However, efficient cell delivery method is urgently needed and the therapeutic mechanism remains unclear. This study focused on developing a minimally invasive open-flow microperfusion (OFM) technique, which combined orthotopic transplantation of human umbilical cord-derived (hUC)-MSCs to liver and in vivo monitoring of liver microenvironment in mice with CClThe therapeutic potential of OFM route was evaluated by comparing OFM with intravenous (IV) injection route in terms of hUC-MSCs engraftment at the fibrosis liver, liver histopathological features, liver function and fibrotic markers expression after hUC-MSCs administration. OFM was also applied to sample liver interstitial fluid in vivo, and subsequent metabolomic analysis was performed to investigate metabolic changes in liver microenvironment.4-induced up-regulation of the fibrotic markers, such as α-SMA, collagen I and TGF-β. In vivo monitoring of liver microenvironment demonstrated the metabolic perturbations induced by pathological condition and treatment intervention. Two metabolites and eight metabolic pathways, which were most likely to be associated with the liver fibrosis progression, were regulated by hUC-MSCs administration.Compared with IV route, OFM route caused more hUC-MSCs accumulation in the liver and was more effective in improving the remodeling of liver structure and reducing collagen deposition in fibrotic liver. OFM transplantation of hUC-MSCs reduced blood ALT, AST, ALP and TBIL levels and increased ALB levels, to a greater extent than IV route. And OFM route appeared to have a more pronounced effect on ameliorating the CClThe results demonstrated that the novel OFM technique would be useful for hUC-MSCs transplantation in liver fibrosis treatment and for monitoring of the liver metabolic microenvironment to explore the underlying therapeutic mechanisms.The online version contains supplementary material available at 10.1186/s13287-021-02599-w. Liver fibrosis remains a serious health problem which affects a significant number of people all over the world . The onsCurrently, mesenchymal stromal cell (MSC) therapy is regarded as a promising strategy for liver fibrosis treatment  because Up to now, the translation of numerous MSC therapies has not fully realized their clinical application potential. One critical but often overlooked challenge has been the administration route of MSCs; indeed, many studies have revealed when the same MSC therapy is introduced through different routes of administration, the engraftment and retention of MSCs within the liver are different, which results in different therapeutic outcomes , 17. ComThe mechanisms of MSC-based liver fibrosis therapy have not been fully delineated , which iIn this study, a novel minimally-invasive open-flow microperfusion (OFM) route was developed to improve hUC-MSCs delivery efficiency to the liver for liver fibrosis treatment. MSCs entrapment in liver and therapeutic effects of OFM route were compared with IV route. In addition, OFM was simultaneously employed to sample the liver interstitial fluid in vivo during fibrosis progression and healing process of liver fibrosis, and subsequently combined with metabolomic analysis. This allowed for the investigation of the potential therapeutic mechanisms.2 at 37 °C and the medium was replaced every 3–4 days until well-developed colonies of fibroblast-like cells appeared. The cells were then digested with 0.25% trypsin and seeded in new culture bottles for further expansion. The cells of fourth generation were harvested as purified hUC-MSCs and taken for further studies. The adhesiveness properties were identified and the morphological characteristics of hUC-MSCs were detected by an inverted microscope . Flow cytometry  was employed for hUC-MSCs characterization according to published protocols [With informed consent, the hUC-MSCs were isolated from fresh umbilical cord of a full-term delivery donor. The entire procedure was approved by the Medical Ethics Committee of The Affiliated Drum Tower Hospital of Nanjing University Medical School. The umbilical cord was washed with sterile PBS and was cut into 3–5 mm long pieces, suspended in Dulbecco's modified Eagle's medium–low glucose  supplemented with 10% FBS  and 1% penicillin/streptomycin . Then the isolated cells were cultured in an atmosphere of 5% COrotocols , data an4 was dissolved in olive oil at the volume ratio of 1:3. Mice were intraperitoneally injected with 150 μL of CCl4 solution twice a week for eight weeks. The normal group received 150 μL olive oil twice a week for eight weeks.C57BL/6 mice  were purchased from Nanjing Medical University and housed in a temperature/humidity-controlled environment with light illumination cycles of 12 h/day and had free access to diet and water. The animal experiments were approved by the Institutional Animal Care and Use Committee of Nanjing University. To induce liver fibrosis, CClA polyimide capillary  was used for the fabrication of the OFM probe. The capillary was perforated with 60 holes at intervals of about 50 μm on a laser ablation platform . The diameter of the hole was 100 μm.4-exposed mice were randomly divided into a model group, a hUC-MSCs OFM-treated group, and a hUC-MSCs IV-treated group. In hUC-MSCs OFM-treated group, the mouse was anesthetized and the abdominal cavity of the mouse was opened. OFM probe with 60 holes (100 μm diameter) was implanted into the left lobe of liver under the traction of catheter. One end of the probe was plugged with an empty syringe, another end of the probe was connected to a push pump and 1 × 106 hUC-MSCs in 300 μL PBS were delivered at the flow rate of 25 μL/min. In IV group, same amount of hUC-MSCs was injected to the caudal vein of the mouse. In model group, mice were administrated with 300 μL PBS at a flow rate of 25 μL/min via OFM route.After induction of liver fibrosis, CCl6 hUC-MSCs were resuspended in 400 μL CM-Dil solution with concentration of 20 μg/mL. The mixed suspension was incubated at 37 °C for 15 min, then at 4 °C for 15 min. Then, the labeled-cells were washed with PBS 3 times to eliminate free CM-Dil residual. Finally, the labeled hUC-MSCs suspension was prepared with a concentration of 3.3 × 106 cells/mL. To evaluate the distribution of hUC-MSCs in liver fibrosis model at day 3, 7, 14 and 21 post-transplantation, the same amount of CM-Dil-labeled hUC-MSCs were administrated using transplantation method of OFM or IV as described above. Mice were sacrificed 3, 7, 14 or 21 days after cell transplantation. Livers, lungs, spleens, hearts and kidneys were collected and fixed in 4% (v/v) paraformaldehyde (PFA) overnights. Then the tissues were transferred to 20% (w/v) sugar for 12 h and 30% (w/v) sugar overnight for dehydration. After dehydration, the tissues were embedded in chilled OCT and frozen at − 80 °C. Cryosections of liver, lung, spleen, heart and kidney samples were prepared with a thickness of 12-μm (three slides each sample) and incubated with DAPI dye  in the dark. The distribution of CM-Dil-labeled cells in different tissues was observed by confocal microscope .hUC-MSCs were labeled by dyechloromethylbenzamido-1,1’-dioctadecyl-3,3,3’,3’-tetramethyl indocarbocyanine perchlorate  in accordance with the manufacturer’s guidance. After digestion and centrifugation, 6 × 10Serum samples were collected and serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total bilirubin (TBIL), and albumin (ALB) levels were measured on day 21 post-MSCs transplantation by a chemistry analyzer .Liver tissues were removed from mice instantly after euthanization on day 21 post-MSCs transplantation and divided into sections. One section was washed with PBS, fixed in 4% PFA and embedded in paraffin. Then the liver samples were cut into 4 μm-thickness slices. To illustrate the histological details, hematoxylin&eosin (H&E) staining was performed. To evaluatethe collagen deposition, masson's trichrome and sirius-red was performed. Immunohistochemistry (IHC) staining was also performed with antibodies against α-SMA , collagen I  or TGF-β . The staining results were detected with an optical microscope .Total RNA was harvested from another unmanipulated liver tissue using TRIzol® reagent  and was reverse-transcribed into cDNA with HiScript®III RT SuperMix for qPCR  using 10 μg RNA. ChamQ Universal SYBR qPCR Master Mix  was used for template amplification with a primer for each of the transcripts examined. PCR reagents were assessed by a three color real-time PCR machine . All reactions repeated three times. Relative quantification of gene expression was performed through normalizing to the expression of β-actin as an internal control. The mRNA expression levels of α-SMA, collagen I, and TGF-β in normal group, model group, hUC-MSCs OFM-treated group, and hUC-MSCs IV-treated group were compared. The primer sequences including α-SMA, collagen I, TGF-β, and β-actin were listed in Table OFM sampling of liver interstitial fluid was performed in normal group, model group and hUC-MSCs OFM-treated group. The operation of OFM sampling was similar to OFM transplantation of hUC-MSCs described above with a little modification. In brief, one end of the OFM probe was connected to a push pump  and another end of the OFM probe was connected to a pull pump  after implantation into the liver. The perfusion fluid was normal saline. The flow rate of the push pump and the pull pump was 2 μL/min. The probe was perfused for 30 min for equilibration, then 80 μL dialysate sample was obtained from each mouse. The dialysates were stored at -80 °C until the time of analysis.g at 4 °C. The supernatant was collected for UPLC-ESI HR MS/MS analysis. Samples were loaded into the UPLC system  equipped with a BEH amide column . The mobile phase A was water containing 20 mM ammonium acetate and the mobile phase B was acetonitrile containing 0.1% formic acid. The LC separations were 30 min per sample with a flow rate at 0.3 mL/min using LC gradient reported in the previous literature [m/z range 150–1000, ion transfer tube temperature 325 °C, vaporizer temperature 275 °C, sheath gas flow 30 Arb, auxiliary gas flow 10 Arb. Metabolite fragments were obtained under high energy collisional dissociation (HCD) mode with a collision energy at 20 eV.The liver dialysates obtained by OFM sampling were analyzed using ultra performance liquid chromatography-electrospray high resolution MS/MS (UPLC-ESI HR MS/MS). In brief, 80 μL dialysate sample was dried with a vacuum dryer, and then re-dissolved in 50 μL of ethanol containing 0.25 μg/mL isoprenaline  as internal standard (IS). The mixture was centrifuged for 15 min at a rate of 10,000terature . The methttps://www.metaboanalyst.ca).The high resolution MS spectra and MS/MS spectra were loaded into Compound Discovery . Database search was performed after peak alignment and peak area integration. The putative annotation of the metabolites was achieved by precisely matching mass with mzCloud, ChemSpider and MassList databases. Further structural confirmation was performed by MS/MS fragmentations obtained in a data dependent acquisition mode. The concentration data of the hUC-MSCs OFM-treated group, the model group and the normal group were acquired by comparing the peak area of each metabolite with that of IS. Principal component analysis (PCA) was performed using SIMCA-P 14.1  with the concentration data. Volcano plot and heat map were obtained using Graphpad Prism5.0  with the concentration data. Metabolic pathway analysis was performed on MetaboAnalyt website . Graphpad Prism 5.0 was used to generate graphs. Statistical significance of differences between groups was evaluated using a standard one-way analysis of variance (ANOVA) or non-parametric test. A The hUC-MSCs exhibited typical fibroblastic-like morphology , AST (p = 0.7133), ALP (p = 0.9966), TBIL (p = 0.5949), and ALB (p = 0.1229), which indicated an approximate return of these liver function biomarkers to normal level.Liver function was evaluated through measurements of serum biomarkers, including serum ALT, AST, ALP, TBIL, and ALB. As shown in Fig. p = 0.7757) and collagen I (p = 0.4934) mRNA expression. The mRNA expression levels of TGF-β in OFM group were significantly lower than those in model group while TGF-β mRNA expression levels in IV group did not show a significant difference to the model group (p = 0.5650). The results indicated that OFM route appeared to have a more pronounced effect on α-SMA, collagen I, and TGF-β mRNA expression.The mRNA expression levels of α-SMA and collagen I, which were the main ECM components of the fibrotic liver and widely used as fibrotic markers, were evaluated through qRT-PCR. The mRNA expression levels of TGF-β, which was known as a strong introducer of HSCs to produce excessive ECM components, were also assayed by qRT-PCR. As shown in Fig. 4 exposure induced an excessive accumulation of α-SMA, collagen I, and TGF-β, and hUC-MSCs transplantation via OFM or IV route alleviated this effect in different degrees. The protein expression of α-SMA, collagen I, and TGF-β in OFM group was lower than that in IV group. The results were confirmed by quantification of the areas positive for α-SMA, collagen I, and TGF β  > 2.0 or < 0.5) during progression and treatment process of liver fibrosis were highlighted. As shown in Fig. As an in vivo and in situ sampling technique, OFM was employed to monitor metabolic changes in liver microenvironment during progression and treatment process of hepatic fibrosis. As shown in Fig. lot Fig. C, D. Thep value < 0.05 was considered as the potential target pathway. It was shown that 10 metabolic pathways were selected as the potential pathways associated with the development of liver fibrosis, 8 of which were regulated by treatment with hUC-MSCs, including alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism, arginine biosynthesis, aminoacyl-tRNA biosynthesis, taurine and hypotaurine metabolism, histidine metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism.The metabolite concentration data set was imported into MetaboAnalyst website to explore the significantly disturbed metabolic pathways under the conditions of this study. Figure 4-induced upregulation of the fibrotic markers, such as α-SMA, collagen I and TGF-β. Metabolomic analysis of liver dialysates showed that pathological condition and treatment intervention induced metabolic perturbations in liver microenvironment. Specially, the levels of 9-cis-retinoic acid and dehydroretinaldehyde, which were significantly decreased in model group compared to the normal group, showed opposite trend in hUC-MSCs OFM-treated group compared to the model group. Moreover, 8 metabolic pathways, which were most likely to be associated with the liver fibrosis progression, were regulated by treatment with hUC-MSCs. In summary, our study strongly suggested the high therapeutic potential of utilizing OFM route in the administration of hUC-MSCs for treatment of liver fibrosis and monitoring of the liver metabolic microenvironment in vivo.The aims of this study were to improve hUC-MSCs administration routes in liver fibrosis models by developing a minimally-invasive OFM, which was simultaneously employed as an in vivo and in situ sampling technique to monitor liver metabolic microenvironment during fibrosis progression and healing of liver fibrosis. To evaluate the therapeutic potential of OFM transplantation of hUC-MSCs in liver fibrosis, OFM route was compared with IV route in terms of hUC-MSCs engraftment at the fibrotic liver, liver histopathological features, liver function and fibrotic markers expression after hUC-MSCs administration. Metabolomic analysis of liver dialysates obtained by OFM sampling was performed to investigate metabolic changes in liver microenvironment.Our results showed that compared with IV, OFM route caused more hUC-MSCs accumulation in the liver. Histopathological data revealed that OFM route was more effective in reducing collagen deposition in fibrotic liver and improving liver structure than IV route. OFM transplantation of hUC-MSCs significantly reduced blood ALT, AST, ALP and TBIL levels and increased ALB levels, to a greater extent than IV route. Consistent with the histopathological and biochemical results, OFM route appeared to have a more pronounced effect on ameliorating the CCl4-induced liver fibrosis in mice [6 cells/mouse to observe any effect in colitis treatment with IV transplantation of MSCs [6 MSCs per kg body weight, and a high dose was also more likely to induce a severe systemic immune response [Considering the complexity of liver fibrosis progression, efficient therapy options are severely limited. Cell therapy, particularly involving MSCs, has gained great interest for the treatment of liver diseases . It has  in mice , 28. The in mice . Hence,  in mice . Xuan et in mice . However in mice . As expe in mice –35. Gao  in mice , 37. The in mice . The pri of MSCs . Wang alresponse . As a re7 cell/mouse was even tried, but no embolism-related death occurred. Combining histopathological and biochemical data and the results of fibrotic markers levels, it was suggested OFM transplantation of hUC-MSCs into livers was a promising technique for treatment of liver fibrosis. However, it’s worth noting that there are still several defects in OFM transplantation of hUC-MSCs. Firstly, the operation difficulty of OFM route is higher compared to IV route. Secondly, as the OFM probe is thin, the injection rate of hUC-MSCs suspension should be limited to avoid probe clogging. Thirdly, although the OFM technique is minimally invasive, the risk of liver bleeding still exists.To bypass cell entrapment in the lung, alternative administration routes, such as hepatic artery injection, portal vein injection, intrahepatic injection and intraperitoneal injection have been investigated in several studies. Sang et al. found that MSCs transplantation through intraportal injection was superior to hepatic intra-arterial injection, intrahepatic injection and peripheral intravenous injection for ALF treatment in swine, which could improve liver function and prolong the life span of swine with ALF . In our 4-induced hepatic fibrosis, were reversed by transplantation of hUC-MSCs. 9-cis-retinoic acid and dehydroretinaldehyde belongs to the class of organic compounds known as retinoids, and 9-cis-retinoic acid is a ligand of retinoic acid X receptor (RXR). Previous study found that the 9-cis-retinoic acid and synthetic RXR agonists had an inhibition effect on synthesis of fibronectin and collagen I, and HSC proliferation [It is a great challenge to completely understand the action mechanisms of liver fibrosis and hUC-MSCs treatment for the disease. Metabolomics is a powerful tool to reveal the potential targets for therapeutic intervention by identifying significantly altered metabolites relevant to the progression of the disease and the healing process . Howeverferation . Lee repferation . Further4-induced chronic liver injury [Using metabolic pathway analysis, we identified 8 metabolic pathways, including alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism, arginine biosynthesis, aminoacyl-tRNA biosynthesis, taurine and hypotaurine metabolism, histidine metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism, which were significantly regulated in hUC-MSCs-treated group versus the model group. These metabolic pathways may play a crucial role in underlying therapeutical mechanism of hUC-MSCs in liver fibrosis. Du et al. recently reported that the transdifferentiation of HSCs to myofibroblastic (MF)-HSCs was strongly dependent on glutamine . The α-kr injury . The over injury . Gloria r injury . Even thThe results presented in this study showed the benefits of using a topical transplantation technique over systemic route. This effective route could possibly be further investigated as an alternative delivery route via B ultrasound or CT-guided percutaneous puncture in clinical therapy and may prove particularly valuable if the liver is the main target organ involved, or if the patient is unable to tolerate systemic exposure of MSCs. Furthermore, OFM systems could be extended to direct local delivery of chemical drugs to the liver in clinical experiments. In addition, the advantages of combining OFM sampling of liver interstitial fluid for microenvironment monitoring have been highlighted in this study. Enabling in vivo detection of minor metabolic changes, OFM could serve as a monitoring tool in clinical routine beyond scientific investigations. Nevertheless, it’s worth noting that there are still a few limitations in the study. The comparison between OFM route and other common local delivery routes such as intraportal injection and intrahepatic injection in terms of stem cell entrapment in liver and therapeutic effect is not performed. Furthermore, as it is widely believed that the liver repair effect of MSCs mainly depend on their paracrine manner by which MSCs secrete various soluble factors, the monitoring of cytokines in liver environment can be included in addition to small molecular metabolites by OFM sampling, which would promote mechanism studies of stem cell therapy.4-induced hepatic fibrosis. 8 metabolic pathways were significantly regulated after hUC-MSCs treatment in liver microenvironment, including alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism, arginine biosynthesis, aminoacyl-tRNA biosynthesis, taurine and hypotaurine metabolism, histidine metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism. Our results indicated that OFM transplantation of hUC-MSCs and OFM sampling of interstitial fluid could possibly be applied in clinical therapy and therapeutic mechanisms study in the future.In summary, a minimally invasive OFM technique, which could be used not only to deliver hUC-MSCs to the liver but also to sample liver interstitial fluid in vivo, was successfully developed in this study. It was demonstrated that OFM transplantation of hUC-MSCs was superior to IV route in treatment of liver fibrosis due to its advantages of promoting hUC-MSCs delivery to the target tissue, inhibiting collagen deposition, improving liver function and reducing expression of fibrotic markers. In vivo monitoring of liver microenvironment demonstrated the metabolic perturbations induced by pathological condition and treatment intervention. Following metabolomic analysis of liver dialysates revealed that hUC-MSCs treatment reversed the levels of 9-cis-retinoic acid and dehydroretinaldehyde that were significantly increased with the development of CClAdditional file 1. Supplementary Figure 1. The characteritics of hUC-MSCs. A Morphology of hUC-MSCs at passage 4. B Surface markers of the isolated and cultured hUC-MSCs were detected using flow cytometry. More than 95% hUC-MSCs expressed CD73, CD90, CD105, and CD29, but not CD34, CD45, CD117, and HLA-DR. C Fluorescence image of CM-Dil-labeled hUC-MSCs. Supplementary Figure 2. PCA analysis of livers obtained from control and OFM groups based on metabolomic analysis.


