their time on the most complex cases ,2,3. InnA low uptake of genetic counseling services is common, not only in clinical and research settings, but also in direct-to-consumer contexts ,9,10,11.Studies examining patient and research participant attitudes about genetic counseling are scant ,12,13,14This study was approved by the Mayo Clinic Institutional Review Board (IRB#: 15-005013).Our inquiry occurred within the Return of Actionable Variants Empirical (RAVE) study, part of a National Institutes of Health (USA) funded consortium that reported pathogenic/likely pathogenic results in 68 disease-related genes and 14 single-nucleotide variants . A totalOf the 2535 individuals who underwent genomic sequencing as part of the RAVE study, 2310 participants received neutral genomic sequencing results in the mail. The mailed materials consisted of a one-page results letter and a laboratory report. The letter reminded participants that they had participated in the RAVE study and that their genetic material had been sequenced. In bold, underlined text in the top third of the letter, they were informed, “Your results: No clinically significant genetic variants were found. No further action on your part is needed at this time.” The letter also described several test limitations and, at the end, invited participants to call a genetic counselor with any questions. The clinical laboratory report presented participants’ sequencing results in more technical language and included several results tables.We recruited participants for our qualitative inquiry from the first 300 participants who received neutral results, which were randomly returned in batches of 50. Consequently, we were limited in terms of the purposive sampling we could conduct. We recruited the first 30 individuals who agreed to participate; for the remaining 20 participants, we oversampled males, younger participants, and individuals whose baseline survey at the time of study enrollment indicated lower genetic knowledge scores . We recrTo assess the demand for genetic counseling following the return of neutral results, genetic counselors completed field notes to document how many recipients reached out to them and the nature of callers’ questions.Qualitative interviews focused on participants’ overall experience in the RAVE study, receiving neutral genomic sequencing results by mail, and their reactions to those results. To gain a better understanding of why so few people accessed genetic counseling services, we asked a series of questions about the study’s offer to speak with a genetic counselor before enrollment and upon receiving results, whether participants were familiar with genetic counseling, and why participants chose not to access genetic counseling services during their study participation.All the telephone interviews were conducted by E.J.S. or A.T.B. Interviews were audio-recorded and transcribed verbatim. Transcripts were reviewed for accuracy and edited accordingly. Transcript review also served as an opportunity for preliminary data analysis. Cleaned transcripts were uploaded into NVivo for data management .We exported all the transcript text that mentioned genetic counseling, genetic counseling services, genetic counselors, or the genetic counseling profession. We included all interview guide questions and probes related to genetic counseling and associated participant responses. Text featuring participant questions of the interviewers were also examined. Three independent coders  participated in codebook development and coding.We began codebook development from 85 pages of transcript excerpts from 19 interviews and transcription analysis notes. We then implemented a three-pronged approach to codebook refinement: first, we applied the codebook to six transcripts taken longitudinally over the course of the study, appreciating the evolution of the interviews over time; second, we applied the modified codebook to five of the most challenging interviews to make sure the codebook was effective in capturing the content of more difficult interviews; third, we applied the codebook to five interviews near the end of the study, three of which were conducted by a second interviewer. Consensus was reached between all three coders at every stage of codebook development. This iterative, comprehensive approach reduced the need for later adjustments to the codebook, as it was applied to the remaining transcripts.All three coders participated in thematic analysis and memo writing . We idenOf the 2310 study participants who received the neutral results packet in the mail, six spoke with a genetic counselor (0.3%). Of the six participants who called, three wanted to discuss their family history of Alzheimer’s disease (a condition not examined as part of the RAVE study), two wanted to confirm that they had understood their results, and one was confused by her results and wanted them explained.We invited, by telephone, 123 RAVE participants, who had been among the first to receive their neutral results by mail, to participate in an interview. Fifty-five individuals agreed to participate; 50 completed the interview see .We spoke to equal numbers of female and male participants who ranged in age from 42 to 71 years. They were predominantly white. All had a high school education and most had some college. All the participants had some form of health insurance, and most reported a comfortable income. The participants offered a number of explanations as to why they did not access elective genetic counseling either before they enrolled in the RAVE study or after receiving neutral genomic sequencing results by mail see .Box 1Participants did not recall that genetic counseling had been offered.Participants