The creation of subepithelial voids within scarred vocal folds via ultrafast laser ablation may help in localization of injectable biomaterials toward a clinically viable therapy for vocal fold scarring.We aim to prove that subepithelial voids can be created in a live animal model and that the ablation process does not engender additional scar formation. We demonstrate localization and long-term retention of an injectable biomaterial within subepithelial voids.A benchtop nonlinear microscope was used to create subepithelial voids within healthy and scarred cheek pouches of four Syrian hamsters. A model biomaterial, polyethylene glycol tagged with rhodamine dye, was then injected into these voids using a custom injection setup. Follow-up imaging studies at 1- and 2-week time points were performed using the same benchtop nonlinear microscope. Subsequent histology assessed void morphology and biomaterial retention.in vivo. Our analysis suggests that the ablation process does not introduce any scar formation. Moreover, these studies indicate localization, and, more importantly, long-term retention of the model biomaterial injected into these voids. Both nonlinear microscopy and histological examination indicate the presence of biomaterial-filled voids in healthy and scarred cheek pouches 2 weeks postoperation.Focused ultrashort pulses can be used to create large subepithelial voids We successfully demonstrated subepithelial void formation, biomaterial injection, and biomaterial retention in a live animal model. This pilot study is an important step toward clinical acceptance of a new type of therapy for vocal fold scarring. Future long-term studies on large animals will utilize a miniaturized surgical probe to further assess the clinical viability of such a therapy. We used hamster cheek pouches, which are considered a good model for VF surgery because their epithelium and LP layers are very similar in composition to humans.In this letter, we demonstrate a proof of concept for the creation of subepithelial voids, injection of a biomaterial, and retention of the biomaterial inside a void using an 2A polyethylene glycol-based hydrogel (PEG30) was prepared by our collaborators at Massachusetts General Hospital’s Department of Laryngeal Surgery and Voice Rehabilitation. The hydrogel’s biocompatibility, mechanical functionality, and retention were shown in canine models as part of previous work.The animal studies were performed in accordance with the PHS Policy on Humane Care and Use of Laboratory Animals, the NIH Guide for the Care and Use of Laboratory Animals, and Animal Welfare Act. We followed protocols approved by the Institutional Animal Care and Use Committee of the University of Texas at Austin. Four male Syrian hamsters (young adults 80 g wt.) were obtained from Charles River Laboratories and housed at the Animal Resources Center where they were acclimatized for 1 month prior to the experiments. Before each procedure, the hamsters were administered buprenorphine  and a three-axis micromanipulator . The micromanipulator was used to precisely position a 1-mL syringe with a 30-gauge needle (BD syringe) to inject a small amount of rhodamine-tagged PEG30 into the void . The neeHamsters were brought back for imaging following a 1- or 2-week recovery period to evaluate biomaterial localization and retention in ablated voids. In both groups of scarred and nonscarred animals, one animal was imaged 1 week after injection, and the other animal was imaged 2 weeks after injection. The ink marks imaged by white light provided a guideline to approximately align the treated area within the FOV of the nonlinear microscope. We performed volumetric TPF imaging and SHG imaging to locate the rhodamine particles and to identify the collagen-rich LP layer containing the void . The ima3Nonlinear imaging of the scar group showed dense and organized collagen fibers, emphasizing the expected difference between scarred tissue in We imaged the voids of one nonscar and one scar animal 1 week after the surgery and found that the imaging results 1 and 2 weeks postoperatively were similar in the nonscar group. For 1 week follow-up imaging studies of the scar group, however, we were not able to see any rhodamine particles. A possible explanation is suggested following the histology for that animal, which is discussed next.Immediately after follow-up imaging studies at 1- and 2-week timepoints, animals were sacrificed and prepared for histology. For the nonscar group, histology revealed a similar picture to that of nonlinear microscopy: we were able to detect the void when stained with collagen I antibody . A consethe void . The voithe void  showed t4In summary, these studies describe our ongoing efforts toward establishing a treatment modality to localize therapeutic biomaterials inside scarred VFs. By enabling precise localization of injectables, this method may be able to help with testing the efficacy of various biomaterials in the treatment of scarred tissue. Further, injection and long-term localization of PEG30 within subepithelial voids in both scarred and nonscarred hamster cheek pouches is an important milestone toward clinical viability of the proposed VF scarring therapy. Retention of PEG30 within these voids over a 2-week period was confirmed both by nonlinear microscopy and histological examination in both scar and nonscar groups. Results for the nonscar group also revealed a disorganized  collagen structure at the voids’ margins and suggest that ultrafast laser ablation does not elicit formation of scar tissue.Although these results were encouraging, clinical translation is limited by the free space delivery of light and the large optics of the benchtop microscope used in these studies. Flexible delivery of tightly focused ultrashort pulses through miniaturized optical systems is required to translate our VF scarring therapy to the clinic. Thus, future studies performed on large animals utilizing miniaturized laser surgery probes with high numerical aperture miniaturized objectives will help to assess the clinical viability of the proposed VF scarring treatment.


O HipercolBrasil é um programa de rastreamento genético em cascata realizado no Instituto do Coração, com mais de 2.000 pacientes identificados com variantes patogênicas5 e, a partir desses resultados, foram selecionados casos índices (CI) de pequenas cidades com variantes patogênicas para amplificar a cascata. O programa realiza testes genéticos para HF em indivíduos com LDL-C ≥ 210 mg/dL confirmados em duas análises separadas: (CI)6 e em parentes de primeiro grau daqueles nos quais foram identificadas variantes patogênicas ou provavelmente patogênicas.A hipercolesterolemia familiar (HF) é uma doença autossômica codominante associada a níveis elevados de LDL-colesterol e a doença cardiovascular aterosclerótica prematura.7 endossadas pela Atualização da Diretriz Brasileira de Hipercolesterolemia Familiar – 2021.8Em nosso país, as recomendações para testes genéticos seguem as Primeira Diretriz Brasileira de Hipercolesterolemia Familiar,9 os autores utilizaram um método de triagem direcionado aplicado a candidatos de 11 pequenas cidades brasileiras (com menos de 60.000 habitantes) com suspeita de alta prevalência de pessoas com HF. Selecionaram quatro cidades com suspeita de efeito fundador ; dois municípios em regiões com altas taxas de dislipidemia e infarto precoce, conforme descrito pelo banco de dados do Sistema Único de Saúde (Bom Despacho e Moema); e cinco cidades geograficamente próximas a outras cidades com alta prevalência de indivíduos com HF . Cento e cinco casos índice e 409 parentes de primeiro grau foram registrados nessas cidades. Usando tal abordagem, os autores encontraram 4,67 parentes por caso índice, o que foi significativamente maior  em comparação com a taxa geral do HipercolBrasil . Os métodos utilizados para confirmar o diagnóstico de HF foram Next Generation Sequencing (NGS) com um painel incluindo LDLR, APOB, PCSK9, LDLRAP1, STAP1, LIPA, APOE, ABCG5 e ABCG8. A triagem genética foi complementada por MLPA (multiplex ligation-dependent probe amplification) no gene LDLR para detectar variações no número de cópias do gene (CNVs) associadas à HF quando nenhuma mutação foi identificada. Este estudo mostrou que as taxas de detecção de HF foram maiores que as do programa HipercolBrasil e foram maiores nos municípios com efeitos fundadores. Por outro lado, em cidades próximas àquelas em que o efeito fundador esteve presente, a taxa de detecção de casos índice foi menor, consequentemente, o número de familiares afetados.No artigo de Jannes et al.,10No entanto, existe uma lacuna importante na percepção de risco, manejo do colesterol e outros aspectos relacionados à HF.11 A estratégia de triagem alvo em cidades pequenas pode ser eficaz, mas algumas questões devem ser levadas em consideração antes de escolher a cidade a ser triada. A participação do sistema de saúde local, a disponibilização de resultados laboratoriais prévios e a divulgação desta campanha em diferentes mídias sociais podem melhorar a adesão da população e melhores resultados.Em uma publicação anterior, os autores relataram que seu programa de rastreamento genético em cascata de HF poderia predizer a inclusão de novos familiares com base nas características do IC, informações úteis para a elaboração de abordagens de triagem melhores e mais eficazes para indivíduos sob risco. 1 The condition is underrecognized, and screening tools must be implemented to improve diagnosis and promote early treatment.2 The screening methods include universal, selective, cascade, reverse cascade, and oportunistic screening;4 however, in small cities in certain regions, where founder effects can be present, target search for affected individuals can be an interesting option. The HipercolBrasil is a genetic cascade screening program carried out in the Heart Institute, with more than 2,000 patients identified with pathogenic variants,5 and from these results, index cases (IC) from small cities with pathogenic variants were selected to amplify the cascade. The program performs genetic tests for FH in individuals with confirmed LDL-C ≥ 210 mg/dL in two separate analyses: (IC)6 and in first-degree relatives of those in whom pathogenic or likely pathogenic variants were identified.Familial hypercholesterolemia (FH) is an autosomal codominant disease associated with high levels of LDL-cholesterol and premature atherosclerotic cardiovascular disease.7 endorsed by the Update of the Brazilian Guideline for Familial Hypercholesterolemia – 2021.8In our country, the recommendations for genetic testing follow the First Brazilian Guideline for Familial Hypercholesterolemia,9 the authors used a targeted screening method applied to candidates from 11 small Brazilian cities  with a suspected high prevalence of people with FH. They selected four cities with suspected founder effect ; two cities in regions with high rates of dyslipidemia and early myocardial infarction, as described by the National Health System database (Bom Despacho and Moema); and five cities that were geographically close to other cities with a high prevalence of individuals with FH . One-hundred and five index cases and 409 first-degree relatives were enrolled from those cities. Using such approach, the authors found 4.67 relatives per index case, which was significantly higher (p < 0.0001) compared with the general HipercolBrasil rate (1.59). The methods used to confirm the diagnosis of FH were next-generation sequencing (NGS) with a panel including LDLR, APOB, PCSK9, LDLRAP1, STAP1, LIPA, APOE, ABCG5 and ABCG8 genes. The genetic screening was complemented by MLPA (multiplex ligation-dependent probe amplification) in the LDLR gene to detect gene copy number variations (CNVs) associated with FH when no mutation was identified. This study has shown that the rates of FH detection were higher than the HipercolBrasil program and were also higher in cities with founder effects. On the other hand, in cities close to those in which founder effects were present, the detection rate of index cases was lower, consequently, the number of affected relatives.In the article by Jannes et al.,10 However, there is an important gap in risk perception, cholesterol management, and other aspects related to FH.11 The strategy of target screening in small cities can be effective, but some issues must be taken into account before choosing the city to screen. The participation of the local health system, availability of previous laboratory results, and advertisement of this campaign in different social media may improve adherence of the population and better results.In a previous publication, the authors reported that their FH cascade screening program could predict family enrollment based on IC features, useful information for devising better and more effective screening approaches for at-risk individuals.