Can genetic screening be used to personalize education for students? Genome-wide association studies (GWAS) screen an individual’s DNA for specific variations in their genome, and how said variations relate to specific traits. The variations can then be assigned a corresponding weight and summed to produce polygenic scores (PGS) for given traits. Though first developed for disease risk, PGS is now used to predict educational achievement. Using a novel simulation method, this paper examines if PGS could advance screening in schools, a goal of personalized education. Results show limited potential benefits for using PGS to personalize education for individual students. However, further analysis shows PGS can be effectively used alongside progress monitoring measures to screen for learning disability risk. Altogether, PGS is not useful in personalizing education for every child but has potential utility when used simultaneously with additional screening tools to help determine which children may struggle academically. Though controversial for reasons related to demographic disparities, fear, and genetic essentialism3, research and use of genetic screening has become more established in the medical field, leading to what is known as “precision medicine”. Precision medicine aims to tailor prevention and treatment based on an individual’s genes, as well as environments, and lifestyles4.This framework of approaching the treatment and prevention of biomedical disease using genetic information has been used with some success5 for both monogenic disorders, as well as disorders with more complex polygenic patterns such as psychiatric disorders6. Assessing the genetics underlying these more complex traits is typically done using polygenic scores (PGS)7. These PGS are developed from genome-wide association studies (GWAS) that screen an individual’s DNA for specific variations in their genome, and the relations said variations have with the specific trait. Said variations are then assigned a corresponding weight and summed to produce a PGS for a given trait. Under the precision medicine framework, PGS have been used for estimating the risk of acquiring certain conditions or diseases11. More recently, similar PGS methods have been used for predicting social outcomes12, such as educational attainment13, personality14, and socioeconomic status15. This extension of genetics into social sciences, including into educational science, has garnered excitement and controversy, with some saying that we can mirror the precision medicine movement in education to “personalize education” by personalizing instruction and prevention based in part on genes17. Though there are many definitions for what personalized education means , here we define personalized education as differentiated and targeted instruction at the individual level. Educationally relevant outcomes have many parallels to biomedical outcomes, including individual differences characterized by quantitative patterns of genetic, environmental, and psychological risk and protective factors, making the extension of precision medicine into education seemingly a useful next step. There are also important differences, including biomedical research having a better grasp of mechanisms of disease, more clear definitions of disease, and fewer concerns of determinism and genetic essentialism—the idea that genes are deterministic. Importantly, as part of the personalized education framework, educators would use various tools and screeners, which could include genetic information via a PGS, to better understand individual students, and provide a personalized education experience that targets each student’s needs. Said approach would have to keep in mind that PGS results are only one  aspect that impacts development and are not deterministic, but rather that the information gained can be used alongside other information to better predict general trends in educational achievement for individual students. With this in mind, the idea of personalized education is thought-provoking and comes with its own share of criticisms, including considerable ethical concerns regarding genetic essentialism and inequitable effectiveness, as well as a lack of portability of results across races and ancestry groups making current genetic technology limited in utility outside of European ancestry groups18. These criticisms withstanding, the practical usefulness of using genetic information as part of a personalized education framework has not actually been tested. It is not clear if genetic technological capabilities support the idea that using PGS would add incremental value to the universal screening system for educational risk already in place in many schools, or do so to the extent the outweighs ethical concerns. In this paper we quantify the incremental value of using PGS in tandem with behavioral screening measures for academic achievement and risk, moving beyond the conceptual idea of a genetically informed personalized education, to explore two research questions. First, what incremental value could a PGS have in predicting student academic achievement? That is, determining if PGS could be used as part of a battery of tools to differentiate students based on their DNA. Specifically, seeing if schools would be able to use PGS to increase their ability to predict the end of the year achievement beyond currently used progress monitoring tools, and therefore, help them better differentiate instruction to target student needs. Second, could PGS be used as part of a screener to increase accuracy in identifying students at risk of developing a learning disability? In other words, testing if PGS could be used as part of a screener to better identify children who might struggle. We purposely do not limit our analysis to a specific kind of academic achievement or learning disability , or to general achievement , but instead keep our modeling flexible enough to represent the range of possible uses for PGS in schools. This allows us to more broadly consider how PGS might be used as part of personalized education across multiple academic content areas. Further, it should be noted that the results to these questions are not deterministic, but rather aim to identify PGS’s ability to predict more potential trajectories in educational achievement rather than predicting a predetermined or fixed level of achievement.Websites such as 23andMe.com and Ancestry.com have gained increased popularity in recent years as individuals are getting more interested in what their DNA says about their ancestry and about some of their traits. Although these commercial ancestry genetic tests are what many people are most familiar with, the uses for similar genetic screening are rapidly expanding19. The primary method used for screening is evaluating student performance once to several times a year using progress monitoring tools . This includes the more novel progress monitoring tools such as computer adaptive testing that have recently become more popular20. Screening using progress monitoring tools in this way is used to determine if at-risk children need supplemental intervention and to predict how a child might perform on end-of-the-year standardized achievement tests. As such, the purpose of screening and progress monitoring tools is to provide teachers the necessary information to guide targeted instruction to students. Specifically, when considering students who are at risk for difficulties, it is important to accurately identify them at the beginning of the school year so that adequate differentiated instruction can be provided. Unfortunately, many of the commonly used progress monitoring tools have moderate predictive validity leading to low classification accuracy, often resulting in 25–60% misclassified students22. Current estimates show that these progress monitoring tools correlate with end of the year scores at a range of about 0.40–0.70, and as such, we used values representing this range  as estimates of effectiveness for our analyses. This low classification accuracy means either some students will receive intervention unnecessarily, depleting school resources, or some students will not receive the supplemental intervention they need and fall further behind. Classification accuracy can be increased by combining multiple progress monitoring tools, or follow-up intensive assessments24, methods that potentially expend school resources. As such, the incremental value of PGS will be examined as an additional tool for universal screening systems that could be used to increase prediction accuracy. To address the research questions presented above, it is important to assess PGS’s potential incremental value alongside progress monitoring tools. Only then, will we be able to see what new value it adds, and determine where it could fit as a tool for educators in authentic school settings.Accurately identifying current student performance and using that to predict their expected later outcomes is essential to meet the instructional needs of individual students. In attempts to accurately predict student achievement, and risk for developing learning disabilities, many schools have implemented various systems to universally screen students. At the core of comprehensive universal screening, systems lie progress monitoring or frequent evaluation of students to determine which students need supplemental intervention27. As molecular genetics methods get better and reference sample sizes get bigger, the predictive power of PGS to educationally relevant outcomes are expected to substantially increase, with some research estimating predictive limits as high as about 50% in student outcomes28. However, adding to the complexity of testing the incremental value of using PGS as part of personalized education, the current and hypothesized future predictive values of PGS are currently debated. It has been argued that the potential upper limit of PGS predictive effectiveness is much lower than 50% as twin estimated heritability is thought to be overinflated due to violations regarding the assumptions of equal environments29. In addition, differences in the predictive effectiveness of PGS are dictated by the characteristics of the individuals used to create the reference datasets to calculate the PGS30, and the populations they are being used to predict. For example, to date, around 79% of the reference samples used to create a PGS are drawn from European ancestry populations31. When using a PGS drawn from these majority European ancestry populations to predict outcomes in individuals who do not come from those same ancestry groups, like ~45% of the student population in the US32, the accuracy of prediction is greatly diminished31. This is due to genetic variants being more common among some ancestry groups than others, and as such the significant variants found in one population not translating as well for outside populations. Adding to the concerns of less predictive power from PGS for individuals from non-European ancestries, children from racial minority backgrounds in the US are already less likely to be identified by current screening systems for special education services 33. This would mean that adding PGS, as the current technology stands, into personalized education would likely further exacerbate educational disparities. Due to the vast majority of literature being conducted using European ancestry groups, the results of this paper should be interpreted with students of European ancestry in mind. For students of differing ancestry groups the results of this study are likely to be far less due to the lower effectiveness of PGS across ancestry groups33, and again points to the need for more research on populations of non-European ancestry34. In addition, PGS are probabilistic, not deterministic, and there is reason to think that the environments students are in would influence the role their genetic variants have on their educational outcomes, although the work in this area is just starting35. One such theory on how this could work is an individual’s genetic variants can make that individual more or less susceptible to the environments around them, with genetic influences being negatively expressed in detrimental environments and beneficial expressed in supportive environments36. The predictive effectiveness of PGS can also vary based on student age and academic measure of interest. For those studies focused