Research has shown that patient experiences and outcomes of genetic counseling are not equitable across racial categories, disadvantaging Black patients relative to White patients. One major factor contributing to such racial disparities might be genetic counselor racial bias. The present study examined the prevalence of and variation in racial bias toward Black (vs. White) Americans among genetic counselors in North America. This study extends the current literature of racial disparities in experiences and outcomes of genetic counseling by distinguishing prejudice (negative feelings or attitudes) and stereotyping (beliefs) at the implicit and explicit levels as well as by including both certified genetic counselors and genetic counseling trainees. Two-hundred and fifteen genetic counselors  completed four measures in a random order: the Race Implicit Association Test , feeling thermometer (for explicit prejudice), the Medical Cooperativeness IAT (for implicit stereotyping), and a self- report measure of explicit stereotypes (for explicit stereotyping). On average, genetic counselors (both certified genetic counselors and genetic counseling trainees) were slightly to moderately in favor of White Americans over Black Americans at the implicit level. They were also slightly more likely to associate “medically cooperative” stereotypes with White Americans more than Black Americans implicitly. In contrast, genetic counselors, on average, did not display either explicit prejudice or explicit negative stereotyping, which may reflect social desirability concerns among genetic counselors. However, genetic counselors as a group strongly endorsed stereotypes related to mistrust  to be more true for Black (vs. White) Americans. Finally, our study revealed relatively large variability in each type of bias across genetic counselors. Future research should examine how such variability in each type of bias is associated with patient experiences and outcomes of genetic counseling. Health Because genetic counselors strive to reduce disease burden by addressing not only medical but also psychological and familial concerns related to genetic disease, genetic counselors have the potential to play a central role in reducing racial health disparities in genetic disease. However, experiences and outcomes of genetic counseling are not equitable across racial categories . One majAlthough prior research provides initial evidence that genetic counselors’ bias contributes to racial disparities in experiences and outcomes of genetic counseling, there are methodological and theoretical limitations that must be addressed to further advance the field. Methodologically, many of these studies that examined genetic counselor bias used data that were collected from the same sample , while aRacial bias consists of prejudice  and stereotyping , both of which can operate at implicit and explicit levels. At the implicit level, feelings and beliefs are activated spontaneously and automatically; in contrast, feelings and beliefs at the explicit level are deliberate and reflect effortful processes . Distingimplicit prejudice is associated with providers’ spontaneous communication behaviors during racially discordant medical interactions, such as verbal dominance . Inclusion of trainees is important because they provide a significant proportion of genetic counseling; 40% of genetic counselors report “supervising students” is a significant part of their job . In sum,Consistent with the previous studies of genetic counselors , we hypoFindings on the presence/absence of explicit prejudice in physicians have been mixed. On the one hand, a study that analyzed data collected from a large sample of physicians (over 2500 physicians) via the Project Implicit demonstration website has documented that physicians in the United States, on the national average, reported a slight preference for White Americans relative to Black Americans. On the contrary, individual studies with smaller samples of physicians generally find no explicit prejudice against Black Americans  or even We also predicted that genetic counselors would display implicit stereotyping  by drawing on prior research that has consistently shown physicians perceive Black Americans to be less medically cooperative than White Americans at the implicit level . Finally2 |2.1 |To recruit certified genetic counselors, we e-blasted a study invitation with a link to an online study to potentially eligible individuals through the American Board of Genetic Counseling (ABGC). To reach genetic counseling trainees from programs that are accredited by the Accreditation Council for Genetic Counseling (ACGC), we emailed members of the Association of Genetic Counseling Program Directors through their group email address, requesting that they share the study invitation with their students. Interested individuals were first asked to complete the screening. Only individuals who met the eligibility criteria  were able to self-enroll into the study. A total of 412 participants self-enrolled in the study. However, 197 participants were excluded from the analysis because they did not complete all four measures of bias , resulting in 215 analyzable cases (107 certified genetic counselors and 108 genetic counseling trainees).2.2 |2.2.1 |D score that ranges from −2.0 to 2.0 (D scores are interpreted as “slightly biased” (±0.15), “moderately biased” (±0.35), and “strongly biased”  Americans with medical cooperativeness, while greater negative values indicate implicit association of Black (vs. White) Americans with medical cooperativeness.Implicit racial stereotyping was assessed with the medical cooperativeness IAT, which is the most commonly used implicit measure of racial stereotyping in racial health disparities research . The des2.2.4 |Explicit racial stereotyping was assessed with two new measures that were created for this study: a 32-item measure designed to assess general racial stereotypes , and a 34-item measure designed to assess racial stereotypes specifically in the medical context . All items that were included in the general racial stereotyping measure were adapted from the study by 2.3 |Participants completed the demographic information  and then proceeded to complete all four measures of bias (which were administered using Inquisit 6 Web). The presentation order of the four measures was randomized across participants. Additionally, the presentation order of items was randomized within each measure. Upon completion of the entire study, participants were directed to another survey administered on Qualtrics and given an opportunity to provide their names and email addresses to receive a $25 eGift card for their participation. Data were collected between August 18, 2021, and November 23, 2021. The study was reviewed and approved by the Virginia Commonwealth University Institutional Review Board (HM20022251) as human subject research.2.4 |t-tests. The secondary analysis aimed to document the presence/absence of each type of bias by conducting a one-sample t-test with score 0 as the reference point for each type of bias. The tertiary analysis aimed to explore the associations between participant demographic characteristics and four types of biases. First, we computed bivariate correlations between all demographic characteristics  and the four types of bias. Second, we proposed to follow-up with a stepwise regression with the backward elimination for each composite score as an outcome. Each model would have started with all demographic characteristics, and we would delete one characteristic at a time until no variable could be removed without losing a model fit . Categorical variables  were dummy-coded, and continuous variables  were grand-mean-centered before being