P < 0.0001] when using the biomarker model to screen for high risk for amyloid PET + status. In simulations, plasma screening also resulted in a 54% reduction of the total number of amyloid PET scans required and reduced total recruitment costs by 43%  compared to no pre-screening when assuming a 16× PET-to-plasma cost ratio. Total savings remained significant when the PET-to-plasma cost ratio was assumed to be 8× or 4×. This suggests that a simple plasma biomarker model could lower recruitment costs in Alzheimer’s trials requiring amyloid PET positivity for inclusion.The extent to which newly developed blood-based biomarkers could reduce screening costs in secondary prevention trials of Alzheimer’s disease is mostly unexplored. We collected plasma amyloid-β42/40, apolipoprotein E ε4 status and amyloid PET at baseline in 181 cognitively unimpaired participants  from the Swedish BioFINDER-1 study. We tested whether a model predicting amyloid PET status from plasma amyloid-β42/40, apolipoprotein E status and age (combined) reduced cost of recruiting amyloid PET + cognitively unimpaired participants into a theoretical trial. We found that the percentage of cognitively unimpaired participants with an amyloid PET + scan rose from 29% in an unscreened population to 64% [;  et al. demonstrated that combining plasma amyloid-β42/40 with APOE status and age could be used to effectively screen cognitively unimpaired individuals in the Swedish BioFINDER study for amyloid PET abnormality. These results add to the evidence that this panel could greatly improve recruitment for clinical trials.Cullen   These failures have led to a shifting focus towards patients at earlier disease stages where cognitive symptoms are minor and Alzheimer’s disease pathology may be present but not widespread.5Alzheimer’s disease is the most common form of dementia and is expected to increase in prevalence during the coming decades due to a growing elderly population.6 Such secondary prevention trials in cognitively unimpaired (CU) individuals may still require confirmation of Aβ pathology before the start of treatment to ensure target engagement, although recent trials in the symptomatic phase of the disease suggest that tau pathology could also be useful for selecting an appropriate trial population.3 The gold standard of detecting Aβ pathology is through PET scanning or cerebrospinal fluid collection, but these modalities are likely to remain prohibitively expensive as a front-line screening tool for secondary prevention  since the prevalence of Aβ pathology is estimated to be around 25% in enriched Alzheimer’s disease study cohorts and as low as 15% in the general population (depending on age).7In the future, the target population of Alzheimer’s disease therapies is likely to include elderly individuals who are at risk for Alzheimer’s disease but do not exhibit any cognitive impairment.5 In particular, plasma Aβ42/Aβ40—considered to directly reflect Aβ pathology—has been shown to provide significant prognostic value for Alzheimer’s disease-related outcomes in a CU population.8 Apolipoprotein E (APOE) is also the strongest genetic risk predictor for the risk of Alzheimer’s disease in healthy elderly individuals and may be included in prognostic models which are utilized for early screening of the disease.10Therefore, there is a need for pre-screening tools which can effectively and affordably identify CU individuals at high risk for Aβ pathology before a more invasive and expensive measurement is obtained for confirmation. Effective pre-screening biomarkers could lower overall recruitment costs by reducing the number of negative  Aβ PET scans which result in exclusion from trials. Recently established plasma biomarkers of Alzheimer’s disease show great promise in filling this role as inexpensive predictors of abnormal Aβ pathology as measured by PET.APOE status and age to predict Aβ PET status in a mixed group of individuals with and without cognitive impairment.11 In this study, we expand on this approach to predict the risk of abnormal Aβ PET scans using this same plasma biomarker panel but focusing on a CU population and exploring a novel cost-benefit perspective throughout a variety of clinical trial designs. Validation of Aβ screening panels in CU populations is of high importance to facilitate its establishment in clinical care and secondary prevention trials. We hypothesized that this biomarker panel could reduce the cost of recruiting Aβ PET + CU participants in a simulated clinical trial. We also placed our risk prediction model into the context of a larger framework of trial recruitment in Alzheimer’s disease involving separate ‘pre-screening’ and ‘screening’ phases  cohort were included in the present analysis. The CU group consisted of (i) cognitively normal (CN), healthy control participants with no objective evidence of cognitive impairment at baseline and (ii) participants with subjective cognitive decline (SCD) who were referred to the memory clinic for investigation but deemed to not have any cognitive impairment after undergoing an extensive neuropsychological battery.13 The inclusion criteria were the following: (i) age ≥60 years for CN, 60–80 years for SCD; (ii) absence of objective cognitive impairment as assessed by a physician with a special interest in cognitive disorders; (iii) mini-mental state examination score of at least 28 for CN participants and at least 24 for SCD participants at screening visit; (iv) fluent in Swedish and (v) does not fulfil the criteria for mild cognitive impairment or dementia according to DSM-5. Exclusion criteria included (i) a significant unstable systemic illness that makes it difficult to participate in the study and (ii) current significant alcohol or substance misuse.Participants from the Swedish BioFINDER-1 . Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and analysis of MS data were then performed.14 Average intra-assay coefficient of variation was 0.72% and the average inter-assay coefficient of variation was 3.46%. There were no failed samples.Plasma Aβ42/Aβ40 was measured at baseline at the Bateman laboratory at Washington University using a method which has been previously described.15 Further, APOE genotype was measured at baseline and, due to a low number of APOE ε4 homozygotes in the sample, was treated as a binary variable indicating presence of at least one ε4 allele or not.To facilitate easier interpretation of results, plasma Aβ42/Aβ40 levels were negated (i.e. multiplied by −1) so that higher values indicate worsening Aβ pathology. Aβ pathology in the brain was measured at baseline using 18F-flutemetamol PET conducted on a Philips Gemini TF 16 scanner. A global neocortical composite standardized uptake ratio (SUVR) was calculated for each individual using cerebellar cortex as the reference region. A pathologically abnormal (‘positive’) Aβ PET scan was defined as SUVR > 0.742 as defined previously through mixture modelling.APOE status and age as predictors. We tested whether using this fitted model to choose which CU participants should undergo amyloid PET scanning could reduce the cost to recruit 500 Aβ PET + CU participants into a theoretical clinical trial compared to obtaining Aβ PET scans on everyone. A value of 500 participants was selected to reflect reasonable sample sizes for recruitment in clinical trials in this population.16A logistic regression model was first fit with Aβ PET status [normal (‘PET-’) versus abnormal (‘PET+’)] as an outcome and with plasma Aβ42/Aβ40, relative risk approach where an individual’s risk value was calculated relative to the entire study population, and also using an absolute risk approach where risk was represented as the absolute probability of being Aβ PET+ . In other words, relative risk is interpreted as an individual’s risk relative to others in the study population  while the absolute risk is interpreted as an individual’s risk relative in absolute terms . Subsequently, we calculated the total cost of recruitment at each risk threshold across a range of PET-to-plasma cost ratios .We did this by comparing the percentage of Aβ PET + participants in the entire study population (representing a scenario without pre-screening) with the expected percentage of Aβ PET + participants found when using the biomarker model for pre-screening across a range of risk thresholds from 0 to 90%. This analysis was done first using a While the primary goal was to validate a pre-specified and well-established panel, a data-driven approach to variable selection was also undertaken where non-significant and non-trending variables were removed from the screening model.P < 0.05. All confidence intervals (CIs) and P-values were derived from 1000 trials of bootstrap sampling to extrapolate what model performance would be on new data. The performance of the logistic regression model was additionally validated using 5-fold cross-validation.All analysis was done in the R programming language (v4.0.3) and all statistical tests were two-sided with P = 0.005). The average age was 73.0 ± 5.3 years, with 111 (61.7%) female participants and an average educational attainment of 11.9 ± 3.3 years. The cohort is further described in A total of 52 of 180 (28.9%) participants had abnormal Aβ PET scans at baseline and 80 of 180 (44.4%) participants had SCD. The distribution of Aβ PET positivity in the SCD group  compared to the CN group  was significantly different (APOE status and age as predictors showed high performance in predicting Aβ PET status  with bootstrap sampling. Moreover, evaluating model performance in a predictive context using 5-fold cross-validation resulted in an out-of-sample AUC of 0.86 [95% CI ]. In this combined model, there was a significant effect for plasma Aβ42/Aβ40  and APOE status , but not age . The effect of each variable on Aβ PET status is visualized in The logistic regression