Ethiopia is considered a center of origin and diversity for durum wheat and is endowed with many diverse landraces. This research aimed to estimate the extent and pattern of genetic diversity in Ethiopian durum wheat germplasm. Thus, 104 durum wheat genotypes representing thirteen populations, three regions, and four altitudinal classes were investigated for their genetic diversity, using 10 grain quality- and grain yield-related phenotypic traits and 14 simple sequence repeat (SSR) makers. The analysis of the phenotypic traits revealed a high mean Shannon diversity index (H′ = 0.78) among the genotypes and indicated a high level of phenotypic variation. The principal component analysis (PCA) classified the genotypes into three groups. The SSR markers showed a high mean value of polymorphic information content (PIC = 0.50) and gene diversity (h = 0.56), and a moderate number of alleles per locus (Na = 4). Analysis of molecular variance (AMOVA) revealed a high level of variation within populations, regions, and altitudinal classes, accounting for 88%, 97%, and 97% of the total variation, respectively. Pairwise genetic differentiation and Nei’s genetic distance analyses identified that the cultivars are distinct from the landrace populations. The distance-based  and Minimum Spanning Network (MSN)) and model-based population stratification (STRUCTURE) methods of clustering grouped the genotypes into two clusters. Both the phenotypic data-based PCA and the molecular data-based DAPC and MSN analyses defined distinct groupings of cultivars and landraces. The phenotypic and molecular diversity analyses highlighted the high genetic variation in the Ethiopian durum wheat gene pool. The investigated SSRs showed significant associations with one or more target phenotypic traits. The markers identify landraces with high grain yield and quality traits. This study highlights the usefulness of Ethiopian landraces for cultivar development, contributing to food security in the region and beyond. Triticum turgidum L. ssp. durum, Desf.) is an amphidiploid (AABB) domesticated species (2n = 4x = 28), originated through intergeneric hybridization and polyploidization between Triticum urartu (A genome) and Aegilops speltoides-related species (B genome) [T. turgidum ssp. aethiopicum [Durum wheat ( genome) . The pri genome)  stated ties 2n = x = 28, oEthiopia is one of the main centers of durum wheat genetic diversity ,6,7,8,9.The success of breeding programs relies on significant genetic variation in source populations . Higher Morphological markers are useful indicators of genetic diversity, but they are not the most reliable, as they are highly affected by the environment . In contThe present study assessed the genetic diversity of Ethiopian durum wheat based on quality- and yield-related morphological traits and SSRs previously reported to be associated with these traits. Ethiopian durum wheat genetic diversity has been previously assessed using SSR markers ,27,28,29This study provided valuable information on the genetic structure and extent of the genetic diversity of Ethiopian durum wheat genotypes. In addition, few landraces showed good levels of performance in yield and quality traits, which highlights the importance of landraces for developing cultivars. This will serve as a basis for further research aimed at identifying superior genotypes for the improvement of desirable traits, such as end-use quality.In this study, 104 genotypes of Ethiopian durum wheat were used, which included 94 landraces and 10 cultivars selected from those used in the study by Dagnaw et al. [−1) was recorded by weighing total economic seed harvested from a plot and converted to hectare. The GY and TKW traits were weighed at the standardized moisture content of 12%. The grain moisture content , gluten content  and grain protein content  were measured using near-infrared spectroscopy (NIRS) as described in AOAC [The data on spikelets per spike , plant height , spike density (SPD), and vitreosity (VTR) were recorded following the descriptor list for wheat . The dat in AOAC  method 9Five healthy seeds from each of the 104 genotypes were randomly selected and planted in pots in a greenhouse at Addis Ababa University (AAU), Ethiopia. Two-week-old leaf samples were collected and dried using silica gel. Genomic DNA was extracted using the cetyltriethyl ammonium bromide (CTAB) method following a protocol optimized by Abdie et al.  at the PFourteen SSR markers associated with grain quality- and yield-related traits of durum wheat from previous research ,38,39,402, 0.25 μL dNTPs (2.5 mM), 0.15 μL of each forward and reverse primer (10 mM), 0.15 μL Taq-polymerase enzyme (3 U/μL) and 1 μL of DNA template (100 μL/ng). The PCR condition was adjusted to 4 min preheating and initial denaturing at 94 °C; 35 cycles of 30 s denaturation at 94 °C, 30 s annealing at a temperature specific to each primer pair, and 90 s primer extension at 72 °C; and 8 min final primer extension at 72 °C. The PCR amplification was carried out in a PCR tube with 10 μL total reaction volume containing 7.3 μL nuclease-free water, 1 μL 10× PCR buffer with 17.5 mM MgClThe PCR products were separated on 8% polyacrylamide gel socked in 0.5× TBE buffer for 3.5 h at a constant voltage (110 V) and current (30 mA). The electrophoresed gels were stained by socking and gentle shaking in 0.5 μg/mL EtBr solution for 20 min and rinsed with distilled water two times. The gels were finally visualized using the Bio-rad Gel-Doc EZ gel documentation machine , and the molecular weight of the bands on the gels was estimated in base pairs, using Image-Lab 6.1 software . The frequencies of phenotypic classes of each morphological trait were calculated based on all genotypes as a single group, populations, regions, and altitudinal classes . Using tPrincipal component analysis (PCA) was computed to identify the diversity pattern among genotypes and the contribution of morphological traits. The data of morphological traits were scaled to unit variance and means of zero using the scale function of Stats package  in R andTo estimate the resolution of the 14 SSR markers used in the present study, a genotype accumulation curve was plotted using the genotype_curve function of the pegas package  in R. MeTo determine genetic relationships between genotypes, the Minimum Spanning Network (MSN) based on Bruvo’s distance  was compThe population structure was analyzed using STRUCTURE version 2.3 software . To estiThe standardized Shannon diversity index (H′) of the 10 morphological traits indicated significant phenotypic variation with an overall mean of 0.78. The value of H′ is classified as high (H′ ≥ 0.60), intermediate (0.40 ≤ H′< 0.60), and low (H′ < 0.40), according to Eticha et al. . All pheThe estimates of the within-region diversity index were high (H′ > 0.60) for all traits except DM (H′ = 0.39) and VTR (H′ = 0.50) . The Orop = 0.001) across durum wheat genotypes.Principal component analysis (PCA) was used to understand the contributions of 10 agronomic and quality traits in terms of magnitude and direction to the variation and grouping of the 104 genotypes. The first two principal components (PCs) explained 49.4% of the total phenotypic variation among the genotypes . The firThe 14 SSR markers used in this study were distributed across the first six chromosomes of the A genome and the seven chromosomes of the B genome of durum wheat . As showThe percentage of polymorphic loci (PPL) ranged from 86% (Southern Tigray) to 100% , with an average value of 95% . The nump < 0.001). The highest genetic differentiation was for Cultivars versus Central Tigray, Bale, and South Wello. In general, the Cultivars population was the most differentiated from the other populations, which is also seen in Sothern Tigray, East Shewa and South Wello populations. However, North Gondar, Central Tigray, East Gojam, and Bale showed lower pairwise genetic differentiation than other populations. The genetic distance and differentiation between the populations were estimated by pairwise population measures of Nei’s genetic distance and Fst coefficient. The highest pairwise Nei’s genetic distance was obtained between Cultivars and South Wello (0.53), and Cultivars and Bale (0.51) . The paip < 0.001) genetic differentiation coefficient (Fst) and high gene flow between populations (Nm = 1.83), regions (Nm = 8.37) and altitudinal classes (Nm = 7.70) (p = 0.412).AMOVA revealed 12%, 3%, and 3% of total variation among populations, regions, and altitudinal classes, respectively. In contrast, 88%, 97% and 97% of the total genetic variations were observed within populations, regions, and altitudinal classes, respectively, with an overall significant ( = 7.70) . In addiThe MSN was used to reveal the genetic relatedness between the 104 Ethiopian durum wheat genotypes representing 13 populations using Bruvo’s distance method. The MSN analysis grouped the genotypes into two clusters at 0.1 cut point distance . Most laThe Bayesian model-based analysis of population structure across 104 genotypes identified two groups as an appropriate number of clusters based on the Evanno method  Figure . The STRAll SSR markers showed significant relationships with their associated traits. For instance, the Xgwm46 locus significantly correlates with GPC and GY . GenotypThis study estimated the extent of genetic diversity and the population structure of 104 Ethiopian durum wheat genotypes using SSR and morphological markers. The SSRs were used to evaluate genetic diversity and genotype population structure. It is extremely valuable to determine genetic variation at loci contributing to grain quality and yield in durum wheat to identify novel alleles that can be used for breeding purposes to improve the crop. A total of 14 SSR markers distributed across the durum wheat genome were used to estimate the genetic diversity and population structure of the durum wheat genotypes. Several previous reports have shown that SSRs are among the most effective DNA-based markers for genetic diversity studies and the fingerprinting of crop plants ,25,26,57Compared to this study, a higher number of alleles were observed by Mondini et al.  Na = 8.. AlamereSeveral studies carried out on durum wheat from other countries also reported different mean values for Na, depending on the number of SSR markers and the landraces or cultivars used. For example, Christov et al.  reportedThe number and frequency of alleles at a given locus in a given population provide valuable information about the genetic diversity of that particular population. The observed high mean values of most diversity indices  across the populations reveal high genetic diversity within Ethiopian durum wheat populations. Previously, high genetic polymorphism in Ethiopian durum wheat populations was reported by Mondini et al.   , which iThe MSN and DAPC analyses were used to identify the genetic relationship between the genotypes used in this study. In the MSN analysis, the landraces were divided into two clusters based on their geographical proximity, and with a distinct distinction between cultivars . Both phThe PCA divided the genotypes into three groups based on the magnitude and direction of the contribution of different traits to the principal components . The posThe model-based clustering method (STRUCTURE) classified the genotypes into two genetic groups with different admixture levels. Some landrace populations showed low admixture, but all received alleles from the two genetic groups . AlthougThe investigated SSR loci showed significant associations with one or more target phenotypic traits. The SSR Xgwm46 showed a significant association with GPC and GY , which aThe molecular and phenotypic diversity analyses carried out in this study revealed considerable genetic variation in Ethiopian durum wheat genotypes. In both molecular and phenotypic diversity analyses, cultivars and landraces were distinguished clearly. The PIC value suggests the potency of the markers in detecting polymorphism. A total of 56 alleles were identified among studied genotypes. The SSR markers used were highly polymorphic, and revealed genetic variation within the populations and genetic differentiation between the populations. The lower observed heterozygosity compared to the expected heterozygosity at all loci is consistent with the inbreeding characteristics of durum wheat. The population structure analysis grouped the genotypes into two clusters. The grouping of landrace genotype 90 with cultivars in both morphological- and SSR-based investigations should be further evaluated and included in breeding programs that may facilitate the development of cultivars with better local adaptation. Interestingly, all SSR loci showed significant associations with one or more phenotypic traits, confirming previously reported associations. As a result, the favorable alleles at these SSRs can serve as markers for marker-assisted selection in durum wheat breeding programs, both for identifying genotypes that can serve as parents for crossbreeding and for pyramiding desirable characteristics of agronomic and quality traits into a standard breeding line.