entered into the model. We used α = 0.05 as the cut-off criterion for the secondary and tertiary analyses. The analysis plan was pre-registered on the Open Science Framework website (https://osf.io/7s5ma).Data were analyzed using SPSS Statistics 27.0. The primary analysis aimed to document means, standard deviations, ranges, and distributions of the four types of bias. We also examined whether group means are significantly different between certified genetic counselors and genetic counseling trainees by conducting independent-samples In addition to the main analyses, we conducted ancillary analysis of explicit racial stereotyping. The composite scores for explicit racial stereotyping computed solely based on the valence of stereotypes (positive vs. negative) can mask potential variations in the stereotype content . To explore how participants responded to each specific stereotype, we also computed a difference score for each item . Specifically, we first examined the means, standard deviations, and ranges to assess the overall level and variability of specific explicit stereotypes among genetic counselors. We also assessed the prevalence of specific explicit stereotypes by examining the percentage of genetic counselors who endorse a given stereotype as more true for Black Americans, White Americans, or neither group.3 |Participant characteristics are presented in 3.1 |As reflected in standard deviations, ranges, and score distributions, there was relatively large variability in each type of bias. That is, some genetic counselors displayed a given type of bias more than other genetic counselors. The visual inspection of figures further suggests that score distributions of explicit prejudice and explicit stereotyping  were highly skewed and leptokurtic, while implicit prejudice and implicit stereotyping were distributed normally. To test our observation statistically, we followed up with examinations of skewness and kurtosis as well as the Kolmogorov–Smirnov normality tests  for eacht-tests revealed that certified genetic counselors and genetic counseling trainees, on average, did not differ in any of the four types of bias. The distributions of the four types of bias were also all similar between certified genetic counselors and genetic counseling trainees. Therefore, the subsequent analyses were conducted with the overall sample.Finally, independent-samples 3.2 |t(214) = 6.96, p < 0.001, d = 0.47. This indicates they were slightly to moderately in favor of White Americans over Black Americans at the implicit level. Genetic counselors also displayed implicit stereotyping that is significantly higher than zero, t(214) = 2.11, p < 0.036, d = 0.37. They were slightly more likely to associate White (vs. Black) Americans with “medically cooperative” stereotypes at the implicit level.Consistent with our prediction, genetic counselors displayed implicit prejudice that is significantly higher than zero, t(214) = −0.98, p = 0.328, or explicit stereotyping specific to the context of medicine, t(214) = −0.09, p = 0.927. Furthermore, genetic counselors displayed explicit general stereotyping that is opposed from what we expected, t(214) = −15.55, p < 0.001, d = 0.60. Specifically, they were more likely to associate Black (vs. White) Americans with positive general characteristics.Contrary to our predictions (and prior research on physicians), genetic counselors, on average, did not display either explicit prejudice, 3.3 |r = −0.222, p = 0.001) and explicit stereotyping specific to the medical context . These coefficients suggest that, compared with White genetic counselors, non-White genetic counselors were less likely to display explicit racial prejudice toward Black Americans but more likely to associate Black Americans with negative medical stereotypes. Because none of the other demographic characteristics were associated with any of the biases, we did not conduct backward regressions.3.4 |p-value using the Bonferroni correction (p < 0.0015), the differences between genetic counselors and genetic counseling trainees became non-significant.The pattern of the endorsement of explicit stereotypes was similar between certified genetic counselors and genetic counseling trainees in general, except for four stereotypes . Specifically, the degree of the endorsement of “humorous” and “cheerful” for Black (relative to White) Americans was greater for genetic counseling trainees, on average, than for certified genetic counselors. In contrast, the degree of the endorsement of “sheltered” for White (relative to Black) Americans was greater for certified genetic counselors than for genetic counseling trainees. Finally, certified genetic counselors endorsed “worried about privacy of genetic test results” to be more true for White Americans, whereas genetic counseling trainees endorse it to be more true for Black Americans. However, these differences should be interpreted with caution given an increased chance of making Type I errors due to multiple testing. In fact, when we adjusted the t-tests revealed that genetic counselors overall endorsed all positive general stereotypes to be more true for Black (vs. White) Americans and all negative general stereotypes to be more true for White (vs. Black) Americans. In contrast, the pattern of the endorsement of medicine-specific stereotypes was more complicated. First, there were a number of medicine-specific stereotypes that were endorsed to be true for neither Black nor White Americans . Second, inconsistent with the results of implicit stereotyping of medical cooperativeness, genetic counselors endorsed stereotypes related to medical cooperativeness  to be more true for Black (vs. White) Americans at the explicit level. Third, genetic counselors strongly endorsed stereotypes related to mistrust  to be more true for Black (vs. White) Americans.One-sample In addition to the average level of endorsement, we also examined the distribution of the endorsement of each specific stereotype . For thi4 |We assessed four types of bias  in a sample of 215 genetic counselors, including both genetic counselors board-certified by the ABGC and genetic counseling trainees in ACGC accredited programs. Based on the previous studies that examined the role of genetic counselors in patient care , we predM = 0.42). This might be due to at least two reasons. First, participants who self-enrolled in the current study and were willing to complete all four measures of racial bias may be less racially biased than the general genetic counselor population. Second, there might be the time effect. Implicit prejudice data for all genetic counselors (not just recent graduates) used in the previous three studies were collected in 2010  across the nation.” This prompt might have made their professional membership salient and increased their desire to present their profession in a positive light. Participants were also told that they would be given an opportunity to provide their contact information to receive participant payments at the end of the study in a separate survey, which might have increased self- awareness before they started the study. Social psychology research has shown self- awareness (even just looking at oneself in a mirror) can increase behavioral responses consistent with one’s personal moral standards .Finally, participating genetic counselors, on average, did not display overall explicit negative stereotypes about Black Americans. Examinations of individual stereotypes also confirmed that many individual stereotypes were not endorsed by the majority of genetic counselors. However, a clear pattern emerged among the stereotypes that were endorsed. Specifically, the majority (ranging from 50.7% to 66.1%) of genetic counselors endorsed six negative general