model with plasma Aβ42/Aβ40, P = 0.0011] and the effect of APOE status trended towards significant .Restricting the logistic regression analysis to CN participants led to only a slight drop in the performance of the combined model for predicting amyloid PET status [AUC = 0.84 ], although the effect of plasma Aβ42/Aβ40 was still significant  with a 25th percentile risk cutoff , increased to 54.6%  with a 50th percentile risk cutoff, and increased to 63.7%  with a 75th percentile risk cutoff. These results are displayed in When using a relative risk approach to pre-screening where predicted Aβ PET risk was normalized based on percentiles derived from the entire population, the expected Aβ PET + rate increased from 28.9% with no pre-screening  to 38.7% , or a 75th percentile risk cutoff , but there was a significant cost saving with a 50th percentile risk cutoff . However, plasma pre-screening always resulted in significant cost savings when PET scanning was assumed to be at least 8 × as expensive as plasma measurement . When Aβ PET scanning was assumed to be 16 × as expensive as plasma biomarker measurement, the savings were strongly significant for plasma . These results are displayed graphically in When assuming that Aβ PET scanning would be four-times (4×) as expensive as plasma biomarker measurement, there were no significant cost savings by employing pre-screening with the plasma panel and a 25th percentile risk cutoff  with a 25% risk cutoff , increased to 63.8%  with a 50% risk cutoff, and increased to 80.2%  with a 75% risk cutoff. These results are displayed in With an absolute risk approach to pre-screening where an unnormalized predicted probability of Aβ PET positivity was used, the expected Aβ PET positivity rate increased from 28.9% with no plasma pre-screening  to 58.3% , with a 50% risk cutoff  and with a 75% risk cutoff . The same results were found when Aβ PET scanning was assumed to be 8 × and 16 × more expensive than plasma measurement. These results are displayed graphically in Additionally, when assuming that Aβ PET scanning would be four-times (4×) as expensive as plasma biomarker measurement, there were significant cost savings by employing pre-screening with the plasma panel and a 25% risk cutoff . Fitting a logistic regression model with only APOE status and age led to a significantly worse model performance than the model with plasma Aβ42/Aβ40 .For pre-screening, the results found using the relative risk approach were almost identical to those found when also including age see . In termAPOE status and age could be employed in the pre-screening phase of Alzheimer’s disease clinical trial recruitment to reduce future costs associated with Aβ PET screening. A sensitivity analysis also showed that plasma Aβ42/Aβ40 alone without age may even be equally as effective while removing plasma Aβ42/Aβ40 results in clearly worse performance. This suggests that the contribution of plasma biomarkers is clearly beneficial for pre-screening, while the role of age remains unsettled in the context of highly selected cohorts.We demonstrated that a blood-based biomarker panel including plasma Aβ42/Aβ40, 17 For secondary prevention trials of Alzheimer’s disease, an important pre-requisite to recruitment is the identification of individuals who are at high risk of having amyloid pathology and are therefore likely to have an abnormal Aβ PET scan.In the pre-screening phase, the ideal biomarkers are those which are non-invasive and inexpensive to collect, thereby allowing for measurement in a large number of individuals.18 For example, an Aβ PET scan costs between $5000 and $8000 in clinical practice and can be higher in a research setting, while a blood test costs approximately $500–$1000.18 With the expectation of PET scanning being 16 × more expensive than plasma measurement, total costs of patient recruitment were estimated to be reduced by more than 40%. In reality, the PET-plasma cost ratio will likely vary depending on the situation and reducing the number of PET scans may also be beneficial in non-monetary ways considering the radiation and time consumption associated with PET scanning. To note, this cost-benefit analysis was done in terms of the relative cost between PET and plasma measurement to ensure generalizability of our results across clinics or locations where the absolute costs of such tests may differ greatly (e.g. between Europe and the US).Our results demonstrated that accessible biomarkers can effectively identify high-risk and low-risk individuals and that pre-screening is cost-effective under the assumption that PET scanning is at least 4 × –8 × as expensive as plasma biomarker measurement. The expectation of PET scanning being 4 × as expensive as plasma biomarker measurement appears quite conservative given the high cost of Aβ PET scanning  compared with the high competition for developing plasma biomarkers which may place downward price pressure.Our cost-benefit analysis suggests that the optimal pre-screening risk threshold is around 50% in terms of relative risk  and 20% in terms of absolute risk . In the relative risk scenario, around half of the individuals who are pre-screened using plasma would be invited to receive an Aβ PET scan. This threshold increased the likelihood of Aβ PET positivity from 28.9 to 54.6%. Since almost every second scan would still be negative with this approach, these results suggest that simply maximizing positive predictive value or negative predictive value is not necessarily the optimal strategy for biomarker-based screening in a CU population. The size of the clinical trial population is also likely to play a role as larger fixed costs are more readily tolerated with larger trials, but a full exploration of this factor was outside the scope of the current analysis.19–21 Other studies have also investigated how Aβ risk assessment using simple cognitive tests and demographics can reduce the economic burden of PET scanning.23 However, our study contributes to the existing literature in its analysis of how a novel but readily available plasma biomarker reflecting amyloid pathology can be leveraged together with APOE status and Aβ PET scanning during trial recruitment to reduce overall costs of trial inclusion.25 Our results are also novel in the targeting of CU individuals at the earliest stages of Alzheimer’s disease pathological development, where recruitment is likely to be difficult without effective pre-screening tools. Our study built upon a previously suggested combination of plasma Aβ42/Aβ40, APOE status and age, but in a sensitivity analysis, we noted that the cost-benefit results were largely unchanged using a more sparse model with only plasma Aβ42/Aβ40 and APOE status. This may suggest that age is not a necessary component in prediction models in CU populations when efficient biochemical tests are employed. Alternatively, the lack of an effect of age in our study (in contrast to Ref. 11) could be a cohort-specific effect. Besides a more thorough evaluation of model variables, additional novel contributions of our study include an emphasize on cost-benefit calculations across a variety of inclusion thresholds and relevant clinical trial scenarios.Previous efforts in the area of reducing unnecessary Aβ PET scans have considered widely available biomarker modalities such as cognition and MRI.APOE by evaluating its ability to predict and screen for Aβ PET status.26 The result presented in that article was validated nicely by our model since the AUC values were similar between studies . The main addition of our study was the use of a more sensitive and well-validated MS-based assay for measuring plasma Aβ42/Aβ40 that is already being implemented for clinical care and clinical trial recruitment.One important previous study did look at a similar panel of plasma Aβ42/Aβ40 and relative risk for screened individuals, where the predicted risk for amyloid PET positivity for each individual is compared to all other screened individuals within a batch of biomarker samples. After biomarkers have been analysed, only a certain percentage of individuals in terms of predicted risk for amyloid positivity are carried forward to PET. The advantage of this approach is that it provides a consistent sample size for downstream screening and is likely to be more robust against the variability of assays or demographic compositions between sites. Ensuring equitable inclusion in clinical trials is a major goal given the acknowledgement that biomarker cutoffs are not necessarily applicable across different socio-economic groups.28 When using the relative risk approach, however, there may be a delay between plasma analysis and the determination of eligibility for PET scanning for the individual participants.Practically speaking, we explored two contrasting methods of applying biomarker-based algorithms in a pre-screening context. The first method is to use the The second option would be to use the absolute risk of Aβ PET positivity for each screened individual. Here, the statistical model is used to generate a predicted Aβ PET risk score based on each individual’s biomarker data, using previously derived model specifications. Only e.g. those with >75% absolute risk of Aβ positivity are carried forward to PET. This approach would require highly standardized assays and generalizable clinical prediction models. The advantage of this approach is that it allows for greater control of positive predictive value or negative predictive value for inclusion of actually amyloid PET + participants compared to the relative risk strategy. This approach also allows for quicker differentiation of participants into those who should move on to PET scanning versus those who are not eligible but requires careful calibration and monitoring of prediction models.14Regardless of whether a relative or absolute risk approach is employed, the