Nge) decreased from 43.8 in 1980 to 11.9 in 2018. The index of genetic diversity based on Nge decreased from 0.99 in 1980 to 0.96 in 2018 due to genetic drift in non‐founder generations. Changes in inbreeding and genetic diversity parameters were similar between Japanese Shorthorn and other Japanese Wagyu breeds, but the magnitude of the changes was lower in the Japanese Shorthorn.The Japanese Shorthorn is a Japanese Wagyu breed maintained at a small population size. We assessed the degree of inbreeding and genetic diversity among Japanese Shorthorn cattle using pedigree analysis. We analyzed the pedigree records of registered Japanese Shorthorn born between 1980 and 2018, after evaluating the pedigree completeness. The average of the actual inbreeding coefficients increased at the same rates annually from approximately 1.5% in 1980 to 4.2% in 2018 and was higher than the expected inbreeding coefficients over time. The effective population size based on the individual coancestry rate largely decreased from 127.8 in 1980 to 82.6 in 1999, and then remained almost constant at approximately 90. Three effective numbers of ancestors decreased over time until 1995, then remained almost constant. In particular, the effective number of founder genomes ( The Japanese Shorthorn has been continuously improved and was certified as a Japanese Wagyu breed in 1957    and its variance are lower in Japanese Shorthorn than those in Japanese Black cattle, and carcass weight has been emphasized as a target for genetic improvement in Japanese Shorthorn cattle MAFF, . The BMSThe Japanese Shorthorn has a unique genetic background compared to that of other Japanese Wagyu breeds .We analyzed the pedigree records of Japanese Shorthorn cattle that were maintained in cooperation with the Japanese Shorthorn Cattle Association of Japan and born between 1970 and 2018 to known parents. The animals born during each year served as reference populations. The total number of bulls and cows in the reference population was 1,833 and 33,850, respectively, and Table 2.2ge) were used as indicators. PCI is an important indicator of pedigree quality for inbreeding estimation, and its value is 1 when all ancestors are known in the paternal and maternal lines traced back to d generations. We calculated the PCI  and maternal (m) lines, respectively. The proportion of known ancestors was calculated as follows:ui,j is the proportion of known ancestors at generation j in line i. The PCI over three and five previous generations in the pedigree were calculated and are referred to as PCI3 and PCI5, respectively. The informative reference population, which we defined as PCI3 > 0.75 and PCI5 > 0.50, was applied to evaluate inbreeding, effective population size, and genetic diversity.The completeness and depth of the pedigree were evaluated before analyzing inbreeding, effective population size, and genetic diversity in our population. The pedigree completeness index (PCI) and discrete generation equivalents  is defined as the sum of the proportion of known ancestors over all generations traced  in each period was calculated asLss, Lsd, Lds, and Ldd are the generation intervals of four gametic pathways: sire to son, sire to daughter, dam to son, and dam to daughter, respectively.The average generation interval , in which FIT is the average inbreeding coefficient of the reference population, and the inbreeding coefficient of each individual in the pedigree was calculated according to Meuwissen and Luo  was calculated from the relationship of 2Cij=aij, where aij is the element of the additive relationship matrix A and an additive relationship coefficient between animals i and j  were calculated based on the individual inbreeding rate (∆Fi)   and ∆Cij (∆C¯) asTwo types of effective population sizes (2.4Nef) is an indicator of founder contributions to the population and is defined as the number of equally contributing founders , also referred to as the founder genome equivalent, is defined as the number of equally contributing founders without loss of founder alleles in the reference population. Thus, Nge is calculated as follows:aij is an additive relationship coefficient between animals i and j  accounts for the loss of genetic diversity due to the latter effect. Hence, Nenf is calculated according to Caballero and Toro  in the reference population and expressed them as predicted heterozygosity  for each reference population between 1980 and 2018. The FIT increased at the same rate, from approximately 1.5% in 1980 to 4.2% in 2018. The FST also increased at the same rate from approximately 0.7% in 1980 to 3.8% in 2018, and FST was lower than FIT in all reference populations. The FIS was positive in all reference populations and almost constant at approximately 0.5% over time. Thus, the population was genetically subdivided in all reference populations .Figure NeFi and NeCi) for each reference population between 1980 and 2018. NeFi remained almost constant over time and ranged from 60 to 80. In contrast, NeCi largely decreased from 127.8 in 1980 to 82.6 in 1999, and then remained almost constant at approximately 90. The harmonic means of NeFi and NeCi for four periods  were calculated and are shown in Table NeFi increased slightly from 63.3 in 1980–1989 to 70.9 in 2010–2018. Conversely, NeCi decreased from 114.7 in 1980–1989, and then remained almost constant at approximately 90.Figure 3.3Nef, Nge, and Nenf) decreased over time until 1995, then remained almost constant at approximately 60 in Nef and 20 in Nge and Nenf. The Nenf largely decreased from 85.1 in 1980 to 28.0 in 1995. The most recent Nef, Nge, and Nenf were 57.2, 11.9, and 15.1, respectively. Figure GD* and GD) in the reference population. The GD* remained almost constant at 0.99, whereas GD decreased from 0.99 in 1980 to 0.96 in 2018. The GD accounts for the effects of unequal genetic contributions of founders and genetic drift, whereas GD* accounts for only unequal genetic contributions of founders. These results indicated that the effect of random genetic drift mainly caused a reduction in genetic diversity over generations of non‐founders compared with the unequal contributions of founders.Figure 44.1Nambu‐ushi differs from that of other Japanese native cattle . The mtDNA haplogroup P was once the most common haplogroup in European aurochs according to surveys of ancient DNA , because a decrease in FST per generation occurred in some periods in our population, leading to a negative value of effective population size  in Japanese Black (Honda et al., Nef, Nge, and Nenf were reduced to 50.3, 7.3, and 8.5 in the Japanese Black, 74.4, 4.9, and 5.3 in the Kumamoto sub‐breed of Japanese Brown, and 79.4, 3.9, and 4.1 in the Kouchi sub‐breed of Japanese Brown, respectively, by 2000. The Nef was much larger than Nenf, and Nef was close to Nge in both breeds. Changes in trends among the three effective numbers of ancestors were similar among Japanese Shorthorn cattle, but the Nge and Nenf in the current population were still higher than those in both breeds. In addition, the GD in Japanese Black and Japanese Brown was reduced to 0.93 and <0.90, respectively, in 2000, while the trends of GD* were almost constant at 0.99 in both breeds. Trends in the two coefficients of genetic diversity were similar in Japanese Shorthorn, but the GD in the current population was still higher than that in both breeds. These results suggest that random genetic drift accumulation in non‐founder generations is a more serious cause of the reduction in genetic diversity than the unequal contributions of founders in the three breeds. In addition, the low value of Nge indicates extremely limited genetic diversity in the current Japanese Brown population (Honda et al., The effective numbers of ancestors, 4.3Changes in the degree of inbreeding and the genetic diversity of the Japanese Shorthorn were similar to those of the other Japanese Wagyu breeds, which means that the inbreeding coefficients increased while genetic diversity decreased over time. However, the magnitude of the change in Japanese Shorthorn was much smaller than that in the other Japanese Wagyu breeds. There are two possible explanations for this maintained genetic diversity. First, a few specific sires were not used intensively. The average BMS of Japanese Shorthorn is 2.1, its phenotypic variance is low, and the estimated heritability is 0.14 (Sato et al., FIT and FST of Japanese Shorthorn were low at 4.2% and 3.8%, respectively, in the current population, and higher genetic diversity was maintained compared with that in other breeds generated by AI. Conversely, local differentiation has been retained among the dams used in each regional public ranch, and each running bull is distributed to regional public ranches by accounting for the inbreeding coefficient. Thus, the population has been genetically subdivided in the past and current populations of Japanese Shorthorn. Another problem with the natural mating system is that it is difficult to completely manage pedigree information. Here, we extracted information about the reference population based on PCI, and pedigree analysis was then applied to the informative reference population. However, incomplete pedigree information might also be involved in maintaining genetic diversity in Japanese Shorthorn.Regarding the impact of natural mating on genetic diversity, natural mating requires more running bulls. Thus, the Although the degree of BMS is lower in Japanese Shorthorn than that in Japanese Black cattle, Japanese Shorthorn cattle have adapted well to a harsh climate; they are also suited to grazing and can nurture calves due to abundant milk production. The Japanese Shorthorn has a unique genetic background compared with that of other Japanese Wagyu breeds. Consumer demand for high‐quality lean meat might increase as a consequence of the aging society and health consciousness. Thus, Japanese Shorthorn genes and their gene combinations may serve as breeding materials. Although the current breeding program can maintain genetic diversity in the Japanese Shorthorn, accumulated inbreeding and the loss of genetic diversity are potentially problematic because of the decreasing number of registered bulls over time. Therefore, it is necessary to perform genetic selection while monitoring the genetic status and managing selection and mating if economic trends change for the intensive use of a few specific sires.5This study evaluated the current genetic status of the Japanese Shorthorn by clarifying the degree of inbreeding and genetic diversity using pedigree analysis. Changes in inbreeding and genetic diversity parameters were similar between Japanese Shorthorn and other Japanese Wagyu breeds, which means that the inbreeding coefficients increased and genetic diversity decreased over time. However, the magnitude of the change in Japanese Shorthorn was much smaller than that in other Japanese Wagyu breeds. The current breeding program of Japanese Shorthorn can maintain genetic diversity, but accumulated inbreeding and the loss of genetic diversity are potentially problematic, and monitoring the genetic status is necessary.The authors have no conflicts of interest associated with this manuscript to declare.