stereotypes  to be more true for White Americans than for Black Americans and two mistrust-related stereotypes specifically in the context of medicine  to be more true for Black Americans than for White Americans.One major difference between those two categories of stereotypes is valence that signals inherent good or bad. The first set of six stereotypes are clearly about negative personal characteristics and reporting that they endorse those negative characteristics to be true for Black Americans more than White Americans can risk appearing prejudiced. In contrast, reporting that they endorse those negative characteristics to be true for White Americans more than Black Americans could potentially signal individuals’ egalitarian values or even knowledge/awareness of systemic racism that produces and reinforces negative stereotypes about Black Americans. Thus, the results that six (out of the eight) general negative stereotypes were endorsed by the majority of genetic counselors might reflect overcompensation . In factIn contrast to the first set of six general stereotypes with clear valence that signals inherent bad, the second set of two medicine-specific stereotypes tended to be valence-neutral or -ambiguous. Mistrust is often induced by actions (or inactions) by other people or organizations, placing a focus on external factors . So, it SD = 0.43) was compatible to that reported in two previous samples of genetic counselors  was similar to that in two samples of physicians  because there were too few racial/ethnic minority genetic counselors. Prior research has shown that the levels of implicit prejudice and explicit prejudice toward Black Americans differ across respondent race/ethnicity . Thus, cThird, although we suspect that genetic counselors’ social desirability bias might have played a role in explaining the absence of explicit prejudice or explicit stereotyping (and the pattern of the results that are consistent with the process of overcompensation), we were unable to test the idea empirically. In the context of the current study that focuses on racial prejudice and stereotyping, social desirability concerns are likely to be associated with motivation to respond without prejudice. That is, individuals who are highly motivated to respond without prejudice are more likely than those with low motivation to modify their responses. Future research may utilize well-validated measures like the Motivation to Control Prejudice Reactions Scale  and the M = 0.38, SD = 0.41, ranged from 0.27 in Serbia to 0.49 in Japan; M = 0.32, SD = 0.44; Fourth, we did not directly ask practicing genetic counselors to indicate whether they were practicing in the United States at the time of data collection. However, because all of the practicing genetic counselors in the current study were board-certified by the ABGC and also recruited through the ABGC’s e-blast, they likely were US-based at the time of enrollment. Additionally, research has shown that implicit preference for White Americans to Black Americans has been reported consistently across 34 countries .4.2 |In contrast to a large literature on physician bias and its role in racial healthcare disparities, research on the role of genetic counselor bias in the genetic counseling process has been limited. This is despite the fact that there has been a rapidly increasing discourse about and recognition of racial disparities in the genetic counseling process . Genetic5 |Although there is potential for genetic counselors to play a central role in reducing racial health disparities, the benefits of genetic counseling are not equitable across racial categories . Prior rTables S1 and S2


Fermentation is a natural process that has been used for thousands of years by humans to produce a variety of foods and beverages. The process involves the use of microorganisms, especially yeasts and bacteria, to convert sugars and other carbohydrates into organic acids, alcohols, and gases ,2. This Fermented beverages have been an integral part of human culture and history for millennia. Their consumption is particularly common in different cultures worldwide. The ability to create these products has been passed down through generations, using a combination of trial and error and meticulous repetition of ancient practices. In Asia, the consumption of mead, or honey wine, was common around 1500 B.C. This beverage was made by fermenting honey and water with yeast, and it was consumed by the Celts, Saxons, Vikings, Greeks, and other ancient cultures ,9. The hThe production of fermented beverages involves several steps, including the selection of raw materials, the preparation of the ingredients, and the fermentation process. The raw materials used in the production of fermented beverages vary depending on the type of beverage being produced. For example, beer is typically made from malted barley, while wine is produced from grapes ,20. The Microorganisms, Yeast Fermentation 2.0, we aim to provide a platform for articles that focus on the involvement of yeasts in fermentative processes. More specifically, we are interested in studies that investigate yeasts’ roles in the production of fermented products that are of significant value to consumers in both the clinical and food sectors. This Special Issue is a continuation of the previous issue published in 2020, which explored similar topics related to yeast involvement in fermentative processes [In this Special Issue of the journal rocesses . By high


Curcuma longa L., is recognized worldwide as a medicinally and economically important plant species due to its curcumin content which is an industrially important compound. In this study, a total of 329 accessions were collected from four states of India and planted in the experimental farm of CSIR-NEIST, Jorhat, India, in augmented design. Among these, 152 high curcumin (> 1.50%) accessions were screened for molecular divergence study using 39 SSR primers. The primers showed the most efficient outcome with 2–8 allele/ loci and a total 163 number of alleles with 100% polymorphism. Cluster analysis revealed the construction of three clusters, out of which one cluster was geographically dependent, and germplasm was particularly from Assam state. Jaccard's pairwise coefficient showed maximum genetic dissimilarity of (0.75) between accession RRLJCL 3 and RRLJCL 126, indicating high variation as it was from two different states viz Arunachal Pradesh and Nagaland respectively and minimum genetic dissimilarity of (0.09) between RRLJCL 58 and RRLJCL 59 indicating significantly less variation as the two accessions were from same state, i.e., Arunachal Pradesh. Analysis of Molecular Variance (AMOVA) revealed high molecular variation within the population (87%) and significantly less variation among the population (13%). Additionally, Neighbour Joining dendrogram, Principal Component Analysis (PCA), and bar plot structure revealed similar clustering of germplasm. This diversity assessment will help in selecting the trait-specific genotypes, crop improvement program, conservation of gene pool, marker-assisted breeding, and quantitative trait loci identification. Moreover, to the best of our knowledge, it is the first molecular diversity report among 152 high curcumin lines of C. longa from North East India using 39 SSR primers. It is an interbreed triploid species (3n = 63) which is procreate vegetatively using the below-ground rhizome4. Morphologically, turmeric is a perennial herb that measures up to 1 m in height with a wee stem, having funnel-shaped yellow flowers and oblong, pointed leaves5.In the present era of great scientific studies, acknowledging the importance of medicinal crops and shifting towards therapeutic agents is increasing