on older students and measuring composite or more general measures of achievement, the current estimates for PGS effectiveness are as high as 15% of the variance in these academic measures explained27. However, studies that have focused on younger students and more specific measures of academic achievement  had considerably lower estimates of effectiveness ranging around 2–5% of variance explained25.Recent studies on PGS in educational settings have shown that currently, PGS can predict a composite academic achievement measure, educational attainment, and reading comprehension abilities with up to 10%, 15%, and 5% accuracy, respectively38. To reiterate our two research questions from above, first, what incremental value could PGS have in predicting student achievement? And second, could PGS be used as a tool to increase accuracy in identifying students at risk of developing a learning disability? The methods taken to answer the two research questions are as follows. For the first research question, a combination of data-simulation and regression techniques were used, simulating data and determining the increased level of variance in end-of-the-year achievement scores  predicted when adding in PGS alongside existing progress monitoring tools. The second question was explored using probability density functions to determine how much overlap there would be between progress monitoring scores, PGS, and end-of-the-year achievement scores at various common cutoffs for learning disability classification . For both questions, data were simulated and density functions computed using 12 combinations of progress monitoring scores’ correlations to end of year scores , and PGS’s ability to predict variance at the end of year scores . These values for PGS effectiveness were chosen from a combination of educational achievement measures representing a realistic range of different student achievement values, including the 2% value representing early grade reading comprehension25, 10% and 20% values representing a range of current estimates of general educational achievement and intelligence and an estimated limit of where PGS for more specific measures will get in the near future27 and 50% representing an estimated theoretical threshold from twin studies25. As mentioned prior, however, each of these values is up for debate, and we do not wish to speak to only one narrow definition of student achievement, and as such results for PGS effectiveness in a wider range  were tested as a supplement for one of the models related to each research questions. If PGS were to ever predict more than 80% of achievement, they would no doubt be useful if ethical concerns were adequately addressed. Further, for correlational values of progress monitoring to student outcomes, predictive values of PGS to student outcome, and potential cutoffs to characterize learning disabilities, not represented in this paper, a ShinyApp (https://jshero.shinyapps.io/two_predictor_screener/) was developed allowing any combination of correlations among the variables to be tested as a screener for any cutoff in the same ways presented in this paper.As it stands now, previous research has examined the extent to which PGS can predict variance beyond that which is predicted by environmental factors such as socioeconomic status, but schools and districts rarely use this type of information in attempts to predict individual student performance. Rather, teachers and schools use a variety of progress monitoring tools as part of a universal screening system to make these predictions, but to this point, research has not examined what value PGS could add as an additional tool in the universal screening system. This paper aims to conduct said examination and provide insight into what PGS might look like alongside the progress monitoring tools regularly being used in schoolsTable r = 0.70) 34.5% of students who were identified as being at-risk were later identified as learning disabled, and 96.1% of students who were considered not at-risk did not develop a learning disability. These values serve as a baseline for comparison when analyzing the incremental value of using PGS. Results of analysis for additional values at the 20th percentile cutoff are presented in Supplementary Tables As stated prior, four separate cutoffs for learning disability were examined in this section of the paper . Since a diagnosis of a learning disability is not something that can be scientifically tested, finding students that fall in the extreme low ends of the distribution is one typical way to classify learning disabilities and was the definition of learning disabilities used here. Results from one cutoff (tenth percentile), are presented in Tables Table https://jshero.shinyapps.io/two_predictor_screener/) was developed allowing any combination of correlations among the variables to be tested as a screener for any cutoff in the same ways presented here.Table r = 0.55), but with varying correlations between progress monitoring and PGS. This showed that a stronger correlation between progress monitoring and PGS leads to lower PPV and higher NPV when meeting both cutoffs. This is because, at higher correlations, these two variables are capturing more of the same variance in end-of-the-year achievement scores than when the correlation between the two is lower. When meeting either cutoff, changes in the correlation have far less impact and lead to slightly lower positive and NPVs as the correlation gets higher. Regardless of the changes seen here, however, the same trends still emerge with PPV increasing and NPV decreasing when making students meet both screener cutoffs, and vice versa when allowing students to meet either screener cutoff to qualify as at risk for a learning disability.One potential issue is the assumption that PGS correlates the same with progress monitoring as it does with end-of-the-year achievement. To show how contradictions to this assumption would impact the overall results, Fig. might add some value. However, when PGS only predicts about 2% in variance in student outcomes , PGS adds very little in terms of predicting general variance at the end of year achievement beyond progress monitoring. This aligns with what was found by Morris and colleagues39, who found limited utility using PGS to predict achievement for individual students. However, as PGS reaches the 10% and 20% values that represent the current predictive power for broader measures of academic achievement and intelligence for older populations, and current PGS technology’s limit for predicting intelligence, the value PGS adds to predicting the end of year achievement becomes larger. At just 10% predictive power, PGS predicts an additional 1–4% variance in end-of-year achievement. Higher levels of PGS predictive power showed increases in this variance predicted across the board, and if PGS is ever able to reach the theoretical threshold of 50% tested in this paper, it could predict as much as an additional 36% at the end of the year achievement. How much given schools value these increases is entirely dependent on the values and views of those schools, but regardless there does appear to be some incremental value, when defined as variance predicted, to the future use of PGS in a school setting. The extent to which this is true additionally hinges on the type and goal of the PGS used, with more specific measures showing less potential benefit than general measures of achievement.These results have powerful implications for personalized education, questioning if there is any incremental value for genetics in school settings. From the first research question, when considering predicting the overall end-of-year academic achievement, the addition of PGS 41. On the other hand, if schools were more interested in saving resources and ensuring those students identified as at-risk and receiving additional services were actually students who would develop a learning disability, then labeling only students identified by both measures as at-risk would also serve to just this.The results of the second research question showed there is some value in the addition of PGS as a screener of learning disabilities alongside progress monitoring. Results showed that adding PGS as a screener could be strategically used to increase either positive or NPV. The choice for how to use these scores would depend on the values and views of the school or locality. If a school’s primary aim was to decrease the number of students missed by current progress monitoring screeners, then allowing students to be identified as at-risk by either progress monitoring or PGS would do just that. This may be especially beneficial in earlier grades when early identification is key to ensuring optimal intervention efficacy for student growth39, indicate the best path to using PGS in school settings may not be through personalized education as we defined it, as allowing for differentiated instruction at the individual level. Personalized education requires us to be able to predict with a large degree of accuracy where a student will fall at the end-of-year achievement distribution, something the first research question showed PGS is not capable of doing at this point. Further, this suggests that with the limited understanding we have of how genetics impact learning and educational achievement, there is simply not enough evidence out there to support the idea of personalized education using genetics as many have suggested. However, using PGS in different ways, such as part of a screener for learning disabilities, would allow PGS to be used in a beneficial way without requiring the same high degree of predictive accuracy. Again, this assumes the definition of personalizing education as being at an individual student level. Personalizing education at a group level does require less precision and using PGS in this fashion could have some utility. In this case, rather than aiming to differentiate to each individual’s specific needs, predicting more broadly which quartile or quantile of achievement a student will fall in could help to differentiate based on group needs rather than individual needs, and is able to be done without requiring as much precision as individual differentiation. This is because grouping can occur with a wider margin of error, and approximations of where a student will fall are acceptable in contrast to differentiation at the individual level which requires more precision. However, the low levels of variance predicted by PGS and the fact that PGS on its own cannot be used to diagnose learning disabilities make it unlikely that on their own they will ever be able to do this, but in conjunction with other screeners as PGS get stronger they may become useful towards personalizing education at this broader group level.These results, combined with those from Morris42. Although some progress monitoring tools report immediate results, this is not the case for all. This could lead to additional time for grading assessments by teachers, further increasing the time cost associated with these tools. This is in contrast to using PGS which would take only the amount of time to gather a sample of DNA and wait for results to come back. Although no comprehensive reports have been conducted on how much schools typically spend towards progress monitoring tools, some research has reported the costs of many popular assessments. Estimates show costs of progress monitoring tools to widely ranging from $6.00 per student per year to up to $55.00 per student per year43. Some progress monitoring tools offer other options, such as per teacher rates that can be as low as $30 per teacher per year, or per school rates42. These fees only include the use of the actual product, however. There are often several additional associated costs such