were unfamiliar with the genetic counseling profession and associated services.Participants believed that genetic counseling was for other people, particularly individuals who receive positive results.Participants were committed to research and understood the risks of study participation.Participants did not know how a genetic counselor could help them before or after screening.The majority of participants did not remember that genetic counseling had been an option prior to undergoing sequencing. More interviewees recalled the availability of post-result genetic counseling services. While half of our participants had their results letter with them during the interview, many were still not aware that they could speak with someone. Participants who did not recall the availability of genetic counselors at different time points throughout the study were not surprised that such a service would have been provided: “[I]f you told me it was there, I believe you” (RNR 51); “[It’s] a reasonable thing to have available” (RNR 31). Some participants explained that while they did not access genetic counseling services, they found the offer reassuring.“I could only assume, by the title—that, if you had a genetic disorder, they be [sic] able to help you understand what it is more and understand the things to watch for or understand what you can do to help mitigate the possibility of somethin’ happening” (RNR 13).Most participants lacked familiarity with the genetic counseling profession. Even those who expressed some knowledge of the field through prior genetic testing for themselves or a relative did not feel confident in their understanding. When pressed to offer “best guesses” as to what a genetic counselor might do, some interviewees provided basic interpretations based on the profession’s title; “I imagine that they counsel you in genetics [Laughter]” (RNR 03). Others endeavored to provide more detailed guesses:Ultimately, we found that the participants had dichotomous understandings of genetic counseling, emphasizing either the genetics or the counseling aspect of the field. Participants who defined genetic counselors by their genetics expertise perceived them as professionals who could explain family history and risk, explain positive diagnoses, clarify a genetic diagnosis, and recommend lifestyle changes as necessary. Those participants who focused on the counseling component, by contrast, perceived genetic counselors “more as emotional support than clinical support” (RNR 41). Under this interpretation, genetic counselors were viewed as skilled providers who help patients cope with positive diagnoses, support emotional and mental well-being, and attend to the patient’s “psyche” (RNR 41). Regardless of where participants fell in their understanding, they believed genetic counseling support was not relevant to their personal situation, but might be good for others.In the minds of our participants, genetic counseling was better suited for people who are uncertain about study participation, worried about genetic discrimination, not in the medical field, or uneducated in genetics. Genetic counseling was seen as a useful resource for people with a family history of disease or individuals living with a genetic condition. Others perceived as ideal candidates for genetic counseling support included young people, parents, people who are nervous, and people who have difficulty coping.Our participants maintained that prior to sequencing they were not afraid of what the results could reveal; they were not worriers. They embraced a philosophy that “knowledge is power” (RNR 45), and that it is better to know than not know. In the words of one participant, “Why wouldn’t you want to know?” (RNR 08). This commitment to knowing, coupled with a seemingly unwavering confidence that they “could react properly to whatever was found” (RNR 37), led participants to conclude, “I don’t think I really need to go be counseled by how I’m gonna react if I find out an answer” (RNR 48).Participant views were also shaped by a strong commitment to research participation: “I decided no matter what was found, you know, I wanted to contribute to the body of knowledge” (RNR 33). The nature of the RAVE study led some participants to conclude that speaking with a genetic counselor prior to undergoing sequencing was unnecessary. They trusted the Mayo Clinic and its biobank, confident that Mayo protects private health information and that “they wouldn’t expose me to anything that would, uh, cause me harm” (RNR 37). Participants perceived the study as “low risk” given that no additional medical procedures were required and felt it was convenient to participate in the study: “I think just contributing to research, and I guess I would add just the fact that it was easy to do. I—[Interviewer: Okay.]—I didn’t have to go into the clinic or get any extra blood drawn or anything” (RNR 05). Ultimately, the participants believed they understood the study aims and procedures well enough to make a decision about their participation: “I, um, have a—enough of a general understanding of, you know the genetics, and […] the results to understand what I was getting into and […] the possibilities of getting, you know, results that weren’t maybe favorable” (RNR 27).Although the participants shared various reasons why they did not need assistance from a genetic counselor, it was clear many questioned the value of such services more generally. They did not know how speaking with a genetic counselor prior to sequencing would benefit them: “I guess I would be interested in exactly what value that would bring to me in terms of my health” (RNR 06). Moreover, the interviewees articulated not knowing what they