tremendously. Among all, the genus Curcuma, which belongs to the Zingiberaceae family, is acknowledged medicinally and economically as one of the most predominate plant species6. In addition to its uses in religious practices, the rhizome part of C. longa has numerous masteries in traditional medicines like stimulants, diabetic wounds, jaundice, to treat liver complaints, stomachic, arthritic, muscular disorders, blood purifiers, rheumatism, cough, and sinusitis like diseases8. The C. longa rhizomes are also widely practiced in treating sprains and swelling caused due to different injuries9. The primary compound of turmeric is curcuminoid which is the most active component, and it prevails in three polyphenolic forms, namely curcumin, demethoxycurcumin, and bisdemethoxycurcumin12. The chemical compounds like curcumin -1,6-heptadiene-3,5-dione), which is also better known as diferuloylmethane is one of the key natural polyphenols found in the turmeric rhizome. It exists in two tautomeric forms: enolic in organic solvents and keto in water13. Curcumin is the foremost essential component present in the C. longa rhizomes, which is responsible for the yellow hue. It is responsible for several pharmacological activities like anti-inflammatory, anti-hepatotoxic, anti-microbial, anti-rheumatic, anti-fibrotic, choleretic, anti-venomous, hypercholesteremic, anti-diabetic, insect repellent as well as anti-cancerous properties14.Since time immemorial, turmeric as flavoring spice powder has been used continuously in the preparation of both vegetarian and non-vegetarian food due to its digestive propertiesC. longa, which is extracted from the leaf, flower, root, and rhizome of the plant, is considered as a highly valuable product in different pharmaceutical and cosmetic industries. The essential oil (EO) is epitomized by the existence of secondary metabolites, which possess a strong odour, volatile terpene, and hydrocarbon15. The chemical composition present in the EO of plants can vary due to various factors like nutrients present in the soil, temperature, humidity, altitude, ultraviolet radiation, luminosity, seasonality, circadian cycle, and portion of the plant18. The prime essential oil constituents of Curcuma longa consist of monoterpenoids and sesquiterpenoids19. Earlier studies outlined that in C. longa, rhizome essential oil comprised of constituents like ar-turmerone, turmerone, curlone, ar-curcumene, and root consists of ar-turmerone, dihydro curcumene, and ar-curcumene where components like turmerone were absent. Moreover, the major constituents found in leaf oil were p-cymene, terpinolene, α-phellandrene, 1,8- cineole, and flower oil consist of terpinolene, p-cymen-8-ol, 1,8-cineole20. These significant constituents were reported to exhibit numerous health benefits, which help to strengthen the immune system, expedite toxin elimination, and elevate blood circulation23.Additionally, the essential oil (EO) of C. longa L. which needs a meticulous study. For screening of the elite germplasm, the study on genetic diversity as well as their association study is of immense significance. Though the value of this golden crop has been recognized since ages, but the demand for this crop in the domain of the food and pharmaceutical industry is still increasing26. Therefore, genetic diversity analysis was performed in order to enhance a deeper study in the productivity and conservation of this crop which forms the basis of the crop breeding program27. The application of phenotypic attribute in germplasm conservation is meagred due to the influence of environment and genotype interactions28; therefore, studies have been performed on molecular diversity using various markers like Simple Sequence Repeat (SSR), Randomly Amplified Polymorphic DNA (RAPD), Amplified Fragment Length Polymorphism (AFLP). The application of RAPD provides insightful information about genetic diversity29 but yields a low level of polymorphism as relative to SSR markers30. Molecular diversity was successfully studied in many crops for phylogenetic assessment and genetic diversity using SSR marker34 as SSR markers are locus-specific, co-dominant markers and have the capability to perceive high levels of miscellany in the genome36.High rhizome yield, disease resistance, and high curcumin content are the desirable characteristics for the turmeric varietal developmental programme, and North East India being a biodiversity hotspot, has ample variability of C. longa germplasm using a large number of accessions (152) gathered from different regions of North East India using SSR markers. A very few reports have been found where Singh et al.37 evaluated 30 genotypes of C. longa using 9 SSR markers, Sahoo et al.38 studied 88 accessions using 50 EST SSR primers, out of which only 11 primers showed polymorphic banding patterns. Till now, to our supreme cognizance, no molecular diversity survey has been performed on such a large number of accessions, and hence this study will help in the progress of worthier germplasm with more yield-related traits and crossing of parents with preferable traits34. This will provide more reliable information in improving traits and will be very effective for the selection and conservation of trait-specific germplasm, which can be further implemented in the study of the food and pharmaceutical industry.The present study was designed to estimate the genetic divergence of Among the 329 accessions collected from four states of India, 152 accessions were selected based on curcumin content (> 1.50%) for further molecular analysis. The total curcuminoids content of selected lines ranged between 1.86 and 12.97%, which is charted in , Resolving Power (Rp), and Marker index (MI) outcomes of the screened primers are depicted in Table C. longa is represented in Fig. The screened 39 primers showed good amplified bands, which ranged between 2 and 8 alleles per individual in all the loci. A total of 163 alleles were achieved, of which all (163) were polymorphic, and no monomorphic bands were observed. The polymorphism percentage of all the primers showed 100%, which is unique to this study. Furthermore, PIC, Rp, and MI values were evaluated to check the proficiency of the screened primers. In the current survey, the PIC value of all primers ranged between 0.20 and 0.89, of which primer (MAGN27) showed minimum PIC value and primer (CuMiSat 31) showed maximum PIC value. The primer proficiency was further analyzed by calculating the Marker Index (MI) and Resolving Power (Rp). The average MI was estimated to be 2.82, of which primer (MAGN27) showed a minimum of 0.40, and CuMiSat 22 showed a maximum of 6.32. Similarly, the Rp value was also evaluated where Rp is the resolving power which objective is to discriminate between genotypes, and from the calculated study, it was observed that the average Rp value was 1.204, of which primer (RM 171) showed a minimum Rp value (0) and primer (CuMiSat 37) showed maximum Rp value (3.46). The gel image of the SSR primer (CuMiSat-37) profile of all the 152 accessions of The genotypes of selected lines were splitted into four populations, namely Pop 1 from Assam, Pop 2 from Arunachal Pradesh, Pop 3 from Nagaland, and Pop 4 from Manipur, based on the geographical location. The genetic variability parameters like the number of observed alleles (na), Nei’s gene diversity (h), number of effective alleles (ne), Shannon’s information index (I), genetic diversity within the population (Hs), genetic differentiation degree (Gst), genetic diversity in the population (Ht), gene flow (Nm) are computed and mentioned in Table C. longa rich in curcumin content analyzed through SSR marker. A comprehensive study on dendrogram revealed a total of three clusters, out of which cluster I