In conclusion, history of previous severe hypoglycemia, eGFR less than 60 mL/min/1.73m2, and insulin use were associated with severe hypoglycemia.This study aimed to describe risk factors of severe hypoglycemia in type 2 diabetes mellitus (T2DM) patients in a tertiary care hospital in Indonesia. This study was a retrospective cohort study in the Endocrinology Outpatient Clinic of Dr. Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia. All subjects more than 18 years old who had been visiting the clinic for at least a year were included. Subjects were interviewed whether they had any severe hypoglycemia events within the past year, while data on risk factor variables of severe hypoglycemia was taken from medical records one year before data collection. We recruited 291 subjects, among whom 25.4% suffered at least one episode of severe hypoglycemia within one year. History of severe hypoglycemia , eGFR less than 60 mL/min/1.73m Neuroglycopenic manifestations of hypoglycemia range from mild to lethal symptoms that can compromise patient safety and are potentially deadly3. Severe hypoglycemia is defined as an event of hypoglycemia associated with severe cognitive impairment requiring external assistance for recovery, regardless of the plasma glucose concentration4. A prospective cohort study by Rudijanto et al.5 showed that 99.4% of type-2 diabetes patients had one episode of hypoglycemia. Moreover, the incidence rate of any hypoglycemia was 25.7 events per patient-year for T2DM patients in Indonesia5. Severe hypoglycemia was associated with increased cardio-cerebrovascular events, fear of hypoglycemia, higher risk of dementia, a longer length of stay (LOS), and higher medical expenses7. Therefore, the identification of clinical risk factors for severe hypoglycemia is of paramount importance.Hypoglycemia is one of the most common acute complications of T2DM13. However, those studies have reported inconsistent results. This might be due to the differences in the essential characteristics of the T2DM patients, study settings, and resources available.Studies in various countries have reported several risk factors that are significantly associated with severe hypoglycemia, including age, level of education, knowledge of hypoglycemia, HbA1c levels, duration of T2DM, chronic kidney disease (CKD), chronic liver disease (CLD), history of previous severe hypoglycemia, self-monitoring of blood glucose (SMBG), and types of hypoglycemic agents14. Despite many diabetes patients in Indonesia, there is a scarcity of data on severe hypoglycemia. If anything, two studies in Indonesia on severe hypoglycemia, such as the International Operations Hypoglycemia Assessment Tool (IO HAT) study5 and a study from Jayanti et al.15 were conducted in specific T2DM populations . Our study aims to describe the prevalence and risk factors of severe hypoglycemia among T2DM patients in an outpatient setting of a tertiary care hospital as well as a national referral hospital in Indonesia. The majority of patients at our hospital are referred from primary or secondary health care, in which patients have to return to primary or secondary health care after three months at our hospital so the data is important as input for evaluating patients in primary or secondary health care. Additionally, the study location acts as a teaching institution for a general practitioner program, 31 specialist programs, and 6 subspecialist programs16.According to the International Diabetes Federation (IDF), there were 19.5 million people with diabetes in Indonesia in 2021, and it predicted raised up to 28.6 million people with diabetes in 2045 so this study is important for health workers, particularly in IndonesiaThis study was a retrospective cohort study conducted at the Endocrinology Clinic, Dr. Cipto Mangunkusumo National General Hospital, a tertiary-care hospital located in Jakarta, the capital city of Indonesia. The clinic treats patients with various complications. Based on medical records, there were 4129 subjects with diabetes in the Endocrinology Clinic in 2022. A consecutive recruitment method was performed from October 2019 to January 2020. The inclusion criteria were T2DM patients, aged more than 18 years, who had regularly visited the clinic for at least one year. The exclusion criteria were pregnancy, psychiatric disorders, and insufficient clinical information recorded. This study was approved by Ethical Committee of Faculty of Medicine, Universitas Indonesia (KET-984/UN2.F1/ETIK/PPM.00.02/2019). Informed consent was obtained from all subjects. All methods were carried out according with relevant guidelines and regulations.17. The risk factors for severe hypoglycemia included in this study were age, level of education, subjects understanding of hypoglycemia symptoms, HbA1c levels, duration of T2DM, chronic kidney disease (CKD), decompensated chronic liver disease (CLD), history of previous severe hypoglycemia, self-monitoring of blood glucose (SMBG) application, sulfonylurea (SU) use, and insulin use. Data regarding those risk factors were taken from the subjects’ medical records one year before data collection, except for subjects’ understanding of hypoglycemia.The primary outcome of this study was any event(s) of severe hypoglycemia within the past year, which was asked to the subject during the subjects’ visit. We used the 2018 American Diabetes Association (ADA) definition of severe hypoglycemia, which was an event of hypoglycemia, associated with severe cognitive impairment requiring external assistance for recovery, with or without plasma glucose concentration18. Chronic kidney disease (CKD) was defined as having impaired renal function for at least three months, assessed based on estimated glomerular filtration rate (e-GFR)18. Decompensated CLD was defined as having Child–Pugh Score of more than 10 (C)19. Self-monitoring of blood glucose (SMBG) application was defined as well-performed if the frequency of blood glucose monitoring in a week was by the recommendation from the Indonesian Society of Endocrinology (PERKENI), which vary according to subjects' T2DM treatment regimen20.Subjects’ understanding of hypoglycemia was defined as subjects’ ability to mention at least three neuroglycopenic symptoms of hypoglycemiaStatistical analysis was conducted using SPSS Statistics for Windows, Version 20.0. Missing risk factors data were filled in using multiple imputation techniques. We presented continuous variables with mean and standard deviations (SD) or median and interquartile range (IQR), while nominal variables with counts and percentages. The normality test was assessed using the Kolmogorov Smirnov. Bivariate analysis was performed using chi-square test. Using a multiple logistic regression test, all variables in bivariate analysis with a p value less than 0.25 were included in multivariate analysis. The estimation of association was presented as odds ratio (OR). The risk factor model was developed by calculating the coefficient formula divided by the standard error for the related variable.We recruited 331 subjects, of whom 40 were excluded for various reasons , and insulin use  were significantly associated with severe hypoglycemia  compared to similar studies in other countries2 associated with severe hypoglycemia . CKD may increase the risk of hypoglycemia, and insulin resistance is increasingly more frequent at progressively lower Glomerular Filtration Rate (GFR) levels25. The Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial study reported that severe hypoglycemia more commonly appears in participants with a lower eGFR26. Moreover, 16.2% of patients in intensive therapy had an excess mortality rate25. Alsahli et al.27 found that eGFR less than 60 mL/min was an independent risk factor for hypoglycemia in 40% of people with diabetes, thus increasing the risk of cardiovascular disease and death. The current study also found that hypoglycemia is higher in patients with CKD than those without CKD due to the decreasing insulin degradation in peripheral tissue28. Moreover, CKD patients are at a greater risk of hypoglycemia in cases involving decreased insulin degradation in peripheral tissue29.This study found eGFR less than 60 mL/min/1.73 m30. Although the presence of a history of severe hypoglycemia is known to have an association with the next event of severe hypoglycemia, the onset of past hypoglycemia events that could predict the next event of severe hypoglycemia has not been specifically studied. Meta-analysis showed that among studies included, the onset of history of severe hypoglycemia varied, ranging from 1 month to 22 years31. Blood glucose levels will decrease in subjects with a history of hypoglycemia. A study by Iwase et al.32 showed that a history of severe hypoglycemia was a risk factor for developing severe hypoglycemia in type 2 diabetes (HR = 2.38). It will trigger the counter-regulatory mechanism so that hypoglycemia can be more severe and unrealized33. Patients who have experienced hypoglycemia can cause the sensitivity of receptors to blood sugar levels to be less sensitive. The signs and symptoms of hypoglycemia are not immediately seen until blood glucose levels are lower than the usual limit for people experiencing hypoglycemia. A history of hypoglycemia indicates hypoglycemia unawareness34. The presence of hypoglycemia unawareness increases the risk of severe hypoglycemia (17-fold in T2DM)35. Several factors influence the emergence of unawareness hypoglycemia, including comorbidities in the patient, advanced age, and length of suffering from diabetes and type 1 diabetes36. Limitations of blood sugar levels that cause severe hypoglycemia are still controversial, but the most important thing is a decrease in consciousness that causes patients to be unable to help themselves from hypoglycemia37. In some literature, severe hypoglycemia is caused by SU class of drugs such as glibenclamide. In this study, our SU patients found 37.5% but there was no significant association between SU and severe hypoglycemia. Probably may be due to the dominance of the use of more insulin or SU in combination with insulin.Our investigation showed association between history of severe hypoglycemia and increased risk of experiencing a subsequent event . The finding was consistent with other studies13. Rudijanto et al.5 showed that 75.1% of T2DM subjects with insulin had hypoglycemia (mild-moderate and severe) within 4 weeks of monitoring. A meta-analysis regarding insulin use showed that the incidence rate of severe hypoglycemia for insulin users in the T2DM population was 23 per patient year38. This study showed a significant association between insulin use and severe hypoglycemia . One of the reasons for the high proportion of severe hypoglycemia in this study was that most of the subjects were already taking insulin , which was 62.7% of them used basal-bolus.Furthermore, insulin is well known to be risk factors for severe hypoglycemia2, history of previous event(s) of severe hypoglycemia, and insulin use were associated with increased severe hypoglycemia risk. Based on the scoring we made, a history of severe hypoglycemia has a score of 2, while the scores for using insulin and having an eGFR less than 60 mL/min/1.73 m2 are both 1. The range of scores in the scoring system model is a risk factor for the occurrence of severe hypoglycemia which is between 0 and 4. A score of 0 is given if the subject has no risk factors. The higher subject's score, the higher possibility of severe hypoglycemia. For clinical use, we believe that our prediction model might be helpful for clinicians in determining the intensity or adjusting the dose of glycemic management for T2DM subjects, where a higher score may be a consideration for clinicians to provide more intensive glycemic therapy.Our multivariate analysis showed eGFR less than 60 mL/min/1.73 mHowever, our study has several limitations, including; (1) study design that leads to potential of recall bias; (2) some variables that are not included as independent variable such as autonomic neuropathy since the diagnosis requires specialized tools, which are not currently used as diagnostic guidelines outside of exceptional cases in our hospital.2, history of severe hypoglycemia, and insulin use were associated with severe hypoglycemia in an outpatient setting of a tertiary care hospital in Indonesia. We managed to develop a simpler severe hypoglycemia prediction model. However, future longitudinal studies are needed to obtain data regarding time-to-event of hypoglycemia.One-fourth of T2DM subjects experienced at least one episode of severe hypoglycemia in a one-year follow-up. Moreover, eGFR less than 60 mL/min/1.73 m