use of an MS-based plasma assay means that plasma samples would have to be shipped to one (or at most a few) central locations which have the expertise to measure such samples. This is a clear drawback compared to automated platforms or immunoassays where samples can be measured on site. However, to date, there does not appear to be any non-MS-based plasma Aβ assays which meet suitable diagnostic standards for Aβ PET screening.29 Our study also features the latest plasma Aβ42/Aβ40 assay which has been shown to be effective in estimating Alzheimer’s disease-related outcomes on its own and combined.30–36 This assay, along with APOE status and age, combines to make up a well-validated test which has been certified for use in predicting risk for amyloid pathology. The fact that this panel is already commercially available for clinical use and is implemented for clinical trial screening makes it important to validate in independent cohorts. Future studies may further compare this panel with models that also incorporate other promising Alzheimer’s disease biomarkers under development.The strengths of our study include a well-characterized cohort with longitudinal follow-up over highly relevant timeframes for secondary prevention trials of Alzheimer’s disease.In terms of generalizability, the model was evaluated using both bootstrap sampling and cross-validation, which showed good concordance between performance on train and test samples. This indicates that the model is likely to generalize to new data, although performance would depend on assay variability and reliability.The limitations of our analysis are a relatively small cohort in relation to the heterogeneity likely to be found in a CU elderly population, even if the size of our cohort relative to previous studies is quite large. A larger collection of CU individuals pulled from cohorts unrelated to Alzheimer’s disease could provide important information towards this end in the future.Additionally, this study included participants who were recruited specifically as part of an Alzheimer’s disease-related study, meaning that the performance of our model may be optimistic compared to a true population screening scenario where specific expertise in plasma biomarkers may not be available. However, when we excluded SCD participants from the analysis to simulate a true population-based screening scenario, the performance of the model dropped only slightly. This indicates that our model was not overly influenced by the inclusion of SCD participants.37 Cerebrospinal fluid biomarkers could also theoretically be used to screen for Aβ PET status, as they show much stronger association than plasma biomarkers. However, they are not as accessible or inexpensive as plasma biomarkers, making their utility for Aβ PET screening limited.We restricted the analyses to a readily available plasma biomarker panel, but the addition of other biomarkers may also be relevant. The optimal biomarker model may likely depend on both the target population and outcome of interest, as previous research has shown that the utility of specific plasma biomarkers varies greatly depending on disease stage.fcad015_Supplementary_DataClick here for additional data file.


Selection over 70 years has led to almost complete fixation of a haplotype spanning ~ 250 Mbp of chomosome 5H in European two-rowed spring barleys, possibly originating from North Africa.Plant breeding and selection have shaped the genetic composition of modern crops over the past decades and centuries and have led to great improvements in agronomic and quality traits. Knowledge of the genetic composition of breeding germplasm is essential to make informed decisions in breeding programs. In this study, we characterized the structure and composition of 209 barley cultivars representative of the European two-rowed spring barley germplasm of the past 190 years. Utilizing high-density SNP marker data, we identified a distinct centromeric haplotype spanning a ~ 250 Mbp large region on chromosome 5H which likely was first introduced into the European breeding germplasm in the early to mid-twentieth century and has been non-recombining and under strong positive selection over the past 70 years. Almost all cultivars in our panel that were released after 2000 carry this new haplotype, suggesting that this region carries one or several genes conferring highly beneficial traits. Using the global barley collection of the German Federal ex situ gene bank at IPK Gatersleben, we found the new haplotype at high frequencies in six-rowed spring-type landraces from Northern Africa, from which it may have been introduced into modern European barley germplasm via southern European landraces. The presence of a 250 Mbp genomic region characterized by lack of recombination and high levels of fixation in modern barley germplasm has substantial implications for the genetic diversity of the modern barley germplasm and for barley breeding.The online version contains supplementary material available at 10.1007/s00122-023-04418-7. Hordeum vulgare L. ssp. vulgare), there are contradictory assessments of the overall genetic diversity over time, with some authors reporting a loss of genetic diversity, while most did not observe any significant changes  and leaf rust (Puccinia hordei)  was performed on the panel clustered by release period  using the function ‘dapc’ from the R package ‘adegenet’ v.2.1.4 and 600,000 randomly selected SNPs Jombart . The 'adTo visualize genetic variation and population structure, a Principal Coordinate Analysis (PCoA) was performed using all 1,509,446 SNPs in the dataset. A neighbor-joining tree was created based on a distance matrix using the ‘nj’ function in the R package ‘ape’ v.5.6–1  were calculated in rolling windows of 10,000 SNPs.The polymorphism information content (PIC) was calculated in rolling windows of 10,000 SNPs using the following equation , we identified gene bank accessions belonging to the cluster corresponding to the newer haplotype. Maps showing the distribution of this haplotype by country and different passport data categories  were made using the R package ‘maps’. Countries for which fewer than 15 accessions per respective category were present were excluded.The origin and geographic distribution of the haplotype present in the cluster of more recent cultivars was analysed in a collection of approximately 20,000 domesticated barley accessions stored in the German Federal ex situ gene bank at IPK Gatersleben for which high-resolution SNP datasets generated by genotyping-by-sequencing (GBS) and morphological and agronomic passport data are available . Assuming two subpopulations, the panel was divided into a slightly older  and a slightly younger population   (Table k = 2 were assigned to one of the younger clusters (cluster 3) at k = 3.In addition to the a priori clustering of cultivars by release year described above, overall population structure was analysed using several different approaches. The PCoA Fig.  and the 0) Table . At k = k = 2 , including the flanking 10 kbp, 1080 BaRTv2.0 gene models are present, of which 827 had an expression of > 1 cpm in > 30% of the cultivars in at least one of the six tissues. A PCoA of the log-transformed cpm expression values of these genes separated the cultivars into the two k-means clusters along the first PC in all six tissues . We define the H. laevigatum-derived haplotype as haplotype 3 and the haplotype found in ‘Reggae’, ‘Apex’ and ‘Livet’ as haplotype 4.In the chromosome 5H region characterized by high FST values between the older cluster 1 and the more recent cluster 2 , BaRT2v18chr5HG238440 and BaRT2v18chr5HG238450 (315.86–315.88 Mbp) showed Blast hits further downstream in haplotype 1 accessions, suggesting that they are present on the translocated segment.A GO enrichment analysis of the 1080 genes located between 68.78 and 320.04 Mbp in chromosome 5H identified six significantly enriched terms suggesting an involvement of these genes in general processes such as calmodulin and abscisic acid binding, phosphatase activity and salicylic acid biosynthesis processes (Online Resource 20).n = 6535) the analysis was not powerful enough to consistently distinguish between haplotypes 1 and 3 and putative additional haplotypes at different values for k. However, at k >  = 7 the cluster corresponding to haplotype 2 remained stable, hence we assumed k = 7 subpopulations (Online Resource 23).The oldest cultivar in the panel carrying haplotype 2 is ‘Union’, a variety released in 1955 and with southern German pedigree which can be traced back to Bavarian landraces and landrace selections. Over the following 40 years, haplotype 2 quickly increased in frequency in the panel. All but one cultivar released after 2000 carry this haplotype Fig. . Eighty-Northern Africa became apparent as the geographic region with the highest percentage of landraces carrying haplotype 2 , suggesting this region as the potential origin of this haplotype  which differ clearly from the adjacent regions with lower FST values. FST values remain fairly high along the entire region but are especially high at the start and the end of the region. The start and end points of this region are very conserved except in a few cultivars. We did not see any signs of recombination between the two haplotypes in modern European cultivars, which is not uncommon for centromeric and pericentromeric regions of barley chromosomes . One of the 46 differentially expressed genes is annotated as a disease resistance protein  family gene (Online Resource 8). Tondelli et al.  and DziuRph2  in their name were assigned to the haplotype 1 cluster, excluding this parent as the likely haplotype donor for ‘Union’. Two out of three accessions named ‘Donaria’ were part of the haplotype 1 cluster while the third one belonged to another group. The collection contains one accession