would discuss in advance of knowing results: “I guess I just wouldn’t know what to ask. I wouldn’t know what to expect or ask or do, really. So—I guess that would be why I wouldn’t do it before I really knew the results” (RNR 09). A few added that they did not want to assume the burden of trying to figure out why they should call: “I guess I didn’t know really what they did and so I guess I wasn’t interested in, you know, finding out anything else about that I guess” (RNR 10). Absent greater familiarity with the value of genetic counseling in advance of pursuing genomic sequencing, the participants felt there was no need to call until they received their results: “Somebody would’ve had to really tell me why it would be a benefit to speak to ‘em first as opposed to doin’ the test first and seein’ if there’s any issues I needed to talk to them about” (RNR 13).“And the genotype, I have no idea what that is either. But—and it goes all back to the statement that—on the front [of the results letter] that no clinical [sic] significant genetic variants were found. And that’s—that’s the main part. And if one—you know, and if they would’ve even said possible one was a little low, I would’ve called” (RNR 39).After receiving neutral sequencing results by mail, the participants still did not feel a need to speak with a genetic counselor: “There’s no reason to have to talk to anybody because…there is nothing to talk about. It’s negative” (RNR3). Even if they admitted not completely understanding everything in the results packet, the participants did not feel compelled to call a genetic counselor because the results explicitly stated that nothing was found:The participants felt they might have been more inclined to talk to a genetic counselor after receiving their results if they had a better sense of how a counselor could help them: “Maybe with more information about […] how I would benefit from having some genetic counseling, I guess. I don’t know” (RNR 26).Even though nobody we interviewed had reached out to speak with a genetic counselor after receiving their results, 16 out of 50 interviewees posed at least one question to the interviewer that would have been appropriately directed towards a genetic counselor. These questions included study-specific questions ; questions related to their individual results ; and highly individualized questions regarding their risk in relation to a known family history .“I feel like the questions I would’ve had, the genetic counselor would not have had the answers to because probably nobody does, you know? [Laughter] It’s probably not known. Like, d—did I have breast cancer markers? So, I mean, they said that they didn’t find anything, variants, so I guess by going and talking to the genetic counselor, I wouldn’t have learned anything more than, you know, what I already know. They’ve already told me that there’s no variants” (RNR 33).In spite of a general lack of familiarity with the role or function of genetic counselors, the participants revealed assumptions about what genetic counselors would not be able to do for them, particularly regarding their genomic sequencing results. A few participants assumed that a genetic counselor would not be able to clarify the lab report (at least not in a way that they would be able to understand), explain why they might still be at risk for the diseases in question, or offer any additional genetic health information beyond the letter:When the interviewers encouraged the participants with questions to reach out to a genetic counselor after the interview, some back-peddled immediately and insisted that getting answers was not important. Others agreed to follow up with a genetic counselor, but none actually did.Our participants conveyed that unless there was something to talk about, there was no point speaking with a genetic counselor. They maintained that if they understood the benefits and risks of genomic sequencing; were not afraid of receiving results, whatever the outcome; and were not “worriers” by nature, genetic counseling support was unnecessary. For our participants, speaking with a genetic counselor was reserved for receiving a positive result. These findings complement a study by Houfek and colleagues that found that many participants were less keen to discuss, with a genetic counselor, topics related to genetic screening or test decision-making and associated ethical issues, as well as the psychosocial aspects of testing .Although many participants felt that genetic counseling was for others—especially following the receipt of neutral sequencing results—that belief was superimposed on vague, incomplete understandings of the role of genetic counselors. Additionally, the participants did not want to assume the burden of finding out why they should call. This lack of awareness about genetic counseling is not a new phenomenon ,14, and n = 16) had questions about their results. These experiences are reminiscent of Levin et al.’s findings that genetic counseling uptake increases when counselors are proactive and reach out to patients or research participants directly [Only 6 out of the 2310 RAVE study participants who received neutral results in the mail elected to contact a genetic counselor for clarification and more information. However, when we interviewed our 50 participants about their experiences receiving neutral results by mail, 32% of our interviewees , there is arguably a moral imperative to ensure that the recipients of neutral genomic sequencing results have an opportunity to have questions answered and that access to genetic counselors is neither limited solely to individuals who receive medically actionable or especially complicated results nor to those who have