European Medicines Agency (EMA) between 2015 and 2019, based on the Arzneiverordnungsreport . For these 20 drugs, the number of prescriptions, sales, defined daily dose (DDD), and DDD costs were identified for the year of approval and for 2020, respectively, based on data from the Wissenschaftliches Institut der Ortskrankenkassen . Moreover, the additional benefit assessments by the Gemeinsamer Bundesausschuss  were considered for each drug. It is shown that the share of a drug in prescriptions, sales, and DDD does not correlate with the clinical benefit of the drug as measured by the additional benefit assessment by the GBA. Lastly, the advertisement pattern of protein kinase inhibitors in a representative oncology journal does not correlate with drug benefit. In conclusion, the immense costs of oncology drugs are therefore largely caused by drugs for which no additional benefit has been proven by the GBA. In order to ensure the long-term stability of health care systems, price-regulation measures are urgently needed, especially for drugs whose additional benefit has not been proven.The prices of oncology drugs have been rising progressively in recent years. Despite accounting for only a small share of prescriptions, oncology drugs are the most expensive drugs on the market. However, the association between drug price and clinical benefit often remains questionable. Therefore, we set out to analyze the development of prescription and benefit assessment of protein kinase inhibitors. We identified 20 protein kinase inhibitors with oncological indications that were newly approved by the The online version contains supplementary material available at 10.1007/s00210-023-02475-9. Gesetzliche Krankenversicherung  are increasing progressively. In 2020, GKV drug expenditures amounted to 45.570 billion euros, which corresponds to an increase of 5.1% compared to the previous year. Despite only accounting for a small share of prescriptions (1.2%), the oncology drug group accounted for the highest costs in the GKV drug market with 9.457 billion euros. Within the group of oncology drugs, the high costs are primarily due to the very expensive “targeted therapeutics,” i.e., monoclonal antibodies (€3.715 billion) and protein kinase inhibitors (€2.391 billion) , the new protein kinase inhibitors for oncological indications, approved by the European Medicines Agency (EMA) between 2015 and 2019, were identified  . In each case, the values from the year of approval of the drug were compared with the values in 2020. The data refer to drugs prescribed by physicians for outpatient use and dispensed via public pharmacies at the expense of the GKV system. Due to changes in classification or DDD, there may be deviations from the AVR data. Table For the 20 drugs, the number of prescriptions, sales, the defined daily dose (DDD) and the DDD costs were determined on the basis of data from the Gemeinsamer Bundesausschuss  for each drug . Compared to the appropriate comparative (standard) therapy, the GBA differentiated between six categories for the additional benefit assessment of a drug: major additional benefit, considerable additional benefit, minor additional benefit, not quantifiable additional benefit, no additional benefit, and less benefit  on which the GBA based its additional benefit assessment, all relevant studies on which the benefit assessment was based were identified . In a further step, additional relevant studies published until March 28, 2022, were identified for the respective drugs through literature searches in the PubMed database . An overview of the analyzed additional benefit assessments is shown in Table Furthermore, the additional benefit assessment was determined by the European Society for Medical Oncology (ESMO) and the Deutsche Gesellschaft für Hämatologie und Onkologie  and similarities and differences were analyzed .Finally, the additional benefit assessments by the GBA for the four drugs with the largest share of prescriptions, sales, and DDD in this analysis were compared with the drug assessments by the Oncology Research and Treatment were analyzed exemplarily . For this purpose, all 10 issues of the journal from 2020 were examined with regard to published advertisements on drugs.In addition, the advertisements published in the oncological journal Comparing the year of introduction and 2020, the number of prescriptions increased for each of the 20 drugs Fig. . PalbociSales increased for 18 of the 20 drugs when comparing the year of introduction and 2020 , nintedanib (990.50 thousand), ribociclib (743.50 thousand), and osimertinib (676.00 thousand) had the most DDDs.DDD costs are calculated by dividing net costs by DDD. For all drugs except ceritinib, both net costs and DDD increased, thus DDD costs decreased. For ceritinib, on the other hand, net costs and DDD decreased, and DDD costs increased. Top 4. Palbociclib and ribociclib are CDK inhibitors used for treatment of hormone receptor positive, HER2-negative locally advanced or metastatic breast cancer. The angiokinase inhibitor nintedanib is used for treatment of metastatic NSCLC and EGFR inhibitor osimertinib is used for treatment of metastatic NSCLC with T790M-EGFR-mutation. The five drugs ceritinib, brigatinib, dacomitinib, gilteritinib, and larotrectinib had the lowest share of prescriptions, sales, and DDD in this analysis and are therefore referred to the Flop 5 below.The four drugs palbociclib, nintedanib, osimertinib, and ribociclib accounted for the largest share of prescriptions, sales, and DDD in this analysis. In the following, these will be referred to the In the initial additional benefit assessment by the GBA, no additional benefit could be determined for 52% of the 20 drugs in 33 indications. Fifteen percent of the 20 drugs were assessed with a minor additional benefit, 21% had a considerable additional benefit, and for 12%, the additional benefit could not be quantified assessment by the GBA, no additional benefit could be identified for 46% of the 20 drugs in 33 indications. Eighteen percent had a minor additional benefit, 27% a considerable additional benefit, and for 9%, the additional benefit could not be quantified  and 63% vs. 52% ). In 2020, the proportion of drugs with no additional benefit in the Top 4 group was considerably lower than in the overall group, whereas the proportion in the Flop 5 group was higher compared to the overall group (14% vs. 46% and 50% vs. 46%).The share of drugs with no additional benefit was initially higher in the group of the Flop 5, the additional benefit assessment “not quantifiable additional benefit” was not assigned, neither initially nor in 2020. In the Top 4 group, the proportion of drugs with not quantifiable additional benefit was higher than the overall proportion both initially and in 2020 (14% vs. 12% and 14% vs. 9%).In the group of the Top 4 as well as in the group of the Flop 5 than in the total of the 20 drugs considered, whereby the share in the group of the Top 4 was higher than in the group of the Flop 5 (14% vs. 15% and 12% vs. 15%). In 2020, the proportion of drugs with a minor additional benefit in the Top 4 group was considerably higher than in the overall group, whereas the proportion in the Flop 5 group was slightly lower than (43% vs. 18% and 13% vs. 18%).Looking at the drugs that are assessed with a minor additional benefit, their share was initially lower in the group of the Top 4 group was lower than in the overall group, whereas the proportion in the Flop 5 group was higher (15% vs. 21% and 25% vs. 21%). In 2020, more drugs in both groups were assessed as having considerable additional benefit than in the overall group, but the proportion in the Flop 5 group was considerably higher than in the Top 4 group (29% vs. 27% and 37% vs. 27%).Initially, the proportion of drugs assessed as having considerable additional benefit in the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS). The score distinguishes ratings for the palliative and curative settings. In the palliative setting, scores of 1 to 5 can be achieved, with scores of 5 and 4 rated as substantial benefit. In the curative setting, a score of A to C is assigned. Here, scores of A and B are considered substantial benefit. ESMO explicitly states that a high ESMO-MCBS score does not automatically imply a high clinical value of a drug but rather serves as an initial assessment of a drug which must be followed by further investigations in order to use available resources wisely and responsibly . Based on this score, an assessment is available for 3 of the Top 4 drugs. Until October 15, 2022, there was no score for nintedanib by the ESMO-MCBS. When comparing the ESMO-MCBS and the GBA evaluation, the ESMO deviates from the GBA evaluation in 4 of 6 evaluations whereby the evaluation of the drug by the ESMO is always considered to be better than by the GBA  through the drug alectinib, launched in Germany in 2017, and the drug lorlatinib, launched in 2019. However, direct comparative studies between the different ALK inhibitors are still lacking . A systematic review from 2020 shows that progression-free survival (PFS) was significantly better when treated with alectinib or brigatinib than with therapy with the first-generation ALK inhibitor crizotinib or ceritinib. Likewise, overall survival (OS) seems to improve only with alectinib, compared to chemotherapy and crizotinib, although confounding cannot be precluded here by the authors  which considered all additional benefit assessments of the GBA from 2011 to 2020. Nevertheless, it was found that the additional benefit assessments of the drug group of oncology drugs as a whole on average were better than those of the entire drug market. These results raise the question of how valid the additional benefit assessments by the GBA are when often no additional benefit is found, although the respective drugs were approved on the basis of high-quality randomized controlled trials (RCT)   and Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften e.V., (AWMF), For the drugs considered in our analysis, the GBA did not assign the best rating of “major additional benefit” in any of its evaluations. It evaluates the drugs with a considerable, minor, not quantifiable, or no additional benefit. It can be seen that in the initial evaluation by the GBA, with 52%, no additional benefit could be determined in more than half of the additional benefit evaluations considered. In the 2020 current evaluation by the GBA, with 46%, no additional benefit could be determined in almost half of the cases. These results are consistent with those of an analysis of the DGHO and the Top 4 group and the Flop 5 group. When a drug is launched there is often no long-term data available, so that often no or only a minor additional benefit can be found. Only subsequent evaluations show the actual extent of the additional benefit of a drug  and interstitial lung disease in patients with systemic sclerosis (SSc-ILD)  and Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften e.V., (AWMF) Oncology Research and Treatment in 2020 primarily promoted "targeted therapeutics", with 71% of advertisements promoting protein kinase inhibitors or monoclonal antibodies. Drugs with the lowest proportion of prescriptions, sales, and DDD in our analysis (Flop 5) were advertised almost twice as often as drugs with the highest proportion of prescriptions, sales, and DDD (Top 4). The newness of a drug to the market had no effect on the number of advertisements published in this sample. We found no correlation between the number of drug advertisements and the clinical benefit of the drug being promoted. Thus, with the scientific information available to us, there is no scheme why an individual protein kinase inhibitor is promoted or not. Therefore, it should be noted that the high drug costs associated with protein kinase inhibitors attract economic interest among pC to make as much profit as possible , it would also not be available for other indications in which it might have a considerable additional benefit. The re-evaluations of the additional benefit after expiry of the deadline by the GBA can result in both an improvement and a deterioration of the evaluation. This makes a constant critical examination of the added benefit of a drug absolutely necessary—even after its approval. Particularly considering the immense cost of protein kinase inhibitors, this needs to be well established in clinical practice.Furthermore, it was shown that the share of a drug in prescriptions, sales, and DDD does not correlate with the clinical benefit of the drug as measured by the corresponding additional benefit assessment by the GBA. The immense costs of oncology drugs are therefore largely caused by drugs for which no additional benefit has been proven by the GBA. This is not only a German phenomenon. In the USA, England, France, and Switzerland, there is also no statistically significant association between monthly therapy costs and clinical benefit of a drug  from October 2022 provides a reform of the Arzneimittelmarktneuordnungsgesetz  of 2011 and is therefore a first step in the right direction. In the future, larger discounts will be levied on the price negotiated with the company if no or only a minor additional benefit has been determined by the GBA. In addition, the negotiated reimbursement amount will be valid retroactively from the 7th month, rather than from the 13th month as it is currently the case (Bundestag Supplementary file1 (DOCX 122 KB)Below is the link to the electronic supplementary material.