called ‘Firlbecks III/621’ and two accessions called ‘Firlbecks III Neu’. The former possesses haplotype 2 while the latter two carry haplotype 1. It is therefore not possible to conclude that ‘Firlbeck 621’ is the haplotype 2 donor of ‘Union’. While ‘Firlbecks Union’, ‘UNION (BENDER) AUSGANGSFORM 4X’ and two accessions called ‘Union’ all have haplotype 2, there is also one accession named ‘UNION AUSGANGSFORM 2X’ which carries haplotype 1. ‘Firlbeck III’ is derived from a cross between ‘Firlbeck II’ and ‘Haisa’. ‘Firlbeck II’ is not part of the genebank collection and none of the accessions including the name ‘Haisa’ had haplotype 2 except for one accession simply named ‘Haisa’. ‘Haisa’ is likely ‘Haisa I’, a cross between ‘Heines Hanna’ and ‘Ackermann Isaria’. None of the accessions containing the names ‘Hanna’ and ‘Isaria’ carried haplotype 2. Despite these limitations of utilizing passport data and genetic information from gene bank collections we do believe, however, that the general patterns of haplotype distribution are reliable, simply due to the large number of accessions analysed.The presence of a 250 Mbp genomic region characterized by lack of recombination and high levels of fixation in modern barley germplasm has substantial implications for barley breeding. Contemporary barley breeding relies primarily on the selection of favourable combinations of alleles generated by crosses between breeder strains  statistics quantifying the extent of genetic variation found between and within groups of cultivars defined by release periods Online Resource 4 Neighbor-joining tree using the genotypes of 1,509,447 SNP markers. The cultivars are color-coded according to the period in which they were released. The inner ring panel shows the chromosome 5H haplotype of each cultivar, the outer ring panels show the k-means cluster membership of each cultivar at k=2 (outermost ring panel) and k=3 Online Resource 5 Principal coordinate analysis of the European two-rowed spring barley panel using the genotypes of 1,509,447 SNP markers. Each dot represents a cultivar, color-coded according to geographic originOnline Resource 6 Left: Cumulative percentage of variance explained by different numbers of PCs. Selecting the number of PCs at which ~95% of the cumulative variance were explained, 110 PCs were retained for k-means clustering. Right: Bayesian Information Criterion (BIC) values at different numbers of assumed subpopulations (k). Low BIC values indicate a good fit of the model to the data. Using the elbow method to determine the value for k at which the curve flattens, two or three subpopulations were assumed to be likelyOnline Resource 7 k-means cluster membership of cultivars at k=2 (A) and k=3 (B) superimposed on PCoA Online Resource 8 Annotation, gene IDs and genetic positions in reference genomes Barke and Morex V3 as well as the reference transcriptome BaRTv2 of the 48 genes differentially expressed in haplotype 1 and 2. These 48 genes form a distinct group in a PCoA of all genes located between 69 and 320 Mbp on chromosome 5H . All genes were highly expressed in cultivars carrying haplotype 2 except for the two genes marked with asterisks which were highly expressed in cultivars carrying haplotype 1 and low/not expressed in cultivars carrying haplotype 2Online Resource 9 Heatmap of the expression of the genes between 69 and 320 Mbp on chromosome 5H forming a separate k-means cluster and showing low or no expression in cultivars carrying the old haplotype and higher expression in cultivars carrying the new haplotype in the crown tissue of one week old seedlings. An exception is BaRT2v18chr5HG233580 which shows the opposite pattern. Gene expression is not correlated with haplotypes 3 and 4.Online Resource 10 Heatmap of the expression of the genes between 69 and 320 Mbp on chromosome 5H forming a separate k-means cluster and showing low or no expression in cultivars carrying the old haplotype and higher expression in cultivars carrying the new haplotype in the root tissue of one week old seedlings. An exception is BaRT2v18chr5HG233580 which shows the opposite pattern. Gene expression is not correlated with haplotypes 3 and 4Online Resource 11 Heatmap of the expression of the genes between 69 and 320 Mbp on chromosome 5H forming a separate k-means cluster and showing low or no expression in cultivars carrying the old haplotype and higher expression in cultivars carrying the new haplotype in the developing inflorescence tissue. Exceptions are BaRT2v18chr5HG233570 and BaRT2v18chr5HG233580 which show the opposite pattern. Gene expression is not correlated with haplotypes 3 and 4Online Resource 12 Heatmap of the expression of the genes between 69 and 320 Mbp on chromosome 5H forming a separate k-means cluster and showing low or no expression in cultivars carrying the old haplotype and higher expression in cultivars carrying the new haplotype in the peduncle tissue. Exceptions are BaRT2v18chr5HG233570 and BaRT2v18chr5HG233580 which show the opposite pattern. Gene expression is not correlated with haplotypes 3 and 4Online Resource 13 Heatmap of the expression of the genes between 69 and 320 Mbp on chromosome 5H forming a separate k-means cluster and showing low or no expression in cultivars carrying the old haplotype and higher expression in cultivars carrying the new haplotype in the spikelet tissue. Exceptions are BaRT2v18chr5HG233570 and BaRT2v18chr5HG233580 which show the opposite pattern. Gene expression is not correlated with haplotypes 3 and 4Online Resource 14 Heatmap of the expression of the genes between 69 and 320 Mbp on chromosome 5H forming a separate k-means cluster and showing low or no expression in cultivars carrying the old haplotype and higher expression in cultivars carrying the new haplotype in the developing grain tissue. Gene expression is not correlated with haplotypes 3 and 4Online Resource 15 PCA of all cultivars in the panel, using 110,914 SNPs at 68.78 - 320.04 Mbp on chromosome 5H color-coded by the haplotype the cultivars carry in this regionOnline Resource 16 Alleles of 10,000 randomly selected SNP markers in the haplotype region between 68.78 and 320.04 Mbp on chromosome 5H and the flanking 10 Mbp on each side. Red indicates the homozygous reference genotype (cultivar ‘Barke’), yellow indicates the homozygous alternative genotype, green indicates the heterozygous genotype, grey indicates missing data. White columns indicate regions without SNP data. Accessions are sorted according to haplotype Online Resource 17 a) Alleles of all SNP markers 30 Mbp upstream and 4 Mbp downstream of the start of the haploblock region on chromosome 5H (38.78-72.78 Mbp). b) Alleles of all SNP markers 4 Mbp upstream and 30 Mbp downstream of the end of the haploblock region on chromosome 5H (316.04-350.04 Mbp). c) Alleles of all SNP markers 0.5 Mbp upstream and 0.5 Mbp downstream of the start of the haploblock region on chromosome 5H (68.28-69.28 Mbp). b) Alleles of all SNP markers 0.5 Mbp upstream and 0.5 Mbp downstream of the end of the haploblock region on chromosome 5H (319.54-320.54 Mbp). Black lines indicate the start  and end  of the haploblock region, respectively. Red indicates the homozygous reference genotype (cultivar ‘Barke’), yellow indicates the homozygous alternative genotype, green indicates the heterozygous genotype, grey indicates missing data. White columns indicate regions without SNP data. Accessions are sorted according to haplotype Online Resource 18 Dotplots showing genome alignments of cv. Morex (carrying haplotype 1) and cv. Barke (carrying haplotype 2) of a) the entire haplotype region on chromosome 5H illustrating the structural variation at the start and end of the region, b) the start of the haplotype region illustrating an insertion in Barke followed by two inversions further downstream and c) the end of the haplotype region illustrating an insertion in Barke followed by a deletion or potential translocationOnline Resource 19 Dotplots showing pairwise alignments of the chromosome 5H haplotypes in genomes of nine cultivars/accessions carrying haplotype 1 or 2. Names of the cultivars/accessions are shown on the bottom and left. Accessions carrying haplotype 1 are ‘Golden Promise’, ‘Morex’, ‘HOR13821’, ‘HOR3081’ and ‘HOR3365’. Accessions carrying haplotype 2 are ‘Barke, ‘Igri’, ‘HOR13942’ and ‘RGT Planet’.Online Resource 20 GO enrichment of the 1080 genes located between 68.78 and 320.04 Mbp on chromosome 5H. Shown are the GO IDs, GO terms, the number of annotated genes per term in BartV2.0, the number of genes observed per term, the number of genes expected per term, the p-value of gene enrichment and the GO categoryOnline Resource 21 PCAs of all domesticated barley accessions of the German Federal ex-situ gene bank at IPK Gatersleben. Red dots represent the 243 accessions carrying the same names as cultivars and landraces from the European two-rowed spring barley panel. The figure was created using the IPK Bridge Portal Online Resource 22 PCoAs of all domesticated barley accessions of the German Federal ex-situ gene bank at IPK Gatersleben. Accessions are color-coded by geographic origin. NZ=New ZealandOnline Resource 23 Clustering of domesticated barley accessions of the IPK gene bank at different k. While the approach is not suitable to clearly distinguish haplotype 1, haplotype 3 and other potential haplotypes clustering together, haplotype 2 is clearly distinguished at k >= 7Online Resource 24 Geographical distribution of haplotype 2 in groups of landraces and cultivars characterized by different row-types and growth habits in the IPK gene bank collection. Colors indicate the frequency of gene bank accessions from a given country carrying haplotype 2. Countries from which less than 15 accessions per respective category are present in the gene bank are shown in greyBelow is the link to the electronic supplementary material.