Anxiety disorders are highly prevalent with an early age of onset. Understanding the aetiology of disorder emergence and recovery is important for establishing preventative measures and optimising treatment. Experimental approaches can serve as a useful model for disorder and recovery relevant processes. One such model is fear conditioning. We conducted a remote fear conditioning paradigm in monozygotic and dizygotic twins to determine the degree and extent of overlap between genetic and environmental influences on fear acquisition and extinction.In total, 1937 twins aged 22–25 years, including 538 complete pairs from the Twins Early Development Study took part in a fear conditioning experiment delivered remotely via the Fear Learning and Anxiety Response (FLARe) smartphone app. In the fear acquisition phase, participants were exposed to two neutral shape stimuli, one of which was repeatedly paired with a loud aversive noise, while the other was never paired with anything aversive. In the extinction phase, the shapes were repeatedly presented again, this time without the aversive noise. Outcomes were participant ratings of how much they expected the aversive noise to occur when they saw either shape, throughout each phase.Twin analyses indicated a significant contribution of genetic effects to the initial acquisition and consolidation of fear, and the extinction of fear  with the remainder of variance due to the non-shared environment. Multivariate analyses revealed that the development of fear and fear extinction show moderate genetic overlap (genetic correlations 0.4–0.5).Fear acquisition and extinction are heritable, and share some, but not all of the same genetic influences. Anxiety disorders affect over 20% of individuals during their lifetime  to what extent are fear acquisition and extinction influenced by genetic and environmental factors; and (2) are the genetic and environmental influences on the acquisition and extinction of fear shared between all processes, or are there distinct influences on each phase? We used the recently developed Fear Learning and Anxiety Response (FLARe) smartphone app  compared to those who did not (n = 3380). Our participant groups were more likely to be older , female  and to have lower socioeconomic status  than those who chose not to take part. However, there were no statistically significant differences in anxiety scores between respondents and non-respondents .Participants were recruited by email invitation sent to 5934 twins enrolled in the TEDS . See Online Supplementary Table S1 for a description of the total sample including age, gender and self-reported clinician-provided diagnoses of any anxiety or depressive disorder.Twin zygosity was determined by parental responses to a twin similarity questionnaire. This has 95% concordance with genotyped zygosity . They were asked to complete the task in a single, undisturbed session. The fear conditioning procedure began with a fear acquisition phase during which 12 presentations each of a large and small circle (CS) were shown in a pseudo-randomised sequence on a background image of an outdoor scene. One of these (CS+) was paired with the aversive ‘unconditional stimulus’  during the final 500 ms of the trial on nine out of 12 presentations (75% reinforcement). The other was never paired with the US (CS−). The use of the large During each trial, participants were asked to rate how much they expected to hear the aversive stimulus on a scale ranging from 1 (completely certain NO scream will occur) to 9 (completely certain a scream WILL occur). Risk-estimates (sometimes known as expectancy ratings) for the CS− were subtracted from those of the CS+ to obtain differential scores, an index of how well participants were able to differentiate between the two experimental cues. Repetition is a core component of memory and learning  were age and sex regressed to avoid artificial inflation of twin correlations  twins share 100% of their segregating genes, dizygotic (DZ) twins share ~50%, while both MZ and DZ twins share their rearing environments . By the consolidation phase (second row), risk-estimates had further increased for the CS+ and decreased for the CS−. Average risk-estimates reduced for both stimuli over the course of extinction. During the first third of extinction trials, ratings were similar to average risk estimates during the initial development of fear. By the end of extinction, almost all participants had ceased to report any expectation of a scream occurring for either stimulus.As shown in See a2) for consolidation was 0.29 (0.18–0.39). In addition, there were significant shared genetic effects (ra) between all variables, with genetic correlations being highest between initial development and consolidation of fear [rG = 0.99 (0.97–1.00)]. In comparison, genetic correlation were significantly lower between each of the acquisition phases and extinction of fear . The effect of non-shared environment (E) was also significant for all variables (range e2 = 0.71–0.83), with significant non-shared environmental correlations (0.31–0.49) between all variables. See Estimates from this model are shown in h2: 0.34–0.36). This may be due to differences in our paradigm (in lab v. remotely administered) and resultant measurement error, or due to differences in outcome variables . However, taken together, the results from present and prior studies provide strong support for the role of genetic influences in the acquisition and extinction of fear.We present the largest twin study of fear conditioning to date, providing evidence for genetic and non-shared environmental influences. Discriminative fear learning is significantly heritable during the initial development, consolidation and extinction of fear conditioning. We demonstrated significant contributions from non-shared environmental influences (which includes measurement error), but no evidence for the contribution of common environment. Shared genetic effects between initial development and the consolidation of fear approached unity. This indicates that the genetic variants involved in the initial development of fear learning are virtually the same as those involved in the later consolidation of fear learning. The genetic correlations between both stages of fear acquisition and extinction were moderate and significant, indicating either that some of the genetic variants implicated in fear learning also play a role in the extinction of these learned associations. However, these genetic correlations were around 0.5, indicating that roughly half the genetic factors on extinction of fear are different from those on fear learning. Although a previous study provided preliminary evidence for complete genetic overlap between acquisition and extinction of fear, these results were difficult to interpret as CIs were not included for estimates from the multivariate model . This cognitive bias is a core feature of anxiety in adults and children  suggests that a relatively small degree of variation in fear extinction in healthy individuals is influenced by genetic factors. The only prior twin study of fear conditioning to date  influencing fear extinction in this study partially reflects high measurement error given our task  and non-shared environment (re = 0.31–0.41) correlations between fear acquisition and extinction. If fear extinction is considered a credible marker underlying treatment response (Craske, Hermans, & Vervliet, There were moderate genetic (We note genetic correlations between fear acquisition and extinction were below 0.5, indicating that distinct influences exist. Crucially, this indicates understanding the genetics of anxiety disorder is not sufficient to fully understand the genetics of psychological treatment response. It continues to be important to find ways of investigating treatment response as a distinct phenotype in large samples of individuals with sufficient depth of phenotyping to explore the role of variance in a range of environmental and individual factors within genetically informed designs.We presented robust evidence for genetic influences on fear acquisition and extinction, shedding light on possible mechanisms underlying known genetic influences on anxiety. We have also shown moderate genetic overlap between fear acquisition and extinction. This indicates that genetic susceptibility to developing fears is likely to have a moderate influence on reducing learned fears, for example through exposure-based treatment. Future studies using genotyped samples with fear conditioning or treatment outcome data will enable more detailed investigation of the genetics of the development and treatment of anxiety.In conclusion, these findings represent a significant advance in the genetics of fear acquisition and extinction, two key processes underlying the development and exposure-based treatment of anxiety disorders.