Microsphaera diffusa, leads to substantial yield decreases in susceptible soybean under favorable environmental conditions. Effective prevention of soybean PMD damage can be achieved by identifying resistance genes and developing resistant cultivars. In this study, we genotyped 331 soybean germplasm accessions, primarily from Northeast China, using the SoySNP50K BeadChip, and evaluated their resistance to PMD in a greenhouse setting. To identify marker-trait associations while effectively controlling for population structure, we conducted genome-wide association studies utilizing factored spectrally transformed linear mixed models, mixed linear models, efficient mixed-model association eXpedited, and compressed mixed linear models. The results revealed seven single nucleotide polymorphism (SNP) loci strongly associated with PMD resistance in soybean. Among these, one SNP was localized on chromosome (Chr) 14, and six SNPs with low linkage disequilibrium were localized near or in the region of previously mapped genes on Chr 16. In the reference genome of Williams82, we discovered 96 genes within the candidate region, including 17 resistance (R)-like genes, which were identified as potential candidate genes for PMD resistance. In addition, we performed quantitative real-time reverse transcription polymerase chain reaction analysis to evaluate the gene expression levels in highly resistant and susceptible genotypes, focusing on leaf tissues collected at different times after M. diffusa inoculation. Among the examined genes, three R-like genes, including Glyma.16G210800, Glyma.16G212300, and Glyma.16G213900, were identified as strong candidates associated with PMD resistance. This discovery can significantly enhance our understanding of soybean resistance to PMD. Furthermore, the significant SNPs strongly associated with resistance can serve as valuable markers for genetic improvement in breeding M. diffusa-resistant soybean cultivars.Powdery mildew (PMD), caused by the pathogen  Three ae to PMD . Althouge to PMD . In contfe cycle . The Rmdectively . Rmd_PI2 Sat_224 . Rmd_PI5_16_1272 . Rmd_V97ectively . Similar28 genes . Recentlof Chr16 .M. diffusa.Genome-wide association studies (GWAS) have become a powerful alternative to linkage mapping for analyzing complex trait variations at the genomic level, utilizing ancient recombination events at the population level . GWAS si22.1In the association mapping population, we observed a substantial variance in PMD resistance based on the disease severity index (DSI) (2.22) dropped to half of its maximum value, was determined to be 109 kb at r2 = 0.422 . After filtering SNPs with a minor allele frequency of less than 5% in at least 80% of genotypes, 30,602 high-quality SNPs were obtained, providing widespread coverage across the entire soybean genome. Population structure analysis and association mapping were performed using these high-quality SNPs. Within a 1000 kb window, pairwise linkage disequilibrium (LD) was estimated, and the LD decay rate, measured by the point at which the correlation coefficient , compressed mixed linear model (CMLM), mixed-model association eXpedited (EMMAX), and efficient mixed linear model (MLM). Quantile-quantile plots (Q-Q plots) of the four models showed initial consistency between observed and expected P values, with significant deviation beginning from the expected P value as the -log10P value increased to approximately 3.5 Figure 42.5https://www.ebi.ac.uk/QuickGO/) and the eukaryotic orthologous groups  databases. Most of these genes displayed similar functional descriptions, including protein kinase activity, innate immune response, ADP binding, apoptosis, protein phosphorylation, protein binding, nucleic acid binding, signal transduction, and other biological and metabolic processes , ss715624939 , ss715624901 , ss715624931 , ss715624926 , and ss715624888 . Soybean plants carrying the favorable allele (GG) on ss715624933, ss715624888, ss715624926, and ss715624931 showed significantly higher PMD resistance  than those carrying the unfavorable allele (AA)   displayed differential expression between ZDD06944 and ZDD00359, and were up-regulated in the highly resistant accession ZDD00359 after M. difusa treatment. In ZDD00359 (HR), the expression patterns of these three putative candidate genes significantly increased at 6 and 12 h after treatment, with peak expression observed at 12 h. Glyma.16G212300 also showed increased expression at 6 h, reaching the maximum value (approximately 216.8-fold) at 12 h, and then rapidly decreased at 24, 48, and 72 h. A comparable expression pattern was observed for Glyma.16g213900, where the expression level peaked (approximately 6.5-fold) at 12 h after treatment, followed by a rapid decrease. In contrast, the expression level of Glyma.16G210800 in ZDD00359 (HR) reached the maximum value (approximately 8.56-fold) at 6 h after treatment and started to decrease at 48 h. Based on these expression patterns, we concluded that these three R-like genes were induced by M. diffusa and may play a role in the soybean’s disease defense mechanism.We analyzed the expression patterns of 20 candidate genes in ZDD06944  and ZDD00359  using real-time reverse transcription polymerase chain reaction (qRT-PCR) analysis Figure 83Rmd_V97-3000 was located within a region of approximately 3.6 Mb, covering all other mapped R-genes. The regions of Rmd_PI24540 differed from those of Rmd_B1, Rmd_ZH14, and PMD_PI567301B. Additionally, the regions of Rmd_B1 and Rmd_ZH14 were distinct from that of PMD_PI567301B. Rmd_B13’s location partly overlapped with the region of Rmd_PI567301B, while it was different from that of Rmd_PI24540. Previous studies have suggested that soybean PMD resistance sources, such as PI567301B, PI243540, and the cultivated variety CNS, may carry different R-genes based on map positions and verification tests of three markers . These gene regions were determined through a blast search of flanking markers based on the cultivated soybean reference genome (Williams 82.a2.v1) Figure 6 markers . CombiniGlyma.16G210800, Glyma.16G212300, and Glyma.16g213900 exhibited differential expression between ZDD06944 and ZDD00359, with up-regulated expression in the highly resistant accession ZDD00359 after M. diffusa infection. Previous studies have suggested that genes showing distinct expression patterns among different accessions tend to be associated with susceptibility or resistance results, either directly or indirectly. Conversely, genes with different expression dynamics across time may represent the general plant reactions to pathogen infections without necessarily conferring increased resistance  presented differential expression between highly susceptible and highly resistant accessions after M. diffusa infection, suggesting their potential as candidate genes. These PMD resistance-associated SNPs could serve as valuable markers for MAS in soybean breeding.In this study, GWAS identified seven SNPs significantly associated with PMD resistance. Three genes  and maintained by the Soybean Institute of Jilin Academy of Agricultural Sciences  with PLINK1.09. Mean LD decay plots were generated using an R script  phylogenetic trees, and population structure analysis. Tassel V5.2.60 was employed for PCA and kinship matrix calculations based on 30,602 SNPs from the 331 SGAs, where the kinship Matrix_Type was Centered_IBS. The NJ tree was constructed using the Maximum Composite likelihood model in MEGA-X, in which the Bootstrap value was 1000 replicates, the Gaps/Missing Data Treatment selected partial deletion, and the Site Coverage Cutoff was 80%. Linkage SNP filtering was performed using PLINK V1.09, with a window size of 50 kb, SNP step size of 10, SNP correlation threshold of 0.2, and retention of unlinked SNPs, resulting in 1643 SNPs for population structure inference using STRUCTURE 2.3.4 . The numR script , plottinum value . The LD um value .5.5Missing SNP genotypes in the filtered dataset were imputed using Beagle software . A total5.6http://www.soybase.org/). Utilizing soybean genome annotations, we predicted putative causal genes associated with the identified regions. Furthermore, functional annotations of genes in the target genomic regions were retrieved from Phytozome (http://www.phytozome.net). For the gene expression analysis, the M. diffusa spore suspension (1×105 cfu/ml) was sprayed onto seedlings of ZDD06944  and ZDD00359  after they had been grown for 10 d. The seedlings were then stored in a growth chamber at 75% relative humidity, 23°C, and a photoperiod of 16 h of light and 8 h of darkness. Primary leaves were sampled at 0, 6, 12, 24, 48, and 72 h after inoculation, and total RNA was extracted using an Easy Pure Plant RNA kit . Reverse transcription was performed on 1.5 μg of DNase-treated RNA utilizing a PrimeScript™ RT Reagent kit with gDNA Eraser from Takara (Japan). The qRT-PCR primers were designed using the Oligo7 software  or known genes within the target genomic regions from the Soybase Database , accession number: CNP0004650; see the following link for details: YS: Data curation, Formal Analysis, Investigation, Software, Writing – original draft. HZ: Funding acquisition, Writing – review & editing. XL: Project administration, Writing – review & editing. CY: Writing – review & editing. GQ: Investigation, Writing – review & editing. YL: Writing – review & editing. LD: Investigation, Writing – review & editing. YNW: Investigation, Writing – review & editing. DW: Conceptualization, Resources, Writing – review & editing. YMW: Conceptualization, Supervision, Validation, Writing – review & editing. YD: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Visualization, Writing – review & editing.