consist of 58 accessions, cluster II consists of 92 accessions, and cluster III consists of 2 accessions which are represented in  between accession RRLJCL 3 of cluster IIa(1) and RRLJCL 126 of cluster IIa(2), indicating high variation between these two lines as it was from two different states viz Arunachal Pradesh and Nagaland respectively and minimum genetic dissimilarity of (0.09) between cluster I of line RRLJCL 58 and RRLJCL 59 indicating significantly less variation as the two lines were from same state, i.e., Arunachal Pradesh.Based on Nei’s genetic study, genetic distance and genetic identity was calculated between four population, where it was revealed that maximal genetic identity (0.9853) was observed between Pop 1 (Assam) and Pop 2  and maximum genetic distance (0.1220) was noticed between Pop 3  and Pop 4 (Manipur) which explains that genetic similarity was very high between Pop 1 and Pop 2 whereas genetic variation is high between Pop 3 and Pop 4  respectively, which provides more information about the divergence among the accessions. The sum cumulative variance perceived was 34.01%, of which 17.53%, 11.34%, and 6.03% were for the first three principal components  was also performed for 152 accessions of 34. In this study, almost all accessions were found to be pure accessions except nine accessions which were intermixture in nature. The admixture accession of population structure 1 (red colour) were RRLJCL-86, RRLJCL-97, RRLJCL-60; and RRLJCL-67, RRLJCL-76, RRLJCL-92, RRLJCL-69, RRLJCL-85, RRLJCL-96 were the admixture accession of population structure 3 (blue colour). All the accessions clustered in population structure 2 (green colour) were pure accessions, as shown in  and significantly less variation among the population (13%) mentioned in the  was conducted for ulation 8% and sighe Table .Table 7AC. longa holds industrial significance worldwide and hence needs improvement through breeding program.The collection and assessment of germplasm diversity are imperative to facilitate genetic improvement in the crop. The present study scrutinized high curcumin lines from 329 accessions where 152 accessions showed curcumin content > 1.50%. Among the 152 accessions, 59 accessions showed curcuminoid content > 9%, out of which 29 accessions were from the state of Assam, 18 accessions were from Arunachal Pradesh, 10 accessions were from Nagaland and 2 accessions were from Manipur. The elevated curcumin content was observed mostly in the accessions grown in Assam and Arunachal Pradesh which can be attributed to the favorable environmental conditions in Assam as the state shares a moderate climate condition, characterized by an average temperature of 25.67 °C and an average humidity of 75.15% with an annual rainfall of 2244 mm. This aligns with the findings of Sandeep et al.39 who also noted that the curcumin content in turmeric is influenced by the specific environmental zones in which the plants are cultivated. Thus, it can be stated that soil and environmental factors plays a significant role in the accumulation of curcumin in turmeric plants.C. longa which hinders the phenotypic attribute in germplasm variation, a molecular diversity study on 152 high curcumin accessions were conducted using SSR markers. The molecular markers or primers serve as indicators of polymorphism in unveiling genetic distinctions among individual organisms or species which may emerge from its genetic constitution, nucleotide alterations or genome locus mutations40. A total of 39 SSR primers were screened, where all the primers showed good amplified bands with 100% polymorphic percentage. Likewise, Singh et al.37 studied 9 SSR primers on 30 genotypes, out of which only 6 primers pairs showed 100% polymorphism with banding patterns ranging between 1–2 allele/loci. A study on 60 genotypes of turmeric was reported where average polymorphism resulted 91.4% and 95.4%, respectively, for 11 RAPD and 6 ISSR primers41. Again 15 DAMD and 13 ISSR primers were studied by Verma et al.42 where the average polymorphism percentage reported was 84.4% and 79.2%, respectively; Sumi et al.43 reported 64.4% average polymorphism percentage on 8 genotypes by using 12 RAPD primers. Aswasthi et al.44 also worked on 56 primers which include both SSR and ISSR on 18 improved varieties. Similarly, in the present survey, all the screened 39 primers represent a high level of polymorphism percentage (100%) as compared to earlier studies, which outlines an indication towards high level of genetic variation among the individual accessions. In addition to that, PIC, MI, and Rp values were evaluated where the PIC value for all the primers ranged between 0.20 and 0.89, where MAGN 27 showed minimum PIC value, and CuMiSat 31 showed maximum PIC value. The average PIC value obtained from 39 primers is 0.64, out of which 31 primers showed a PIC value higher than 0.50 signifying that SSR primers used in this study are highly informative as PIC value greater than 0.50 (> 0.50) illustrate high efficiency of primers45. The primer proficiency was further analyzed through MI and Rp where the average MI was 2.82, with the lowest value showed by primer (MAGN 27) and the highest value showed by primer (CuMiSat 22). Again, average Rp was calculated to be 1.204, where the lowest value showed by primer (RM 171), and the highest value showed by primer (CuMiSat 37). Based on study and observation of data for all the screened primers, eleven primers, namely CuMiSat-19, CuMiSat-21, CuMiSat-22, CuMiSat-31, CuMiSat-35, RM125, RM135, RM153, RM154, CBT-05, and CBT-08 showed most efficient outcome with PIC, Rp and MI values more than the mean values .The findings of the above analysis unveiled efficiency of the primers which will serve as a resource for future conductance of molecular diversity analysis in different species of Zingiberaceae family.Keeping in view the effect of environmental factors on the genotypes of 46. The N-J method is a distance-based method that relies on assessing shared alleles to calculate distances between taxa, creating distance relationships of different accessions or germplasms in representation of tree47. In the present investigation, the results obtained through cluster dendrogram revealed the presence of geographically independent clusters as majority of the accessions did not exclusively grouped according to geographical provinces, and the reason may be due to the migration of genotypes from one location to another caused by overexploitation of natural habitat48. However, region-specific clustering was restricted to cluster III, where 2 lines (RRLJCL78 AND RRLJCL76) were from Assam, and the reason may be due to uninterrupted wild varieties in natural habitat1. Likewise, Verma et al.42 identified two prime clusters from the dendrogram constructed on account of the combination of DAMD and ISSR markers, where it was revealed that genotypes in the clusters grouped independently of their geographical location which align with our study. Again, Sahoo et al.49 worked on 10 Curcuma species using EST SSR primer, revealed the presence of two main clusters, one of which consists of all nine species and the other of only Curcuma longa species. The clustering of accessions in different clusters unveiled more genetic distance, revealing more genetic variation which would be conducive to the future hybridization programme. The genetic diversity analysis of intra and inter-population genotypes were also studied in the four populations which was divided based on its collection site. All the populations showed good polymorphism, with only moderate polymorphism in the Manipur population, which may be due to the low number of accessions collected from Manipur compared to other populations used in the study. All the genetic