a clear understanding of how a genetic counselor can help them. If we know that patients and research participants are less likely to call a genetic counselor directly, more proactive measures may be necessary to facilitate follow-up among individuals who have questions about neutral results. These efforts might include creating educational materials that describe the potential value of genetic counseling support and foregrounding these services more prominently in study materials and results letters. Not surprisingly, individuals’ motivations for accessing genetic counseling services will be different depending on whether they are considering undergoing genomic sequencing or have received sequencing results see  and suppOur sample population was very homogenous. Additionally, our biobank participants had previously provided broad consent for future genetic research, which might explain in part their comfort with uncertainty and perceived lack of need for genetic counseling support in the context of the RAVE study. Information about the availability of genetic counseling also was presented in a very neutral way, not in a manner that encouraged utilization. Although the recruitment packet outlined the kinds of questions a genetic counselor could answer, it did not explain the value of accessing genetic counseling services prior to sequencing or after receiving neutral results. Finally, we recruited participants and returned genomic screening results by mail, which may have caused some participants to conclude that follow-up with a genetic specialist was not warranted.That so few people wanted to speak to a genetic counselor provides additional support for the current shift away from pre-test counseling towards a more post-results-focused model that prioritizes individuals with medically actionable results. However, that individuals did not appreciate the potential value of genetic counseling at different stages in the evaluation process underscores a need for large sequencing initiatives to consider creating educational materials that describe genetic counseling, the services provided, and why individuals might find speaking with a genetic counselor helpful, irrespective of the type of result received.Our participants had questions about their results, but did not see answers to those questions as immediately relevant to their health and well-being. While the participants did not take the initiative to call a genetic counselor, when we called them many were eager to have their questions answered, intimating that demand for elective genetic counseling may not align with individuals’ needs. The low uptake of elective genetic counseling suggests that more actively encouraging individuals with questions to access genetic counseling services is unlikely to overwhelm limited genetic counseling resources. To address the lack of familiarity with genetic counseling, a more detailed presentation of the value of the profession should accompany all alternatives to in-person genetic counseling, particularly approaches that require individuals to opt-in to receiving such support. Such efforts are critical in helping those individuals who might benefit from genetic counseling to appreciate their unmet support need and seek access.


Biology can be misused, and the risk of this causing widespread harm increases in step with the rapid march of technological progress. A key security challenge involves attribution: determining, in the wake of a human-caused biological event, who was responsible. Recent scientific developments have demonstrated a capability for detecting whether an organism involved in such an event has been genetically modified and, if modified, to infer from its genetic sequence its likely lab of origin. We believe this technique could be developed into powerful forensic tools to aid the attribution of outbreaks caused by genetically engineered pathogens, and thus protect against the potential misuse of synthetic biology. A key security challenge with biosecurity threats is determining the responsible actor. In this Perspective, the authors review recent developments in using genetic sequence to assign a lab-of-origin and the potential protection it provides against misuse of synthetic biology. Biotechnology is in an era of rapid and accelerating progress. Qualitative breakthroughs such as CRISPR and artificial gene drives unlock new capabilities, and quantitative trends show biotechnology as an area of increasing investment, decreasing costs, and expanding access.1. The last known cases of smallpox and SARS were both caused by laboratory exposures, and involved secondary transmission from infected researchers to individuals outside of the laboratory2. The 1977 influenza pandemic was caused by a strain closely related to those isolated in the 1950s, suggesting an anthropogenic origin3.However, alongside the benefits of this advancing technology for science, medicine, agriculture, and industry, there are concerns over its potential for accidental or deliberate misuse. Laboratory accidents have caused outbreaks before. The 2007 Foot and Mouth disease outbreak in the UK was attributed to a leaking pipe at Institute of Animal Health at Pirbright5: notable among these are Al-Qaeda’s unsuccessful attempts to develop biological weapons6; Aum Shinrikyo’s ineffectual bioterrorist attacks7; and the Rajneeshee cult’s use of Salmonella to cause 751 cases of food poisoning in Oregon in 1984.Both state and non-state actors have attempted to develop biological weapons in the last century. Although 183 states are party to the Biological Weapons Convention, which categorically bans the development and production of biological weapons, multiple states have