CACNA1A have been identified in a subset of HM patients, we investigated whether HM patients without a mutation have an increased burden of such variants in the “CACNA1x gene family”. Whole exome sequencing data of an Australian cohort of unrelated HM patients (n = 184), along with public data from gnomAD, as controls, was used to assess the burden of missense variants in CACNA1x genes. We performed both a variant and a subject burden test. We found a significant burden for the number of variants in CACNA1E (p = 1.3 × 10−4), CACNA1H (p < 2.2 × 10−16) and CACNA1I (p < 2.2 × 10−16). There was also a significant burden of subjects with missense variants in CACNA1E (p = 6.2 × 10−3), CACNA1H (p < 2.2 × 10−16) and CACNA1I (p < 2.2 × 10−16). Both the number of variants and number of subjects were replicated for CACNA1H  and CACNA1I , respectively, in a Dutch clinical HM cohort (n = 32), albeit that CACNA1I did not remain significant after multiple testing correction. Our data suggest that HM, in the absence of a single causal mutation, is a complex trait, in which an increased burden of missense variants in CACNA1H and CACNA1I may contribute to the risk of disease.Hemiplegic migraine (HM) is a rare subtype of migraine with aura. Given that causal missense mutations in the voltage-gated calcium channel α1A subunit gene The online version contains supplementary material available at 10.1007/s12035-023-03255-5. Hemiplegic migraine (HM) is a rare subtype of migraine with aura with attacks that are associated with motor weakness or hemiplegia during the aura phase . HM is cCACNA1A, ATP1A2 or SCN1A [PRRT2 [2+-dependent neurotransmitter release machinery [A subset of HM patients exhibits an autosomal dominant phenotype with single high-penetrant causal mutations present in ion transport genes or SCN1A –7. Howevor SCN1A , 9. WherA [PRRT2 , a key cachinery , are invachinery . This suachinery .Following along this line of evidence, it has been hypothesised that complex disorders can be the result of an accumulation of genetic variants in a disease pathway, where the crossing of a certain threshold leads to disease . MoreoveCACNA1A was the first HM gene discovered and encodes the pore‐forming α1A subunit of the neuronal voltage‐gated calcium channel (VGCC) CaV2.1 (P/Q‐type) [V2.1 channels are predominantly localised at presynaptic terminals and play a prominent role in controlling neurotransmitter release at most synapses of the nervous system [CACNA1A is a member of a family of rather conserved α1 subunit genes, hereafter referred to as “CACNA1x”, which are part of VGCCs that are classified as either high-voltage-activated (HVA) or low-voltage-activated (LVA) channels that are present on the membranes of excitable cells  , 18. CaVs system –21. CACNCACNA1x genes varies considerably and, with the exception of CACNA1S, all are expressed in the brain [CACNA1A being a well-known HM gene, there have been rare reports on other CACNA1x genes possibly involved in HM-relevant phenotypes. For instance, a link between hemiplegic migraine and brain stem aura migraine has been suggested for CACNA1E [CACNA1H [CACNA1B as one of the genes commonly mutated in migraine families. Finally, CACNA1A was identified as a risk locus for common migraine, as well as being one of the three genes specific for migraine with aura [CACNA1x genes an interesting candidate for burden testing in HM, with relevance, foremost, to patients with a complex genetic basis.The expression of he brain . In addi CACNA1E , and hea[CACNA1H , a gene [CACNA1H . Further[CACNA1H  identifiith aura . AlthougCACNA1A, ATP1A2 and SCN1A mutations [all patients, the accumulation of both rare and common protein-altering variants may be relevant in terms of disease susceptibility. The use of large publicly available WES datasets from general population controls can be incorporated in burden testing to gain more reliable estimates of gene-wide susceptibility.Whole exome sequencing (WES) enables comprehensive exploration of missense variants and investigation of their role in complex traits. When considering that these missense variants are unlikely to be causing HM as monogenic factors as occurs for patients with specific utations –7, burdeCACNA1x genes increases the risk for HM, burden being the aggregation of both rare and common variants as well as the increased presence of a variant in cases compared to controls. To this end, we here used WES data from a large Australian HM patient cohort to identify missense variants in eight calcium channel genes  and determined whether the aggregated effect of the variants across the genes was higher than observed in general population controls. Results were validated in an independent Dutch clinical HM cohort.We hypothesise that the burden of multiple missense variants in CACNA1A, ATP1A2 and SCN1A) or HM-related genes with mutations confirmed by Sanger sequencing [The study consisted of two cohorts of HM patients: an Australian cohort of 184 patients (discovery cohort) and a Dutch cohort of 32 patients (replication cohort). Importantly, patients were a priori excluded in case a pathogenic mutation was present in one of the three HM genes  and HM-related genes [The Australian cohort was selected out of over 300 patients that had been referred to the Genomics Research Centre (GRC) Diagnostic Clinic for genetic diagnostic testing after a suspected diagnosis of HM from the referring neurologist. From this cohort, a subset of 184  unrelated individuals tested negative for known HM gene mutations  libraries for WES were constructed using the Ion AmpliSeq™ Exome RDY library kits  according to the manufacturer’s protocol. The Ion Chef was used to load sample libraries (barcoded fragments of 200 bp). WES was performed in the Genomics Research Centre (GRC), Australia via the Ion Proton and GeneStudio S5 plus (ThermoFisher Scientific) instruments using default settings for Ion AmpliSeq Exome RDY Kit 4 × 2 (Thermo Fisher Scientific). The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Human Research Ethics Committee of the Queensland University of Technology .The cohort consisted of 32 patients  with FHM/SHM according to ICHD-3 criteria . All patGenomic DNA was extracted from peripheral blood leukocytes according to the standard salting-out protocol . WES wasAs a control dataset, we used summary statistics from gnomAD (see below in the paragraph on TRAPD methods). The gnomAD database was chosen as it consists of a large number of individuals and contains a detailed catalogue of exome-wide genetic variation. Furthermore, gnomAD provided ancestry information. The gnomAD database contains exome variant summary statistics for 56,885 non-Finnish Europeans, with a female-to-male ratio of ~ 1.27:1, depending on the available genotypes at each specific locus. As HM is a very rare disorder with a prevalence of 0.01% , confounFollowing WES, the Ion Torrent Server was used to generate quality metrics, align reads to the Human Genome 19 (Hg19), and the Ion Torrent Variant Caller (TVC) was used to call sequence variants and produce variant calling format (VCF) files.Following sequencing, the sequence reads were aligned to the UCSC Genome Browser hg19 reference sequence using the Burrows-Wheeler Alignment tool . The genCACNA1x genes were assessed for patients of the Australian cohort. For the Dutch cohort, only those missense variants present in TRAPD-associated CACNA1x genes were investigated.Prior to performing burden testing, all variants were assessed to determine whether there were obvious, high-penetrant disease-causing mutations detected outside of the known HM genes that could cause HM in patients of either cohort. In the absence of such pathogenic mutation, individual missense variants in all the CACNA1x genes were included with a 5-bp pad on either side of the exon. New VCFs (one merged for each cohort) were annotated with VEP Ensembl [As VCFs were exported from different platforms, the respective analyses had to be unified. The first step for both cohorts was to normalise VCFs using BCFtool; this ensures that any platform-specific formatting differences are removed and also expands multi-allelic variants . VCFs we Ensembl . For theCACNA1x gene in both cases and controls. These “qualifying variants” for both the case and the control cohort were compared. Insertions and deletions (Indels) were not included in the analysis due to their higher percentage of sequencing artefacts, especially given the differing sequencing platforms used across cohorts.To determine the number of variants, we selected “qualifying variants” being variants that meet the criteria of inclusion. Only those variants classified (annotated) as missense variants were considered as “qualifying variants”. The number of individuals in the case cohort who carried at least one “qualifying variant” in that gene and the total number of variants were used in the analysis. For the gnomAD control dataset, only summary statistics were available. Therefore, to approximate the number of control subjects carrying at least one qualifying variant in a