Background: Hereditary breast and ovarian cancer and Lynch syndrome are associated with increased lifetime risk for common cancers. Offering cascade genetic testing to cancer-free relatives of individuals with HBOC or LS is a public health intervention for cancer prevention. Yet, little is known about the utility and value of information gained from cascade testing. This paper discusses ELSI encountered during the implementation of cascade testing in three countries with national healthcare systems: Switzerland, Korea, and Israel.Methods: A workshop presented at the 5th International ELSI Congress discussed implementation of cascade testing in the three countries based on exchange of data and experiences from the international CASCADE cohort.Results: Analyses focused on models of accessing genetic services (clinic-based versus population-based screening), and models of initiating cascade testing (patient-mediated dissemination versus provider-mediated dissemination of testing results to relatives). The legal framework of each country, organization of the healthcare system, and socio-cultural norms determined the utility and value of genetic information gained from cascade testing.Conclusion: The juxtaposition of individual versus public health interests generates significant ELSI controversies associated with cascade testing, which compromise access to genetic services and the utility and value of genetic information, despite national healthcare/universal coverage. Approximately a decade ago the Centers for Disease Control and Prevention (CDC) categorized hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS) as Tier-1 genetic conditions based on the validity of genetic testing results and the utility of genetic information for healthcare practitioners and public health . HBOC acHBOC and LS are monogenic disorders caused by pathogenic variants in autosomal dominant genes . HBOC- aCascade testing can reduce disease risks to entire cohorts of at-risk relatives , but alsIn order to fully understand the value of genetic and genomic interventions for the individual, family, and society, to upscale the implementation of cascade testing programs, and to ensure a global benefit from technological advances in genomics, a meaningful consideration of ELSI associated with such interventions is required. This paper focuses on ELSI identified in Switzerland, Korea, and Israel from the implementation of cascade testing for HBOC and LS. The three countries have national healthcare systems  that entitle all citizens to a basket of services. National laws for the protection of genetic information are similar to the US Genetic Information Non-Discrimination Act (GINA) . The speThe international CASCADE cohort was initiated in Switzerland in 2016, and was implemented in Korea and Israel in 2020 and 2022, respectively. CASCADE is a family-based, open-ended cohort focusing on HBOC (and LS for Switzerland and Israel). The cohort has been approved by local ethics committees  and the protocol has been published  . SettingRates of cascade testing are assessed with self-administered questionnaires that are available in local languages. Additional outcomes include cancer status and surveillance, risk-reducing surgeries, coordination of medical care, psychosocial and family factors, and quality of life. Participants are asked to complete a questionnaire after they provide written consent and they enter the cohort (baseline questionnaire). They are asked to complete follow-up questionnaires on a continuous basis, approximately 24 months apart, for as long as the study is running or for as long as they want to remain in the cohort. In Switzerland and in Korea narrative data are also collected from a purposeful sub-sample of participants, to get an in-depth understanding about dissemination of genetic information and access to services .n = 91 tested individuals. Israel was in the first stages of recruitment and data from the Israeli cohort were not available.On 31st May 2022 a workshop presented at the 5th International ELSI Congress discussed insights from the international CASCADE cohort based on discussions among the study investigators and initial responses from index cases and relatives. The cohort was initiated in Switzerland, which had acquired most data at the time of the workshop. The development of the Swiss CASCADE and rates of genetic testing among relatives have been presented . In shorThe workshop focused on presenting ELSI associated with models of accessing genetic services and disseminating genetic information to at-risk relatives that are in effect in Switzerland, Korea, and Israel. Comparison of contextual factors is an important first step in understanding rates of cascade testing and management of hereditary cancer risk in the three countries. This paper provides a synopsis of the discussion addressing each specific aim, synthesized into concrete key points and supported by references. Hence, only data relevant to ELSI are presented.The most common model for offering genetic services is the “clinic-based” model. Genetic specialists determine eligibility for services based on personal and/or family history indicating a hereditary cancer syndrome. Many countries require genetic counseling prior to and after genetic testing by law. Providers are the main “gate-keepers” and access to genetic services depends on referrals. Self-referrals are also possible, with cost-coverage depending on individual insurance plans. Costs of testing range from $300-$3,600 for targeted testing and full sequencing, respectively. Co-payments vary from 10% to 60%; in Israel co-payments can be 0% for individuals of Ashkenazi Jewish background. Co-payments of testing relatives range from 0% to 100% depending on family history and degree of relationship with the index case. Costs for risk-reducing surgeries for individuals with pathogenic variants are also not equally covered, i.e., risk-reducing mastectomy for unaffected carriers is not covered in Korea.n = 378 tested individuals from the CASCADE cohort (287 from Switzerland and 91 from Korea) show that more than 65% in both countries reported having genetic testing because it was recommended by a healthcare provider. Other common reasons for having genetic testing related to knowing about one’s personal cancer risk and family benefits  did not have cascade testing; among untested relatives 68.2% (n = 30) were first-degree relatives and 93% (n = 41) were cancer free  of Swiss and 32% (n = 29) of Korean tested individuals did not remember receiving such a recommendation  . The proHowever, population-based screening has also shortcomings. Individuals can have genetic testing without pre-testing counseling, which significantly compromises decisional autonomy since there is no solid informative basis for making an informed choice. When genetic testing reveals a pathogenic variant, the carrier receives post-testing counselling regarding interpretation of results, risk management options, and implications for relatives. However, when genetic testing does not identify a pathogenic variant, there is no post-testing counseling and no interpretation of the negative result as indicating an “uninformative negative” versus a “true negative” case. This may create misconceptions regarding management of cancer risk and implications for relatives, which can compromise the utility of genetic information. The program also raises concerns regarding health disparities, since Ashkenazi Jewish men, non-Ashkenazi Jews, and minority communities, e.g., Arab subgroups are not included .The legislation in Switzerland, Korea, and Israel, and in many countries worldwide, characterizes genetic information as private and delegates the responsibility for disseminating cancer risk to the tested individual. This strategy has significant limitations in both ensuring contact with the appropriate individuals and the transmission of accurate information . Less thn = 17) of Swiss participants and 17% (n = 16) of Korean participants reported that they would rather not discuss genetic testing results with relatives. Similarly, 17% (n = 49) of Swiss and 19% (n = 17) of Korean participants who had genetic testing stated that they did not believe that genetic testing was important for their relatives. Carriers of pathogenic variants who are also affected by cancer may embrace that genetic information provides the opportunity to prevent disease in relatives, whereas healthy mutation carriers may not realize the value of this information for preventing disease in relatives  law in the U.S.  suggesteIn Switzerland, Korea, and Israel, the legal basis for the protection of genetic information is similar to the GINA and HIPPA , leavingn = 287 Swiss CASCADE index cases (had genetic testing) show that they prefer to maintain an active role in disseminating genetic information to relatives  of Korean index cases reported that they would like their physician to contact their family members and explain their genetic testing results (Provider-mediated dissemination of genetic information should not be equated to bypassing index cases in the dissemination process. Data from elatives . Similarelatives . Data fr results . It is nImproving access to genetic services and cascade genetic screening are significant milestones for increasing the number of individuals who benefit from advances in genomic technologies at a global level. This paper discusses two models of accessing genetic services, a clinic-based model versus population-based screening, and two models of disseminating genetic information to relatives, the patient-mediated dissemination versus provider-mediated dissemination. Each model has its own specificities, advantages, and limitations .The Israeli population-based screening program for HBOC values the public health interest and prioritizes access to genetic services over individual concerns about privacy. Cancer is defined as a reportable condition; therefore, population-based genetic screening is ethically justified . PopulatProvider-mediated dissemination of genetic information may help reconcile the inherent tension between carriers’ and relatives’ interests regarding ownership of genetic information . AdvantaThe most common practice worldwide is the clinic-based model with patient-mediated dissemination of genetic information . The resThere is a need to address ELSI regarding genomic healthcare in light of disparities in accessing services and implementing cascade genetic screening. The CASCADE cohort highlights practices that may compromise access to services for segments of the population. The clinic-based model with patient-mediated dissemination of genetic information, maximizes privacy but compromises the utility of genetic information, especially for countries with publicly-funded healthcare systems. Population-based genetic screening programs compromises informed decision-making and the ability for self-reflection and self-determination. Within these models, we suggested enhancing relational autonomy and individual reflexivity as key ingredients of promoting the utility of genetic information, while complying with privacy laws, ethical boundaries, and healthcare systems.