Despite the importance of winter wheat in Central Asian countries, there are limited reports describing their diversity within this region. In this study, the population structures of 115 modern winter wheat cultivars from four Central Asian countries were compared to germplasms from six other geographic origins using 10,746 polymorphic single-nucleotide polymorphism (SNP) markers. After applying the STRUCTURE package, we found that in terms of the most optimal K steps, samples from Kazakhstan and Kyrgyzstan were grouped together with samples from Russia, while samples from Tajikistan and Uzbekistan were grouped with samples from Afghanistan. The mean value of Nei’s genetic diversity index for the germplasm from four groups from Central Asia was 0.261, which is comparable to that of the six other groups studied: Europe, Australia, the USA, Afghanistan, Turkey, and Russia. The Principal Coordinate Analysis (PCoA) showed that samples from Kyrgyzstan, Tajikistan, and Uzbekistan were close to samples from Turkey, while Kazakh accessions were located near samples from Russia. The evaluation of 10,746 SNPs in Central Asian wheat suggested that 1006 markers had opposing allele frequencies. Further assessment of the physical positions of these 1006 SNPs in the Wheat Ensembl database indicated that most of these markers are constituents of genes associated with plant stress tolerance and adaptability. Therefore, the SNP markers identified can be effectively used in regional winter wheat breeding projects for facilitating plant adaptation and stress resistance. Wheat is the third most important crop in the world after rice and maize and the most important in Central Asia. In 2023, the FAO pegged wheat production at 784 million tons globally . In fiveVrn genes in the vernalization response process [Wheat is commonly classified into spring and winter types; the former requires a long period of low temperature to accomplish vernalization . Genetic process ,5. In Ce process . However process . However process , which i process . The ave process . As the Geographically, Central Asia is situated along the ancient Silk Road, and the earliest records of wheat cultivation are from Uzbekistan, dating from the second millennium BCE . The reg® arrays. Notably, with the use of the 90 K SNP Illumina array, a set of 90 accessions of modern spring wheat from Kazakhstan were genotyped and compared with the SNP genotyping data of 690 wheat accessions representing landraces and varieties from Asia, Australia, Canada, Europe, and North America [In the last decade, the reliable evaluation of the genetic variation in wheat collections has become feasible due to the significant progress made in the development of efficient SNP (single-nucleotide polymorphism) genotyping platforms, established via the efforts of the international wheat community ,19. To d America . As a re America . Hence, Nei’s unbiased diversity index values in the studied populations ranged from 0.212 in Kazakhstan to 0.325 in the USA . The higNei’s genetic distances ranged from 0.003 (between Uzbekistan and Kyrgyzstan) to 0.28 (between Afghanistan and Europe) . Within The PCoA indicated the genetic relatedness of accessions from Uzbekistan, Tajikistan, and Kyrgyzstan, all of which were positioned at a distance from the Kazakh accessions . The PC1The results of the “Evanno test” and the “elbow method” suggest that the optimal number of K steps throughout the entirety of the studied collection was 4 , at whicThe STRUCTURE output at step K4 indicates that most samples from CA belonged to cluster 1 and cluster 3 . InteresEvaluations of the following K steps (from K5 to K10) yielded similar trends, as samples from Tajikistan and Uzbekistan fit into clusters that they shared with samples from Afghanistan, and samples from Kazakhstan and Kyrgyzstan had patterns in common with accessions from the Russian Federation . NeverthIn addition to the PCoA plot of SNPs that applied to the entire genome , individThe evaluation of SNP markers with opposing frequencies of alleles within accessions of the Central Asian countries yielded 1006 informative SNPs that may play a role in plant adaption at the regional level . The assTraesCS5A02G208500 and TraesCS5A02G208600) and on chromosome 6A (gene TraesCS6A02G407500)  gene is involved in the regulation of the circadian clock and flowering time [WRKY on chromosome 2B (TraesCS2B02G552800) and ERD  on chromosome 2D  . The ZEIing time . Plant-ding time . In the osome 2D .The winter wheat collection used in the current study consisted of 667 accessions from Europe , Central Asia , Afghanistan , Russia , Turkey , the USA (19), and Australia . The Central Asia collection itself is composed of 115 accessions, including accessions from Kazakhstan , Kyrgyzstan , Uzbekistan , and Tajikistan  . The colA set of 192 winter wheat accessions of the 667 accessions in the CAWBIN panel were genotyped using a 35K Affymetrix Axiom SNP array  in this p > 0.001, a confidence score of 0.5, and a minor allele frequency (MAF) of >5% were considered to meet the requirements. An analysis of the population structure of the collection was performed using the Bayesian Markov Chain Monte Carlo (MCMC) algorithm in STRUCTURE [In total, 10,746 polymorphic SNP markers were selected for phylogenetic analysis using the previously published criteria to identify informative SNPs . AccordiTRUCTURE . K valueTRUCTURE  and the TRUCTURE . A princTRUCTURE .It has previously been established that historic wheat development in Central Asia was based on two primary gene flow directions ,14,15,17As wheat was initially domesticated in the Middle East , the samOur previous study seeking to identify the genetic structure within a barley collection suggested that evaluation of the PCoA plots of groups of SNP markers in individual chromosomes may provide additional insights into the identification of genes associated with plant adaptation . As suchOne of the most logical approaches to identifying genes associated with plant adaptation at both intra- and interregional levels is the evaluation of SNPs with opposing allele frequencies in samples with different origins . In thisWRKY and ERD, located on chromosomes 2B and 2D, respectively (ZTL (chromosome 6B), which controls the circadian clock and flowering time [A further study of the 1006 SNPs with opposite allele frequencies identified in the four groups of accessions from Central Asia revealed that 555 are components of the genes listed in the Wheat Ensembl database . A cursoectively . As expeing time . Other eing time ,28. ThusThis evaluation of winter wheat samples collected from different parts of the world using 10,746 polymorphic SNPs suggests that the range of wheat genetic diversity within Central Asian countries is similar to that in other studied regions. Within the Central Asian region, Nei’s genetic diversity index values of accessions ranged from 0.212 in Kazakhstan to 0.282 in Uzbekistan, and these values are comparable to those in Russia (0.258) and Afghanistan (0.259). The PCoA plots constructed using 10,746 SNPs showed the genetic identities of samples from four Central Asian countries, which differed from those from two neighboring countries. Unlike Kazakhstan, where samples showed genetic similarities to samples from Russia (0.02), the accessions from Kyrgyzstan (0.01), Tajikistan (0.012), and Uzbekistan (0.01) were genetically close to samples from Turkey. Furthermore, assessment using “STRUCTURE” confirmed that, at step K4 (one of the most important steps in the differentiation of samples), the accessions from Uzbekistan and Tajikistan grouped together with samples from their southern neighbor, Afghanistan, while accessions from Kazakhstan and Kyrgyzstan grouped with samples from their northern neighbor, the Russian Federation. These results suggest that historic breeding projects in Kyrgyzstan have actively used genetic resources of winter wheat from Kazakhstan and Russia. A comparative evaluation of the samples from the four Central Asian countries, using 10,746 SNPs, allowed us to identify 1006 SNPs with opposite allele frequencies, suggesting that these markers may be associated with plant adaptation traits at the intraregional level. The results of this study illustrate the genetic structures of accessions with different origins and suggest the possible directions of gene flow in the winter wheat accessions from the four countries in Central Asia. In addition, we identified a set of SNPs showing opposite allele frequencies in the four groups of samples from Central Asia. This set can be further utilized to search for candidate genes relevant to regional winter wheat breeding activities.