given gene, the allele counts for all qualifying variants in that gene were summed. This summation-based approximation probably is an overestimation as it is likely that some individuals carry multiple variants in the same gene. Contrary to rare variant analysis where only the locations of the qualifying variant in cases are used for controls, we selected all variants across the entire gene in controls, in the same way as what was done for the cases. As a result, we had a total number of all missense variants per CACNA1x genes and subjects carried a significant burden of missense variants in cases. TRAPD produces counts of “collapsed” variant groups across each gene and for the respective case or control cohort. To conduct the test, a group file with the qualifying variants was created for each of the eight genes . Of note, CACNA1S was excluded from the analysis as it encodes the pore-forming CaV1.1 α1S subunit that is exclusively expressed in skeletal muscle, so not in the brain and CACNA1F was excluded as this gene is located on the X-chromosome, and TRAPD is currently not configured to test non-autosomal chromosomes.Gene-based burden testing was performed for all variants that met the quality filters, which are referred to as “qualifying variants”, using adaptation on the TRAPD test (Testing Rare vAriants using Public Data) . TRAPD wCACNA1x gene in the case group were compiled into a “SNP file”, (2) a Python script was used to interrogate the VCFs and count the occurrence of variants in each gene in both the case and the control cohorts independently. This generated variant count data for each gene, and (3) the one-sided Fisher exact test was used on the allele count tables to identify the probability of excess in the number of allele counts in cases relative to controls . (4) The one-sided Fisher exact test was used on the subject count tables to identify the probability of excess in the number of subjects with variants in cases relative to controls . P-values < 6.25 × 10−3 were considered significant (Bonferroni corrected for testing 8 genes). Odd ratios were calculated to assess the magnitude of the burden effect. Genes exhibiting statistically significant burden in HM from the Australian discovery cohort were also tested in the Dutch replication cohort.We performed gene-based burden testing for all single-point variants in each cohort. The following steps, in brief, were performed: (1) variants for each CACNA1x genes were identified from the WES data in patients from either the Australian or the Dutch cohorts. However, the number of variants in CACNA1x genes prompted us to perform burden testing. In the Australian cohort, we identified 79 different missense variants in the eight CACNA1x genes examined in the 184 HM patient group from Australia , CACNA1H (p < 2.2 × 10−16) and CACNA1I (p < 2.2 × 10−16). For the Dutch cohort, this replicated for CACNA1H  and CACNA1I , but not CACNA1E (p = 0.85), albeit that CACNA1I did not remain significant after correction for multiple testing. In addition, the number of subjects carrying a variant was also higher cases in CACNA1E (p = 6.2 × 10−3), CACNA1H (p < 2.2 × 10−16) and CACNA1I (p < 2.2 × 10−16)  and CACNA1I , but not for CACNA1E (p = 0.88), albeit CACNA1I did not remain significant after correction for multiple testing. All but four variants were outside the transmembrane domains that are typically affected in case of a pathogenic mutation in either CaV channel , but we consider this a minor factor compared to the ascertainment bias.In the general population, females are overrepresented in most forms of migraine including hemiplegic migraine. The overall female to male sex ratio in our HM cohort was ~ 1.97:1. This observed difference in prevalence will in part be explained by the fact that females are more inclined to consult a physician and thus are diagnosed earlier and more often than males . We cannCACNA1H and CACNA1I plays a role in HM.We have hypothesised that HM may not be autosomal dominant in a substantial proportion of cases, but rather is genetically a more complex trait. The difficulty in confirming this hypothesis lies in how to identify such variants, as they are neither identified by gene association approaches, nor in genome-wide association studies (GWAS). In order to identify such variants, we have used a methodology adapted from a TRAPD analysis. The methodology has proven itself by the identification of functional genetic variants in idiopathic hypogonadotropic hypogonadism . By sligCACNA1H and CACNA1I encode the α1 subunits of CaV3.2 and CaV3.3 LVA T-type calcium channels, respectively Supplementary file2 (XLSX 16 KB)Below is the link to the electronic supplementary material.


Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer’s disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III–IV and V–VII ( Alzheimer’s disease (AD) is a progressive illness that can start with no noticeable symptoms and advance to severe symptomatic forms , 2. MildThe amyloid cascade hypothesis in AD , 5 postuThe association between regional tau accumulation and cognitive decline is somewhat established –21. HoweHerein, we propose machine learning approaches that seek to estimate the multivariate predictive relationships between Aβ biomarkers and cognitive impairment. We hypothesized that cognitive dysfunction can be estimated from regional Aβ biomarkers via ML modeling methods. We further hypothesized that the Aβ-related dominant brain regions involved with such cognitive processes can be identified by integrating feature importance and predictive modeling methods.We used data from the OASIS database  as per oThe mini-mental state examination (MMSE)  is the mData acquisition and processing were performed as per our previous work , 29. BriThis study used two ML techniques, SVR –48 and dmentclass2pt{minimC values: 2n, n = − 5, − 4, − 3, …, 13, 14n, n = − 12, − 11, …, 1, 2Gamma: 2n, n = − 7, − 6, …, − 1, 0Epsilon: 2The model was optimized using mean absolute error (MAE) as the loss function, which was then used to predict the MMSE. The same SVR model designs were applied to each Braak stage and their combinations.). This model was trained and optimized on the training data for each fold using the Adam optimizer [We used a consistent tenfold CV approach with standard scaling for the ANN model to predict the MMSE score. This ANN sequential model has four dense layers, each with a random normal kernel initializer, and was implemented on Python using TensorFlow  and Kera2ptimizer  and MAE ptimizer  and Seabptimizer  in PythoIt is a challenging task to understand the predictions made by SVR/ANN as the internal mechanisms of these models are complex, non-intuitive, and less interpretable. However, neuroimaging data sets and the development of ML have made it possible to identify areas that are strongly associated with a specific trait or feature , 67. FeaThe LIME technique interprets predictions of a complex ML model f with inputs x by locally approximating the model in the vicinity of the prediction. This simple explainable model follows the additive feature attribution  given byLIME is obtained by minimizing the function given by the following equation:In each fold of CV, the LIME explainer was trained in regression mode with ‘lasso_path’  as the ‘LIME generates the interpretable model by creating perturbed versions of the original data and training a model on the new data. Although LIME is one of the most stable methods , 74, dueU test [p value was used to examine the relationship between MMSE and DVR and to determine the predictive associations between actual MMSE and predicted MMSE. These values were calculated using the scipy [We used the Mann–Whitney U test  to compahe scipy  library he scipy  employinp = 0.012). Compared to controls, MCI had higher DVR in Braak stages III–IV (FDR-p = 0.008), Braak stages V–VI (FDR-p = 0.002), and all stages combined (FDR-p = 0.007).Figure ρ) was computed between DVR and MMSE in MCI and controls , whereas stronger and highly significant correlation was found in later Braak stages: III–IV , V–VI , and all stages combined . Figure ρ = 0.033; FDR-p = 0.854), III–IV , V–VI , and all stages combined .Spearman’s correlation  , V–VI , and all stages combined  (panels b–d). Using ANN , III–IV , V–VI , and all stages combined  (panels a–d). The superiority of ANN over SVR is confirmed by the average training and test loss of the SVR and ANN models throughout all 10 folds of data. The average training and test loss of the SVR and ANN models for each Braak stage is presented in Table We tested whether regional DVR values could predict MMSE in MCI patients at different Braak Stages using our SVR and ANN models. Using SVR, the predicted and actual MMSE in MCI did not significantly correlate for Braak Stages I–II (We opted to focus only on the ANN model to compute feature significance, because the SVR model failed to perform well in predicting MMSE and had a smaller correlation between actual and predicted MMSE, as shown in Fig. In this study, we proposed a novel multi-folded analytical