Genetic testing for Parkinson’s disease (PD) is increasing globally, and genetic counseling is an important service that provides information and promotes understanding about PD genetics and genetic testing. PD research studies have initiated outreach to underrepresented regions in North America, including regions in Latin America, such as the Dominican Republic (DR); some studies may include return of genetic test results. Thus, understanding what individuals know about PD, genetic testing for PD, and their interest in speaking with a genetic counselor, is crucial when assessing readiness. In this cross-sectional study, a survey was distributed to people with Parkinson’s disease (PwP) and their unaffected biological relatives in the DR. Questions assessed genetics knowledge, attitude toward genetic testing, and interest in genetic testing and counseling. Of 45 participants, 69% scored the maximum on the attitude scale, indicating an overall positive attitude toward genetic testing; 95% indicated interest in genetic testing for PD, and 98% were at least somewhat interested in meeting with a genetic counselor. The mean PD genetics knowledge score was similar to previously published data. Through free text responses, participants expressed a desire to know more about PD treatment and management, prevention, cause, and their personal risk for PD. These results provide further evidence of readiness for genetic testing in this country but also underscore some gaps in knowledge that should be addressed with targeted educational efforts, as part of building genetic testing and counseling capacities. Current research suggests that up to 10% of people with PD (PwP) have a monogenic hereditary form of the disease4. Genetic testing can be used to identify pathogenic variants in PwP, which can provide an explanation for their diagnosis and allow unaffected family members to undergo familial variant testing to assess their own risk to develop PD. Another purpose for genetic testing in PwP is determining eligibility for gene-targeted therapy trials and genomic studies. The landscape of drug development for neurodegenerative diseases is promising, which will hopefully lead to an increase in genetic testing and personalized medicine to identify the most effective treatment for PwP6.Parkinson’s disease (PD) is the second most prevalent neurodegenerative condition, affecting 1 in 100 people over the age of 608. Multiple studies have reported a significant interest in genetic testing among PwP and their at-risk family members13 but have also found participants’ genetics knowledge to be low13. Genetic counseling can address these gaps in knowledge and ensure understanding of the benefits, limitations, and risks of genetic testing for PD11. Two studies, to date, have surveyed PwP in the United States (U.S.) about their interest in meeting with a genetic counselor11 and found that 56% and 43% were interested, respectively. However, the results of these studies are not necessarily generalizable to other populations or predictable. Firstly, the majority of participants in these previous studies were White and well-educated, reducing the usefulness of these results to individuals of differing racial, cultural, and socioeconomic backgrounds. Another consideration is that data from research in Huntington disease suggest interest may not translate into actual uptake of genetic testing and counseling15, although data from a large clinical trial in North America supports a robust interest and participation in PD testing in North America, with high participant satisfaction, when barriers to testing and counseling are reduced8. This clinical trial known as the PD GENEration study (PD GENE) launched in 2019 and offers free genetic testing and counseling in a clinical setting to those with PD. Currently, it is rapidly expanding in the U.S. and into Latin America, including the Dominican Republic (DR), surpassing recruitment goals (https://www.parkinson.org/advancing-research/our-research/pdgeneration). Other international studies, such as GP2, could involve return of PD genetic test results to research participants in Latin American countries in the future (https://gp2.org/).However, there can be many barriers to accessing genetic testing and counseling and its benefits. Some of these include lack of knowledge, interest, and availability of services, as well as cost16. As of 2016, only one out of 1,853 public health facilities in the DR had a dedicated clinical genetics service and, outside of this facility, genetic counseling was provided by a limited number of physicians with little to no formal training in medical genetics17.As genetic testing for PD continues to expand in the DR and other Latin American regions, it is crucial to understand what PwP and their at-risk relatives know about PD, genetic testing for PD, and their interest in speaking with a genetic counselor regarding their risk for PD. This assessment is particularly important to gauge perceptions and readiness in regions where access to and experience with genetics services are limited. Genetic counseling is not yet recognized as an independent profession within the DR or in most of Latin America17, is growing due to new research initiatives8. The results of this study will help evaluate the demand for PD genetics services, identify gaps in genetics knowledge, and inform educational strategies and capacity-building within the DR.The primary aim of this cross-sectional study was to assess readiness for genetic testing and counseling in PwP and their at-risk relatives in the DR, where access to clinical genetic services, though currently limitedThe survey was distributed to 200 PwP via WhatsApp text blast and to 20 PwP via paper fliers that indicated that PWP or unaffected biological relatives were eligible to participate. PwP were encouraged to share the study invitation with their unaffected biological relatives. We received 45 total responses, yielding a 20.4% response rate. Of the 45 individuals, 25 (56%) self-reported a confirmed medical diagnosis of PD (PwP) and 20 (44%) had only a family history (≥1 blood relative) of PD (unaffected relatives).N = 21/44). The majority  identified as either Mulatto or Mestizo, and 84% (N = 38/45) lived in a city. Approximately one-third  of participants reported primary school as their highest level of education and 28% (N = 12/45) had a university or post-graduate degree. Among PwP, the mean age was 60, and mean age at diagnosis was 56 years . The majority of PwP reported that PD made them feel at least somewhat less independent  and that PD had decreased their enjoyment of life at least somewhat . Most PwP  did not have a family history of PD.Participant demographics and characteristics are summarized in Table N = 44), 52% reported knowing “very little”, 41% reported knowing “some”, and 7% reported knowing “a lot”. Most participants  said they had heard of genetic testing before, while 33% (N = 15/45) said they had heard of genetic counseling. Unaffected relatives were more likely to report knowing about genetics (p = 0.048) and more likely to have heard of genetic testing (p = 0.020) compared with PwP.When participants were asked how much they knew about genetics (N = 32/42) of participants scored ≥3 out of a possible 4, putting them in the “high” genetics knowledge category. Most knew that genetic testing may find gene mutations that increase a person’s chance of developing a genetic disease  and that healthy parents could have a child with a genetic disease . Slightly more than half  answered correctly that some people with a genetic mutation may not develop the genetic disease, but 44% (N = 20/45) were unsure. The mean PD genetics knowledge score was 2.75 (SD = 1.10) with two participants  receiving the maximum score of 5. More than half  were unsure whether scientists had found mutations that increase the risk of getting PD. About 16% (N = 7/44) knew it was incorrect that a normal genetic test for PD meant that person would not get PD. Most participants knew it was incorrect that if a person has PD, all their family members will get PD one day . Participants who self-reported knowing either “some” or “a lot” about genetics had similar PD genetics knowledge scores  as participants who reported knowing “very little” (Wilcoxon p = 0.13). There was no association of general genetics knowledge (“high” vs “low” genetics knowledge) or PD genetics knowledge score with any other variables . PD genetics knowledge results are depicted in Fig. For general genetics knowledge, 76% (.75 SD = .10 with N = 31/45). Almost everyone  agreed that people who want genetic tests should be able to get them, and that they would like to be able to find out through a genetic test if they might have a disease . The majority  said they would have a genetic test that told them how quickly a disease would progress. One-quarter  of participants were either uncertain or would not want a genetic test for a disease if there was no treatment. Participants who reported primary school as their highest level of education had significantly lower attitude scores than those with a secondary level education or higher . There was no association of genetic testing attitude score with any other variable . All results are depicted in Fig. Participants responded to four questions regarding attitude toward genetic testing. Likert responses were summed to represent a total genetic testing attitude score, with a higher score representing a more positive attitude. The mean genetic testing attitude score was 7.3 (SD = 1.34), with 69% of participants scoring the maximum of 8 (N = 7/45) reported that they had already undergone genetic testing for PD. Almost all participants  without genetic testing expressed at least some interest in doing so, with 76% (N = 29/38) being “very interested.” There were three participants  who had already met with a genetic counselor to talk about PD. When the remainder were asked how interested they were in meeting with a genetic counselor, 30% (N = 12/40) were “somewhat interested” and 68% (N = 27/40) were “very interested.” There was no significant difference between PwP and unaffected relatives in their interest in genetic testing (Fisher’s exact p = 0.83) or interest in genetic counseling (Fisher’s exact p = 0.30).Seven participants  of responses. Other common categories included “Develop new treatments,” “Learn about the disease,” and “Guide treatment” .The answers to two free text questions were analyzed via inductive content analysis as described in the “Methods” section. When asked, “What are some ways you think genetic testing for PD would be useful?,” 12 categories were identified among 33 total responses. Responses of “I don’t know” and unintelligible responses were combined into one category that was excluded from further analysis. The remaining 11 categories are presented in Table N = 7/38) was “Treatment/management,” and the next most common category was “Prevention” . All responses to these two questions are available in the Supplementary Results.When asked, “What questions would you want to ask a genetic counselor about PD?,” 17 categories were identified among 38 total responses. Responses of “I don’t know,” “None,” and unintelligible responses were combined into one category that was excluded from further analysis. The remaining 16 categories are presented in Table 11 had more males , more individuals with at least some college education , and more White individuals . There was no significant difference in current age (Wilcoxon p = 0.54) or age at diagnosis (Wilcoxon p = 0.87) between the two study samples. PD genetics knowledge scores were similar between the two samples (Wilcoxon