The Ovis aries natriuretic peptide C (NPPC) could be considered as a candidate gene for the defined significant associations. The results of the current study will help to increase understanding of the variation in weaning weight and ADG until weaning of Akkaraman lambs and help enhance selection for lambs with improved weaning weight and ADG. However, further investigations are required for the identification of causal variants within the identified genomic regions.Small ruminants, especially sheep, are essential for sustainable agricultural production systems, future food/nutrition security, and poverty reduction in developing countries. Within developed countries, the ability of sheep to survive on low-quality forage intake could act as buffer against climate change. Besides sheep’s importance in sustainable agricultural production, there has been less ongoing work in terms of sheep genetics in Near East, Middle East and in Africa. For lamb meat production, body weight and average daily gain (ADG) until weaning are critical economic traits that affects the profitability of the industry. The current study aims to identify single nucleotide polymorphisms (SNPs) that are significantly associated with pre-weaning growth traits in fat tail Akkaraman lambs using a genome-wide association study (GWAS). A total of 196 Akkaraman lambs were selected for analysis. After quality control, a total of 31,936 SNPs and 146 lambs were used for subsequent analyses. PLINK 1.9 beta software was used for the analyses. Based on Bonferroni-adjusted  The wild ancestors of sheep have first domesticated approximately 11,000 years ago in the present-day countries Turkey, Iran, and Iraq . The demBody weight is one of the most important growth and development components in sheep as it both directly and indirectly influences lamb meat yield, wool production, and reproduction. Support of the importance of weaning weight for reproductivity comes from the high 0.80) genetic correlation between these two components . However0 geneticEstimated heritability for lamb daily weight gain and weaning weight was low to moderate depending on the sheep breed studied ,10. CompFor understanding the genetic background of economical traits, QTL are one of the most useful tools in livestock genetics. In the last couple of decades, several QTLs have been found by genome scanning technology and candidate gene approaches in livestock. Numerous QTL studies for different quantitative traits have been performed on pigs, cattle, and chickens . It is iGenome-wide association studies (GWAS) have widely been applied to identify and localize genes related to quantitative traits in several economically important breeds compared to indigenous breeds in farm animals . Additioad libitum) was supplied to lambs, and they were weaned at an approximate age of 90 ± 2 days. The lambs were weighed at birth (T0), and on the 30th (T30), 60th (T60) and 90th (T90) day as weaning weight. Average daily gains (ADG) were calculated taking the weight a lamb has gained since the last weight and dividing the weight by the number of days since that last weight. For instance, ADG0_90 was the ADG between birth weight and T90. Each lamb was weighed at least twice by the same person to reduce manmade errors in BW measurements and the average value was considered as the final measurement. BWs were taken in a quiet, relaxed, state in the four herds of Akkaraman sheep. Approximately 10 ml of blood was taken from the jugular vein using vacutainer tubes containing EDTA as an anticoagulant. Genomic DNA was isolated from blood samples using a commercial kit according to the manufacturer’s instructions . NanoDrop ND-1000 spectrophotometer  was used to check the quantity and quality of the isolated DNA. DNA samples were brought to a final concentration of 50 ng/μl (as required by the Illumina Infinium protocol) and stored at 4°C until use.A total of 192 Akkaraman male lambs of four nucleus herds were used to record body weights (BWs) until weaning in the Kayseri province of Turkey. All experimental procedures were performed in accordance with the guidelines of the Local Ethics Committee for Animal Experiments at Erciyes University (#13/130-13.11.2013). No animal was sacrificed or subjected any kind of suffering. Commercial husbandry practices were applied during experiment. All the ewes were two years of age. The flocks were fed on pasture except for the months of February and March, which coincides with the highest rate of lambing. During housing, animals were fed a ration composed of wheat stubble, barley, and alfalfa. The mating period extends from the second half of August to late September, where ewes are randomly assigned to rams in field conditions. Lambing started in late December and continued until March. The date of birth of all lambs was recorded and immediately after lambing, they were ear-tagged. From 15 days forth, creep feed  was used to genotype individual SNPs that include 54,241 SNPs. After genotyping all SNP markers were mapped to let_v1.0 . After tlet_v1.0 . QualityGenome-wide estimates of identity-by-descent (pi-hat) were used to select distantly related individuals. Genotyped lambs were randomly ordered and selected if they were related to every other member of the group below a set threshold. A pi-hat threshold of ≤0.26 was used for lambs included in the association analyses (“sibships of size one”), as this removes duplicate or closely related pairs of lambs. This selection process was repeated for 1,000 iterations, after which the largest set of lambs was identified for further use. In order to assess the population structure, Two-dimension reduction routines provided by PLINK 1.9 were performed: first, variance-standardized relationship matrix was used generate the “pca” for principal components analysis (PCA) ,21, and p<0.05) were identified at both the genome-wide and chromosome-wide levels following Bonferroni testing correction. The cut-off for significance was calculated using 0.05/n or 1/n, where n  is the number of SNPs analyzed at the genome-wide or chromosome-wide level. Thus, the 5% threshold for chromosome-wide significance was 1.565 × 10−6. Quantile-quantile plots (Q-Q plot) were visualized by plotting the distribution of obtained vs. expected genome-wide p-values  regression tests, where the first 17 principal components were used as covariates for unrelated lambs, farms (where samples are collected), and birth types. Significantly associated SNPs  using TaqMan SNP Genotyping Custom Assays  was performed in 373 Akkaraman lambs. For genotyped lambs, husbandry conditions and number of farms were same as mentioned under the subtitle of animals and phenotypic data. Briefly, genotyping was performed using allelic discrimination primers  and hydrolysis probes  for rs427117280 . The remainder of the reaction consisted of TaqMan Genotyping PCR Master Mix  and 50 ng of DNA, where the final reaction volume was 10 μl. The reaction was run in an ABI StepOnePlus Real-Time PCR System . Genotyping assays aimed for 95% genotyping rates in these data sets. An online software (http://www.husdyr.kvl.dk/htm/kc/popgen/genetik/applets/kitest.htm) was used to analyze the Hardy-Weinberg equilibrium (HWE) and allele frequency for each SNP and statistical significance was defined as p<0.05. The genotype-phenotype association was analyzed with a generalized linear model using the GLM procedure in SAS v9.0. The reduced model included fixed effects of farm (four levels), birth type (single or twin), and genotype. Genotypic comparisons were reported following Tukey-Kramer adjustment, and a p≤0.1 was considered significant.Genotyping of −6  and 3.13 × 10−5  were regarded as genome-wide and chromosome‐wide suggestive significant values, respectively.The descriptive statistic values of body weights and body weight gains until weaning are presented in Ovis aries Oar_rambouillet_v1.0 as a reference on the Ensembl Release 105 (accessed 13 January 2022) was used to query within a distance of 500 Kb for genes surrounding these suggestive markers. This led to the identification of candidate gene NPPC (Ovis aries natriuretic peptide C) in association with the highest significance SNP, where this SNP was found to be significantly associated to both the T90 and ADG0_90 traits. The second SNP on OAR2, rs430444267, was identified as being significant at a chromosome-wide level (MAMDC2 (MAM Domain Containing 2) and associated with the ADG0_30 trait. While this SNP was located within MAMDC2, there is no evidence to suggest that this gene is responsible for growth traits  on OAR7, which was determined to have chromosome-wide significance associated with the birth weight trait (FBN1 (Fibrillin1). The last suggestive significant SNP for T30 trait  was located close to this region.In total, 5 SNPs reached the suggestive significance level , where ade level . This SNh traits . Furtherht trait . Referen30 trait  was locars427117280 SNP was significantly associated with traits T90 and ADG0_90 in Akkaraman lambs. Therefore, SNP rs427117280 was subjected to genotyping, population genetic analysis, and further association analysis within a larger Akkaraman sheep population. Three genotypes for SNP rs427117280 were found. TT was the most frequent genotype with a frequency of 0.911. Consequently, the TG (0.088) and GG (0.001) frequencies were much lower. The most frequent allele was T, with an allele frequency of 0.951, which was higher than that of G at 0.049. The χ2 value was 1.414, confirming that the frequency distribution of SNP rs427117280 was in accordance with the Hardy–Weinberg equilibrium (HWE) law in the Akkaraman sheep population .Analyzing SNP 0 traits . Specifi0 traits . NotablyMost of the important economic traits in livestock maintain a polygenic complex genetic architecture. Therefore, research focusing on the responsible candidate genes underlying these traits is always intriguing and crucial to support livestock genetics and breeding ,24. To tGrowth related traits comprise an essential measuring basis of production performance in lambs, where these traits directly influence the economic benefit. It was reported that the heritability of body weight is moderate in sheep, with the heritability of birth weight and weaning weight ranging from 0.30 to 0.35 ,26. Impo427117280 was involved in both T90 weight and ADG0_90. This result clearly indicates that the rs427117280 might be a candidate SNP for liveweight and/or average daily gain in Akkaraman lamb. Supporting this finding, genotyping of SNP rs427117280 in a further 373 Akkaraman lambs validated preliminary findings and showed association not only with ADG0_90 and T90, but also with ADG60_90 and ADG30_90. Association of other ADGs that were not found during the original GWAS may be due to the increased lamb number during the TaqMan genotyping study.Results from the current study revealed two genome-wide significant QTL regions on OAR2 that had not been previously reported as affecting liveweight and ADG in the Akkaraman sheep breed. It is important to note that the GWAS results demonstrated that rsrs427117280 is correctly associated with liveweight phenotypes. Further reinforcement of this concept is provided by a study