been alleged to have violated the treaty. Non-state actors have also sought to use biological weapons8. The increasing ease and accuracy of genetic engineering both widens the possibilities and lowers the barriers to entry to research that could be misused to produce pathogens more dangerous than naturally arising strains9.Technological progress magnifies these dangers: falling barriers to entry increases the risk that reckless or malicious actors will access biotechnology. Emerging capabilities may worsen the potential impact if this risk is realised. The 2011 ‘gain of function’ influenza experiments raised concern that adapting a highly virulent avian influenza strain to be transmissible between mammals posed an unacceptable risk since a laboratory escape could lead to a pandemicAddressing these biological threats is an urgent and formidable challenge. One element of this challenge is attribution: being able to determine, in the wake of a human-caused biological event, who was responsible. Attribution has three main security benefits. First, knowledge of who was responsible can inform response efforts by shedding light on motives and capabilities, and so mitigate the event’s consequences. Second, it can identify the responsible parties for appropriate civil, criminal, or diplomatic penalty. Third, successful attribution followed by meaningful actions to hold perpetrators accountable can deter those inclined to reckless or malicious practice in the first place.Information for attribution can be roughly divided into three categories. The first category includes non-technical indicators that provide contextual clues to intent, such as the victims, the location of the event, and epidemiological features. For example, if an incident occurs in the midst of an ongoing conflict, suspicion falls on the belligerents, while if it occurs near laboratories working on the causative agent, there is a greater chance of it being attributed to an accidental release.Another category informing attribution is intelligence. This ranges from human sources, such as informers or whistleblowers; to intercepted communications; to surveillance data. All can potentially identify those responsible for the release of a biological agent.The final category is technical forensics: the properties and characteristics of the agent that caused a given outbreak may provide clues as to who made it and/or who was responsible for releasing it.10. Nonetheless, further improvement of these forensic capabilities are a recognised need11. Two capabilities would be important: first, to establish whether the causative organism was genetically engineered; and second, if it was engineered, to identify the actor who engineered it.The nascent field of microbial forensics helped the FBI identify a suspected lab of origin for the anthrax used in the 2001 attack and a suspected perpetrator responsible for the attack12. Under the auspices of the UN Secretary-General’s mechanism for investigating alleged biological attacks, there are separate efforts to develop an international trusted laboratory network that would provide forensic support to such investigations. As performance across laboratories in detecting genetic modifications is currently variable, the network may be strengthened through additional tools and access to existing technologies13.To detect engineering, tools are being developed which can interrogate the genome of the causative organism for indicators of genetic engineering. The IARPA Finding Engineering-Linked Indicators project, FELIX, seeks to develop new experimental and computational tools for this purposeIdentification of the engineer poses a further challenge, since determining that an organism has been genetically engineered, and what that engineering involved, does not establish who the engineer was. A given set of edits could conceivably be performed by a multitude of different actors: from individuals working out of a community lab, to university research groups, to industrial laboratories, to a state-run bioweapons facility.Fortunately, the very diversity of design approaches and technical options that are now available to achieve a given result  offers a means to approach the attribution problem. Which option a genetic engineer chooses will be influenced by a variety of factors, including their training, prior experience, habits, and available resources. In aggregate, these choices compose a ‘methodological signature’, and thus a way of tracing these design choices back to the likely designer.14, although with a limited accuracy of 48%. Most recently, Alley and colleagues deployed deep learning techniques to predict lab-of-origin for plasmids submitted to the Addgene database - the largest repository of its kind, with 70,000 submissions from labs in 37 countries. Their approach offers an accuracy of 70% when distinguishing between over 1000 labs15.That machine learning could be used to detect and interpret these signatures was demonstrated in late 2018They also pioneered further capabilities: uncertainty estimation, tracking ‘genealogies’ of genetic engineering groups, and inferring the nation in which the originating laboratory is located. Each of these has security promise: uncertainty estimation enhances robustness and can aid the integration of technical indicators with other available information for making an overall attribution decision; tracking lineages may identify other groups who knowingly or otherwise assisted the actor responsible; and the nation of the originating laboratory may provide a useful investigative clue in the absence of finer-grained information.These rapid developments have potential as techniques, alongside publications and patents, to