p = 0.51). The proportion of PwP in the DR study  who said they were “very interested” in meeting with a genetic counselor was higher than the proportion in the Maloney study who expressed interest in meeting with a genetic counselor (43%) (Fisher’s exact p = 0.0006). There was no difference between the two samples regarding genetic testing interest (Fisher’s exact p = 0.94).The Maloney studyAs PD genetic testing increases in the U.S. and globally, it is important to evaluate readiness in regions where access to clinical genetics services is currently limited but may be expanding. This cross-sectional study aimed to assess general and PD genetics knowledge, attitude toward genetic testing, and interest in genetic testing and counseling among Hispanics in the DR.2. Predictive testing for untreatable neurodegenerative conditions remains controversial and choosing to undergo such testing is a personal decision18. Knowledge of genetic status may help inform financial and reproductive planning for those at risk to develop PD, but it may also lead to negative outcomes, including genetic discrimination and psychological distress for individuals and their families19.Overall, participants expressed an overwhelmingly positive attitude toward genetic testing, in general, and were in favor of open access. Responses to the quantitative attitude assessment indicated strong support for genetic testing in the context of perceived value in personal risk predictions that may accompany genetic testing, as well as determining disease prognoses. A majority of participants remained in support of genetic testing even for conditions where no treatment is available; however, this statement received the largest proportion of “uncertain” or “negative” responses relative to the other attitude statements. While the quantitative attitude assessment was not specific to PD genetic testing, it is important to note that testing does not necessarily predict prognosis and that there are currently no therapeutic treatments available to delay the onset of PD or slow its natural progressionThis finding of a generally positive attitude toward genetic testing, but hesitancy toward predictive testing for untreatable conditions, identifies a need for genetic counselors to discuss the benefits, risks, and limitations of genetic testing for PD, especially in asymptomatic individuals. This need is further highlighted by the second free text question to which multiple participants responded with questions related to PD risk assessment and the implications of test results—topics that genetic counselors are trained to handle routinely. Additionally, genetic testing attitude scores were significantly lower for individuals with a primary education compared with those with a secondary education or higher, emphasizing the role that education may play in broadening attitudes toward genetic testing.17. Additionally, we found that the proportion of PwP and family members in our DR cohort that were interested in meeting with a genetic counselor was higher than the proportion of PwP in a primarily White, American cohort11. Common PD-related topics that our participants wished to discuss during genetic counseling included:questions about symptomstreatment and managementPD causationmethods of preventionpersonal risk assessmentinheritanceimplications of genetic testing resultsParticipants expressed an almost unanimous interest in both genetic testing and counseling for PD, despite only about one-third of participants having previously heard about genetic counseling. This illustrates an immense gap between the need for, and interest in, genetic counseling services and local availability in the regionWe also found that individuals in the DR are interested in meeting with a genetic counselor and would likely utilize genetic counseling services if they were readily accessible, supporting the need for additional capacity building for the provision of genetic counseling in the DR. Importantly, participants offered multiple topics of interest that could be personalized for counseling.Altogether, participants self-reported having generally low genetics knowledge, but unaffected relatives were more likely than PwP to report knowing “a lot” about genetics, which is likely due to differences in age or education level. General genetics knowledge questions assessed for understanding of inheritance, genetic tests, carrier status, and reduced penetrance. Interestingly, the majority of participants demonstrated overall “high” general genetics knowledge by answering at least three out of four of these questions correctly. The most misunderstood genetics concept was reduced penetrance.Most participants knew that genetic testing could identify mutations that increase a person’s chance of developing a genetic disease, but less than half knew that scientists had found mutations that increase the risk of developing PD. This finding suggests general awareness of inherited diseases, but a lesser realization that PD, specifically, may have a hereditary component. Most participants were familiar with the concept of environmental risk factors for PD but were less certain about the existence of genetic risk factors. Additionally, many individuals were unsure about the significance of a negative genetic test result regarding their personal risk for PD. Free text responses from participants support these findings as multiple individuals expressed a desire to know more about the cause of PD and the genetics of PD. Since PD causation is complex in nature, including multifactorial inheritance and reduced penetrance, this can create difficulties in understanding PD genetics concepts. Although overall knowledge was high, there were misperceptions and gaps as noted above . This could be addressed with educational materials and programs within the DR, targeted to these topics. It is also recommended that providers who counsel PwP and their relatives have awareness of some of these gaps in knowledge, which may extend to individuals outside this region.It is important to highlight that this study had a small sample size due to time limitations of the project to recruit. Most of our participants lived in a city or urban area, similar to the general population distribution in the DR, where most reside in the cities, and may not accurately represent the perspectives of those living in small towns or rural areas. Nonetheless, it is important to recognize the value of all these perspectives considering the scarcity of research done within this specific population. Another limitation of this study is that a small number of participants had already undergone genetic testing and counseling for PD. These participants likely had a basic education about general and PD genetics, thus, skewing general genetics knowledge and PD genetics knowledge scores for our sample. Self-selecting bias may have also influenced the study results, as participants may have been more interested in PD genetics/counseling, skewing responses to knowledge questions and resulting in greater interest to meet with a genetics specialist. Additionally, the recruitment flier emphasized genetic testing, potentially attracting participants with a higher baseline interest in genetic topics, as evident in some of the free responses. The authors further note that the survey data of affected and unaffected individuals was combined due to the overall sample size and small numbers in each subset. We recognize that this may have blurred some of the unique responses from each group.The inclusion of unaffected relatives in this study could lead to bias as related family members oftentimes share similar views and experiences. Since responses were anonymous, there was no way to assess how many members of the same family participated in the survey. It was also unfeasible to prevent individuals from taking the survey more than once; however, there were no incentives that might encourage individuals to do so. Additionally, some participants took the survey on an iPad at their in-person neurology visits which could have resulted in response bias. A final limitation of this study is the relatively small number of questions utilized to assess general genetics knowledge and PD genetics knowledge. These sections contained four and five questions, respectively, which may not be enough to assess true understanding of genetics concepts. It is also possible that scores were skewed by guessing, although this was discouraged by including “unsure” as a response option.16.In summary, this study revealed an overall positive attitude toward genetic testing and a high interest in both genetic testing and counseling for PD in a DR cohort consisting primarily of Hispanics residing in the city and other urban areas. Interest in these services was either similar to or greater than that of a primarily White study cohort in the U.S., despite no significant differences in PD genetics knowledge between the two. These findings support the need for clinical genetic services in the DR as part of greater capacity building. Researchers may be encouraged to make additional efforts to perform outreach to these underserved populations. An updated study on the current state and availability of genetic services in the DR would be quite valuable, as the most recent review was from 2016https://pdnexus.org) and videos, currently in development, addressing PD genetics misperceptions. Future endeavors based on our findings should include continued expansion of clinical genetics services where resources allow and collaboration with clinicians and researchers in the region to develop and distribute culturally relevant and useful educational materials to both patients and providers to increase knowledge surrounding PD genetics and testing.In addition, our study uncovered questions about PD and key genetics concepts that were commonly misunderstood among PwP and family members in the DR. This may assist providers in delivering tailored genetic counseling to this population and others to ensure understanding of the benefits, limitations, and risks of genetic testing. These results also emphasize a need for genetics education within the DR, targeted to gaps in knowledge. This could be addressed by utilizing Hispanic resources already developed by the author team, including a user-friendly website  Institutional Review Board in Santo Domingo, Dominican Republic, and the Dominican Republic National Council of Bioethics and Health (CONABIOS). Documents required by CECANOT and CONABIOS were submitted in Spanish. This study was conducted with faithful observance to the ethical principles and guidelines stated in the Belmont Report. The authors designed and carried out the study with the utmost consideration of the rights, welfare, and dignity of all participants.Recruitment took place via convenience sampling from July 2022 to September 2022. Participants with PD were recruited through a neurology clinic at CECANOT, which is an urban hospital located in Santo Domingo, DR. Eligibility included (1) a confirmed clinical diagnosis of PD or at least one biological relative with PD, (2) fluency in Spanish, (3) age of ≥18 years, and (4) ability to provide informed consent. Confirmation of all eligibility criteria was based on self-report. Capacity to consent was assessed by asking if the participant had a representative who made medical decisions for them. Electronic consent was obtained via an information page at the beginning of the survey which told participants that by clicking “Next Page” they were agreeing to participate in the study.All recruitment materials were provided in Spanish. Two different methods were used to recruit participants. Approximately 