parameters for diversity revealed highest variation in population 1 (Assam) and lowest in population 4 (Manipur). Here, the reason for highest variation in population 1 may be due to the exchange of accessions from the neighboring states or random mutation. Moreover, significant genetic diversity was confirmed from the results of total species diversity within the population (Ht) (0.27 ± 0.03) and among the population (Hs) (0.23 ± 0.02), with genetic differentiation degree (Gst) of 1.24. The parameter like gene flow (Nm) is the shifting of genetic distinction from one population to another, and when gene flow increases among populations, it indicates less variation and more homogeneity whereas when gene flow increases within population, it indicates high variation and less homogeneity. In this study, gene flow (Nm) was found to be 3.51, which is significantly higher than the threshold value (Nm ≤ 1.0)50, and represents high gene flow, whereas Sahoo et al.49 reported working on genetic variation of Curcuma species using EST SSR primer where Nm value observed was 0.0781 which revealed that whole distribution is highly constricted and genetic distinction is highly noteworthy. Again, Basak et al.51 while working on turmeric cultivars using ISSR and RAPD markers reported the Nm value to be between 0.33 and 0.37 for both markers. Here the present study revealed moderate genetic heterozygosity and genetic differentiation degree, which determine moderate genetic variation among and within the populations, and it can be concluded in context to the previous reports52. The results in gene exchange in the present study may be due to human intrusion like genetic swamping, introgression and hybridization1.The genetic diversity analysis led to the construction of cluster dendrogram using Neighbor-Joining (N-J) method as it is a rapid tool and reliable in nature53. Similarly, Nei’s genetic study on Cymbopogon winterianus was studied by Munda et al.,34 where low variation was found among the population and mentioned that the reason for low genetic distance was due to gene exchange among the adjoining population. Moreover may have occurred through human intrusion since gene exchange cannot be facilitated through pollen or seed because the showy flower which blooms in C. longa plant is not a true flower but is actually a bract. Thus, the data procured through this study could be used as baseline data for selection of desired traits in breeding programmes and conservation of highly variable germplasm54.Through Nei’s genetic study, genetic identity among the population ranges from 0.9853 to 0.1220 with maximal genetic identity of 0.9853 was found between Pop 1 (Assam) and Pop 2  and the minimal genetic identity of 0.1220 was found between Pop 3  and Pop 4 (Manipur), and the reason for low variation, i.e., maximal genetic identity between Assam and Arunachal Pradesh population may be due to gene exchange or duplication of germplasm between adjacent states as gene exchange among two population leads to decrease of genetic variation and increase of homogeneity55, where PCA is useful in clustering of similar accession while non-similar accession plot distance apart in the presentation56. In the present survey, the PCA plots closely align with the dendrogram clusters, except for a few outliers  that are plotted at a significant distance from the main cluster. The PCA and cluster analysis provide more reliable information if the first three component scores more than 25% variance57. In the present study, the first three components  come up with 34.01% variability, which is higher than previous reports and indicates more reliable data. Unfortunately, there is currently limited data available regarding the intra and inter-specific relationships and genetic diversity within large number of germplasm of C. longa collected from North East India. Thus, this study will contribute a novel insight to researchers and breeders towards development of superior variety.In addition to that, Principal Component Analysis (PCA) is broadly used to study the structure of data47. From the population structure survey, a total of three population structure were constructed where population 1 is represented with red colour, population 2 is represented with green colour and population 3 is represented with blue colour. As accessions scoring more than 0.80 can be considered as pure accessions, here in the present analysis, almost all accessions were found to be pure accession except nine accessions  were found intermixture in nature. The admixture accessions  were found in the population structure 1 (red colour) and the admixture accessions  were found in the population structure 3 (blue colour) and remaining all the accessions were found to be pure accessions.In the population structure 2 (green colour), no intermixture accessions were found. The pure accessions which were retrieved from this study could be used for conservation of gene pool due to distinct genetic makeup for longer survival of the turmeric species, and this could be later applied for cross breeding programme for development of superior genotype. Through this study, it can be concluded that the SSR markers used in this survey for the genetic divergence of C. longa accessions is suitable and satisfactory for population studies. The reason for the genetic divergence or intermixture accessions found within the populations is mainly due to several factors like gene flow, mating process, selection, mutation1. Moreover, accessions grouping from bar plot resembled mostly with dendrogram clusters in the current survey which provide more reliability and satisfactory result towards genetic diversity studies. Thus, the neighbor-joining tree and Structure analysis of the SSR data complement each other, offer distinct methodologies for investigating the relationships among different accessions and together yield a robust analysis.Further, the structure analysis employs a Bayesian clustering approach, assigning individuals to grouped as populations based on their genotypes and seeking population structures characterized by linkage equilibrium and Hardy–Weinberg equilibrium26 also performed genetic divergence of turmeric using microsatellites where genetic divergence within the population was 75.29% and among the population was 24.71% which belongs only to Brazilian states. In contrast, when the accessions were collected from different countries like Brazil, India, and Puerto Rico, the genetic variability was observed to be 63.42% among the countries and 27.05% within the countries. Singh et al.41 also conducted the analysis of molecular variance using the RAPD marker, which showed 42% genetic variability among the population, and the remaining 58% was within the population, whereas for the ISSR marker, 48% showed genetic variability among the population and 52% was within the population. Similarly, in present investigation, molecular variation within the population was notably more extensive than among populations, serving as the primary source of overall genetic variation. The lower diversity among populations suggested a heightened level of gene exchange and the higher diversity within the population may be due to random drift, mutation, transgressive segregation58 or collection of all the accessions from adjoining states of India which is in accordance to Sigrist et al.26 study. As per the genetic theory of populations, an increase in diversity enhances a species potential to adapt to evolving environments. Hence it becomes necessary to delve into knowledge of genetics, as the loss of heterogeneity could risk the population's sustainability and ultimately lead to the extinction of the species1.Furthermore, AMOVA study revealed high molecular