completed on an Awassi and Merino cross, which suggested that a QTL for body weight at slaughter was present at 253 cM Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: NoReviewer #3: Partly**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: YesReviewer #3: No**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: YesReviewer #3: No**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: NoReviewer #3: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Comments to the authorsPONE-D-22-26401 Genome-wide association study of early liveweight traits in fat-tailed Akkaraman lambsÇinar et al.In general, the manuscript is well written. The study was intended to identify SNPs associated with early live weight traits in fat-tailed Akkaraman lambs using GWAS. Line-specific comments are provided below:Line 188: Is Animals and Phenotypic Data a subtitle?Line 212-214: Sentence is repeated.Line 227-228 and elsewhere: Suggest describing the parameters such as T90, and ADG0_90 on first use.Line 234-237: The trait associated with SNP rs406101124 is birth weight and not T30, as per table 1. The candidate gene for this was not reported in table 1 either.Line 237-240: The trait is not reported in the text.Reviewer #2: Dear Authors,Unfortunately, I cannot recommend your article for publication in PLOS ONE. This journal is designed to present research at the cutting edge of science. Your research certainly deserves attention. However, there are a number of remarks that do not allow us to consider it advanced. The number of animals (146) is too small for a full GWAS. The DNA chip you used is currently used primarily for routine genotyping. The search for associations at the modern level is performed on a consortium 600K chip or using whole genome sequencing data. There are too many mistakes and repetitions in the text, I started to make comments, but it turned out that there were too many of them. Here are just a few of the research results.L215 – Is it Bonferroni correction? And “1.565 × 10−6 ” is genome-wide. Fix it.L218 – In Table showing 5 SNP.L228 – Don’t repeat data from table in text. Just analyze it.L238 – Gene was localizing in intergenic region? Fix it.L242 – Describe region of gene, if SNP localizing in it.L250 – p-value not correcting by Bonferroni correction. It is threshold of p-value with significance level.L253 – Why you are using 500 kbp distance?L256 – What is the lines in graph?L264 – You are fully repeat table data. In this case remove table.L275 – “significantly heavier” wrong sentence. Change it.L277 – Not. In statistic no tendencies. Significant difference presents or not.In conclusion, I would recommend that you finalize the article and submit it for publication in a journal with a lower impact factor, where it can be published.Regards,Reviewer #3: OVERALL:-The authors aimed at identifying SNPs associated with body weight in fat tail Akkaraman lambs. However, the number of sheep utilized for this analysis is low and could lead to false associations. Additionally, a low number of animals reduces the chances of identifying regions controlling the traits of interest. This could be one of the reasons why the authors only found one SNP on chr 2 associated with weaning weight at day 90 and average daily gain (ADG) from day 0 to day 90.INTRODUCTION:-Line 33: Check for typos .MATERIALS AND METHODS:-Please include the protocol number of the approved animal use protocol  utilized in this study.-The authors mentioned that pedigree was unknown. However, you can estimate this from SNP data. Please include the G matrix in the association model.- Please include a better description of the model used for the association analysis. Please use a mixed model and explain the components of the model . Please also include sex if male and female lambs were evaluated and re-analyze the data.- It is possible that authors won’t find the same results after adjusting the association model. Please revise the data and results.- Submit your SNP data to ENSEMBL/NCBI and provide number. This information must be part of the manuscript (Genotyping section).- Revise abstract, results, discussion, and conclusion sections after adjusting the association model.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: NoReviewer #3: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  10 Aug 2023Reviewer #1: Comments to the authorsPONE-D-22-26401 Genome-wide association study of early liveweight traits in fat-tailed Akkaraman lambs Çinar et al.Comment: In general, the manuscript is well written. The study was intended to identify SNPs associated with early live weight traits in fat-tailed Akkaraman lambs using GWAS. Line-specific comments are provided below:Response: Thank you for comment.Comment: Line 188: Is Animals and Phenotypic Data a subtitle?Response: The sentence was corrected.Comment: Line 212-214: Sentence is repeated.Response: Additional sentence was deleted.Comment: Line 227-228 and elsewhere: Suggest describing the parameters such as T90, and ADG0_90 on first use.Response: The sentence was added in the section of “Animals and phenotypic data”. Additionally, footnote #2 is covering the explanation of ADGs. Comment: Line 234-237: The trait associated with SNP rs406101124 is birth weight and not T30, as per table 1. The candidate gene for this was not reported in table 1 either.Response: The phenotype “birth weight” was added in the revised manuscript. Since SNP rs406101124 was located within the CEP152 gene. The candidate gene FBN1 was given in the text and table in the revised version. Comment: Line 237-240: The trait is not reported in the text.Response: The trait information was added in the revised version. Reviewer #2: Dear Authors,Comment: Unfortunately, I cannot recommend your article for publication in PLOS ONE. This journal is designed to present research at the cutting edge of science. Your research certainly deserves attention. However, there are a number of remarks that do not allow us to consider it advanced. The number of animals (146) is too small for a full GWAS. The DNA chip you used is currently used primarily for routine genotyping. The search for associations at the modern level is performed on a consortium 600K chip or using whole genome sequencing data. There are too many mistakes and repetitions in the text, I started to make comments, but it turned out that there were too many of them. Here are just a few of the research results.Response: We agree your comment. But Akkaraman is a native breed that recording of matings could be sometimes biased. We have started this reseach based on farmer’s data but we also understood that inbreeding was relatively higher in the population. Additionally, lack of breeders organization for specific native breeds, rams generally has been used repetetively for mating in years in Turkish sheep husbandry system. Thus, relatidness of half-sib could be analyzed only with whole genotyping. We have started with 192 lambs. However, due to inbreeding 46 lambs were excluded from the analysis. Furthermore, our results based on 146 animals was validated uding TaqMan genotyping assay in 400 animal population. Results showed that the rs427117280 SNP was found to be associated with weaning weight at day 90 and average daily gain (ADG) from day 0 to day 90. Our experiments is ongoing that covers other native breeds of Turkiye for validation of rs427117280 SNP with early growth traits.600K chip could be a good option, however we followed similar researchers and literature during the experimental design. Following articles are showing that similar number of animals using 50K chip published for ovine GWA studies. In further studies, we are planning to enlarge animal population and to utilize 600K ovine chip.  Hernández-Montiel, W., Martínez-Núñez, M. A., Ramón-Ugalde, J. P., Román-Ponce, S. I., Calderón-Chagoya, R., & Zamora-Bustillos, R. (2020). Genome-wide association study reveals candidate genes for litter size traits in pelibuey sheep. Animals, 10(3), 434. James, C., Pemberton, J. M., Navarro, P., & Knott, S. (2022). The impact of SNP density on quantitative genetic analyses of body size traits in a wild population of Soay sheep. Ecology and Evolution, 12(12), e9639. Almasi, M., Zamani, P., Mirhoseini, S. Z., & Moradi, M. H. (2021). Genome-wide association study for postweaning weight traits in Lori-Bakhtiari sheep. Tropical Animal Health and Production, 53, 1-8. Hernández-Montiel, W., Martínez-Núñez, M. A., Ramón-Ugalde, J. P., Román-Ponce, S. I., Calderón-Chagoya, R., & Zamora-Bustillos, R. (2020). Genome-wide association study reveals candidate genes for litter size traits in pelibuey sheep. Animals, 10(3), 434. Mastrangelo, S., Ben Jemaa, S., Sottile, G., Casu, S., Portolano, B., Ciani, E., & Pilla, F. (2019). Combined approaches to identify genomic regions involved in phenotypic differentiation between low divergent breeds: Application in Sardinian sheep populations. Journal of Animal Breeding and Genetics, 136(6), 526-534. Hernández-Montiel, W., Cob-Calan, N. N., Cahuich-Tzuc, L. E., Rueda, J. A., Quiroz-Valiente, J., Meza-Villalvazo, V., & Zamora-Bustillos, R. (2022). Runs of Homozygosity and Gene Identification in Pelibuey Sheep Using Genomic Data. Diversity, 14(7), 522.Comment: L215 – Is it Bonferroni correction? And “1.565 × 10−6 ” is genome-wide. Fix it.Response: The sentence was fixed. Yes, it is Bonferroni corrected values, mentioned in Genome-Wide Association Study sub-section.Comment: L218 – In Table showing 5 SNPResponse: The sentence was corrected in the revised manuscript. Comment: L228 – Don’t repeat data from table in text. Just analyze it.Response: Sentences were removed and table 1 was referred.Comment: L238 – Gene was localizing in intergenic region? Fix it.Response: The rs428733788 SNP was found to be intergenic, however, ADAMTS1 was identified as closest candidate gene to be responsible for related growth trait. The sentence was rephrased in order to avoid misunderstanding.Comment: L242 – Describe region of gene, if SNP localizing in it.Response: The gene was given in the rephrased sentence. Comment: L250 – p-value not correcting by Bonferroni correction. It is threshold of p-value with significance level.Response: Thank for comment. Footnote section was corrected and Bonferroni term was removed. Comment: L253 – Why you are using 500 kbp distance?Response: According to literature, we have observed that different distance were used between detected SNP and candidate gene among various experiments. Here we listed some literature that were identified and discussed candidate genes in different distances to identified SNP: Esmaeili-Fard SM, Gholizadeh M, Hafezian SH, Abdollahi-Arpanahi R (2021) Genome-wide association study and pathway analysis identify NTRK2 as a novel candidate gene for litter size in sheep. PLOS ONE 16(1): e0244408. (1 Mb distance was investigated) Bolormaa, S., Swan, A.A., Stothard, P. et al. A conditional multi-trait sequence GWAS discovers pleiotropic candidate genes and variants for sheep wool, skin wrinkle and breech cover traits. Genet Sel Evol 53, 58 (2021). (0.97 Mb distance was investigated) Wang Z, Zhang H, Yang H, Wang S, Rong E, Pei W, et al. (2014) Genome-Wide Association Study for Wool Production Traits in a Chinese Merino Sheep Population. PLoS ONE 9(9): e107101. (0.40 Mb distance was investigated) Sutera AM, Moscarelli A, Mastrangelo S, Sardina MT, Di Gerlando R, Portolano B, Tolone M. Genome-Wide Association Study Identifies New Candidate Markers for Somatic Cells Score in a Local Dairy Sheep. Front Genet. 