20 paper invitation fliers were handed out to PwP and any accompanying family members at their in-person neurology appointments. These participants had the option of taking the survey on a personal device at home or on an iPad in the clinic. On the first day that fliers were provided, a text invitation was sent via WhatsApp text blast to a recipient list of 200 PwP inviting them and their unaffected biological relatives to participate. The text invitation and physical fliers contained a link and scannable QR code to access the survey. The survey was open for 10 weeks in total.11 and a validated genetics knowledge questionnaire developed by Milo Rasouly et al.20. The adapted questions were then combined with novel questions developed by the research team. All questions were translated into Spanish by P.D.H., a native Spanish speaker with experience developing Spanish healthcare materials. The Spanish version of the survey was piloted by multiple native Spanish-speaking individuals, and modifications were made to enhance understanding of the questions. The authors decided to use the more familiar term “mutation(s)” rather than “variant(s)” for survey language. Study data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Indiana University22.A quantitative and free text survey was utilized to evaluate general genetics knowledge, PD genetics knowledge, attitude toward genetic testing, and interest in genetic testing and counseling. The survey was created by adapting an existing survey developed by Maloney et al.The survey contained 29–34 questions that varied depending on the participant’s diagnostic status and prior experience with genetic testing and counseling. Questions included information about demographics, self-perceived genetics knowledge, general and PD genetics knowledge, and attitude toward genetic testing. Interest in genetic testing and counseling was also assessed. Free text questions asked all individuals in what ways they thought genetic testing for PD would be useful, and a second free text question asked all individuals what questions they would want to ask a genetic counselor about PD. Please refer to the Supplementary Methods for the full questionnaire.20. We used a five-point PD genetics knowledge score to summarize PD genetics knowledge based on the number of correct responses to the five multiple choice questions. This score ranged from 0 (low genetics knowledge) to 5 (high genetics knowledge). Attitude toward genetic testing was summarized based on the Likert responses for 0 = “negative” responses, 1 = “neutral” responses, and 2 = “positive” responses to each of the four multiple choice questions. This score ranged from 0 (negative attitude) to 8 (positive attitude). When comparing interest in genetic testing and counseling to the sample in Maloney et al.11, we used only the most affirmative response (Very interested) among PwP in our sample.To summarize general genetics knowledge, a four-point scale was developed based on the number of questions participants answered correctly. Participants were then classified as having either “high genetics knowledge,” if they scored ≥3, or “low genetics knowledge,” if they scored ≤2, consistent with the classification designated by Milo Rasouly et al.We examined whether the following variables differed significantly between PwP and unaffected biological relatives: self-reported genetics knowledge, familiarity with genetic testing and counseling, general genetics knowledge, PD genetics knowledge, attitude toward genetic testing, genetic testing interest, and genetic counseling interest. We explored if age, education, geographic location, and self-reported genetics knowledge were associated with the “high” vs “low” genetics knowledge. Due to the small sample size, non-parametric statistics were employed for all analyses. To assess factors predicting the PD genetics knowledge score and the genetic testing attitude score, we tested age, sex, education, and geographic location  using a Kruskal–Wallis test. All associations between categorical variables were evaluated using a Fisher’s exact test.11, using only the most affirmative response (Very interested) from the DR sample. Comparisons of PwP between the DR sample and numbers reported from the Maloney sample were performed using either a Fisher’s exact test  or a non-parametric Wilcoxon test (quantitative variables). Variables compared between these two cohorts included current age, age at diagnosis, PD genetics knowledge, sex, education, race/ethnicity, familiarity with genetic counseling, and interest in genetic testing and counseling. Two-sided p values are reported for all analyses. All analyses were performed in SAS v9.4. As this is an exploratory study, we did not correct for multiple testing.We compared interest in genetic testing and counseling among PwP in the DR to the sample in Maloney et al.23. The last author (P.D.H.) compiled responses in an Excel spreadsheet and translated them from Spanish to English. The first author (M.H.) reviewed all translated responses, generated initial codes using open coding, and combined related codes into higher level categories. The second author (L.C.) audited the categories and provided suggestions for recategorization, which were then reviewed by M.H. and, when needed, discussed with L.C. until an agreement was reached. A response could be placed in multiple categories. Responses that were unclear or did not answer the original question were placed into a singular category, which was excluded from further analysis. Frequencies were calculated for all remaining categories.Free text data derived from the two free text questions were analyzed via inductive content analysisFurther information on research design is available in the Supplemental MaterialReporting Summary


N = 2,871) using the VVIQ, self-identification of a reduction in visual imagery, Questionnaire upon Mental Imagery (QMI), and Verbalizer-Visualizer Questionnaire (VVQ). We found that 3.7 and 12.1% fulfilled the VVIQ and self-identification criteria, respectively, roughly replicating the proportions reported in previous studies. The self-identification criterion—but not the VVIQ criterion—contains items related to face recognition; hence, we suspected that face recognition ability was factor contributing to this discrepancy and conducted another online sampling study (Study 2: N = 774). We found a significant correlation between VVIQ and face recognition ability in the control group with self-identification, but not in the group defined by low VVIQ (VVIQ ≤32). As the participants in the control group with self-identification tended to exhibit moderately high VVIQ scores but low face recognition ability, we reason that the discrepancy can be partially explained by the contamination of individual differences in face recognition ability. Additional analyses of Study 1 revealed that the aphantasia group included participants who lacked all types of sensory imagery or only visual imagery in multisensory imagery and exhibited a non-specific cognitive style. This study indicates that the VVIQ alone may be insufficient to diagnose individuals who report an inability to form visual imagery. Furthermore, we highlight the importance of multiple assessments—along with the VVIQ—to better understand the diversity of imagery in aphantasia.Aphantasia—a condition wherein individuals have a reduced or absent construction of voluntary visual imagery—is diagnosed using either the Vividness of Visual Imagery Questionnaire (VVIQ) or self-identification. However, a significant discrepancy exists between the proportions of aphantasia in the populations assessed using these two criteria. It is unclear why the reported proportions differ excessively and what percentage of people cannot form visual imagery. We investigated the replicability of the proportion of people with aphantasia using both criteria in the same population of participants. Therefore, we explored the potential causes of the discrepancy and characteristics of putative aphantasia in terms of multisensory imagery, cognitive style, and face recognition ability. First, we conducted an online sampling study (Study 1:  FurtherIndeed, various definitions of aphantasia exist—as discussed by 1.1.N = 1,004), including undergraduate students and the general population  used in a previous study  along with visual imagery. Moreover, cognitive style may be an important characteristic in people with aphantasia because people with aphantasia may tend to employ non-visual thinking strategies, such as the verbalizer type, if they exhibit a reduction in—or absence of—visual imagery. However, as the criteria for visual imagery (VVIQ or self-identification) have been used to define aphantasia, multisensory imagery and cognitive style have not been examined extensively. This constitutes our second research question.1.2.1.N = 317) and a low VVIQ score (VVIQ ≤32). Furthermore, they demonstrated that all sensory imagery in the QMI  was weaker among people with aphantasia than among those without aphantasia. However, few studies have examined multisensory imagery in people with aphantasia, and further research is needed. Furthermore, we used the QMI to discuss multisensory imagery in people with aphantasia.1.2.2.Almost no studies have examined the cognitive style of people with aphantasia. To measure cognitive style, 1.3.We conducted a large-sample investigation using online sampling  to investigate the proportion of people with aphantasia using the VVIQ and self-identified reduction in visual imagery criteria. First, participants with visual aphantasia were classified using the criteria primarily used in previous studies. After collecting responses based on the VVIQ and self-identification criteria from the same participants, we examined whether the proportions were consistent between the VVIQ and self-identification criteria. Although collective considerations may be advantageous, previous studies have calculated the proportions of each criterion . Consequ2.2.1.2.1.1.SD = 11.5), most of whom were recruited through online sampling ; the other participants were recruited through face-to-face sampling . Most participants were office workers, executives, or businesses owners , we excluded the term “aphantasia” from the survey. However, we used the term “imagery” to indicate the survey’s theme, which was required to ensure informed consent from the participants.SD = 11.5) in the analysis and excluded 29 participants who either provided incomplete responses or reported a psychiatric disorder. We aimed to investigate congenital aphantasia in participants; psychopathological factors may play a role in the context of acquired aphantasia  to 5 . Lower scores indicate weaker vividness of the visual imagery. The VVIQ’s Japanese version has been used in previous studies, and its validity and reliability have been confirmed , 2 (I can barely imagine it), and 3 (I can imagine it), and asked the participants, “Can you create imagery, such as furniture in your room or a friend’s face?” We categorized people as having aphantasia if they scored 1  or 2  in this study.We adopted the self-identification of reduction in visual imagery. We used a three-point Likert scale, based on scores of 1  regarding their episodes, following If they answered, “2.1.2.3.no image at all, you only “know” that you are thinking of the object) to 7 . Lower scores indicate weaker vividness of the sensory imagery. The QMI’s Japanese version has been used in previous studies and its validity and reliability have been confirmed (We adopted the QMI’s Japanese version  to examionfirmed .2.1.2.4.yes” or “no”), was developed to analyze verbalizer and visualizer scales