variation within the population (87%) and significantly less variation among the population (13%). Similarly, Sigrist et al.59. From an evolutionary perspective and for the long-term survival of species, population genetic diversity of C. longa holds immense importance. Therefore, through this genetic assessment study, eleven most efficient primers were found which showed PIC, Rp and MI values greater than the average PIC, Rp and MI values. A total of three clusters were formed through Neighbour Joining dendrogram, structure bar plot and PCA analysis which resembles with each other. Analysis of variance revealed high molecular variation within the population (87%) and very less variation among the population (13%) and maximal intra-population diversity was distinguished in Pop 1 (Assam) and minimal in Pop 4 (Manipur). Hence it is necessary to conserve the highly variable population through both in-situ and ex-situ conservation, which may otherwise lead to a loss of heterogeneity in a population, causing species extermination. In the present study, population 1 (Assam)which showed highest variation can be invaluable for hybridization efforts in future crop improvement programs and the identification of closely related species which are represented as pure accessions can serve as major sources of genes for conservation of gene pool and future breeding programs. Therefore, result obtained through genetic assessment study of Curcuma longa L. will provide a handful of resources and great potential for enhancing crop improvement programs, facilitating the selection of desired genotypes, aiding in marker-assisted breeding, identifying quantitative trait loci, and preserving the genetic diversity. The growing global demand for curcumin, the most active component found in turmeric, due to its numerous health benefits underscores the importance of conserving and cultivating curcumin-rich superior genotype and hence urgent steps must be taken to effectively conserve and breed these industrially significant plants. Hence, the present research report will play a pivotal role in promoting the sustainable growth of this economically important medicinal species.To devise effective conservation strategies for various species, understanding the genetic makeup within populations is of utmost significance and to prevent the depletion of genetic foundations, selection of genetically diverse genotypes, analysis of plant molecular diversity is an indispensable toolC. longa was collected from four states of India, specifically from Assam, Nagaland, Arunachal Pradesh, and Manipur during the year 2019  using the Soxhlet apparatus. The solvent extract was allowed to evaporate through the rotary evaporator to obtain the dry mass. The dry mass was put forwarded for HPLC analysis where a Thermo Scientific Dionex Ultimate 3000 HPLC system was used with Syncronis C-18 column (Dim. 150 × 4.6 (mm), SN: 10,879,123, LOT: 16,092, Particle size (µ) 3), acetonitrile and 0.1% formic acid as mobile phase, the flow rate of 1 mL/min, injection volume of 20 µL, UV: 426 nm, temperature: 25ºC as conditions. For the development of a standard calibration curve, 1 mg of standard total curcuminoids batch number ANE/CL/01/047/21  was dissolved in 1 mL of HPLC grade acetonitrile from which different concentrations were prepared  and injected into the system. Samples were prepared by using 1 mg of dried extract in 1 ml of HPLC grade acetonitrile, filtered with 0.2 μm nylon membrane, and injected into the system. Chromatographic data were obtained from which data were analyzed using Thermo Scientific Dionex Chromeleon Chromatography Data System version 7.2.Curcuma longa were collected in separate zip lock bags from the experimental field at CSIR NEIST, Jorhat, Assam. Leaf samples were then washed, cleaned properly, and lyophilized at − 40 °C for 48 h. Isolation of plant DNA was carried out using HiMedia Kit (HiMedia Mumbai) by modifying the CTAB method (Cetyltrimethylammonium Bromide) as instructed in the kit. Agarose gel 0.8% was used to check the purity of extracted DNA, and bands were observed in the gel documentation system . To check the quantification of stock DNA concentration, 3 µl of DNA sample was assessed in a Nano Bio Spectrophotometer  at λ260/λ280 ratio.Fresh tender leaves of all the selected high curcumin-rich accessions (152 no.) of C. longa accession outlined 39 primer pairs out of a sum of 44 pairs of SSR primers which showed the best amplification for genetic study analysis. The primers were selected based on the earlier literatures on molecular diversity analysis in the Zingiberaceae family, which was then obtained from Bioserve Biotechnologies (India) Private Limited, Hyderabad, India. The primers obtained were in lyophilized form, which was then prepared to stock solution followed by preparation of working solution at a concentration of 10 picomol. The extracted DNA of all the accessions was also converted to a working solution with a concentration of 30 ng. The required mixture for PCR amplification consists of 5μL of DNA sample, 1μL each of forward and reverse primer, 10 μL of master mix (HiChrome), and 3μL of doubled distilled water, making a final volume of 20μL. For amplification, Prima 96 thermocycler  was used, which was conditioned at 95 °C for 1 min for initial denaturation, then continued with 35 cycles of 95 °C for 55 s (denaturation), 55 s with primer melting temperature ± 5 °C , 72 °C for 5 min (extension), and finally by 72 °C for 8 min as a concluding extension. The amplified results were then observed in Agarose gel (2%) using 1X TBE buffer, and electrophoresis was run for 1 h at 90 constant voltage. The amplified bands were then observed in a gel documentation system .PCR analysis of In order to obtain genetic variation, the banding patterns were scored after observing in the gel documentation system. The data was obtained by using input as 1 for the presence of band and 0 for the absence of band for each primer in the genotypes. The Jaccard's similarity coefficient was evaluated to obtain all pairwise differences between the accessions to build a genetic dissimilarity matrix. The final cluster dendrogram and principal coordinate analysis (PCA) were plotted based on the dissimilarity matrix and Neighbor Joining (N-J) method using Darwin software version 6.0.45. Marker Index (MI) was calculated by the formula MI = EMR × PIC where EMR is the Effective Multiplex Ratio and is defined by the multiplication of a number and a fraction of polymorphic loci61 while Resolving Power (Rp) is the summation of the band informativeness calculated by the formula [Ib = 1 − [2 ×|0.5 − P|] where p = proportion of the individuals containing the bands61. In addition, the POPGENE (Version-1.31) software was utilized to study the genetic diversity variables such as genetic differentiation degree (Gst), genetic diversity in the population (Ht), gene flow (Nm), genetic diversity within the population (Hs), Shannon’s information index (I), number of observed alleles (na), number of effective alleles (ne) and Nei’s gene diversity (h). Through Analysis of Molecular Variance (AMOVA), inter and intra-population diversities were analyzed by implementing GenAlex software version 6.5. To evaluate the genetic relationship among 152 accessions using 39 SSR primer STRUCTURE software version 2.3.4 has been utilized to construct a model-based population structure where the software was pass multiple times to evaluate the number of populations among all the accession. The most foreseeable genetic population group obtained from the study was computed by using an online tool named Structure Harvester.Polymorphic Information Content (PIC) values were calculated by using the formula PIC = 1 − Supplementary Figure 1.