In a recent original research article in this journal, Myers et al. reported findings supporting the widely held assumption that white female trainees have higher odds of passing the American Board of Genetic Counseling (ABGC) certification examination on their first attempt than their non‐female non‐white peers  Board of Directors on these issues, focusing on the relationship between evolving practice standards for genetic counselors and our fervent wish to build a more diverse, equitable, and inclusive profession. We begin with a reference to how genetic counseling practice standards are currently set and existing gaps in the standards. Subsequently, we emphasize relevant history and action items resulting from two significant professional initiatives: an effort to define genetic counseling outcomes and develop rigorous genetic counseling metrics; and a comprehensive diversity, equity, and inclusion (DEI) assessment for NSGC, which offers a detailed exploration of common barriers and potential new pathways to the genetic counseling profession. Finally, we share our vision for how North American genetic counseling professional organizations might work together to realize the interrelated goals of professional excellence and social justice.1Despite a growing body of research on genetic counseling processes and outcomes, there are few consensus‐adopted, valid, replicable measures of processes and outcomes of high‐quality genetic counseling  sets standards for training program accreditation and curriculum development. The ACGC’s practice‐based competencies (PBCs) are used as a foundation for training program curricula and preparation for the ABGC certification examination. The certification examination is based on an ABGC practice analysis of currently employed genetic counselors. The practice analysis defines the job activities and responsibilities in which knowledge, skills, and expertise are required for competence in genetic counseling. The certification examination is used as a requirement for licensure in all US states licensing genetic counselors  within the profession. This included addressing barriers to entry and collecting data to explore and support solutions.Since its inception in 1979, NSGC has recognized the need to advocate for DEI, both for the populations that genetic counselors serve and within the profession itself. These efforts have evolved over the years, with a timeline of activities available on the NSGC Web site: Quantitative member data .Member and staff DEI survey (responses from 622 members and 24 staff).25 focus groups (representing 22 diversity dimensions).15 stakeholder interviews.Best practice research.Policy, process, and procedure review (63 documents).Benchmarking.In 2018, the NSGC Board of Directors included D&I as a strategic area of focus in its Strategic Plan, codifying it as a priority for the organization. A Task Force convened to explore this and future work, with efforts later expanding to include justice and equity and, thus, J.E.D.I. NSGC created a committee as a permanent group to continue these efforts. NSGC’s D&I Task Force recommended engaging an external DEI consultant to guide its efforts, and The Exeter Group (Exeter) was selected. Exeter undertook an organizational assessment for NSGC in 2020–2021, which included:Exeter's completed assessment yielded several themes. Those relevant to the AGCPD Task Force's efforts described by Myers et al. were as follows: ‘Address Barriers to Entry to the Profession’, ‘Provide DEI Training and Resources’ and ‘Develop DEI Metrics and Communications’. Many under‐represented individuals shared concerns relevant to the ABGC certification process. Several described difficulties with taking the ABGC Board examination. Quotes within the report indicated potential Euro‐centric bias in ABGC certification examination questions, such as a bias toward individuals whose language of fluency is English. Others shared exclusionary training program experiences and feeling unwelcome as they entered the field, with calls for transformation and accountability.Increase diversity within the genetic counseling field. Data from the member DEI survey show that 75% of respondents identified as White alone; 91% identified as women; 86% identified as heterosexual; 90% identified as having no disability; and 85% stated English as their primary language spoken.Address barriers and diversify pathways to entry including: low awareness of the field in communities with under‐represented individuals; training program requirements for admission; and the ABGC certification examinations.Develop DEI resources, metrics, and communications to build accountability. Limited collection of demographic data of NSGC volunteers and leaders hampers the understanding of diversity dimensions. There were also few to no DEI‐related continuing education opportunities and professional requirements.Collaborate on sustainable action to promote DEI throughout NSGC and the field. Grow and leverage partnerships across organizations to address potential bias in credentialing and certification examinations, as well as promote DEI in training programs.The assessment identified needs and action steps to:The full DEI report and an Executive Summary are openly available on NSGC’s Web site, along with a video summarizing key findings of the report and the open comment period that followed . We propose that these definitions should be aligned upon broadly throughout the profession.3.2’s findings.We propose that genetic counseling organizations commit to the collection and sharing of data related to recruitment and retention of genetic counselors representing diverse identities into the field. This is a necessary first step to identifying and assessing barriers and disparities in admissions guidelines, training, certification, ongoing education, and professional development. NSGC has made Exeter's DEI report publicly available and will continue to collect and share data through the Professional Status Survey and other means. We recognize and applaud genetic counseling training programs’ and organizations’ data collection and sharing efforts, an example being Myers et al.3.3We encourage the creation and sharing of resources for fostering greater justice, equity, diversity and inclusion in the training, supervising, education, mentoring, recruitment, and retention of genetic counselors. Such resources could include, for example, implicit bias and antiracism training, best practices for demographic data collection, and high school career fair outreach materials. We recognize that each genetic counseling organization has a unique purpose with specialty expertise and that access to monetary, volunteer, and staff assets vary. NSGC recognizes the need to bridge gaps and limitations to foster inclusive and equitable access by all partner organizations. Shared and easily accessible resources would maximize breadth and depth of content and eliminate duplicative efforts and redundancies, so that all genetic counselors could benefit and contribute to J.E.D.I. initiatives.3.4Finally, we see the measurement of quality genetic counseling in training and professional development as an area for future research efforts and collaboration. Myers et al. suggest that collectively there could be broader inclusion of non‐cognitive variables such as self‐confidence, leadership, community service, critical thinking, and writing skills. Incorporating these qualities may help to redefine professional success. Using alternative assessment methods beyond the ABGC board examination could be considered to lead to better assessment of competence without bias. Furthering relationships with federal granting agencies such as the National Human Genome Research Institute could create avenues for filling existing research gaps in evidence‐based genetic counseling measurement.4Our goal in writing this response is to highlight and acknowledge the progress of our predecessors while recognizing the tall hill we have yet to climb. We also acknowledge that other healthcare professions with some parallels to genetic counseling have already explored similar issues in their respective fields  reviewed the Commentary, gave final approval of this version to be published, and were in agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Heather Zierhut researches genetic counseling skills, processes, and outcomes and owns stock in Genome Medical / GeneMatters, LLC.Deepti Babu owns stock in ThinkGenetic, Inc., and is the founder of Integrity Content Consulting, a company that provides medical writing/content strategy services.Sara Riordan owns stock or equity in Thermo Fisher Scientific, ixLayer and Unified Patient Network, and works with health systems and biopharma in genomic and clinical data sharing initiatives.Heather Hampel is on the Scientific Advisory Board for Invitae Genetics, Genome Medical, and Promega. She has stock/stock options in Genome Medical and GI OnDemand.Aishwarya Arjunan is an employee and shareholder of GRAIL/Illumina and also owns stock in Myriad Genetics.Altovise Ewing is a full‐time employee of Roche/Genentech. She is on Medical Advisory Boards for The Chrysalis Initiative, Tulsa Innovations Lab (TIL) and Susan G. Komen's Stand for Health Equity Revolution Initiative‐ Genetic Counseling & Testing Advisory Group. She is a co‐founder of The Health Equity Solution and a paid consultant for the Genomes2People Consortium. She is a 23andMe and Roche shareholder.Carolyn Applegate, Meghan Carey, Leila Jamal, Barry Tong, Sandy Prucka, Jodie Vento, and Patrick Wilson have no conflicts of interests to disclose.Classifications: Diversity, Genetic Counseling, Diversity, Workforce.