framework demonstrating the multivariate relationships between Aβ DVR and cognition. We took Aβ DVR and cognition from the same individuals, trained SVR and ANN models on those data using standard CV techniques, and predicted cognition from Aβ DVR alone. Our analyses suggest that MCI had higher DVRs in later stages, strongly correlating to cognitive decline in stages III–IV and VI–V. When LIME and ANN were combined, the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most dominant Aβ regional hubs for predicting cognitive decline. This suggests that these regions may play a key role in early AD mechanisms. Our approach is crucial for Aβ biomarker research for early detection of cognitive decline and aids in advancing computer-assisted diagnostic ML approaches. These approaches are different, and the results are promising compared to the established Aβ analysis approaches and related patterns , 81.The clear difference between DVR in controls and MCI at different Braak stages supports our hypothesis that regional Aβ accumulation is a critical and sensitive biomarker for disease progression compared to other established biomarkers . The effThe significant correlation between MMSE and DVR, especially in later Braak stages III–VI of MCI, further supports the hypothesis that more Aβ accumulation leads to worse cognitive outcomes, eventually leading to a higher risk of developing AD dementia. This correlation might also help clarify the relationships between Aβ accumulation and tau and their mutual effect on cognitive abilities and disease progression in later stages of the disease, i.e., symptomatic AD. The correlation between DVR and MMSE was weak in the early Braak stage I–II spatial distribution. Surprisingly, controls showed slightly higher DVR than MCI at this stage, albeit with a small effect size. This finding may be attributed to some controls being on the borderline of meeting the clinical diagnosis of MCI or transitioning to the MCI phase. Using ANN and SVR models, we reliably predicted cognitive impairment in the MCI population starting at Braak stages III–IV. The success of these models in predicting MMSE based on DVR at different Braak stages is a highly promising proof-of-concept approach. These results highlight the significance of ML algorithms in understanding and diagnosing AD pathophysiology, which supports previously published work on the reliability of ML models , 95.Using the combination of ANN and LIME methods, we determined the significance of Braak staging brain regions, and it was found that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus Aβ biomarkers were the top three significant features for predicting cognitive dysfunction. These findings broadly align with previous research indicating that alteration in the parahippocampal gyrus and hippocampus is an early biomarker of AD –100. TheML approaches typically require larger sample sizes for robust results. However, with our proof-of-concept approach, we were able to generate clear predictions and outcomes with a relatively small sample size. As a result, we anticipate strong findings in future studies with larger sample sizes. While using ML algorithms in only Braak staging regions may limit the generalization of Aβ accumulation as a predictor of cognitive abilities, combining other regions can enhance the ability to predict cognitive dysfunction in individuals with MCI and other populations. By incorporating additional brain regions, such as those involved in memory and executive functioning, we may improve our predictive models’ accuracy and robustness. The PET scans we studied were obtained from only two sites. The future studies should also focus on considering a larger number of features, including voxel measures, and associated dimensional reduction techniques, and studying tau PET and other imaging modalities from a larger number of sites. These approaches might increase the reliability of the findings from multiple aspects of AD mechanisms.Our findings demonstrate a significant difference in Aβ accumulation between MCI and controls, particularly in the later stages of the spatial distribution of Braak stages. The strong association between Aβ DVR and cognitive dysfunction highlights the importance of Aβ as a biomarker for MCI. The most important Braak regions associated with cognitive impairment were carefully identified by combining LIME feature significance and ANN techniques. This integration offers a promising strategy for comprehending the links between regional Aβ biomarkers and cognitive impairment. Our approach highlights the ML model’s potential in conjunction with the feature importance attributes in enhancing biomarker identification and suggests that further research utilizing these tools may lead to earlier diagnosis and intervention in the progression of MCI and related disorders.


OsGA3ox1 has been proposed to regulate pollen development through the gametophytic manner, but cellular characterization of its mutant pollen is lacking. In this study, three heterozygotic biallelic variants, “-3/-19”, “-3/-2” and “-3/-10”, each containing one null and one 3bp-deletion allele, were obtained by the CRISPR/Cas9 technique for the functional study of OsGA3ox1. The three homozygotes, “-19/-19”, “-2/-2” and “-10/-10”, derived from heterozygotic variants, did not affect the development of most vegetative and floral organs but showed a significant reduction in seed-setting rate and in pollen viability. Anatomic characterizations of these mutated osga3ox1 pollens revealed defects in starch granule accumulation and pollen wall development. Additional molecular characterization suggests that abnormal pollen development in the osga3ox1 mutants might be linked to the regulation of transcription factors OsGAMYB, OsTDR and OsbHLH142 during late pollen development. In brief, the rice GA3ox1 is a crucial gene that modulates pollen starch granule accumulation and pollen wall development at the gametophytic phase.The rice GA biosynthetic gene  Pollen is essential for plant reproduction, and rice pollen is comprised of a multilayered pollen wall and is full of starch granules in the central cytoplasm. Pollen wall is a sturdy structure and functions to protect pollen from various environmental stresses, and the accumulated starch granule provides energy for pollen germination and pollen tube elongation. The pollen wall formation starts from an early stage of pollen development, and the development of the outer layer, exine, is mainly regulated through tapetum, a sporophytic tissue , while toscps mutant affects stamen and pollen development  are included in the manuscript file. Please remove them and upload them with the file type 'Supporting Information'. Please ensure that each Supporting Information file has a legend listed in the manuscript after the references list.http://journals.plos.org/plosone/s/supporting-information. 6. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: 7. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.Additional Editor Comments:The results of the manuscript are intriguing; however, the reviewer has raised a few important points that must be clarified. Therefore, this manuscript required revision prior to publication consideration by PLOS ONE. Please refer to the attached report for detail.[Note: HTML markup is below. Please do not edit.]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: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: Yes**********  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: Yes**********  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: 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: -The authors analyzed the molecular functions of rice GA3ox1, essential for the development of tapetum and exine as well as the starch accumulation. osga3ox1 mutants generated by CRISPR/Cas9 showed a reduced number of total pollen grains and a significantly lower seed setting rate. Furthermore, the histochemical analysis revealed that the osga3ox1 mutant induces tapetum deficient anther and that the pollen grains with osga3ox1 allele showed morphological changes in the exine layer including bacula and tryphines, which can cause disrupted pollen wall development. And the transcriptional reductions of OsGAMYB, OsTDR, and OsbHLH142 support morphological defects of osga3ox1 knockout anther and pollen grain.https://doi.org/10.1038/s42003-022-03008-5). Current manuscript added cellular gametophytic features in GA3ox1 mutants, which further confirmed the previous finding. I would like to ask authors to improve few things.