Litchi chinensis Sonn.) is a highly valuable fruit crop that is widely grown in tropical and subtropical areas of the world. Studying its genetic diversity and population structure is critical for effective conservation and breeding programs. In this study, we developed 150 single-nucleotide polymorphism (SNP) markers that were evenly spaced across litchi genome and applied them to the evaluation of the genetic diversity of 84 litchi accessions, including old cultivars, modern cultivars, hybrids from known parents and wild accessions. Ninety-one SNP markers, showing high levels of polymorphism and high genotyping success rates, were used for further analysis. The newly developed SNP markers captured a relatively higher level of genetic diversity (He = 0.364) in litchi cultivars and could be successfully applied for the identification of synonymous cultivars and hybrids with close genetic backgrounds. Cluster analysis grouped all genotypes into three clusters that showed perfect association with their fruit maturation period, among which wild accessions clustered with their corresponding domesticated cultivars, and hybrids from different parent combinations showed different inheritance tendencies. Our study not only provided a set of efficient SNP markers for future genetic research, but also laid an important foundation for the conservation and genetic breeding of litchi.Litchi ( Litchi chinensis Sonn.), a member of the family Sapindaceae, is a highly valuable fruit crop that is widely grown in tropical and subtropical areas of the world [Litchi  are more informative markers and have several advantages over other molecular markers, including being more frequent in genomes, mostly bi-allelic and highly reproducible among laboratories and detection techniques ,36. AddiIn our previous study, we investigated the genetic relationship of 96 litchi accessions in China using 90 SNP markers . HoweverA set of 150 candidate SNP loci, evenly spaced across the 15 chromosomes of the litchi genome , were seGenetic diversity was investigated using the parameters of the expected heterozygosity (He) and observed heterozygosity (Ho). The He and Ho of the 91 SNP loci ranged from 0.107 to 0.500 and 0.000 to 0.690 . For theFor genetic identification of the 84 litchi accessions, pairwise multilocus match analysis was applied among individual accessions using GenAlEx 6.5 . The resIn addition to cultivar identification, the results also showed that ‘Guinuo 1’, ‘Guinuo 2’ and ‘Guinuo 3’, which are full siblings from same parents (‘Guiwei’ × ‘Nuomici’); ‘Zaogui’ and ‘Zaonuo’, which are half siblings from same maternal parent (‘Zaoli1hao’); as well as ‘Honggui’ and ‘Guihong’, which are hybrids derived from the reciprocal crossing of ‘Hongxiuqiu’ and ‘Guiwei’, could all be discriminated accurately based on genotypes on the newly developed 91 SNP loci, indicating a high discriminative power of our SNP marker set for hybrid identification of litchi.According to the phylogenetic tree constructed via the UPGMA method , the 84 K = 2  was also performed, and we separated 84 litchi accessions into three groups in accordance with their fruit maturation period. This was consistent with the result of UPGMA clustering analysis . PC 1 acp < 0.001) of the total variation, while the variation among fruit maturation group explained 51% of the total variation (p < 0.001)  to explore genetic variation among the 84 litchi accessions. The accessions were divided into four groups based on origin  and three groups based on fruit maturation period , respectively. The AMOVA results showed that the variation among origin only explained 5% (< 0.001) , confirmST statistics estimated from pairwise comparisons. As for four groups of different origin, the lowest genetic differentiation was found between modern cultivars and wild accessions (FST = 0.004), the highest genetic differentiation was found between the modern cultivars and old cultivars (FST = 0.118) , the highest genetic differentiation was found between the EEM and MLM group (FST = 0.691)  and fruit maturation period  were also tested using F= 0.118) , and the= 0.691) , and theTo promote litchi breeding, it was necessary to conduct a comprehensive survey of the genetic diversity of litchi germplasm resource at a genome-wide scale with molecular markers. However, previous studies generally used a limited number of molecular markers with unknown distributions at the genome ,29. In tGenetic diversity was further compared among old cultivars, modern cultivars, hybrids of known parents and wild accessions, showed expected heterozygosity (He) values of 0.371, 0.259, 0.348 and 0.365, respectively. In general, cultivars may contain lower levels of genetic diversity than that of wild germplasm owing to domestication bottleneck and intensive artificial selection ,43. The Due to the rapid development of the litchi industry in China, the introduction of different litchi cultivars among different litchi-producing provinces is rather frequent . SometimIn addition to cultivar identification, molecular markers also play important roles in hybrids identification ,44,45. OST) analysis further confirmed that the majority of genetic variation was found among three groups with different fruit maturation periods. In our UPGMA dendrogram, the EEM group contained all EEM cultivars like ‘Sanyuehong’, as well as wild accessions of YNW, GXDXW and VNW. The MLM group consisted of all MLM cultivars like ‘Guiwei’ and wild accessions of GXBBW, GDW and HNW. These results were incongruent with the two independent domestication events for litchi cultivars with different fruit maturation period, which found that EEM cultivars was domesticated from YNW, while MLM cultivars was domesticated from HNW [Assessing the genetic relatedness and population structure of germplasm resources is essential for allowing breeders to identify optimal parental combinations ,47,48,49from HNW . The indST = 0.691) with the MLM group. Similar results have also been reported in other genetic diversity analyses of F1 hybrid populations of litchi using EST-SSR markers, which found that the genetic distance between cross-parents may affect the inheritance tendency and hybrid progeny from more genetically distant parents. Additionally, litchi showed closer genetic relationships with maternal rather than paternal parent [In addition to cultivars and wild accessions, some hybrids from known parents were also included in order to analyze their genetic relationships with their parents. Among these, ‘Guinuo 1’, ‘Guinuo 2’ and ‘Guinuo 3’, which are full siblings derived from crossing two old MLM cultivars ‘Guiwei’ and ‘Nuomici’, all clustered closely with their paternal parent ‘Nuomici’ rather than maternal parent ‘Guiwei’. However, ‘Zaonuo’ and ‘Zaogui’, which are hybrid progeny created using the same modern EEM cultivar ‘Zaoli1hao’ as the maternal parent and two different old MLM cultivars, ‘Nuomici’ and ‘Guiwei’, as paternal parents, respectively, both showed closer genetic relationships with their maternal parent ‘Zaoli1hao’. We speculated the different inheritance tendencies of hybrids from different combinations may owe to the genetic relationships of parents because ‘Guiwei’ and ‘Nuomici’ both belong to MLM group, while ‘Zaoli1hao’ belongs to the EEM group which showed relatively high genetic differentiation . One locus that could not design genotyping primers successfully was abandoned and the remaining 149 loci were genotyped using the MassARRAY platform  according to the manufacturer’s instructions  at BGI . This study utilized the newly reported SNP loci from litchi resequencing research  as a basST statistics using Arlequin 3.5 software [GenAlEx 6.503 software  was usedsoftware .In conclusion, this is the first study to develop a novel set of SNP markers that were evenly spaced across the litchi genome. We successfully applied them to genetic diversity and population structure analysis of litchi accessions with diverse genetic backgrounds. Our results demonstrate that the newly developed SNP markers are able to capture a relatively higher portion of genetic diversity compared to SNP markers with unknown locations in the genome. The SNPs set could also be effectively used for both cultivar and hybrid identification. Clustering analysis based on the SNPs confirmed that litchi accessions could be divided into three main clusters showing perfect association with their fruit maturation period, among which wild accessions clustered with their corresponding domesticated cultivars, and hybrids from different parent combinations showed different inheritance tendencies. The SNP markers developed in this study provide a valuable tool for future population genetics studies and breeding programs.