The complicated interactions between genetic background, environment and lifestyle factors make it difficult to study the genetic basis of complex phenotypes, such as cognition and anxiety levels, in humans. However, environmental and other factors can be tightly controlled in mouse studies. The Collaborative Cross (CC) is a mouse genetic reference population whose common genetic and phenotypic diversity is on par with that of humans. Therefore, we leveraged the power of the CC to assess 52 behavioral measures associated with locomotor activity, anxiety level, learning and memory. This is the first application of the CC in novel object recognition tests, Morris water maze tasks, and fear conditioning tests. We found substantial continuous behavioral variations across the CC strains tested, and mapped six quantitative trait loci (QTLs) which influenced these traits, defining candidate genetic variants underlying these QTLs. Overall, our findings highlight the potential of the CC population in behavioral genetic research, while the identified genomic loci and genes driving the variation of relevant behavioral traits provide a foundation for further studies. Genetic studies of behavioral diversity demonstrated heritable variation for traits such as locomotor activities, cognition, anxiety, novelty seeking, and wildness. In the 1970s, behavioral geneticists began utilizing recombinant inbred (RI) strains for behavioral genetic analysis ,3. HowevMus musculus, capturing approximately 90% of the common genetic variation present in the Mus musculus species  score) for each genomic position. Genome-wide permutations with ectively . A LOD 2ectively .


STF) indicated a high genetic diversity between P2 and P3, and a moderate genetic diversity between P1 and P2, and P1 and P3. Furthermore, the 43 core germplasm were exploited for creating cytoplasmic male sterility (CMS) lines and cultivating new radish varieties. The high genetic diversity of 217 radish germplasms will not only provide valuable resources for future genetic mapping and functional genomic research, but also facilitate core germplasm utilization and the molecular breeding of radish.Radish is an economically important root vegetable worldwide. In this study, the 217 cultivated radish accessions were collected and genotyped. To detect the genotypes of these accessions, a total of 24 structure variation (SV) markers distributed on nine chromosomes were employed to analyze genetic diversity and construct a core germplasm collection of radish. The results of polymorphism information content (PIC) indicated a good polymorphism of these SV markers. Population structure analysis and principal component analysis (PCA) results showed that the 217 radish accessions fell into three main populations . Genetic diversity analysis showed that these populations were highly associated with geographical distribution. The values of the fixation index ( Raphanus sativus L., 2n = 18) is one of the most important vegetables worldwide, especially in Asia. Radishes are cultivated as vegetable crops, oil crops, cover plants, ornamental plants, and medical plants [R. sativus var. hortensis), European small radish (R. sativus var. sativus), oil radish (R. sativus var. chinensis), black radish (R. sativus var. niger), and rat-podded radish (R. sativus var. caudatus) [R. raphanistrum, R. maritimus, and R. landra have been discovered in the Raphanus [Raphanus is regarded as a good model for investigating population genetics and gene flow among species and their relatives [Radish (Raphanus . Raphanuelatives . Additioelatives .Zea mays L.) [Cucumis sativus L.) [Brassica oleracea var. italica) [The analysis of genetic diversity and population structure can reveal the genetic relationships of the investigated populations, and it is essential for large-scale association mapping and genomic selection . Generalmays L.) , cucumbeivus L.) , and broitalica) . AdditioWith the development of biotechnologies, three generations of molecular marker technologies have emerged, including restriction fragment length polymorphism (RFLP), simple sequence repeat (SSR), and single nucleotide polymorphism (SNP), respectively . CurrentThe objective of this study was to investigate the genetic background information of 217 radish accessions collected from Asia, Europe, and Africa. Using the 24 selected SV markers, DNA fingerprint profiles of these collected radishes were constructed, and their genetic diversity and population structure were analyzed. The relationships between different leaf phenotypes of radish subpopulations and their geographical distribution were further explored. Moreover, we identified the 43 core radish germplasms covering 91.88% of the effective alleles. Our findings lay a foundation for further genetic research and molecular breeding of radish.The 24 SV markers distributed on nine chromosomes were used to identify the genotypes of the 217 collected radish accessions . DifferiTo explore the genetic background of 217 collected germplasms, STRUCTURE 2.3.4  was emplTo validate the classification of 217 radish accessions, a principal component analysis (PCA) was performed. The results showed that the contribution rates of the first and second principal components were 9.3% and 8.2%, respectively . PCA resNa) was two (Ne) varied from 1.2064 to 1.9966, with an average value of 1.7120. The average values of observed heterozygosity (Ho) and expected heterozygosity (He) were 0.1430 and 0.4018, respectively. The value of Shannon’s information index (I) ranged from 0.3128 to 0.6923. Moreover, the polymorphism information content (PIC) ranged from 0.1711 to 0.4991 with an average value of 0.4011. These results indicated that these SV markers had a suitable polymorphism and thus they were qualified for further background selection and evolution analysis.In this study, the observed number of alleles  was conducted for further assessing the diversity of radish germplasms. The results showed that the variations among populations and within populations reached 7.36% and 93.64%, respectively . The ranTo promote the utilization and breeding of radish, we continuously collected radish germplasms for over twenty years. The classification of subpopulations was highly correlated with the geographical distribution of all radish accessions . P1 consp < 0.05) and P1P2 (p < 0.01). Moreover, leaf lengths and leaf widths displayed significant differences between P2 and other populations  in the all accessions. These results indicated that the screened 43 core germplasms were able to represent a major part of the genetic information of all accessions.The core accessions were screened on 217 radish accessions using the M-strategy algorithm of Core Hunter II ,21. BaseNa , we deteNa , of whicTo utilize the core germplasms in radish breeding, an Ogura cytoplasmic male sterility (CMS) line 04–28A was employed to construct the novel CMS lines for further hybrid breeding A. Three R. sativus var. niger) and rat-podded radish (R. sativus var. caudatus) collected in our study. Our AMOVA results showed that there was a high genetic differentiation between the P2 and P3 and moderate genetic differentiation among main populations . In our study, these three populations possessed a strong association with geographical distribution. For example, P1 and P2 were almost derived from Eastern Asia, and Europe, respectively  . Howeverectively . To our We developed 24 pairs of large fragment insertion/deletion markers (>50 bp), namely SV markers, derived from the whole-genome sequencing data in the study. This was the first report of the assessment on genetic diversity using SV markers in plants. To date, numerous studies have been performed to analyze genetic diversity and population structure in many higher plants, such as rice , cabbageIn general, the accuracy of the molecular marker detection is improved with the increase in the number of markers. Therefore, the genome-wide SNPs have been gradually exploited for genotyping via the next-generation sequencing (NGS) in higher plants. For example, the 43,735 high-quality genome-wide SNPs were applied to analyze the genetic diversity in maize . In radiHo) showed significant differences between the screened germplasms and all of the germplasms  between the screened germplasms and all the germplasms, indicating that the 20% screening rate could act as a criterion for screening the core germplasm.In this study, the Core Hunter II package  was applrmplasms . TherefoIncreasing yield and improving commercial quality by enhancing disease and pest resistance are two important breeding goals . For inshttps://CRAN.R-project.org/package=maptools, accessed on 9 May 2022).A total of 217 radish accessions were collected from Asia, Europe, and Africa over the past twenty years, of which 143 radish accessions (>65%) were collected from China , 40 acceThe phenotypes of six two-month-old individuals from each accession were investigated in our study. At the mature stage of taproot, three leaf-related phenotypes, including petiole length, leaf length, and leaf width, were examined. The experiments were performed with three biological replicates and three technical replicates. Leaf-related phenotypes were plotted using the ggplot2 package .The young leaf of radish was sampled at 63 days after germination (DAG) for DNA extraction. Genomic DNA was extracted by the CTAB method  with a m2O, and 1 μL of genomic DNA (50 ng/μL). The procedures of PCR were as follows: pre-denaturation at 95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s, 50–60 °C for 20 s, and 72 °C for 30 s, ending up with a final extension at 72 °C for 5 min. PCR products were detected by 2% agarose gel electrophoresis, and images were captured by the real-time gel imaging system Gel Doc XR+ .PCR amplification was performed by our previously study reported method . The 11 https://CRAN.R-project.org/package=factoextra, accessed on 7 May 2022) in R. Firstly, we converted the uniformly coded data into corresponding genotypes. Then, the dimension values were obtained from genotype data. Finally, the two main dimensions were selected for plotting the PCA diagram.A principal component analysis (PCA) of the 217 radish accessions was conducted by FactoMineR  and factThe polymorphism of the 24 SV markers of the collected radish populations was estimated using PopGene software. The original data type  was converted to the ‘bp’ type using DataFormater software for further analyses . The misSTF − 1) [The population structure was investigated using the STRUCTURE2.3.4 software  with theFST − 1) .Core germplasms of radish with the minimal size were screened using Core Hunter II in R package ,42. FiveIn this study, we constructed fingerprint profiles of 217 radish accessions collected during the past twenty years using 24 SV markers. The population structure and PCA results indicate that these 217 accessions fall into three main populations  and three admixtures . Genetic diversity analyses revealed a moderate or high genetic differentiation among these populations. In addition, the genetic diversity of our collected accessions was strongly correlated with geographical distribution and leaf-related traits, especially with leaf size. Furthermore, we screened 43 core accessions and further mined their utilization. Overall, we elucidated the genetic background of radish accessions and constructed radish core germplasm collections, based on which we cultivated three new radish varieties.


Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin , a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single‐seed descent‐purified accessions , will be developed, exploiting germplasm available both from genebanks and on‐farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri‐food and non‐food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.Food legumes are crucial for all agriculture‐related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant‐based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co‐benefits for human health. The characterization, maintenance and exploitation of food‐legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea ( Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin , develops new approaches to conserve, manage and characterize genetic resources. INCREASE is working with Intelligent Collections, consisting of nested core collections composed of single‐seed descent‐purified accessions, exploiting germplasm both from genebanks and on‐farm; these will be subjected to different levels of genotyping and phenotyping in order to meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers, citizen scientists and agri‐food and non‐food industries, including traits related to the value chain, to agroecosystem characteristics and to adaptation to climate change.INCREASE, focusing on important food legumes, i.e., chickpea ( Such transition is particularly relevant in the light of recent health concerns regarding the prominence of zoonosis . Production of industrial fertilizers contributes approximately 3% of global CO2 and is a primary source of the pollutant NO2 . This implies that a crucial aspect leading to added value to European and worldwide primary production will be the improvement of nutritional and quality traits, considering the development of innovative products, possibly linked to sustainable agroecosystems, and the local production in line with agricultural and food tradition, which are important factors in consumer preferences. Thus, the challenge is to meet citizens’ needs and preferences  regarding impact on health, environment and climate change mitigation. For this purpose, healthy and environmentally friendly food based on the local production of novel varieties is needed and existing genetic resources must be properly exploited in breeding within sustainable agroecosystems. In addition, the value chain needs re‐enforcing with new varieties with higher adaptation to the environment of cultivation, better yield, and improved qualities such as high organoleptic, technological and nutritional values.Almost half of the current global food production depends on planetary boundary transgressions infringing on biosphere integrity, land‐system change, freshwater use and nitrogen flows. As highlighted by Gerten et al. , the traHowever, especially in the case of food legumes, investment in breeding research has been modest, leading to a largely unexplored genetic potential of these important staple food crops. The European Union devotes only 3% of its arable land to protein crops, and imports more than 75% of its plant protein  and the EU has developed a new protein plan , being also in line with the Sustainable Development Goal 15.Thus, efficient and cutting‐edge genetic resource management is required to attract additional private and public investment. Without the correct handling of legume genetic resources, indeed, the development of sustainable agriculture to match economic, environmental, climatic and socio‐economic challenges including the promotion of healthier diets will be unattainable.vice versa; moreover, if available, single‐nucleotide polymorphism data are mapped to a single reference genome, which exclude variants present only in a subset of accessions, without the possibility to access structural variations and to obtain a complete picture of the functional genetic variation  the genetic structure of accessions; in most cases, accessions have an unknown genetic structure and are heterogeneous, which impedes the projection of the phenotypic information to the genotype and (ii) limited information on plant genetic resources and data quality; passport data  are often incomplete and phenotypic evaluations are often limited to morphological and a few major agronomic traits;(iii) limited access to available information; the heterogeneous nature and non‐standardized (biased) way of data collection and integration cause an under‐use of the huge amount of existing information and data; databases are centralized, not interconnected and not designed to integrate data obtained by external users, strongly limiting the access to available information, which remains separated in genebank/project information systems; also, the available information is not easily accessible to users due to unfriendly searching and visualization tools ‐purified accessions, bringing with it the possibility to better associate phenotypes to reliable genotypic information  and artificial intelligence (AI), in order to develop an innovative platform for conservation and sustainable use of genetic resources.The general objective of INCREASE is to improve the sustainable use of plant genetic resources in food and agriculture working with the four abovementioned important food legumes and promoting the conservation and use of all kinds of their genetic resources. The INCREASE goal will be achieved through the improvement of food‐legume genetic resource data management and sharing, by development of optimized databases, data‐management solutions and web‐based searching and data visualization tools, based on data findability, accessibility, interoperability and reusability (FAIR) principles to facilitate, with much more useful and easily accessible information, the access of stakeholders to plant genetic resources  stakeholders. Moreover, citizen science activities will connect farmers and consumers with the plant genetic resource conservation system to raise awareness in the general public and to stimulate the use of plant genetic resources among different stakeholders and to test a decentralized solution on data and germplasm sharing. INCREASE will, furthermore, acquire comprehensive and more precise genotypic and phenotypic information on materials, to improve our understanding of the connections between them and how they vary in different environmental contexts; the project will put in place appropriate (bioinformatics) tools for data processing, exchange and visualization. INCREASE will characterize the plant genetic resources of the four different food legumes, and a representative sample of diversity will be developed for each one, including wild and domesticated accessions, that will be characterized at the genotypic and phenotypic levels using a combination of ‘‐omics’ tools, image analysis and field phenotyping.Using an interdisciplinary approach, INCREASE aims to implement and improve data management solutions and visualization tools to facilitate user access, also involving different users and stakeholders in the dynamic management and conservation. Thus, INCREASE will pave the way for informed use of plant genetic resources based on the user and stakeholder inventory needs.Intelligent Collections  as a set of nested core collections of different sizes that represent the entire diversity of each crop , for each crop, ICs will be developed, a set of nested core collections of genetically purified accessions  from a large sample of representative accessions from genebanks and from dynamic conservation on‐farm. ICs will: (i) have a memory, as they are based on collections of derived inbred lines, with all genotypic and phenotypic information obtained in different experiments and by different actors referring to the same identical genotype; (ii) learn, from the analysis of the data integrated into the databases about the structure of genetic diversity and its relationship with phenotypic diversity and the environment; and (iii) improve and evolve, by correcting mistakes and making progressive adjustments of the sampling procedures according to the novel information that will be obtained.Along with genotypic data, sub‐cores of nested core collections will be phenotyped in evaluation activities carried out by different actors, both internal and external to the INCREASE project consortium. The genotypic information and related genomic predictions will constitute the links between phenotypically characterized and non‐characterized accessions, as a very powerful tool to exploit the whole collection of genebank accessions, as far as the genotyping data are available. Genome‐wide association (GWA) analysis will be performed using conventional  a Reference‐CORE (R‐CORE): the thousands (approximately 2000–4000) of genetically purified accessions obtained by SSD will be genotyped using a low‐coverage approach ; all of the R‐COREs of each species will be preserved as units in one or more genebanks that participate in the project, for long‐term and active conservation;(ii) a Training‐CORE (T‐CORE): comprising a subset of R‐CORE, the Training‐CORE will be based on a few hundreds of genotypes (approximately 400–500) with comprehensive passport and georeferenced information; these will be used for phenotypic characterization (including transcriptomics and metabolomics) and genomic characterization, using whole‐genome sequencing;(iii) a Hyper‐CORE (H‐CORE) of approximately 40–50 genotypes sampled on an evolutionary transect will be used for deep genomic and phenotypic characterization, to explore the ‘phenotypic space’  and to identify key traits of interest for future phenotyping.Thus, in detail, on each of the four INCREASE food legume crops, the project will develop nested core collections, including wild, landraces and cultivars Figure :(i) a RThese ICs will be shared as a tool among genebanks, safety duplicated in the Svalbard Global Seed Vault Fowler, , and wil(i) removing as much as possible redundancy of accessions;selection of R‐CORE lines (approximately 2000–4000 lines): (a) maximizing diversity for geographic origin and distribution, and environmental adaptation; (b) maximizing diversity