as annual licensing fees as high as $500 or initial technology fees as high as $1700, data housing fees over $500 per school or an additional $4 per student, and training and upkeep fees reaching as high as $6600 per 30 people42. The cost for PGS is also somewhat unclear. Though commercial offerings for genetic sequencing are now as low as $100, the actual cost of genetic screening that would be of interest to educators is less known. Several options are available depending on the number of SNP’s, and the specific SNP’s every screener is focusing on, with widely ranging costs. There is also the question of how to analyze and house this data, which would incur additional costs to schools. One known benefit however for the cost of genetically screening students is that results could be reused for years to come, and in turn, the overall cost dispersed throughout a student’s entire school career. Before implementation of any PGS in school settings, comprehensive cost-benefit analyses should be conducted analyzing everything from current costs of progress monitoring tools to the potential overhead costs of implementing PGS and the benefit they would bring. After doing so, it may well be the case that schools may not value the additional incremental value of PGS as much as it costs to implement.Beyond understanding variance predicted and positive and negative predictive power, the question of time and monetary cost to school when adopting PGS is also important. The use of another progress monitoring tool, such as a different behavioral assessment, could lead to similar results as using a PGS. However, using additional progress monitoring tests could take a considerable amount of instructional time. Many progress monitoring tests take as long as 30 min to complete excluding the indirect time cost associated with organizing the classroom and situating students to complete the assessmentAnother important factor to consider is the amount of shared variance predicted by PGS and progress monitoring  and East Asian ancestry (19%), with very few studies have used cohorts of individuals with African, Hispanic, or Indigenous ancestries (3.8%)44. Another ethical concern is in the way that using PGS is framed. PGS in its current state, just as with all existing screening systems for academic risk, points towards risk factors and probabilities, rather than an absolute diagnosis of any status or condition. If schools were to utilize PGS, they must accurately express to teachers and parents that the results are not absolute or diagnostic of learning disabilities. Interpreting the results as such could lead to potentially erroneous outcomes, with teachers and parents potentially viewing a student’s ability as pre-determined by their PGS. Research in the field of genomics has found that individuals tend to overemphasize the importance of genetics, leading to a deterministic and often fearful view of the results3. To combat this, genetic counseling has become common alongside genetic testing in the medical field45 and has been found to be effective in reducing anxiety and distress related to genetic test results as well as increasing accurate understanding of what genetic risk means47. For school settings, a similar approach to genetic counseling should be considered, as well as proper educational outreach and professional development with not only the members of the school but with all educational stakeholders connected to the school, including parents and the larger community. Further, issues related to genetic essentialism and biodeterminism can be tackled from an early age through instruction aimed at increasing genetic literacy49. This is likely only true however if said instruction is intelligently designed with a humane approach combatting these views in mind. If not, genetic instruction with only simple explanations of sex or population differences can actually increase belief in these ideas51.There are also grave ethical concerns tied with using genetic information in school settings mentioned prior. There is potential that genetic information will be used against children from marginalized groups. Particularly, the idea of scientific racism presents a large ethical concern and will require necessary conversations before any discussion of the implementation of PGS in schoolsOverall, it does appear as though PGS could have practical value in education settings, but that this value depends largely on several other factors, and must be weighed against ethical concerns. We found that though genetic screening may not be useful for tailoring education for individual students, it has the potential to provide utility as a time-sensitive and inexpensive additional screening tool.52 found that curriculum-based measurement (CBM) showed no signs of violating multivariate normality, even when varying assessment difficulty and grade level. Further, studies examining progress monitoring at the extremes of student achievement found progress monitoring to be equally as effective for gifted53, and low achieving students54. For PGS, it is assumed that a large number of genetic variants contribute to complex traits and that at the population level the distribution of these genetic variants and overall PGS are normally distributed56. The assumptions were tested by Krapohl et al.57, finding these assumptions to be accurate. The extent to which one accurately predicts another is the direct result of the correlation between the variables. When the correlation is strong, there is a more limited range of values that the second variable can take on given the individual’s value for the first variable, and in turn, the prediction is more accurate. When the correlation weakens, however, the range of possible values for the second variable greatly increases, and the prediction accuracy decreases. Thus, assuming multivariate normality, knowing the correlations among multiple variables can be used to directly estimate the overall and unique predictive powers of the variables on one another. These assumptions were carried into this paper and applied using estimates of the correlations between the variables of interest in attempts to answer the research questions.We sought to explore what benefit PGS could offer in a school setting by predicting student ability, above and beyond current progress monitoring efforts. To evaluate this potential impact, we took two approaches. For both approaches, we used assumptions of the multivariate normal distributions, as well as existing estimates of PGS and progress monitoring effectiveness. Assumptions of the multivariate normal distribution state that when multiple variables are normally distributed with one another, with no signs of heteroskedasticity, skewness, or other violations of normality, that knowledge of where an individual lies one variable is indicative of where that individual lies on the others. Research on several progress monitoring tests has found progress monitoring scores to be normally distributed with end-of-the-year achievement outcomes. A study by Hintze and ChristThe first research question aimed to understand what value PGS could add to predicting scores for an entire distribution of students. For this, an approach was taken that examined PGS’s effectiveness to predict the overall end of the year academic achievement and address the question of PGS could be used to increase educators’ ability to predict general education scores for all students. The second research question focused on if PGS could be used effectively as a screener tool for learning disabilities. For this, an approach was taken that used statistical software to mathematically compute cumulative distribution functions using various cut-points for learning disability classification. That is, mathematically determined how many individuals would get accurately screened into or out of learning disability services based on the effectiveness of PGS and the relations between PGS, progress monitoring scores, and end of the year achievement scores.Being that this study involved no human participants or their data of any sort and rather used simulated data, no formal ethics approval was required by a university or institutional review board for the completion of this study.25. This 2% value was used to estimate the lowest range of effectiveness for PGS in this study, in particular allowing us to generalize to early subject-specific measures and for at-risk and minority populations for whom PGS are less effective. For general intelligence and educational attainment in later grades, these estimates are higher, with current ability to predict at around 10–15%, with new SNP chips and technologies being estimated to increase this up to 20% of the variation in intelligence in just the near future27. Here, 10% and 20% values represent the range of PGS’s ability to predict the more general outcomes , as well as provide a realistic ceiling for PGS effectiveness with current technology. As such, these estimates were also used in the subsequent analysis. Finally, behavioral genetics research has pointed to an overall heritability of around 50% for educational outcomes13. Although technologies are not yet able to predict educational outcomes at this level58 this represents a theoretical upper limit of PGS effectiveness13 and was the final value tested.The ability of PGS to predict educational outcomes has been studied and reported in recent literature. Current estimates, drawn from very large cumulative samples, of PGS’s ability to predict reading comprehension in early grades, range between 2% and 5%Square roots were taken of these 2%, 10%, 20%, and 50% values of variance predicted, to compute correlations between PGS and end of the year achievement. Since progress monitoring is also a measure of academic ability, the same correlations were used between PGS and progress monitoring as is used for PGS and end-of-year achievement. That is, for instances in which PGS predicts the end of the year reading achievement with 2% accuracy, it was also estimated that PGS would predict the reading achievement progress monitoring tests attempting to measure the same construct with a 2% accuracy as well. It could be the case that this is not necessarily the case, however, no data or studies are available that make the argument otherwise. Further values were tested for the level of PGS effectiveness including every 10 percentage points increase up to 80% to account for the variability and debatable nature of the actual maximum threshold of heritability. If PGS were to ever predict more than 80% of achievement, they would no doubt be useful if ethical concerns were adequately addressed.59. For the values of progress monitoring effectiveness for both approaches, this paper used the median value of this range  and upper and lower values 0.15 from this value  to capture the bulk of the estimates for much popular progress monitoring assessments. Although this range was set by the literature focused on reading achievement, these values served as a good rule of thumb to anchor the values needed for progress monitoring predictive effectiveness. Using this range allowed us to examine what differences may arise in PGS’s incremental value when progress monitoring effectiveness is high or low.The accuracy of commonly used screeners for student achievement varies widely on their ability to predict the standardized end-of-the-year achievement assessments across early elementary grades, with ranges between 0.33 and 0.76 when examining reading achievement60 that was multivariate normally distributed with the different combinations of existing relations across variables based on prior research. That is, every combination of PGS effectiveness  and progress monitoring effectiveness  was computed, for a total of 12 combinations of PGS and progress