help understand patterns of influence and performance within the synthetic biology community, and also a means to identify and protect intellectual property. Our interest is in the biosecurity promise of using these advances to develop forensic tools which can aid attribution of genetically engineered agents and organisms.The central benefit would be an increase in the actual and perceived accuracy of attribution decisions. This increases the likelihood of the right people being implicated in any misuse of genetic engineering in case of either an accident or an attack. The converse—avoiding mistaken attribution—is also key, given the potentially catastrophic consequences of one state mistakenly believing it is a victim of a biological attack.An indirect effect of this improved accuracy is deterrence of misuse in the first place. Some actors may be incentivized to be reckless if they believe they are unlikely to be held accountable for any accidents arising from their actions. Malicious actors may be attracted to biological weapons as a means of clandestine violence. Better attribution tools deter both by increasing the risk of discovery.Three additional features of genetic engineering forensics make it particularly attractive as a biodefense technology. First, unlike other instances where the interests of science and security conflict, the development of genetic engineering forensic tools does not impede scientific enquiry. If anything, it offers co-benefits for the overwhelming majority of well-intentioned and responsible genetic engineers: further means of receiving due credit and recognition, and further safeguards of their intellectual property.16. A given state’s biodefense activity, even if wholly defensive in intent, may nonetheless provoke concern in other states that this activity could both harbour and be co-opted for offensive purposes. Mutual suspicion can drive an arms race. Compared to other aspects of biodefense, genetic engineering forensics has more limited prospects for offensive use, and so state investment in this aspect of biodefense poses a lower risk of triggering suspicions and insecurity in its peers.Second, biodefense activity can paradoxically worsen security, by what is known as a security dilemmaThird, the efficacy of genetic engineering attribution is coupled to biotechnological progress, so the trends that make misuse more concerning also enhance this approach to help address them. The rapidly growing corpus of genetically engineered sequence information provides more data that can be fed into these forensic tools; the increasing diversity of biotechnological methods also increases the diversity of ‘methodological signatures’ among practitioners.The security benefits of genetic engineering attribution, even in the ideal case, would have limits. Attribution techniques are not techniques to detect whether engineering occurred in the first place: determining attribution is a process that would follow detection of engineering, and is not a substitute for it. Great caution should apply to using genetic engineering attribution as an improvised means of genetic engineering detection. Inability to attribute does not rule out genetic engineering: a sequence may show clear signs of engineering even if the engineer cannot be identified. There are also risks of false positives: improper use of genetic engineering attribution could ‘attribute’ non-existent engineering, such as identifying the ‘engineer’ of a wild-type pathogen genome.Genetic engineering attribution is also not applicable to releases of non-engineered agents or organisms, for which other forensics methods remain necessary. Technical forensics may help identify the designer of the genetically engineered organism, but this may not be the actor who misuses it .The deterrence value of attribution, and thus of better forensic tools to inform it, is sensitive to political context. Forensic identification offers little deterrence to actors intending to claim rather than conceal responsibility, nor to those who plan to evade the consequences of being held responsible by disinformation campaigns or other political means .Realistic circumstances, rather than ideal ones, imply further limitations. 70% accuracy is far from a smoking gun, and although this may improve further, the performance ceiling is not known. Genetic engineering forensics should be seen as an important forensic tool in the attribution toolkit, instead of a standalone silver bullet.A key uncertainty is that genetic engineering forensics has so far been developed on—and tested against—data from genetic engineers operating ‘in the clear’: those who publish their sequences to public repositories and make no attempt to conceal authorship. In the case of an attack rather than accident, sophisticated adversaries may also attempt to find ways of obfuscating or misdirecting attribution indicators—genetic engineering forensics included. For example, an attacker could attempt to adopt the ‘methodological signature’ associated with other practitioners in an attempt to deflect attribution or at least confuse the analysis.Such attempts could leave their own trace, and forecasting how any potential contest between forensics and counter-forensics would play out is difficult; one side or the other may have an intrinsic advantage. Yet even in the worst case where an adversary is justifiably confident that they can evade genetic engineering forensics, doing so imposes a further cost, a further design constraint, and a residual risk of discovery. Each is a disincentive.Genetic engineering forensics is at an early stage; there is a long way to go from published proof of principle studies to a robust forensic capability. These