on the basis of availability of previous genetic and phenotypic characterization; (c) including accessions with passport data  available; (d) including accessions from historical and current collections of on‐farm and ex situ populations; (e) availability of SSD seeds; (f) availability of genomic data; (g) including approximately 20% of heterogeneous accessions to cover gaps and balance sampling structure and to include samples collected from heterogeneous populations ;(ii) selection criteria for T‐CORE (approximately 400–500 lines): (a) maximizing diversity present in R‐CORE; (b) availability of SSD lines seeds; (c) availability of genomic data. Part of the T‐CORE will include lines that will be used for field experiments; these lines will be exclusively chosen from the domesticated gene pool, and among those adapted to European agroecosystems; the remaining lines of the T‐CORE will be wild lines, domesticated lines not adapted to European environments and CWRs, which will be used mostly for controlled condition experiments. Thus, T‐CORE phenotyping will be based on different sub‐samples based on the type of experiment and the level of complexity of phenotyping ;(iii) selection of H‐CORE lines (approximately 40–50 lines): this core collection will be used for deeper genotyping and phenotyping. Selection criteria will be to maximize the overall genetic diversity and to include highly characterized accessions . Also, the H‐CORE’s characterization will be based on the entire set of lines or sub‐samples depending on the type of experiments.(iv) As INCREASE aims to develop a worldwide sample of wild forms and landraces, as well as cultivars and improved varieties of different market classes and origins, with special attention given to germplasm adapted to European environments, including landraces cultivated on farm and crop wild relatives (CWRs), the selection of lines to be part of the ICs includes the following steps:Having assembled the above‐described ICs they will be subjected to extensive phenotyping at three levels: (i) multi‐location field trials (MLFTs), (ii) metabolomics and transcriptomics and (iii) root traits, disease and symbiosis as detailed here below.Comprehensive agronomic and morphological phenotyping of the T‐CORE accessions will be performed under field conditions using standard phenotyping to assess the adaptation and the agronomic performances of the genotypes of the four species along with the effect of genotype versus environment (G × E) interaction , applying crop‐specific phenotyping protocols based on standard crop descriptors, also included in crop ontologies  to match online databases   using an in‐house library (with >3500 compounds) and (ii) using the accurate Crop plants produce compounds that affect their market quality, taste and nutritional value, as well as numerous compounds that contribute to human health. Therefore, in addition to metabolomics and transcriptomic analysis and taking advantages of the metabolomics profiling, INCREASE will determine several quality traits including antioxidant content as well as that of other health‐related compounds and anti‐nutritional metabolites, such as seed alkaloids  gene enrichment sequencing.A specific test will be conducted under controlled conditions for root traits, using paper blotting for chickpea, common bean, lentil and lupin on the T‐CORE including those evaluated for root traits in MLFTs. The same set of lines will be evaluated under control conditions for disease resistance to anthracnose, white mold, powdery mildew and ascochyta in common bean and lentil to combine association genetics and resistance  for the four INCREASE species. The already available genotypic data of all the species  will be exploited and integrated, including the reference genomes of all the species.The most recent and advanced sequencing technologies will be applied to sub‐samples of H‐CORE genotypes to develop pan‐genomes of common bean and lentil and obtain new high‐quality High‐quality whole genome assemblies and pan‐genomes (core and dispensable genes) will be developed and we will use several approaches to exploit the structure of the genetic diversity and identify the functional diversity that could have a major role in the conservation, adaptation and improvement of food legumes. Nucleotide and structural variations  will be identified in order to highlight and explore crucial functional roles and associations with several phenotypic traits  involved in local adaptation and rely on demographic scenarios inferred and ML algorithms to detect footprints of selection. In addition, we also intend to associate allele frequencies to environmental variation by taking latitude and longitude as proxies. During these field evaluations of the association panel, traits such as grain production will be taken as proxies for fitness.In order to seek adaptive variation and its determinants, we will further identify the alleles underlying the ability of genotypes to perform in any given environment, thanks to GWA studies applied to both the core and pan‐genomes.R gene clusters using resistance‐gene enrichment  Single‐seed descent‐purified genotypes derived from on‐farm conservation, using at least two genotypes per population (farmer’s field), will be used to estimate the diversity within the population; population sequencing using Pool‐Seq  will be carried out to determine the allelic diversity within populations and within heterogeneous accessions, to follow, in the future, their changes over time and to test strategies to infer the haplotypic structure of heterogeneous accessions from Pool‐Seq.ex situ collections. Our goal will be to access such haplotype information using both sequencing of pools and multiple individuals (Pool‐Seq) from a single accession, alongside the collection of haplotypes recovered from the H‐CORE.Accessions are composed of a mixture of genotypes and current breeding schemes use one or a few SSDs out of an accession to represent it, capturing only a small fraction of the existing adaptive diversity within this accession. On the other hand, genetic diversity within one accession is usually described by allele frequencies, but characterization of the mosaic of haplotype blocks contained in an accession would increase the predictive power of their breeding potential. An example of such an application would be to screen uncharacterized accessions for haplotypes known to be associated with traits of interest. Another would be the improved monitoring of haplotype frequencies in Hordeum vulgare) IPK collection  reveal accessions with mismatching passport information and potentially correct those, and to reduce duplicated accessions and redundancy as in the barley . INCREASE will verify the potential of direct genotyping of heterogeneous accessions from pooled DNA samples, as a way of by‐pass SSD purification using R‐CORE haplotype data as the reference dataset, inside an open and traceable data‐sharing policy.genotype × environment interactions for adaptation to different environmental conditions. It is intended that this will not only encompass agronomic performance, but also include information concerning nutritional content and quality traits. Moreover, the combined use of transcriptomic and metabolomic approaches and their associated networks will allow identification of putative genes responsible for phenotypic plasticity , not only considering conventional traits, but also accounting for specific features of the value chain  and of the target agro‐ecosystem , with the possibility to explore R gene clusters, which can be difficult to characterize as they can have a highly repeated structure  based on the data stored in the project’s databases. It will provide features to navigate between and combine the different search results of the domains . The concept of the IC should make it possible to suggest suitable candidates of plant genetic resources to the user that match his or her requirement profile. However, experience from existing platforms, such as BRIDGE for barley  concept to a smart contract , schools, citizens) interacting with each other under the coordination of INCREASE. The aims are to create and work within a ‘legume research community’, genebanks, plant breeders, plant and crop scientists, farmers , gardeners, seed suppliers, food industry, database and computational experts, NGOs related to environmental conservation and associations of citizens that will contribute to the project and, at the same time, benefit from the data produced and from the outcomes reached, enhancing the management and use of genetic resources on these important food legumes and maximizing the project impact.www.pulsesincrease.eu/experiment; Figure Moreover, within INCREASE, a citizen science experiment on common bean has recently been launched  the international organization FAO, which has a major role in promoting the sustainable use and sharing of plant genetic resources and in the implementation of the related policy;(ii) two associations of stakeholders (TERRES INOVIA and MASP);(iii) three subject matter experts (SMEs) from the seed (ISEA SRL), IT (DCS‐Fuerth) and service (EURICE) sectors;(iv) 19 research institutions, spread across 12 countries , including European and international universities, institutions focusing on fundamental research and research institutions focusing on agricultural research, including five EU and three non‐EU genebanks.The INCREASE consortium is composed of 25 partners, highly diverse in the type of organization and highly complementary in expertise Figure . The INCde novo genome sequences, genomic and phenotypic data) with SSD‐purified accessions.INCREASE brings together and coordinates the main international efforts towards the acquisition of genomic information and phenotypic evaluation of hundreds of accessions of the four target species. The involvement of non‐EU partners expands the scope and ambition of INCREASE by integrating a significative amount of additional data and plant genetic resources (e.g., All authors contributed to the writing of this manuscript.All authors of this manuscript declare no conflict of interest.