as either one-dimensional  completed the questionnaire on Google Forms, while only 50 participants from face-to-face sampling completed the paper-based questionnaire.2.1.4.To investigate the relationships among the QMI’s multisensory imagery and among the VVQ’s verbalizer and visualizer types, we adopted cluster analysis using Ward’s method . Cluster2.2.2.2.1.2.2.1.1.Following These results indicate that approximately 3.7% of participants scored 16–32 on the VVIQ—in line with a previous study : 3.9%. H2.2.1.2.I cannot imagine it at all”) or 2  for self-identification. As some people with aphantasia could create imagery for a moment, we included those with a score of 2.We defined people with aphantasia as those with a score of 1  and 11.5%  responded with 2 .We calculated the proportion of participants who reported a reduction in visual imagery. These results indicate that approximately 12.1% of participants reported a reduction in visual imagery. These findings are similar to previously reported results (2.2.2.n = 46) reported that they could not form visual imagery under the self-identification criterion, responding with 1  or 2  and exhibited a low VVIQ score (VVIQ ≤32). However, some participants (n = 59) reported that they could form visual imagery under the self-identification criterion, responding with 3  despite exhibiting a low VVIQ score (VVIQ ≤32). Moreover, for VVIQ scores ≥33, some self-identification frequency was observed. These participants (n = 301) reported that they could not form visual imagery in the self-identification measure (1 or 2), despite exhibiting a high VVIQ score (VVIQ ≥33). By contrast, other participants  reported that they could form visual imagery in the self-identification measure (3) and exhibited a high VVIQ score (VVIQ ≥33). Study 2 examines these discrepancies in detail.We found a discrepancy between the proportions of methods used for measuring visual imagery. 2.2.3.We used the criteria for VVIQ (VVIQ ≤32) and self-identification, and divided these individuals into three groups—specifically, an aphantasia group (VVIQ ≤32) and two control groups with and without self-identification. We considered that a low VVIQ (VVIQ ≤32) might indicate aphantasia, as proposed in several previous studies . Moreove2.2.3.1.We examined the cognitive profiles of multisensory imagery in the QMI to investigate the relationship between visual imagery and other sensory imagery in people with and without visual aphantasia, as defined by the VVIQ and self-identification criteria. We calculated the z-score using the mean scores of each subfactor  in the QMI and performed a cluster analysis (Ward’s method) for each group. We classified the clusters using distance <10 in the dendrogram for each group.F = 34.27, p < 0.001, η2p = 0.50; auditory: F = 37.76, p < 0.001, η2p = 0.53; cutaneous: F = 49.69, p < 0.001, η2p = 0.60; kinesthetic: F = 38.21, p < 0.001, η2p = 0.53; gustatory: F = 64.78, p < 0.001, η2p = 0.66; olfactory: F = 34.67, p < 0.001, η2p = 0.51; organic: F = 15.92, p < 0.001, η2p = 0.32). A multiple comparison analysis using the Bonferroni correction ; however, no significant differences were found between clusters 3 and 4 for auditory , and clusters 1 and 2 for olfactory  and organic  imagery. Regarding visual imagery, cluster 1’s z-score was the lowest, and cluster 3’s z-score was the highest , but we found no significant difference between clusters 2 and 4 .The cluster analysis indicated that the participants in the aphantasia group could be classified into four clusters . Subsequrrection  revealedF = 13.72, p < 0.001, η2p = 0.08; auditory: F = 103.92, p < 0.001, η2p = 0.41; cutaneous: F = 154.71, p < 0.001, η2p = 0.51; kinesthetic: F = 165.77, p < 0.001, η2p = 0.53; gustatory: F = 223.77, p < 0.001, η2p = 0.60; olfactory: F = 168.59, p < 0.001, η2p = 0.53; organic: F = 98.48, p < 0.001, η2p = 0.40). A multiple comparison analysis using Bonferroni correction . For visual imagery, we found that the z-scores for cluster 3 were higher than that for cluster 1  and that for cluster 2 , but there was no significant difference between clusters 1 and 2 .In the control group with self-identification, the cluster analysis revealed that the participants could be classified into three clusters . We perfrrection  revealedF = 459.38, p < 0.001, η2p = 0.27; auditory: F = 789.46, p < 0.001, η2p = 0.39; cutaneous: F = 1168.77, p < 0.001, η2p = 0.49; kinesthetic: F = 1091.15, p < 0.001, η2p = 0.47; gustatory: F = 1368.67, p < 0.001, η2p = 0.53; olfactory: F = 1196.78, p < 0.001, η2p = 0.49; organic: F = 666.26, p < 0.001, η2p = 0.35). A multiple comparison analysis using Bonferroni correction .In the control group without self-identification, cluster analysis revealed that the participants could be classified into three clusters . We perfrrection  revealedCollectively, multiple profile types were identified in the aphantasia group. Some participants scored low for all sensory modalities, whereas others scored showed low for visual imagery only. However, no significant differences were found between clusters in visual imagery , though the scores for other type of sensory imagery had a similar profile. This unique tendency toward a reduction in visual imagery was not observed in the control groups with and without self-identification.2.2.3.2.We examined cognitive style using the VVQ in people with and without visual aphantasia, as defined by the VVIQ and self-identification criteria. We calculated the z-score using the mean scores of each subfactor  in the VVQ and performed a cluster analysis (Ward’s method) for each group. We classified the clusters using distance <10 in the dendrogram for each group.t-test using clusters as a between-participants factor. In both the verbalizer and visualizer factors, no significant differences were observed in the z-scores between clusters 1 and 2 .The cluster analysis indicated that the participants in the aphantasia group could be classified into two clusters . To examF = 119.25, p < 0.001, η2p = 0.45; visualizer: F = 269.91, p < 0.001, η2p = 0.64). A multiple comparison analysis using Bonferroni correction revealed that the z-scores were higher in the order of clusters 1, 3, and 2 in the verbalizer factor . For the visualizer factor, the z-scores were significantly higher for cluster 1 than for clusters 2 and 3 ; however, no significant difference was observed between the z-scores for clusters 2 and 3 .In the control group with self-identification, cluster analysis revealed that the participants could be classified into three clusters . We perfF = 2257.51, p < 0.001, η2p = 0.73; visualizer: F = 1838.93, p < 0.001, η2p = 0.69). A multiple comparison analysis using Bonferroni correction . In the visualizer factor, the z-score of cluster 2 was higher than that of cluster 1, and these z-scores were higher than those of clusters 3 and 4 . No difference was observed between the z-scores of clusters 3 and 4 .In the control group without self-identification, the cluster analysis revealed that the participants could be classified into four clusters . We perfrrection  indicateCollectively, the aphantasia group exhibited non-specific characteristics pertaining to cognitive style , though the control group with self-identification exhibited verbalizer and visualizer types, and the control group without self-identification exhibited verbalizer, visualizer, and mixed types.2.2.4.r = −0.198), among the QMI subfactors (0.235 ≤ r ≤ 0.733), and among the VVQ subfactors (r = 0.511). Moreover, we found significant correlations between the QMI and VVIQ subfactors (0.202 ≤ r ≤ 0.503), QMI and VVQ subfactors (0.213 ≤ r ≤ 0.255), and VVIQ and VVQ subfactors (0.249 ≤ r ≤ 0.264).We conducted a correlation analysis among all questionnaires in the three groups. In the aphantasia group , we obser ≤ 0.767), QMI subfactors (0.220 ≤ r ≤ 0.640), and VVQ subfactors (r = 0.330); and between the QMI and VVIQ subfactors (0.127 ≤ r ≤ 0.610), QMI and VVQ subfactors (0.120 ≤ r ≤ 0.194), and VVIQ and VVQ subfactors (0.114 ≤ r ≤ 0.161).In the control group with self-identification , we founr ≤ 0.513), QMI subfactors (0.220 ≤ r ≤ 0.640), and VVQ subfactors (r = 0.330); and between the QMI and VVIQ subfactors (0.127 ≤ r ≤ 0.610), QMI and VVQ subfactors (0.120 ≤ r ≤ 0.194), and VVIQ and VVQ subfactors (0.114 ≤ r ≤ 0.161).In the control group without self-identification , we founSignificant correlations were found for most questionnaires in all groups. However, no significant correlations were found among the VVIQ subfactors in the aphantasia group. Furthermore, these participants might have provided disparate answers unlike the other participants who responded consistently.3.I can barely imagine it” or “I cannot imagine it at all,” to assess their self-identified visual imagery deficit. Specifically, we considered the following question; “How did you realize that you could not visualize?” Notably, the responses were related to a difficulty in face recognition or face imagery abilities, such as “I cannot visualize face” or “I cannot remember faces, so I cannot match the faces and names of people I meet at work” in the aphantasia group (12.8%) and self-identification group (22.9%). Thus, a certain number of individuals in each group had poor face recognition ability, including face imagery.In Study 2, we explored the factor pertaining to discrepancies in proportions between the criteria for the VVIQ and self-identification. We confirmed free descriptions reported by those who answered, “Considering the VVIQ and self-identification items’ contents, the VVIQ did not include any face-imagery question, but the self-identification measure did. Even if the participants scored high on the VVIQ (VVIQ ≥33), if they were aware of a reduction in face imagery during self-identification, they might be judged as the control group with self-identification. We speculate that some participants in the control group with self-identification might have had poor face recognition ability or face imagery. Therefore, face recognition ability might be one of the factors contributing to the discrepancy between the criteria for the VVIQ and self-identification. In Study 2, we administered a novel questionnaire survey and compared face recognition abilities among the groups.3.1.3.1.1.SD = 10.6) who did not participate in Study 1. Most participants were office workers, executives, or businesses owners. Questionnaires were distributed to participants after they received a comprehensive explanation of the study and provided informed consent. Participants consented to participate in the study and responded anonymously. The ethics committee of Fukushima University approved the study protocol . This study was conducted in accordance with the Declaration of Helsinki and Ethical Guidelines for Medical and Biological Research Involving Human Subjects in Japan.Participants included 816 Japanese individuals  to 5 . Higher scores indicate a lower ability for face recognition. The PI20-J’s validity and reliability have been confirmed  in the analysis and excluded 42 participants who provided incomplete responses.We used the data of 774 participants  of the total score of PI20-J in each group: The aphantasia group, control group with self-identification, and control group without self-identification scored 59.80 (14.05), 63.13 (13.76), and 51.55 (14.42), respectively.We calculated the mean  and a previous study 3.9%: , but it 3.2.2.We performed a correlation analysis between the VVIQ and PI20-J scores in the three groups. If the correlation coefficient is higher in the control group with self-identification than in the aphantasia group, the larger proportion of VVIQ scores in the self-identification