2021 Mar 22;12:643531. (0.50 Mb distance was investigated)As we can see there some more literature that used various range surrounding each significant SNP. There is no specific range has been reported in literature for ovine GWAS studies. Comment: L256 – What is the lines in graph?Response: Blue line represents chromosome-wide significance while red one represents genome-wide significance levels.Comment: L264 – You are fully repeat table data. In this case remove table.Response: The table 2 was modified as supplementary table give as Table S1.Comment: L275 – “significantly heavier” wrong sentence. Change it.Response: The sentence was modified and highlighted as yellow. Comment: L277 – Not. In statistic no tendencies. Significant difference presents or not.Response: Thanks for comment: The sentence was modified: From: Notably, statistical analysis exhibited a trend toward higher significance values between genotypes and measured traits in the larger Akkaraman sheep population (p<0.1).To: Notably, statistical analysis exhibited a significant association between genotypes and measured traits in the larger Akkaraman sheep population (p<0.1).Comment: In conclusion, I would recommend that you finalize the article and submit it for publication in a journal with a lower impact factor, where it can be published. Response: We are aware of animal number is relatively small. However, though Akkraman is consisting of approximately the 45% of whole sheep population in Turkiye, it is accepted as extensively bred local sheep breed. Therefore, lack of herd book and breed organization is limiting to access regular phenotyping data. Additionally, GWAS comprising similar or lower number of animals have been publishing in PloS One and other scientific journals.  Esmaeili-Fard SM, Gholizadeh M, Hafezian SH, Abdollahi-Arpanahi R (2021) Genome-wide association study and pathway analysis identify NTRK2 as a novel candidate gene for litter size in sheep. PLOS ONE 16(1): e0244408. (n=91) Kominakis A, Tarsani E, Hager-Theodorides AL, Mastranestasis I, Gkelia D, Hadjigeorgiou I (2021) Genetic differentiation of mainland-island sheep of Greece: Implications for identifying candidate genes for long-term local adaptation. PLoS ONE 16(9): e0257461. (n=237) Tuersuntuoheti M, Zhang J, Zhou W, Zhang C-l, Liu C, Chang Q, et al. (2023) Exploring the growth trait molecular markers in two sheep breeds based on Genome-wide association analysis. PLoS ONE 18(3): e0283383. (n=184)Regards,Reviewer #3: OVERALL:-The authors aimed at identifying SNPs associated with body weight in fat tail Akkaraman lambs. However, the number of sheep utilized for this analysis is low and could lead to false associations. Additionally, a low number of animals reduces the chances of identifying regions controlling the traits of interest. This could be one of the reasons why the authors only found one SNP on chr 2 associated with weaning weight at day 90 and average daily gain (ADG) from day 0 to day 90.INTRODUCTION:Comment: Line 33: Check for typos .Response: The sentence was corrected.MATERIALS AND METHODS:Comment: Please include the protocol number of the approved animal use protocol  utilized in this study.Response: The following sentence was added into material and methods part. “All experimental procedures were performed in accordance with the guidelines of the Local Ethics Committee for Animal Experiments at Erciyes University (#13/130-13.11.2013)”Comment a: The authors mentioned that pedigree was unknown. However, you can estimate this from SNP data. Please include the G matrix in the association model.Comment b: Please include a better description of the model used for the association analysis. Please use a mixed model and explain the components of the model . Please also include sex if male and female lambs were evaluated and re-analyze the data.Response a and b: PCA based association analysis is the most popular approach for GWAS due to its simplicity and effectivenes . Linear mixed model based associations are also used for GWAS due to their capacity of handling population stratification and criptic relatedness . However, due to several reasons such as their treatment to the estimations for fixed versus random effects, simplicity and effectivenes PCA-based association is defined as the ‘gold standard’ . To account for the mild population stratification, we used a PCA based association test, where top 10 PCs were fitted in the model for the GWAS of each trait. The model of PCA-based linear regression for GWAS is provided below: y= µ+Xβx+Zβz+ eWhere y is the vector of observations, µ is the intercept, X and Z are the design matrices mapping SNP effect and 10 PCs constructed by PCA from a large number of genetic variants to the observations, respectively . βz is the effect of eigenvectors and βx is the effect of SNP being tested for association. e ~ MVN  is the error term. The goal of an association analysis is to test the null hypothesis H0: γ1 = 0. Zhang Y, Pan W. Principal component regression and linear mixed model in association analysis of structured samples: competitors or complements? Genet Epidemiol. 2015 Mar;39(3):149-55. doi: 10.1002/gepi.21879. Epub 2014 Dec 23. PMID: 25536929; PMCID: PMC4366301. https://doi.org/10.1038/s41598-022-14395-4 Elhaik, E. Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated. Sci Rep 12, 14683 (2022).  Chen, W.-M., Abecasis, G.R., 2007. Family-based association tests for genome-wide association scans. Am. J. Hum. Genet. 81, 913–926.Comment: It is possible that authors won’t find the same results after adjusting the association model. Please revise the data and results.Response: Due to explanation which is found above we have not revised the data.Comment: Submit your SNP data to ENSEMBL/NCBI and provide number. This information must be part of the manuscript (Genotyping section).Response: SNPs those were given as results are already abundant in the sheep reference genome. Their rs numbers are unique and represents each SNP.Comment: Revise abstract, results, discussion, and conclusion sections after adjusting the association model.Response: Due to explanation which is found above we have not revised the data.AttachmentResponse to reviewer.docxSubmitted filename: Click here for additional data file. 6 Sep 2023Genome-wide association study of early liveweight traits in fat-tailed Akkaraman lambsPONE-D-22-26401R1Dear Dr. Çınar,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Muhammad Abdul Rehman Rashid, PhDAcademic EditorPLOS ONE 15 Sep 2023PONE-D-22-26401R1 Genome-wide association study of early liveweight traits in fat-tailed Akkaraman lambs Dear Dr. Cinar:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. 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Ginkgo biloba is an economically valuable tree worldwide. The species has nearly become extinct during the Quaternary, which has likely resulted in reduction of its genetic variability. The genetic variability is now conserved in few natural populations in China and a number of cultivars that are, however, derived from a few ancient trees, helping the species survive in China through medieval times. Despite the recent interest in ginkgo, however, detailed knowledge of its genetic diversity, conserved in cultivated trees and cultivars, has remained poor. This limits efficient conservation of its diversity as well as efficient use of the existing germplasm resources. Here we performed genotyping-by-sequencing (GBS) on 102 cultivated germplasms of ginkgo collected to explore their genetic structure, kinship, and inbreeding prediction. For the first time in ginkgo, a genome-wide association analysis study (GWAS) was used to attempt gene mapping of seed traits. The results showed that most of the germplasms did not show any obvious genetic relationship. The size of the ginkgo germplasm population expanded significantly around 1500 years ago during the Sui and Tang dynasties. Classification of seed cultivars based on a phylogenetic perspective does not support the current classification criteria based on phenotype. Twenty-four candidate genes were localized after performing GWAS on the seed traits. Overall, this study reveals the genetic basis of ginkgo seed traits and provides insights into its cultivation history. These findings will facilitate the conservation and utilization of the domesticated germplasms of this living fossil plant. Ginkgo biloba L. is a world-renowned living fossil plant and the last extant representative of a large group of Mesozoic gymnosperms. Ginkgo was widespread in the Northern Hemisphere, based on available fossil evidence, and became nearly extinct with the climatic oscillations of the Quaternary [aternary , 2. Todaaternary  and is eaternary –6. Beyonaternary . Materiaaternary , 9 and gaternary .While the genetic diversity of ancient populations of ginkgo is relatively well known, it remains largely unknown what genetic variation may be hidden in ancient trees and a number of cultivars derived from the various existing ginkgo resources , 11, 12.Plasmopara viticola resistance loci in grapes [Genotyping-by-sequencing (GBS) is a common, simplified genome sequencing technique that is widely used in population genetics , and is n grapes . Beyond n grapes  has maden grapes .G. biloba cultivars and reveal genetic resources underlying the variation of seed traits, which will facilitate efficient conservation and utilization of ginkgo germplasms.Here we aimed to study genetic structure, kinship, inbreeding, and average linkage disequilibrium (LD) decay in cultivated ginkgo germplasm, through GBS-based identification of SNPs and runs of homozygosity (ROHs). In addition, we mapped SNPs and candidate genes related to seed length, width thickness, and embryo-free rate based on GWAS. The findings of this study will parse the genetic relationships between the widely cultivated The average of 102 samples each yielded 30 227 882 bp of clean data after filtering, and the average effective restriction fragment length was 237 bp. The alignment ratio of clean reads and reference genomes for all samples ranged from 98.15 to 99.34%, with an average of 99.14%.After alignment and filtering, we identified a total of 9 147 579 variants, of which 8 796 251 were SNPs and the rest were indels, with an average density of one variant/1.12 kb. The overall distribution of variants on each chromosome was relatively uniform (Supplementary Data File S1). Around 90% of the identified variants (8 237 491 out of 9 147 579) were found in intronic regions. In addition, a total of 812 322 variant sites were located in the intergenic region, affecting ~97.35% of all genes in the latest reference genome of ginkgo. The genotypic call rates of all samples met the requirements for subsequent analysis.K = 4 after running STRUCTURE starting with K = 1  of the entire population was 0.001951. The nucleotide diversity of the G2 and G3 subpopulations was significantly