monitoring effectiveness. Further values were tested for the level of PGS effectiveness including every 10 percentage points increase up to 80% to account for the variability and debatable nature of the actual maximum threshold of heritability. Using this data a stepwise regression approach was used, in that first end-of-year academic achievement was regressed on both progress monitoring scores and PGS, and then again regressed only on progress monitoring scores. By doing so, the overall amount of variance predicted in end-of-year achievement by progress monitoring scores and PGS, dependent on the given effectiveness of each, was estimated. In addition, by comparing regression models, the unique variance predicted by each was determined. This comparison was important for understanding the value in predicting student scores that PGS could add beyond what current progress monitoring tools and strategies are already doing.To answer the question of what incremental value PGS could have in generally predicting student academic achievement overall, a combination of data simulation and regression procedures was used. For this, data were simulated using the R “MASS” statistical package61. The cumulative distribution functions of complex multivariate normal distributions with many dimensions can only be exactly computed by hand, however extensive research has been conducted on the most accurate way to estimate these distributions66, and has resulted in several statistical packages effective at computing these estimates within 10–6 of the exact value when less than 5 dimensions are present. We used the method developed by Miwa and colleagues65 within the pmvnorm function in the R “mvtnorm” statistical package67 to evaluate the multivariate cumulative distributions of PGS, progress monitoring, and end-of-the-year achievement score. This method used the same estimates for PGS and progress monitoring effectiveness for correlations among the variables, but rather than simulating data, mathematically calculated the predictive values of PGS and progress monitoring for identifying students with learning disabilities. Different cutoffs  for classification of a learning disability were tested to examine if PGS incremental value differs depending on how learning disabilities are defined. Since this is testing effectiveness as a screener, the cutoff for students being labeled at-risk by either PGS or progress monitoring was set at 10 percentile points higher than the cutoff for having said disability . Having a cut-off higher than the diagnostic criteria allows the false-negative rate to be reduced68, which is useful when the costs for false negatives are greater than the costs for false positives such as school settings69. This is not necessarily the way that all screeners work, and it could well be the case that for some schools at-risk cut-offs are set at the same cut-off as a diagnosis for a learning disability. To account for the difference in screener cut-offs and usage, a shinyapp was developed (https://jshero.shinyapps.io/two_predictor_screener/) allowing for any combination of screener and diagnosis cut-offs to be tested for any combination of screening tools with knowable or able to be estimated correlations between said screeners and the outcome of interest. Further, to account for the variability and debatable nature of the actual maximum threshold of heritability additional values were tested for the level of PGS effectiveness including every 10 percentage points increase up to 80% effectiveness for one model (20th percentile cutoff).The second approach taken was to examine the incremental value of PGS as a screener for learning disabilities. The multivariate normal distribution can be used to determine the prevalence and accuracy of predicting learning disabilities when the correlations among all variables are known, and the variables are an outcome variable by which learning disabilities can be measured (academic achievement) and independent variables with meaningful relations to said outcome variable (screener assessments or PGS)For every combination of PGS and progress monitoring, the cumulative distribution provided 8 resulting values.Result 1—a student’s end-of-the-year achievement score classifies them as having a learning disability, and both PGS and progress monitoring accurately predicted this. This is a shared true positive.Result 2—a student’s end-of-the-year achievement score classifies them as having a learning disability, but only PGS accurately predicted this. This is a unique true positive for PGS and a false negative for progress monitoring.Result 3—a student’s end-of-the-year achievement score classifies them as having a learning disability, but only progress monitoring accurately predicted this. This is a unique true positive for progress monitoring and a false negative for PGS.Result 4—a student’s end-of-the-year achievement score classifies them as having a learning disability, but neither PGS nor progress monitoring accurately predicted this. This is a shared false negative.Result 5—a student’s end-of-the-year achievement score classifies them as not having a learning disability, and both PGS and progress monitoring accurately predicted this. This is a shared true negative.Result 6—a student’s end-of-the-year achievement score classifies them as not having a learning disability, but only PGS accurately predicted this. This is a unique true negative for PGS and a false positive for progress monitoring.Result 7—a student’s end-of-the-year achievement score classifies them as not having a learning disability, but only progress monitoring accurately predicted this. This is a unique true negative for progress monitoring and a false positive for PGS.Result 8—a student’s end-of-the-year achievement score classifies them as not having a learning disability, but neither PGS nor progress monitoring accurately predicted this. This is a shared false positive.These resulting values were then computed into PPVs and NPVs. That is, PPV representing the percentage of students identified as at-risk that ended up classified as having a learning disability, and NPV representing the percentage of students identified as not at-risk that did not end up classified as having a learning disability. PPV and NPV were computed separately for the three different following scenarios: (1) students meeting the progress monitoring cutoff were classified as at-risk, (2) students meeting the cutoff for either PGS or progress monitoring were classified as at-risk, and (3) students meeting the cutoff for both PGS and progress monitoring were classified as at-risk. These distinctions were made to provide a baseline of current predictive values (scenario 1), and to reflect that some schools may value a broader screener (scenario 2), whereas others want to make classification more strict (scenario 3).57 we felt justified in this assumption. Second, both sets of models assume that progress monitoring tools and PGS would work additively to predict the end of the year achievement scores. That is, progress monitoring and PGS will both individually predict the end of the year student achievement, with a certain proportion of their individual levels of prediction being unique and the other proportion being shared. The unique variance predicted by each was a focal point in the results for the first research question but was not heavily discussed for the second. The reason for this is that it is unlikely that PGS be used on its own to screen students. As such, understanding what PGS adds to current screening efforts could be discussed without needing a discussion of unique variance predicted. The unique variance predicted by PGS related to the prediction of learning disabilities was calculated and is presented in the supplemental tables.Across all analyses and for each of the two sets of analyses corresponding to each research question, model assumptions were made. Across both sets of analyses, assumptions of multivariate normality were made. That is, if we collected data on the constructs modeled in this paper, they would have a univariate distribution showing no signs of skewness or kurtosis, and a multivariate distribution showing no signs of heteroskedasticity. Based on the research included in the prior sectionsAnother key assumption of the modeling is that PGS is equally correlated with end-of-the-year student achievement as it is with progress monitoring. There is potential that these values are different, however, there simply is no research to support this or suggest what values would be appropriate to use otherwise. To address this, a sensitivity analysis was conducted for one combination of PGS and progress monitoring values to see how results would change if this assumption were to be violated. For this sensitivity analysis we chose the 20th percentile as the cutoff due to this cutoff having the largest range of PPV and NPV’s in the original analyses, and in turn, had the largest potential impact when violating this assumption. The results from the sensitivity analyses suggested that changes were negligible, with PPV only being impacted for the “either cutoff” result, and NPV only being impacted for the ‘both cutoffs’ result. When PGS was less correlated with progress monitoring as compared to end-of-year achievement, this impacted the PPV when meeting either cutoff by <5%, and impacted NPV when meeting both cutoffs by <3% when PGS was almost entirely uncorrelated with progress monitoring scores. This represents the best-case scenario for proponents of using PGS in school settings, as less variance predicted at the end of year achievement by PGS and progress monitoring will be shared. When PGS were more correlated with progress monitoring scores than end-of-the-year achievement, results again were only significantly shifted for the PPV “either cutoffs” and NPV “both cutoffs” results. Here, if PGS and progress monitoring were correlated at 0.9, this led to changes of ~13% and ~7% for PPV and NPV respectively. This is the worst-case scenario for advocates of using PGS in classrooms, with a higher correlation between progress monitoring and end-of-year achievement meaning the PGS would be less effective.71, and before any of these dynamic influences, is a benefit of using PGS.A final assumption was that the correlation between PGS and end-of-year achievement would hold constant after the initial screening and intervention took place. It is certainly possible that once students were identified as being genetically at-risk for a learning disability and interventions were imposed, that the relation between PGS and end-of-the-year achievement would deteriorate. This is because the PGS value would remain constant, but scores, in theory, would increase due to the intervention. The analyses presented within this paper do not address this potential dynamic situation. This idea however was one driving factor for focusing on both PGS as an additional screener for learning disabilities, and early intervention and use of PGS. Even if PGS became less correlated with end-of-the-year achievement for those students who had received the intervention, PGS would have indeed proven to be useful when used as an early screener for learning disability risk, as children would have grown in achievement scores, the outcome of interest. Further, using PGS is a tool part of personalized education early in education, done at the most beneficial time for interventionFurther information on research design is available in the Supplementary InformationReporting Summary