group is attributable to face recognition ability. If so, it can be demonstrated that the self-awareness of reduced visual imagery in the control group with self-identification is affected by face recognition ability.r = 0.271, n.s.), whereas we found a significant moderate correlation in the control group with self-identification  and a significant weak correlation in the control group without self-identification .In the aphantasia group, we observed no significant correlation . Based on this cutoff point, we divided our participants  as follop < 0.05. In the residual analysis after the chi-square test, the adjusted coefficient (±1.96) was used as a criterion of significant difference.In the cross-tabulation, we performed a chi-square test and residual analysis . The sigχ2 = 40.50). In the residual analysis, significant differences were observed in the control groups with and without self-identification, but not in the aphantasia group.If an association exists between aphantasia and face recognition ability (PI20-J), there may be significant differences in the aphantasia group. A chi-square test revealed that aphantasia was significantly associated with lower face recognition ability . Thus, an association was found between aphantasia and face recognition ability, thus reflecting the self-identification and control groups’ results.To directly examine the association between aphantasia and face recognition ability (PI20-J) in the aphantasia group, a chi-square test was performed again only in the aphantasia group. The results revealed no significant difference  study examined the proportion of people with aphantasia in terms of visual imagery criteria using the VVIQ and self-identification of a reduction in visual imagery. Our data revealed a ratio of 3.7% for the VVIQ criterion (VVIQ ≤32) and 12.1% for the self-identification criterion. However, a large discrepancy was found between these proportions, whereby some participants reported self-identification of a reduction in visual imagery despite exhibiting VVIQ ≥33. Our additional questionnaire research suggested that this discrepancy may be caused by individual differences in face recognition ability. Moreover, we examined the imagery properties of people with aphantasia in terms of their multisensory imagery (QMI) and cognitive style (VVQ) profiles. For cognitive profiles of multisensory imagery, we found that some individuals lacked all sensory imagery, whereas others lacked the specific capacity for visual imagery, despite their ability to form all other types of sensory imagery. Regarding cognitive style, although we tried identifying verbalizer or visualizer types in people with aphantasia, these types were unclear in people with aphantasia.This large-sample  and self-identification of a reduction in visual imagery (12.1%). How can we explain the discrepancy between the proportions of VVIQ and self-identification criterion? Certainly, the 12.1% with self-identification included 3.7% with low VVIQ (VVIQ ≤32), but what were the imagery characteristics of the remaining 8%? This discrepancy is attributable to the absence of face recognition ability based on free descriptions.Study 2’s results revealed face recognition ability as a possible factor contributing to this discrepancy. We assessed the relationship between VVIQ and face recognition ability using correlation analysis in each aphantasia group (VVIQ ≤32) and the control groups with and without self-identification (VVIQ ≥33). If the correlation coefficient is higher in the control group with self-identification than in the aphantasia group, the higher coefficient of the VVIQ in the control group with self-identification can be explained by face recognition ability. Based on this, we predicted that self-awareness of a reduction in visual imagery in the control group with self-identification depends on face recognition ability. A significant moderate correlation existed in the control group with self-identification and a significant weak correlation existed in the control group without self-identification, but not in the aphantasia group. Thus, we assume that face recognition ability was a factor causing this discrepancy.However, one key point should be noted. The discrepancy could be an artifact resulting from the participants’ attention to “a friend’s face” in the questionnaire on self-identification. In other words, they may have responded only to the item regarding whether they could imagine the faces of their friends. If so, our present study included participants who, in fact, do not have a deficit in visual imagery but instead have a deficit in face recognition. Therefore, we believe that self-identification questions should be modified to measure the ability to form general object imagery  rather than predominantly face imagery. This might reduce the discrepancy between the VVIQ and self-identification.One might argue that while the VVIQ may be a strict measure for identifying people with aphantasia, self-identification is not. Indeed, self-identification may be associated with a higher risk of false positives. Hence, our argument does not imply that VVIQ is unsuitable or that self-identification is preferable; instead, we propose using them according to the given purpose. Furthermore, numerous participants experienced problems during the interviews because they were aware of a reduction in visual imagery. To support these individuals, a broad and easy extraction is necessary. It can be used as a preliminary step prior to the VVIQ as a simple screening tool; we believe that the VVIQ should only be administered to those who agree to it as a research study. For example, a person who suspects that they may lack visual imagery would perform this simple screening. Alternatively, if a child has learning difficulties owing to a reduction in visual imagery, the questionnaire can first be administered in a simplified manner at school. Assumedly, these individuals will contact the researcher to conduct the VVIQ. As has already been highlighted, the self-identification question’s content should be carefully examined. The VVIQ is appropriate for recruiting participants for an experiment, whereas the self-identification method may help easily detect a wide range of individuals challenged by a reduction in visual imagery.4.3.Our second aim was investigating the imagery properties of aphantasia by focusing on the cognitive profiles of multisensory imagery using the QMI and cognitive style  using the VVQ.4.3.1.For cognitive profiles of multisensory imagery, while a previous study had already used QMI and examined multisensory imagery , we usedAlong with the visual imagery criteria for the VVIQ and self-identification, we used QMI and observed both visual and multisensory aphantasia. However, we would have been unable to distinguish multisensory aphantasia from visual aphantasia if we had used only the VVIQ. This indicates that visual aphantasia—as defined by the VVIQ—includes both visual and multisensory aphantasia. Notably, we are not opposed to using only visual criteria such as VVIQ to define visual aphantasia. However, we suggest that using visual and multisensory criteria can distinguish visual and multisensory aphantasia. Similarly, previous studies  have demA similar observation may be made for auditory or other sensory types of aphantasia. For example, if we only use auditory criteria to identify auditory aphantasia , they could know or imagine these objects. Thus, this may have affected the discrepancies between the two criteria. Therefore, we must investigate an instruction that focuses on the aspects of “imagery” and “imagination.”Moreover, to underscore the discrepancy between these criteria, we asked participants to create an image of the furniture in their room and a friend’s face in the questionnaire using the self-identification criterion. A possibility exists that the discrepancy is a matter  of “imagr = 0.251, p < 0.01; Spearman correlation: sr = 0.238, p < 0.01). Thus, we assume that the data’s linearity minimally affects the results’ interpretation.Arguably, the self-identification scale does not vary linearly between numbers, while using the VVIQ and self-identification criteria to separate groups may be considered inappropriate. Thus, to confirm that these criteria comprised linear relationships, we performed a correlation analysis using the Pearson correlation (parametric) and Spearman correlation (non-parametric) between the scores for the VVIQ (scores 16–80) and self-identification (scores 1–3). The results revealed that the correlation coefficients were similar for both criteria , multisensory imagery, cognitive style, and face recognition ability. We found multiple types of visual aphantasia using self-identification of a reduction in visual imagery and face recognition ability, along with the VVIQ. Moreover, multisensory aphantasia and visual aphantasia could be classified using multisensory imagery (QMI). In terms of the verbalizer and visualizer types (VVQ), people with aphantasia exhibited non-specific characteristics pertaining to cognitive style. Although the VVIQ is appropriate for diagnosing visual aphantasia, the use of self-identification and face recognition items reveals the diversity of visual aphantasia. Furthermore, by examining multisensory imagery, we investigated the existence of both multisensory and visual aphantasia. Therefore, these findings contribute to the evidence underscoring the diversity of imagery in people with aphantasia.https://drive.google.com/drive/folders/1QKunQ2-Z0SAkee6mVirBayh7KNlhx3qC?usp=sharing.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: The studies involving human participants were reviewed and approved by the Ethics Committee of Fukushima University . The patients/participants provided their written informed consent to participate in this study.JT built the study design, collected the data mainly, and performed analysis. GS and KO also collected the data. DY, SSu, and SSa checked the questionnaires and study protocol. JT drafted the first version of the manuscript. SSu and TH checked the manuscript. TH provided critical revisions. JG provided advice for the manuscript and present survey. All authors developed the study concept and reviewed the manuscript.This work was supported by the Casio Science Promotion Foundation (grant number: 39-50), the Inamori Research Grants to JT, the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (22H03910), the Cooperative Research Project of the Research Institute of Electrical Communication, Tohoku University (R03/B11), and the Competitive Research Funds for Fukushima University Faculty (21RI001).TH was employed by Nippon Telegraph and Telephone Corporation.