lower than that of G1 and G4, showing lower genetic diversity  is greatly improved compared with other analysis methods . We chosGene regulation of seeds traits is one of the aspects that concern us most. High-density SNP markers combined with GWAS offer the possibility of identifying quantitative trait loci (QTL) or candidate genes for plant traits. We collected historical data on seed length, width, thickness, and embryo-free rate for 52 cultivars in the China Ginkgo Germplasm Resource Nursery, and used GWAS analysis in ginkgo for the first time. All data conformed to a normal distribution, and the distribution of best linear unbiased predictors (BLUPs) is shown in Gb_39567 was obtained after gene annotation of this SNP, which was enriched in the transferase activity pathway.For seed length, a significantly associated SNP was included in the interval 370.54 to 372.15 Mb on chromosome 9. For seed width, a total of 50 significantly associated SNPs were identified, with distribution on all 12 chromosomes. Twenty-two genes were obtained after annotation of all associated SNPs, 10 of which were distributed in the region from 344.23 to 693.35 Mb of chromosome 1. These candidate genes are mainly involved in transcription factors encoding the plant hormones abscisic acid, auxin and ethylene.Gb_20425 was annotated as a candidate gene for regulating seed thickness.For seed thickness, one SNP in the region from 664.01 to 664.93 Mb of chromosome 5 was significantly associated with it. CAT (Gb_02867).For seed embryo-free rate, only one SNP in the region of 844.28 to 845.64 Mb on chromosome 11 was related to it, and it was annotated to a gene named CAT is a candidate regulator gene for both seed width and embryo-free rate. No SNPs and candidate genes associated with seed weight were found.The selection and breeding of seed cultivars is one of the important directions for the development and utilization of ginkgo germplasm resources. We used the regions associated with seed traits in the GWAS results to construct phylogenetic relationships for female ginkgo populations. All female cultivars of ginkgo were classified into three major subpopulations according to the phylogenetic structure . The phyThe genetic diversity of cultivated populations used to be reduced compared with that of wild populations, which is in line with natural laws . The freThe G2 subpopulation basically originated from the Tai’an area of Shandong Province, indicating that rarely large-scale gene flow occurred here and that significant population size expansion began to occur during the Sui and Tang dynasties ~1500 years ago. We speculate that the reason for the expansion of the ginkgo population is mainly related to the prosperity and development of Buddhism at that time. Buddhism was supported by the government, and Buddhist temples were also funded and protected by the state during the Sui Dynasty, especially in Shandong, which had a high political, economic, and cultural status. Ginkgo has an important position in Buddhist culture. Many monks like to plant ginkgo around the temple and give each other ginkgo seeds. In Korea and Japan, many of the largest or oldest ginkgo trees are associated with Buddhist temples . The TaiPyrus spp. [Saccharum spp. [Saccharum spp. In the further analysis of ROH islands, it was found that different subpopulations faced different selection pressures and directions. High compound content is one of the main directions of artificial selection. The improvement of the ability to cope with environmental stress and the enhancement of photosynthesis are the results of natural selection. The highest degree of inbreeding in subpopulation G2 may be related to its concentrated distribution in a small geographical area.Exploring the affinities of cultivars plays an important role in the utilization of germplasm resources, especially in artificial breeding. Most of the ginkgo cultivars are distantly related, except for a few that are closely related due to geographical proximity, which affirms the accuracy of the earlier germplasm collection. The average length of ROHs in ginkgo was small, much smaller than in rus spp.  and Saccrum spp. , but theCAT genes exist in multiple copies in plants, and their regulatory patterns and tissue specificity are also different [CAT plays an important role in the regulation of ginkgo seed width and embryo-free rate. The content of ginkgotoxin in embryo-free seeds is greatly reduced [CAT gene represents a novel approach for enhancing innovation in ginkgo germplasm.Seed traits became the most obvious basis for partitioning different ginkgo cultivars. The phylogenetic topology of candidate genetic regions based on seed shape was basically consistent with that of the whole genome (Supplementary Data File S3), and neither supported the basis for classifying ginkgo seeds based on shape. There was no significant difference between the two topologies, suggesting on the one hand that the SNPs in the GBS data are more evenly distributed, and on the other hand proving that the seed shape cannot fully represent the taxonomy. Since this study represents the first attempt to identify genetic associations with ginkgo seed traits using GWAS, and there have been no prior investigations into regulatory genes for these traits, further validation is needed to confirm the potential candidate genes identified in this analysis. GWAS suggested that loci controlling seed length and seed thickness are mostly independent. Abscisic acid and auxin are key phytohormones involved in the growth and development of fruits and seeds . Based oifferent . The str reduced . Control<fig id="f6"><label>Supplementary file S3</label><graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uhad136f6"/></fig>Eucalyptus [Pyrus pyrifolia Nakai [The positioning of biological traits by GWAS has achieved great success, especially providing a cost-effective means for the mapping of long-generation woody trait-associated genes, such as in calyptus , Pyrus pia Nakai , and Koria Nakai , etc. GWia Nakai . While cThis study has yielded novel insights into the genetic structure and dynamic history of cultivated populations of ginkgo, as well as the localization of candidate genes that are associated with seed traits. Our large-scale GBS analysis of ginkgo cultivars revealed that they can be grouped into four subpopulations with lower low level genetic diversity, and most of the cultivars displayed no apparent genetic relatedness. The expansion of the G2 and G3 subpopulations can be traced back to 1500 years ago. Through long-term survey and GWAS analysis of seed traits, we successfully located a total of 54 SNPs and 24 candidate genes. Thus, this study improves our understanding of the genetic structure and dynamic history of cultivated ginkgo populations, as well as the candidate genes that are associated with seed traits, which will facilitate the effective conservation and utilization of ginkgo germplasm.A total of 102 ginkgo cultivars  were col® MLtra™ DNA Library Prep Kit , with the addition of A-tails and Illumina sequencing adapters. DNA fragments ranging from 300 to 400 bp were PCR-amplified and purified with an AMPure XP system . Library quality was assessed using an Agilent 2100 Bioanalyzer , and quantification was performed with real-time PCR. Sequencing was carried out using the PE 150 strategy on a Novaseq 6000 sequencer.DNA extraction from frozen ginkgo leaves was performed using the CTAB method . DNA quaFASTP (v.0.18.0) was used to filter the raw data from the Illumina platform . Reads wAll of the raw sequencing data have been submitted to the National Genomics Data Center (NGDC) with the accession number CRA006613, ensuring that the data are publicly accessible and reproducible.r2) >0.2 using PLINK version 1.90p17 [K = 1 to K = 9 clusters, and the one with lowest cross-validation error was chosen as the best K. The R package Pophelper [We filtered samples with abnormal heteronomy variants, missing rate of variants >0.2, high heterozygosity for a threshold of mean + 3*standard deviationsd, and those recognized as an outlier were filtered. The ADMIXTURE  (v.1.3)  1.90p17 . We ran ophelper  (v.2.2.7P) of sharing 0, 1, or 2 identity by descent (IBD) alleles at each locus using the method-of-moments algorithm implemented in PLINK version 1.90p17, based on pairwise identity by state (IBS) distances and allele frequencies. LD decays of the whole population and each subpopulation were analyzed using the PopldDecay (v.3.41) software [We conducted PCA  kinship software  suite baFROH using the ROH method to assess the degree of inbreeding within populations. FROH was calculated by determining the ratio of the total length of ROH fragments in the genome to the total length of the genome. The following formula was used to calculate FROH:We utilized Vcftools v.0.1.14 , 55 to eThe ROH ratio for each SNP site was calculated when considering the population as a whole. A Manhattan map was produced based on the ROH ratio of each SNP site, and the threshold line for high-frequency SNPs was set at the ROH ratio where the top 1% of SNPs were located. By examining the distribution of SNP sites exceeding this threshold throughout the genome, ROH islands were identified.In 2019, 2020, and 2021, phenotype observations were carried out on the seeds of 52 cultivars without the outer testa in the China Ginkgo Germplasm Resource Base. All these cultivars had three clonal repeats and grew in uniform conditions for >40 years. Each phenotypic variable was measured 30 times in each clone, and the tree averages were calculated for each cultivar. The BLUPs of each phenotypic variable were calculated using the Lme4 R package  and the Genome-wide association mapping was implemented in the GCTA (v1.92.2) program ; using tK of ADMIXTURE results), whereas kinship was accounted for using the kinship matrix generated by GCTA software. Considering the rare variants and the extremely unbalanced case–control ratios, REGENIE and SAIGE were implemented. REGENIE partitioned SNPs into consecutive blocks and generated ridge regression predictions from each block. Then, a second ridge regression was used to combine the predictors into a single predictor, which was then decomposed into 23 chromosome predictors for GWAS [P-values [ρ to determine the minimum P-value.Population structure was accounted for with the first five principal components . We then identified candidate genes (CAGs) located within a 50-kb region upstream or downstream of the associated markers.To determine significant associations, a Bonferroni correction threshold was applied , the China Scholarship Council (202108320301), and the Czech Science Foundation (19-18545S).Y.H. and Q.G. designed the experiments; Y.H., Z.Y., X.G., Y.Z. and G.L. performed experiments; Y.H., Z.Y.,X.G. and P.L. analyzed data; Y.H. and Q.G. wrote the manuscript; P. Š. reviewed the manuscript.https://ngdc.cncb.ac.cn/) with the accession number CRA006613.The high-throughput sequencing data in this study are all open access and have been uploaded to the National Genomics Data Center (NGDC, The authors declare no competing interests.Horticulture Research online.Web_Material_uhad136Click here for additional data file.