next steps include: First, starting a dialogue with the forensics and biodefense communities for what capabilities would be useful, and how technical forensic innovations can be brought into practice. Second, corralling further sources of data to improve accuracy and assess how performance scales. Third, leveraging ongoing improvements in machine learning and the creativity of practitioners to further improve the state of the art.As biotechnology continues to pose a security challenge, it promises new tools to address the same. We believe it is the responsibility of the scientific and policy communities to identify opportunities to create these tools, like genetic engineering attribution, which reduce the risk of misuse. By engaging in this enterprise pro-actively, we can continue to realize the benefits of rapidly improving biotechnology while safeguarding biological security.


Bubalus bubalis) in Germany and their potential relations to herds in other parts of Europe or worldwide. To this end, animals from different herds in Germany, Bulgaria, Romania, and Hungary were genotyped and compared to genotypes from other populations with worldwide distribution and open to the public. The pilot study analyzed population structure, phylogenetic tree, and inbreeding events in our samples. In buffalos from Germany, a mixed genetic make-up with contributions from Bulgaria (Murrah breed), Romania, and Italy was found. All in all, a high degree of genetic diversity was identified in European buffalos, and a novel genotype was described in Hungarian buffalos by this study. We demonstrate that European buffalos stand out from other buffalo populations worldwide, supporting the idea that buffalos have not completely disappeared from the European continent during the late Pleistocene. The high genetic diversity in European buffalos seems to be an excellent prerequisite for the establishment of local breeds characterized by unique traits and features. This study may also be considered as an initial step on the way to genome characterization for the sustainable development of the buffalo economy in Germany and other parts of Europe in the future.This is the first study to explore the genetic diversity and population structure of domestic water buffalo ( Bubalus bubalis) is a multipurpose animal, producing meat, milk, leather products, and dung. In developing countries, buffalos are used as draught animals, providing more than 40% of farm labor  and river buffalo . While their taxonomical status is still being debated, B. bubalis is supposed to be descended from Indian wild buffalo  domesticated ≈5,000 years ago  have been lacking until now (Bos primigenius), wisent (Bos bonasus), and buffalo . This linguistic approach supports the hitherto controversial bone finds from the same area at about the same time. It is unclear whether water buffalos repopulated Europe after the ice age or whether they survived in refugial areas. Because rock reliefs, dated back to ≈16,000 BC in France, clearly display buffalos , when buffalos were also present in Central Europe . Althougontinent . The preontinent  but geneontinent . Based oontinent . Howeverntil now . Accordibuffalos . This, iDomesticated water buffalos in Europe are referenced during the centuries VI–XII C.E.  and had While local Bulgarian buffalos were a significant European Mediterranean population raised for draft power, meat, and milk purposes, they started to be crossed with Murrah breed since 1972, mainly to improve their performance for milk production. A selection program was initiated to develop a typical Bulgarian Murrah breed for milk with high butterfat content .® Buffalo Genotyping Array 90K from Affymetrix, now enables genome analysis of this comparably novel farm animal species and could be useful for the initiation of genetic selection in German buffalo. A subsequent release of the first assembly of the water buffalo genome  genotyping assay specific for river buffalo , Axiom® o genome  further All sampling occurred as part of commercial buffalo breeding programs and strictly adhered to national and international laws.® Buffalo Genotyping Array 90K from Affymetrix1 were performed by ATLAS Biolabs GmbH . Allele calling was carried out using Axiom Analysis Suite software V4.0.1 (Applied Biosystems by Thermo Fisher Scientific) following the pipeline for Affymetrix Axiom genotyping workflow  through the commands –update-map, –update-chr, –update-alleles of PLINK V1.7 software   analysis was performed to explore the genetic relationship between four different buffalo populations. To this end, a symmetric matrix of the identity-by-state (IBS) distances, for all pairs of individuals, was generated in PLINK V1.7 software  based onST)  matrix oST) .ROH) using R package “DetectRUN”  was exclusively conducted on Central and East Europe buffalo populations genotyped in this study  and basetectRUN”  and applLROH is the sum of the length of ROH for each individual, and Lgenome is the length of the genome analyzed, which was around 2.65 Gb in buffalo.