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Phelipanche ramosa (L.) Pomel, is a globally distributed parasitic weed of economic importance. In Europe, where it is native, it can infest several crops, notably tomato, tobacco, and hemp. In western France, it has recently adapted to a new host crop, oilseed rape, causing substantial damage. The aim of this study was to investigate the evolutionary relationships and genetic differentiation among P. ramosa populations infesting different hosts. We collected 1611 P. ramosa samples from 109 fields cultivated with six different crops  and distributed among six European countries. All samples were genotyped for ten microsatellite loci and a subset of samples was sequenced for two nuclear genes and two chloroplast genes. Genetic differentiation among populations was high (FST = 0.807) and mainly driven by differentiation among different host crops, with no significant geographic structure. Genetic structure analysis identified up to seven biologically meaningful clusters that matched with host crops of origin. Reconstructed networks of sequence haplotypes and multilocus SSR genotypes showed a large genetic divergence between samples collected on oilseed rape and samples collected on other crops. The phylogeny inferred from DNA sequences placed samples collected from oilseed rape as a basal lineage. Approximate Bayesian Computations were used to compare different evolutionary scenarios of divergence among the three main genetic clusters, associated, respectively, with oilseed rape, tobacco, and hemp as host crops. The best‐supported scenario indicated that P. ramosa infesting oilseed rape derived recently from an ancient, unknown lineage. Our results suggest that a more complete description of the genetic diversity of P. ramosa is still needed to uncover the likely source of the recent adaptation to oilseed rape and to anticipate future new host shifts.Branched broomrape,  Phelipanche ramosa, is a parasitic weed that can infest several crops, notably tobacco, hemp, and tomato. In western France, it has recently adapted to a new host, oilseed rape. We describe the genetic diversity of populations sampled from six different crops across Europe. We observed strong host‐associated genetic differentiation and found that the host shift toward oilseed rape involved a recent divergence from an ancient, unknown lineage.Branched broomrape,  These agronomically important parasitic plants vary widely in their host range. Some are highly specialized, while others have a broad, diversified host range  Pomel, the branched broomrape, is the most widespread broomrape in the world. It originates from Europe and Asia and was introduced in Australia, China, North America, and South America  and were sampled in six different host crops . The internal transcribed spacer (ITS) of nuclear ribosomal DNA was PCR‐amplified following Schneeweiss et al.  designed based on the available s et al. . Obtaines et al. , using ts et al.  were use2.3Phelipanche aegyptiaca was used as an outgroup for the phylogenetic analyses. For the rpl16 intron and trnK‐trnQ chloroplast regions, homologous sequences were retrieved from the available chloroplast complete genome sequence (Genbank accessions KU212370.1). For the ITS sequences, we used GenBank accession MG948171.1. For BO1, we used GenBank accession KM037755.1. Due to low levels of polymorphism detected .The sequences for the two chloroplast loci and the two nuclear loci were trimmed in MEGA version 11  with the package adegenet  variable sites. The chloroplast trnK‐trnQ marker had sequence lengths of 1007 bp with 11 (1.1%) variable sites. The ITS sequences had a length of 842 bp with 9 (1%) variable sites. The BO1 sequences had a length of 1018 bp with 14 (1.37%) variable sites. Only three haplotypes were identified among the rpl16‐intron sequences, while 9 haplotypes were identified among the trnK‐trnQ sequences and 7 haplotypes were identified among each of the nuclear sequences  were concatenated for their 28 common samples and a Bayesian phylogeny was inferred for the 12 haplotypes identified among them . Low levels of genetic diversity were detected within most populations . Observed heterozygosity was very low at all markers , as expected under a predominantly selfing mating system. The estimated value of the fixation index A total of 194 multilocus genotypes were observed across the whole data set. Most MLGs were rare, as 102 were singletons , and only three were observed at a frequency higher than 5%. The most frequent MLG had an overall frequency of 19.7% and was observed in samples collected from oilseed rape in France, representing 56% of them. Among the 92 non‐singleton MLGs, only 14 (15%) were shared between populations collected on different host crops. All the shared MLGs were observed in both tobacco and a single other crop, which could be tomato, hemp, celery, oilseed rape from Greece or oilseed rape from France . An AMOVA was conducted on the data for the three most represented host crops: hemp, oilseed rape from France and tobacco. It revealed highly significant genetic differentiation among the different host crops and among populations within host crops, with the highest amount of genetic variation (approximately 70%) found among host crops  for the 75 populations. Similar values of Jost's D were observed among populations sampled in hemp  and in tobacco , while significantly lower D values were observed among populations sampled in oilseed rape fields in France . The Mantel's tests performed separately for each of the three host crops were all non‐significant, indicating that there was no isolation by distance among populations.The genetic differentiation among the 75 populations with at least 10 individuals genotyped, as measured by Weir and Cockerham's ps Table . Jost's 3.3K = 2, cluster 1  was associated with one oilseed rape field from France, phera41, which had also an intermediate position in the phylogeny , only one genetic cluster was observed. In the other populations, different patterns could be observed: (i) the presence of likely migrants, that is, genotypes assigned with a membership probability higher than 0.9 to a genetic cluster different from the main cluster in their population, (ii) a mix of genotypes assigned (with a threshold higher than 0.9) to different clusters, and (iii), the presence of admixed genotypes, each assigned to two or more clusters with thresholds lower than 0.9. The second pattern was observed in all four oilseed rape fields sampled in Greece, where 42% of the individuals were assigned to cluster 2, 26.5% to cluster 1, 15.6% to cluster 4, 9.4% to cluster 5, 4.7% to cluster 6 and 1.5% to cluster 7. This suggested that these fields were recently colonized from multiple origins. The third pattern (admixture) was rare, as only 2.9% admixed individuals were observed overall. Except for one individual admixed between the clusters 6 (associated with lentil) and 7 (associated with hemp), all observed admixture events involved the genetic clusters 2 (associated with tobacco) and 7 (associated with hemp) and were observed in 3 hemp fields and 6 tobacco fields.GENECLASS identified first‐generation migrants based on a priori defined populations , without considering the genetic clusters identified by DAPC. Overall, 85 migrants were identified in 52 populations Table , and the3.5K = 4: the cluster associated with oilseed rape from France (hereafter OR), the cluster associated with hemp (hereafter HP), and the cluster associated with tobacco and other crops . We did not include genetic cluster 3, only observed in one oilseed rape population from France (phera41), as it seemed to result from an admixture between cluster 1 and cluster2 . In our study, this genetic group was not observed in any other host crop or geographical region, except in some populations sampled from oilseed rape fields in northern Greece. Cluster 1 is very likely to be identical to group 1 previously described by Stojanova et al. . Host specialization may filter out genotypes not adapted to the local host crop. Indeed, migration events in which incoming seeds were assigned to a different genetic cluster than the receiving population were comparatively much less frequent than migration events in which the same genetic cluster was involved. As a result, most migrants are more genetically similar to individuals already present in the recipient populations than would be expected if migration was random.The allelic diversity and genotypic diversity among plants sampled within a given cultivated field was low, with observed values very similar to those reported by Stojanova et al. . This isP. ramosa collected from a tobacco field was shown to be able to germinate and grow on various species of Brassicaceae . Both cluster 5 (associated with tomato) and cluster 7 (associated with hemp) were found within tobacco fields, which is consistent with the fact that they are genetic subgroups within cluster 2. The close genetic proximity between clusters 2, 5, and 7 was attested by the observation of admixed individuals. In addition, shared multilocus genotypes were observed and first‐generation migrants were detected between populations sampled on tobacco, hemp, and tomato. These results are consistent with co‐culture experiments by Stojanova et al. , where t4.3P. ramosa populations at the European scale, maybe due to human transportation of crop seeds and harvests. Populations sampled in oilseed rape fields in Greece had an outstanding pattern of genetic variation, as they contained a mix of genotypes assigned to all genetic clusters. This suggested some migration events from other fields cultivated with different crops, and/or the emergence of a diversity of genotypes from the seedbank, especially if a variety of host crops had been cultivated locally in the past. These Greek oilseed rape fields were the only locations outside of western France where we observed the oilseed rape genetic cluster, so that long‐distance dispersal of P. ramosa seeds between western France and northern Greece is a possibility. More details on the past agronomic history of the fields would be needed to better understand how they were colonized by P. ramosa. Likewise, some future temporal monitoring and resampling of the populations would allow the evolution of their genetic composition and host preference to be tracked.Mantel tests gave no indication of isolation by distance among fields cultivated with the same crop. On the other hand, a same multilocus genotype could be observed in very distant populations, for example in tomato fields from Greece and tobacco fields from Spain, and several MLGs were shared between tobacco fields from eastern France, western France, and Hungary. Long‐distance dispersal is likely very important in distributing genetic variation among 4.4P. ramosa in this crop were observed as early as the beginning of the 1990s and were thus considered to be the result of a recent and rapid host shift , were introduced. The first infestations of a et al. , very fe Parker, . On the 5P. ramosa is a generalist species whose genetic diversity is mainly structured by host preference. A complex pattern of host‐associated genetic differentiation is observed, so that classification into discrete pathovars seems impossible. A highly specialized genetic group infesting oilseed rape coexists with more generalist, non‐exclusive host‐associated genetic groups and subgroups with an overlapping continuum of host ranges. Most importantly, our study suggests that the recent shift to oilseed rape as a new host originated from an ancient, unknown source population that may have been pre‐adapted. The description of genetic variation in the species is, therefore, likely to be far from complete. Our results support the need to further investigate the genetic composition of P. ramosa populations from under‐sampled geographic regions and host plants, especially wild and weedy hosts, as this may lead to the identification of reservoirs for future adaptations and host shifts to new crops.Our study confirms that the agronomically important parasitic plant Valérie Le Corre: Conceptualization ; data curation ; formal analysis (lead); investigation ; methodology ; writing – original draft (lead). Carole Reibel: Data curation ; investigation ; methodology ; writing – review and editing (supporting). Vaya Kati: Data curation ; investigation ; methodology (supporting); writing – review and editing . Stéphanie Gibot‐Leclerc: Conceptualization ; data curation ; funding acquisition (lead); investigation ; methodology (supporting); writing – review and editing .The authors declare no competing interest.