Solanum spp.) are not only crucial for food security and sustenance in Andean communities but are also deeply rooted in the local culture. The crop originated in the Andes, and while a great diversity of potato persists, some landraces have been lost. Local communities and the genebank of the International Potato Center (CIP) partnered to re-establish some of these landraces in situ by supplying clean seed potatoes to farmers. Over time, the genebank formalized a repatriation program of potato landraces. Repatriation is the process of returning native germplasm back to its place of origin, allowing a dynamic exchange between ex situ and in situ conditions. So far, no comprehensive description of CIP’s repatriation program, the changes it induced, nor its benefits, has been carried out.Potato landraces  abandoned the repatriated material by the fourth year after receiving it, the in situ survival probability of the remaining material stabilized between 36% in year 5 and 18% in year 15. Households where the plot manager was over 60 years old were more likely to grow the repatriated landraces for longer periods of times. While male plot management decreased survival times compared to female plot management, higher levels of education, labor force, wealth, food insecurity, and geographic location in the southern part of Peru were associated with greater survival times. Most farmers reported nutritional and cultural benefits as reasons for maintaining landrace material. Repatriated potatoes enabled farmers to conserve potato diversity, and hence, re-establish and broaden culinary diversity and traditions.Our study is the first to apply an economic model to analyze the duration of in situ landrace cultivation by custodian farmers. We provide an evidence base that describes the vast scope of the program and its benefits. Solanum spp.) are strongly embedded in the local culture and therefore constitute the backbone of food security and livelihoods. The crop originated in the Andes, where a great diversity developed by natural and human selection still exists today . In 1997, the first repatriation effort by CIP started when a group of farmers participating in a local potato fair contacted the genebank to help them quantify the level of available potato diversity in their communities . Within this context, the idea of redistributing healthy landrace germplasm back to the places of origin for in situ conservation evolved dynamically through dialogues between CIP staff and Andean communities , and in exchange novel landraces identified from these communities are deposited into the genebank for long-term conservation and safe keeping (exchange from in situ to ex situ). In the latter case, the landraces fill genetic gaps in the potato germplasm collection at CIP and add value to the germplasm collection. Such a dynamic model of ex situ and in situ conservation has a long intellectual legacy developed by Bellon et al. , BerthauThe processes and stakeholders of CIP’s repatriation program are mapped out in Fig. The repatriation process begins with a formal written request from a community authority or head to CIP. This request is then reviewed by those continually involved in the repatriation program who check which accessions are suitable and available for repatriation to a specific location. Selection is made based on two criteria: availability (1) and suitability (2) of the material. The first criterion is the most important as this is the bottleneck regarding repatriation. Availability is determined by the multiplication planning of the genebank and production constraints in San José de Aymara. This means that the repatriation team decides on a best fit between all available material and its suitability for the communities requesting repatriation. When the accession or landrace tuber’s origin is from the region of a requesting community a good suitability is implied. Hence, endemic potatoes are frequently redistributed back to their places of origin which means that most landraces are strongly associated with the cultural heritage of the receiving communities and will grow well in that area. Additionally, other material with a broader geographic distribution range can also be repatriated to a community when the repatriation team deems it suitable according to their experience and the specific request(s) made by the community such as for specific traits, tastes and textures.Nevertheless, it is important to note that the specific landrace selection is not completely demand-driven as availability is the binding criterion. In most cases, communities request a repatriation without stating specific preferences e.g., on traits or growing requirements and they cannot select the specific landraces they will receive since the CIP genebank contains thousands of landraces derived from Peru. When the communities state specific preferences, they are accommodated if suitable material is available. After this selection, usually the applying communities receive samples of 8–10 tubers per landrace . After receiving the tubers, the communities organize the communal tuber multiplication and the corresponding division of labor and plots. When the community has produced enough tubers, the landraces are redistributed to individual farming households for inclusion in their own potato-planting portfolios. The lower large arrow denotes the flow of material between in situ and ex situ conservation. Here it is important to note that the reciprocal exchange is indispensable for the repatriation program to ensure longevity and safe backup of these landraces. Since certain potato varieties have been grown in their place of origin for over 8000 years, the Andean landscapes and farmers, have conserved the landraces over time and also generated new diversity Hawkes .The smaller circle describes the flow of repatriation work that is necessary to prepare (multiply) the material in the genebank for redistribution back to the Andean farmers. The genebank prepares the material by planting in vitro generated plantlets in greenhouses to produce mini tubers. The mini tubers are then used for planting in potato plots to finally produce the tubers for repatriation. The final multiplication occurs jointly with the community of San José de Aymara, located at about 4000 m above sea level (m.a.s.l.) in the province of Huancavelica in Peru. The high altitude of the community ensures less biotic stressors for the regenerated material, and hence, facilitates the growth of healthy material for distribution  accession-level genebank distribution data on all repatriated landraces from 1997 to 2020 and (2) household survey data that were collected in August and September 2018 with the purpose of investigating and better understanding the changes and benefits resulting from the repatriations.The CIP genebank maintains distribution data on the repatriated landraces and receiving communities. This data includes accession number, Global Information System Digital Object Identifier (GLIS-DOI), cultivar name, taxon name, geographic origin, collecting site, genebank accession status, requestor details , and some morphological traits. On the community level, the data contains the names and locations of the receiving communities, their reasons for participating in the repatriation, the year when the repatriation was carried out and which landraces they received. The data set covers the complete time span of the repatriation program starting in 1997 and for the analyses presented here, all data from 1997 to 2020 was used.The household survey was conducted by CIP with the goal to create an evidence base of the changes induced by the repatriation program and to document how the repatriated landraces were used by the farmers. For this survey, 301 households and community leaders who had participated in the repatriation program were interviewed in 65 communities, covering half of all the communities that received repatriations over the history of this program CIP . HousehoWe employed survival or duration models, which originated in the field of medicine for analyzing the survival of patients after specific diseases and treatments  in the presence of censored data. In our case, censored items were farming households where the landraces still survived when the survey was conducted in 2018, but about whom we had no follow-up data that told us if the landraces still survived today. It is important to note that the surveyed farming households participated in different years in the program. The repatriation program started in 1997, and since then, many farmers received repatriated material each year. The sample included households in the sample who received material in 1997, but also those who had participated for only one year when being surveyed. The analysis integrated the censored items in such a manner that it did not distort the duration of the uncensored items.The Kaplan–Meier is a maximum likelihood estimator, denominated This model is univariate (time is the only variable), and it is characterized by the assumption that survival does not increase over time, all participants have the chance to change their status, and all respondents will change their status at some point in time. This function estimated the probability that landrace maintenance will continue based on observed times. The resulting stepwise function showed the probability that the repatriated landraces still survive in a farming household at a certain point in time.As a second step, we conducted a Cox proportional-hazards model to calculate the probability of survival, given certain household or community characteristics and given that this household has maintained the landraces until a certain point in time. The Cox proportional-hazards model was calculated as:The incentives for smallholder farmers to grow landraces are not well understood. Brush et al.  were theTests were carried out to confirm the suitability and robustness of the model. To avoid multicollinearity, the correlations among independent variables were checked and found not to be statistically significant. Model diagnostics were conducted, including a test on the Weibull distribution and a test of the proportional hazard assumption based on scaled Schoenfeld residuals. Additionally, the model results were tested for influential observations.Finally, we analyzed the changes and benefits perceived by farmers based on the feedback from the household survey. This analysis further explained why farmers maintained and abandoned their repatriated material, and hence, provided valuable insights on how the repatriation program produced changes and how possible benefits can be enhanced and upscaled as the repatriation program continues. This information also enabled us to better understand the reasons for landrace abandonment by revealing foregone benefits. For the benefit and change analysis, multiple choice, and open-ended survey responses to relevant questions were analyzed descriptively. Therefore, all answers were categorized and then the number of farmers whose answer belonged to a certain category was counted.Between 1997 and 2020, 14,950 landrace samples,Over the years, the activities of the repatriation program have changed. Table The landrace accessions that have most frequently been repatriated Table  are acceThe genebank repatriation survey data also included the reasons why communities participated in the program. Most communities wanted to participate to restore their potato diversity and productivity . The other justifications focused on potato diversity recovery (16%) and mitigation of climate change and natural disasters (2%). The mention of climate change shows that it is a push and pull factor for increasing potato diversity. Several communities mentioned that they lost some landraces due to unprecedented weather extremes (pull factor), and others noted that the repatriated material is needed for adaptation (push factor).Over the program duration (1997–2018), 57% of all farming households included in the duration model ceased growing the repatriated landraces, 