-Previously, one article showed that the same GA3ox1 has a higher GA7 synthesis ratio and the bioactive GA synthesis by GA3ox1 is critical for starch accumulation in rice pollen . -L34: While the SSR of the knockout mutants were dramatically decreased , a high proportion (~51.3%) of the stained pollen were observed in the present study  which differs from the low proportion (~5%) reported previously [21]. This variation could be due to the different rice cultivars (TNG71 vs Nipponbare) and culture environments (Taichung Taiwan 24°0′N 120°7′E vs Nagoya Japan 35°2′N 136°9′E).Though this manuscript suggested that the phenotypical difference from Kawai et al. (2022) might be from the different background cultivars or growth environment, I think the differences may come from the unique features of the CRISPR/Cas9 system. Many publications reporting the CRISPR/Cas9-mediated mutants have used transgene-free homozygous/biallelic (or heterozygous) T1 (or T2) plants to prevent functional variations due to chimeric mutations from the constitutive expressions of sgRNA and Cas9 of the remaining T-DNA. In this case, a transgene-free homozygote  and transgene-free heterozygotes  are proper materials for further investigations. However, it seems that the authors used T1 plants containing the transgene, according to Figure S2A and Figure 1A-C, unlike the former publication used transgene-free T1 lines as their materials. Thus, this data needs to be re-verified using such transgene-free homozygous mutants lines.-Fig 4D: For RT-qPCR, currently it has only one WT and one mutant. To clarify this result, repeat this experiment from at least two homozygous mutants.Reviewer #2: This paper described a detailed study on the mutants of OsGA3ox1 (generated by the authors using the CRISPR/Cas9 technique) with aim to answer the following questions: Whether OsGA3ox1 knockout would affect plant growth and grain setting? How the knockout affects pollen development? What is the molecular mechanism underpinning pollen sterility? The study is well designed and implemented using state of the art techniques, the findings were reported against sound analysis of background studies and were well supported by the data, and the writing is easy for understanding. Data are properly analyzed using statistic means and are nicely presented in various formats.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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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,  17 Sep 2023Response to Reviewers’ comments:Reviewers’ CommentsReviewer #1: - The authors analyzed the molecular functions of rice GA3ox1, essential for the development of tapetum and exine as well as the starch accumulation. osga3ox1 mutants generated by CRISPR/Cas9 showed a reduced number of total pollen grains and a significantly lower seed setting rate. Furthermore, the histochemical analysis revealed that the osga3ox1 mutant induces tapetum deficient anther and that the pollen grains with osga3ox1 allele showed morphological changes in the exine layer including bacula and tryphines, which can cause disrupted pollen wall development. And the transcriptional reductions of OsGAMYB, OsTDR, and OsbHLH142 support morphological defects of osga3ox1 knockout anther and pollen grain.https://doi.org/10.1038/s42003-022-03008-5). Current manuscript added cellular gametophytic features in GA3ox1 mutants, which further confirmed the previous finding. - Previously, one article showed that the same GA3ox1 has a higher GA7 synthesis ratio and the bioactive GA synthesis by GA3ox1 is critical for starch accumulation in rice pollen . I would like to ask authors to improve few things.-L34: While the SSR of the knockout mutants were dramatically decreased , a high proportion (~51.3%) of the stained pollen were observed in the present study  which differs from the low proportion (~5%) reported previously [21]. This variation could be due to the different rice cultivars (TNG71 vs Nipponbare) and culture environments (Taichung Taiwan 24°0′N 120°7′E vs Nagoya Japan 35°2′N 136°9′E).Question #1:Though this manuscript suggested that the phenotypical difference from Kawai et al. (2022) might be from the different background cultivars or growth environment, I think the differences may come from the unique features of the CRISPR/Cas9 system. Many publications reporting the CRISPR/Cas9-mediated mutants have used transgene-free homozygous/biallelic (or heterozygous) T1 (or T2) plants to prevent functional variations due to chimeric mutations from the constitutive expressions of sgRNA and Cas9 of the remaining T-DNA. In this case, a transgene-free homozygote  and transgene-free heterozygotes  are proper materials for further investigations. However, it seems that the authors used T1 plants containing the transgene, according to Figure S2A and Figure 1A-C, unlike the former publication used transgene-free T1 lines as their materials. Thus, this data needs to be re-verified using such transgene-free homozygous mutants lines.Response to question #1:Thank you for the comments. We may not have provided a sufficiently clear description of the materials we analyzed. Usually, the homozygous lines produce very few seeds or are sterile, as such they are difficult to be used for progenies analysis. Therefore, the materials used for most of our experiments and progenies analysis were from heterozygous parental lines. For example, the “-3/-19” biallelic variant presented in Figure 1A was T2 progenies segregated from transgenic T1 heterozygous lines #1-1, #1-2 and #1-5 . Then the transgene-free T2 heterozygote, such as the line #1-2 from Figure 1A, was selected to obtain various genotypes of progenies and their phenotypical data and SSR were presented in the Figures 1D and 1H respectively. The other two biallelic variants “-3/-2” and “-3/-10” followed the same processes to get the various genotype of T2 progenies and data were presented in Figures 1E, 1F and 1I. So, the “-3/-19” materials used in the present study were all transgene-free hetero/homozygotes , as well as the “-3/-2” variant but not the “-3/-10” variant (the transgene-free “-3/-10” lines were not yet available).The materials used for Figure 2D were transgene-free hetero/homozygotes. The percentage of stained pollens differ from previously reported  could be due to the different rice cultivars and culture environments. It has been reported that different genetic background  and colder temperature  affect GA biosynthesis and these differences might also affect the SSR, leading to of ~80% in TNG71  vs ~60% in Nipponbare . Furthermore, besides a few heavy stained pollen, most pollens in the knockout mutant were stained lighter than WT  and these pollens were counted as stained pollen even though they might not be viable.To clarify the description in the manuscript, we added the “transgene-free” wording in the sentence of “Detailed agronomic traits of the WT host and progenies segregated from the transgene-free “-3/-19” variant/line were compared, and the plant height, panicle number and grain number/per panicle were the same for WT and all mutant genotypes (Table 1).” in line 10, page 9; and in the sentence of “To determine whether the low SSR was due to defects in any gametophytic features, the flower organs of the three genotypes segregated from the progeny of the transgene-free “-3/-19” variant were examined.” in line 22, page 9.Question #2:-Fig 4D: For RT-qPCR, currently it has only one WT and one mutant. To clarify this result, repeat this experiment from at least two homozygous mutants.Response to question #2:Thank you for the comment. We have compared the expression of the three TFs  in WT, “-10/-10” and “-19/-19”, and both “-10/-10” and “-19/-19” showed similar results. We presented only the data of “-19/-19” in the manuscript, since “10/-10” was not yet a transgene-free mutant. We have added these results as supplementary information  and also indicated in the text of line 18, page 13.Attachment2023-09-17-Response to Reviewers Comments.docxSubmitted filename: Click here for additional data file. 20 Sep 2023Rice GA3ox1 modulates pollen starch granule accumulation and pollen wall developmentPONE-D-23-25784R1Dear Dr. Chen,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. 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Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: Yes**********  6. 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: Authors well addressed all comments I raised previously. I think this revised manuscript is now ready for publication.**********  7. 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.  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Endoscopic submucosal dissection (ESD) with double-clip traction (DCT) with a rubber band and two clips allows excellent en bloc and curative resection rates and is probably the cheapest traction system availableWe report the case of a 63-year-old woman with a partially resected cecal granular laterally spreading tumor (LST) and a clip placement, referred for ESD. Initial evaluation showed a 50 × 40-mm granular LST with a retractive aspect on a fold and persistence of the clip, without signs submucosal invasion . After Video 1 Multitraction with a single rubber band and clips: a simple tip for endoscopic submucosal dissection of a recurrent lesion with previous clip.Multitraction with a single rubber band is a cheap and easy-to-use technique that could help to expose lateral edges of submucosa even better in challenging cases of ESD like recurrent lesions.Endoscopy_UCTN_Code_TTT_1AQ_2AD