ΣThe frequency of occurrence of SNPs into an ROH was analyzed, identifying the genomic regions most commonly associated with ROH. Regions were selected containing the top 1% of most common ROH-associated SNPs (ROH hotspots or ROH islands) . GraphicN = 14). The MDS plot  with 0.41–0.35 for observed values (Ho) and 0.40–0.35/0.32 for expected values (He), respectively. Similar heterozygosity values were found in the Hungarian and Romanian samples, lowest in relation to the other population groups (from 0.35 to 0.38 Ho and from 0.32 to 0.36 He), while German buffalos had the highest values of diversity, after the Bulgarian buffalo population, between groups, and within its group . To suppx matrix  was consx matrix . Two priROH = 0.047). Romanian samples from Mera village had 185.31 Mb of the genome in ROH with an FROH of 0.074, lower than the second farm (Rom_Serc) with 252.31 Mb and FROH 0.102. The buffalo population in Mera (Rom_Mera) is distributed in smaller herds from multiple private farms. The second Romanian population (Rom_Serc) studied here is located in Sercaia and is managed by the Institute of Research and Development for Buffalo Breeding. Buffalos from Sercaia were characterized by ancient inbreeding, showing the highest proportion of ROH in the category 5–15 Mb. These values are in agreement with the results presented in ROH (0.144 and 0.173), Földes (Hun_HT) with lower FROH (0.121). The different values of FROH obtained within the German group were an indication of the high variability between farms  enrichment was found. All the hotspot SNPs in each population were in the short ROH length, likely suggestive of ancient inbreeding or selection events  showed the highest proportion of long size ROH that confirms the small population size but is also indicative of recent inbreeding events. It may be due to the application of selection programs and the sign of environmental adaptation of the Mediterranean Carpathian buffalo in Rom_Serc population, while in Mera the animals are less subjected to predefined or joint selection programs. According to ROH values between Central and East European populations. Indeed, the Bulgarian is the biggest population analyzed in this study, and the samples were collected from two different locations, which also could be related to comparably low levels of inbreeding. In the German populations, genomic relationships with Bulgarian Murrah, Italian Mediterranean breeds, and Romanian genetics were identified. Buffalos in Germany had the highest observed and expected heterozygosity values within all Central Eastern European populations studied here. The observed genetic diversity is a significant factor for their potential adaptation to the local environment and provides a valuable substrate for an upcoming selection program. Drift rather than mutation has likely created this variability due to the short period in generations and the relatively small effective population sizes  were separated from the other farm located in a former Hungarian village (Mera), now part of Romania. The origin of Romanian buffalo is still unclear. According to Borghese, it is a Mediterranean type adapted to the cold climate and local environment and is classified as a Mediterranean Carpathian breed . Becauseon sizes . Buffaloon sizes . The higIl18bp, Rhog) and autophagy (Atg16l2) were only found in Saxony. It is reported that the gene Atg16l2 (autophagy related 16 like 2) influences the adaptation of the immune system to the recovery from mastitis in Danish Holstein cattle (The control of inbreeding in a population is necessary to avoid depression of phenotype traits . Considen cattle . From thThis work aimed to contribute to an improved understanding of Central and Southeast European buffalo populations from a genomic perspective. We defined the genetic characteristics of European buffalo populations in Romania, Bulgaria, and Hungary. Together with the Italian Mediterranean breed, these populations contribute to the high degree of genetic diversity of European buffalos. Incidentally, we may have genetic evidence for a novel or original Hungarian breed, characterized by outmost uniformity in three different farms with no clear relations to any other genotype described so far. Since all farms are in the close neighborhood, follow-on studies may help to unravel the origin of the Hungarian population. This novel genetic identity found in Hungarian buffalos thus adds one additional specific breed to the existing genetic diversity in Europe, which so far consisted only of the Italian Mediterranean, Bulgarian Murrah, and Romanian genetics.This is the first genetic characterization of buffalos in Germany, where farming started only about 40 years ago and therefore is in its infancy. We have identified familiar relations to all genotypes identified in European buffalos so far with the exception of Hungary. Importantly and based only on a comparably small number of herds, a diverse genetic make-up is available in European and German buffalos, which can be seen as an excellent prerequisite for the development of Buffalo-based bioeconomy in Europe based on local breeds characterized by unique features.https://datadryad.org/stash/share/eT_rs3441UHCMlN4qpbRi8u6sFPHdX-ZtDPuVS5TpSI and https://datadryad.org/resource/doi:10.5061/dryad.h0cc7.Our data set has been uploaded in the Dryad Repository, Ethical review and approval was not required for the animal study because sampling occurred as part of livestock breeding programs which are granted by international law and therefore are not reviewed by ethical committees. Written informed consent was obtained from the owners for the participation of their animals in this study.AN, KW, and AH conceived the project and designed the experiments. AN performed the laboratory part. AN, SQ, RG-P, and ML-S performed the data analysis. AN, SQ, RG-B, JB, RK, and AH contributed to the interpretation of results and wrote the manuscript. MiT, M-AF, HP, AB, LV, CH, LH, PP, YI, TP, WL, MaT, and AH collected or provided samples and contributed to data production. All authors read, made corrections, and approved the final version of the manuscript.M-AF, MiT, HP, and RK are employed by the companies Gut Darss GmbH & Co., Hof am Meer GmBH, Wiesenburger Land eG, and Energiequelle GmbH, respectively. The remaining 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.