The calculated cut-off value was 0.067, and there were no differences in the results of tests that utilized positive and negative sera (p < 0.05). The characteristics of two diagnostic tests were evaluated using a conditional dependent and one-population Bayesian model. The Se value of an in-house indirect ELISA were almost similar to ELISA test kit. On the other hand, the Sp value of the in-house ELISA test was lower than that of the commercial ELISA test with the median values of 89% (95% PPI = 75.9–99.3%) and 91.4% (95% PPI = 85.3–95.5%), respectively. A posterior estimate for the prevalence was 66.9% (95% PPI = 60.8–83.3%) and higher than initially expected.Lumpy skin disease (LSD) is a contagious disease among cattle and buffalo worldwide. Currently, an enzyme-linked immunosorbent assay (ELISA) has been recognized as an efficient diagnostic tool that is less time-consuming and easier than the viral neutralization test to measure the antibody levels. In the present study, an in-house method of indirect ELISA was developed to detect the bovine antibodies against Lumpy skin disease virus (LSDV) and its performance was assessed using field samples. This in-house method has been compared with the commercial ELISA test kit for detection of bovine antibodies against LSDV. The sensitivity (Se) and the specificity (Sp) of the test were estimated using a Bayesian latent class model. Checkerboard titration was performed using the naturally LSDV-infected bovine sera and colostrum-deprived calf sera. The LSDV antigen concentrations (1 TCID LSDV is a double-stranded DNA virus containing around 150 kilobase pairs (kbp) enclosed in a lipid envelope. It is relatively large (230–260 nm). In addition to LSDV, the genus Capripoxvirus also consists of the sheeppox virus (SPV) and the goatpox virus (GPV) exhibiting a high nucleotide identity (97%) , whereas the median value of the posterior estimate for Sp of the test was 89.8% (95% PPI = 75.9–99.3%) as is shown in For the purposes of convergence testing, the model exhibited proper convergence and autocorrelation was eliminated by burning out one every ten iterations. With regard to sensitivity analysis, no major changes (change in median or 95% probability percentiles >25%) were observed in the posterior estimates of the Se of the commercial ELISA test and disease prevalence when non-informative distributions were used as prior values for any of the parameters. This was interpreted as evidence of the robustness of the model. However, changes in the posterior estimates for the Sp of the commercial ELISA test were observed with a lower estimated posterior specificity (from 91.4% to 59.1%) .4The ELISA method has been empolyed as diagnostic tool to evaluate the immunity response against LSDV in several studies ,14,31,32The use of a specific antigen is crucial for the ELISA test because of its specificity to antibody detection. Thus the search for an appropriate coating antigen is very important. Previous studies have been conducted to identify a suitable antigen to be coated onto the ELISA plate. Consequently, an inactivated sucrose gradient-purified SPPV , a maturA Bayesian latent class analysis has been performed for the estimation of accuracy of a diagnostic test and has been used to evaluate the values of Se and Sp of various diagnostic techniques in veterinary medicine, especially with regard to the development of a novel diagnostic test ,11,13,15The posterior estimates for disease prevalence were higher than the prior values, which had been inferred using the outcomes of a single study conducted in the northeastern part of Thailand in conjunction with the opinions of an expert. This outcome might be explained by the fact that all samples were collected from infected herds and were considered to be high prevalence samples. Moreover, the results might suggest that the prevalence of the LSD in this region was higher than the northeastern region by what had initially been expected. With regard to the model sensitivity analysis, there were no major changes in the posterior estimates when non-informative prior distributions were applied in the model with the exception of the posterior estimates for the Sp of the commercial ELISA test. This would suggest that the prior values of this parameter did have a stronger influence on the results of the model. Thus, there may have been a strong effect of the prior selections on the posterior estimates, and further studies with larger populations ideally including more prior information are needed in order to verify the results from this study.550/well, a sample serum at a dilution of 1:500, and HRP-conjugated goat anti-bovine IgG at a dilution of 1:10,000. The calculated cut-off value was 0.067. The posterior estimates for the Se and Sp values of an in-house ELISA  developed in this study were notably higher than the Se value of the commercial ELISA kit. However, the Sp value was lower than the commercial ELISA kit of the commercial ELISA kit . From these results, we can conclude that our in-house ELISA produced higher sensitivity levels along with decreases in specificity levels than those of the commercial test kit. Furthermore, the commercial ELISA kit in conjunction with the method described herein, will assist local laboratories in establishing an in-house diagnostic tool for the monitoring of an antibody to LSDV.An in-house ELISA method has been successfully developed in this study. Our method used a local strain of an inactivated whole LSDV particle as a coating antigen at 1 TCIDThis study was conducted according to the guidelines of and approved by the Animal Care and Use Committee (FVM-ACUC) approval number S26/2563, Faculty of Veterinary Medicine, Chiang Mai University and Chiang Mai University Institutional Biosafety Committee (CMU-IBC) approval number CMUIBC A-0764012.Nattawooti Sthitmatee, DVM,MS,PhD: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Pallop Tankaew, M.T.,M.S: Performed the experiments; Contributed reagents, materials, analysis tools or data.Wittawat Modethed, DVM; Paramintra Vinitchaikul, DVM,MS,PhD: Conceived and designed the experiments; Performed the experiments.Amarin Rittipornlertrak, DVM,PhD; Anucha Muenthaisong, BS.,MS; Nisachon Apinda, DVM; Pongpisid Koonyosying, BS.,MS; Boondarika Nambooppha, DVM,PhD; Paweena Chomjit, BS; Kanokwan Sangkakam, BS: Performed the experiments.Tawatchai Singhla, DVM,MS,PhD: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Kittikorn Boonsri, DVM.,MS; Kidsadagon Pringproa, DVM,MS,PhD: Conceived and designed the experiments.Verrasak Punyapornwithaya, DVM,MS,PhD; Khwanchai Kreausukon, DVM,MS,Dr.med.vet: Conceived and designed the experiments; Analyzed and interpreted the data.National Research Council of Thailand (NRCT) [Grant No: FF66/021], Chiang Mai University [R000029530] and Faculty of Veterinary Medicine, Chiang Mai University [08/2566].This work was supported by the No data was used for the research described in the article.The authors declare no competing interests.


Sphenostylis stenocarpa (Hochst. ex A. Rich.) Harms.) (Fabaceae) is a versatile crop of nutritional, nutraceutical, and pharmacological value widely grown for its edible seeds and underground tubers. Its high-quality protein, rich mineral elements, and low cholesterol make it a suitable source of food for age groups. However, the crop is still under-exploited and constrained by factors such as intra-specific incompatibility, low yields, indeterminate growth pattern and long gestation period, hard-to-cook (HTC) seeds, and the presence of antinutritional factors (ANFs). To efficiently utilize its genetic resources for improvement and utilization, it is necessary to understand the crop's sequence information and select promising accessions for molecular hybridization trials and conservation purposes. A total of 24 accessions of AYB were collected from the Genetic Resources center of the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria, and subjected to PCR amplification and Sanger sequencing. The dataset determines genetic relatedness among the twenty-four accessions of AYB. The data consist of partial rbcL gene sequences (24), estimates of intra-specific genetic diversity, the maximum likelihood of transition/transversion bias, and evolutionary relationships based on the UPMGA clustering method. The data identified 13 variables (segregating sites) as SNPs, 5 haplotypes, and codon usage of the species that can be explored further to advance the genetic utilization of AYB.African yam bean ( Specifications TableValue of the Data•Sphenostylis stenocarpa using information from the partial rbcL gene sequences, nucleotide polymorphisms, haplotype diversity, and codon usage.The dataset provides insight into the genetic relationship among twenty-four accessions of African yam bean (AYB) – •The data identified 13 polymorphic sites (segregating sites) nucleotide pairs and 5 haplotypes that can be explored to broaden/enlarge the narrow genetic base of AYB via mutation breeding, nuclear techniques, and modern biotechnologies.•Twelve accessions  showed SNPs in the 13 variable sites.•The SNPs can be used to generate specific markers and genetic linkage maps for AYB.•Accession TSs 303 showed consistency in base substitution (transition and transversion) in all the 13 variable sites and accounted for 42% of the total variable sites.•The sequence data will benefit the scientific community in the area of agriculture and researchers can deploy rbcL gene sequences in the genetic characterization of the species to infer evolutionary history toward the unbiased selection of promising accessions of AYB for breeding and conservation purposes.1The dataset aimed to assess the partial rbcL gene sequences intra-specific variability and phylogenetic relationships among some accessions of African yam bean.2Sphenostylis stenocarpa (Hochst. ex A. Rich.) Harms.) belong to the Order: Fabale and Family Fabaceae. It is one of the nutritionally, and medicinally important orphan legumes in Central, Western, and Eastern Africa [S. stenocarpa. S. stenocarpa. The estimates of genetic diversity and Maximum Likelihood of Transition/Transversion Bias are represented in African yam bean (AYB)  of the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria, while twenty-four accessions were used for this study . One of 3.2Genomic DNA was extracted from the sample leaves  using Zy3.3PCR reactions for the rbcL regions were amplified using the composition as follows: 12.5 µL of NEB OneTaq 2X Master Mix with Standard Buffer , 2 µL of genomic DNA (10–30 ng/µl), 0.5 µL RbcL 1F (5′-ATGTCACCACAAACAGAAAC-3′) and 724R (5-GTAAAATCAAGTCCACCGCG-3) (10 µM), and 9.5 µL of nuclease-free water  to a final volume of 25 µL. The PCR reaction conditions and the integrity of the PCR samples followed standard procedures. The rbcl gene structure and primer binding sites are illustrated in 3.4PCR products were cleaned using an enzymatic method (ExoSAP) whereas the PCR amplicons were sequenced at Inqaba biotechnical Industries (Pty) Ltd, South Africa using the Nimagen, Brilliant Dye™ Terminator Cycle Sequencing Kit V3.1, BRD3-100/1000 according to manufacturer's instructions.3.5https://galaxy.pasteur.fr/).Sequences were cleaned and aligned using default settings in Geneious Prime 2022 The seeds were acquired under the Standard Material Transfer Agreement (SMTA) of the International Treaty on Plant Genetic Resources. No: SMTA-00AF05-00BO89-201210 collected from the Genetic Resources center (GRC) of the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria. The seeds were cultivated and leaves were harvested under standard conditions specified by the agreement. The experiment in this article does not involve animals.Jacob Olagbenro Popoola: Conceptualization, Methodology, Supervision, Validation, Writing – original draft. Davelyne Ifechukude Eruemulor: Supervision, Formal analysis. Omena Bernard Ojuederie: Writing – review & editing. Abiodun Sunday Oyelakin: Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.