32% continued to plant the repatriated material at the time when the survey was conducted in August and September 2018, and for 11% no status could be retrieved. The longest survival time of local bundles of repatriated landraces was 20 years, the shortest was 1 year.The Kaplan–Meier curve Fig.  shows thThe second part of the duration model is the Cox proportional hazard analysis, which quantified the effect of household and regional characteristics on the survival time of the repatriated materials and revealed the statistically significant determinants of survival. Holding other covariates constant, the likelihood that a household abandons the repatriated material increased when the person in charge of the plots with repatriated material was male. The HR value of 1.71 indicated that a male plot manager increased the likelihood of abandonment 71% in comparison to a female in charge of the plots. This effect is statistically significant, and hence, is an important factor to predict survival. Farmers under 30 years of age were also significantly more likely to abandon the repatriated materials than older adults. On the contrary, the likelihood of abandonment decreased by a factor of 0.76 or 24% when the person managing the plots with repatriated material was over 60 years old. Nevertheless, the effect of these two age variables was not statistically significant. A higher level of education, larger labor force, and location in the southern geographical zone, had a statistically significant effect on survival times, indicating a lower risk of abandonment and hence implying longer survival times of repatriated landraces. The covariates household wealth and food insecurity in the district also had positive, but non-significant, effect on survival times.The overall model was robust and statistically significant, based on tests for multicollinearity, the Weibull distribution, the proportional hazard assumption and influential observations.Most farmers (56%) noted nutritional improvement as the most important benefit induced by the program. These improvements were achieved through increased food consumption, food security, health, and an increased availability of landraces for traditional processing, such as “chuño” (freeze-dried potato). In the survey, 31% of participants reported nutritional and economic benefits, while only 13% reported pure economic benefits—related to improved possibilities to sell their product at local markets.Culinary diversity was increased by the repatriation program. The surveyed farmers reported that they experienced an increase in the number of distinct flavors and textures. Unique flavors and textures were defined by different levels of sweet and bitter and a watery, starchy, or chewy texture. This increase in culinary diversity was also confirmed when female household members and those respondents who were mainly responsible for cooking were interviewed separately. Women reported an average of eight new combinations of flavors and textures brought to their kitchens by the repatriated material. Of the interviewed women, 72% felt healthier from eating landraces, and 81% acknowledged that their family or community benefitted from the program.Of all the surveyed farmers, 77% said that the program helped them to recover lost landraces. They also perceived a union of traditional farming and scientific knowledge, and they enjoyed improved reputations from growing repatriated landraces.The changes were not clear-cut regarding the effects of on-farm productivity. Of all the surveyed farmers, 57% stated that yield increases were induced by the program, but also a quarter of them revealed that these were not realized, and the remainder did not answer this question. Meanwhile, 41% of the respondents noted with the repatriated landraces an overall increase in total potato production, irrespective of the area sown. But 38% of the farmers did not notice a production increase, and again, the remainder did not provide an answer. Further, many respondents (44%) observed that they sold less outputs to the markets, as most repatriated material was used for home consumption, but 31% of them confirmed they had more surplus for the markets. The respondents were also divided about whether the program generated more income from market sales, and about the same number of interviewees responded yes and no.Of the overall participants, 105 farmers (or 65%) stated that the main reason why they stopped cultivating the repatriated material was production loss due to abiotic stresses, especially loss from frost and hail (n = 33), which affected their production areas during extreme cold conditions. Other reasons for abandoning cultivation were biotic stresses, insufficient labor force or knowledge by the farming household, and lack of planting material.Since 1997, CIP’s repatriation program has contributed to re-diversifying Andean potato production systems while creating nutritional, economic, and traditional benefits to the farmers. This re-diversification was enabled by a dynamic model of conservation, involving the CIP genebank and Andean communities. Currently, 135 Peruvian Andean communities received 14,950 landrace samples that comprise 1519 unique landrace cultivars.The survival analysis showed that, on farms, much of the repatriated material did not survive the first 4 years, but later, the survival rate stayed relatively constant for 10 years, ranging between 36% in year 5 and 18% in year 15 Fig.  and MengThe factors that increased the likelihood of long-term maintenance of the repatriated material on farms Table  are in lWe found that older farmers were more likely to maintain repatriated landraces, nevertheless the variables’ effects are non-significant. Older farmers usually have more experience with potato production and stronger traditional ties to the crop and its production and likely place more value on conservation of the potato landraces. These factors are considered to positively influence farming household’s landrace diversity and maintenance , supports food security, as it provides readily available food when the fresh tubers are depleted. Also, the culinary diversity was increased by providing landraces with new tastes and textures. Such an increased diversity is very welcomed by farmers whose diet is dominated by potatoes and therefore it is a “luxury” for them to choose from a wider set of potato tastes, textures, shapes, and colors de Haan , p. 179.Economic and cultural benefits, such as a union of traditional and scientific knowledge, were noted by survey respondents, suggesting that farmers perceived and implemented the dynamic model of conservation in practice. These findings underscored that farmers recognized the private economic value of repatriated landraces. Devaux et al.  showed tThe need to support farmers’ adaptation capacity was underscored by the finding that the main reasons for a loss of the repatriated material are abiotic and biotic stressors. Climatic and environmental conditions have been changing the Andean region . Only ten communities received more than one repatriation, the maximum of repatriations to one community was four. Communities could participate as often as they wanted, however this program is community-driven, and it takes active effort for the communities to organize and request material from CIP. CIP makes it as easy as possible but often initial efforts to organize the repatriations are initiated by NGOs working with the communities and in the absence of the NGOs initial organizational efforts, the communities lack the resources, drive or information for subsequent requests.The household data set was very comprehensive and detailed, yet there were some limitations. A few well-established variables for duration analyses, such as the potato plot or farm size, were not included in the questionnaire, and therefore, could not be integrated in our model. We had a limited number of quantitative and continuous variables per household and used recall data for the duration model, which means that farmers might have incorrectly recalled how long they have been planting repatriated material. Therefore, if such factors are to be included in the future, monitoring is advised and should be done in close collaboration with experts in survey design and socioeconomics. Another limitation was that the specifics of the material a household received was not documented. Based on the survey we know what material was received by the community. As we cannot assume that all participating farmers in one community received planting material for all landraces repatriated to the community, we could not analyze how survival times differed according to specific landraces or their traits. Such information could be valuable for future identification of potentially valuable landraces. Nevertheless, it is again important to note that the risk-minimizing planting strategy, chaqru, was most valuable due its overall trait spectrum of landraces. Further, since we had no information on farmer-to-farmer distribution of the repatriated material, we could not deduce the survivability of repatriated material beyond an individual household to the community scale or beyond the individual community to multiple communities. This last element is crucial for conservation strategies. Such seed exchange is generally common amongst Andean farmers to renew or replace their potato portfolios de Haan .Our analyses have led to the first comprehensive description of the potato repatriation activities conducted by the CIP genebank. Further, we identified household and community factors that foster long-term maintenance of the repatriated landraces and the perceived benefits of repatriation for participating farmers and their communities.Due to climatic and demographic changes, it is vital to revive and maintain the rich agricultural systems in the Andes and allow genetic resources to naturally adapt and evolve. Overall, a third of the interviewed participating farmers still conserved the repatriated material. Significant factors in the duration analysis and reasons cited by farmers for loss of the repatriated material were diverse and ranged from a shortage in labor force and knowledge, abiotic and biotic stressors, to community disorganization. Most participating farmers confirmed that they perceived a multitude of benefits induced by the repatriation, even if they stopped planting the material. The most mentioned benefits were the recovery of lost landraces and nutritional benefits. The program has fulfilled its main objective to return lost landraces by distributing healthy planting material, increasing farmers’ food security as well as the intraspecific potato diversity they manage. These results show that the program goals are very complex and highly interlinked with other factors that influence farmers’ livelihood. One factor is climate change, which is altering growing conditions at an unprecedented speed and intensity, including through abiotic and biotic stressors. Also, environmental degradation, urbanization, and changes in dietary and lifestyle preferences have intensified the changes in farming communities in the Andes.We provide evidence that a continuation or upscaling of CIP’s repatriation activities could generate benefits to food insecure regions and is a way to improve the livelihoods of many farming communities that depend on potato production for sustenance and traditions. Due to the large number of smallholder farmer communities in Peru and other Andean countries, the upscaling potential of the repatriation activities would be large and would require substantial investment. Up to and including the year 2020, 135 different communities have participated in the repatriation program, but there are about 6000 smallholder communities in Peru (Diez-Hurtado