Functional Metabolomics Describes the Yeast Biosynthetic Regulome.

Genome-metabolism interactions enable cell growth. To probe the extent of these interactions and delineate their functional contributions, we quantified the Saccharomyces amino acid metabolome and its response to systematic gene deletion. Over one-third of coding genes, in particular those important for chromatin dynamics, translation, and transport, contribute to biosynthetic metabolism. Specific amino acid signatures characterize genes of similar function. This enabled us to exploit functional metabolomics to connect metabolic regulators to their effectors, as exemplified by TORC1, whose inhibition in exponentially growing cells is shown to match an interruption in endomembrane transport. Providing orthogonal information compared to physical and genetic interaction networks, metabolomic signatures cluster more than half of the so far uncharacterized yeast genes and provide functional annotation for them. A major part of coding genes is therefore participating in gene-metabolism interactions that expose the metabolism regulatory network and enable access to an underexplored space in gene function.

Exposing omitted moderators: Explaining why effect sizes differ in the social sciences.

Policymakers increasingly rely on behavioral science in response to global challenges, such as climate change or global health crises. But applications of behavioral science face an important problem: Interventions often exert substantially different effects across contexts and individuals. We examine this heterogeneity for different paradigms that underlie many behavioral interventions. We study the paradigms in a series of five preregistered studies across one in-person and 10 online panels, with over 11,000 respondents in total. We find substantial heterogeneity across settings and paradigms, apply techniques for modeling the heterogeneity, and introduce a framework that measures typically omitted moderators. The framework's factors (Fluid Intelligence, Attentiveness, Crystallized Intelligence, and Experience) affect the effectiveness of many text-based interventions, producing different observed effect sizes and explaining variations across samples. Moderators are associated with effect sizes through two paths, with the intensity of the manipulation and with the effect of the manipulation directly. Our results motivate observing these moderators and provide a theoretical and empirical framework for understanding and predicting varying effect sizes in the social sciences.

Harvesting of aerial humidity with natural hygroscopic salt excretions.

Plants and animals that thrive in arid regions utilize the diurnal changes in environmental temperature and humidity to optimize their water budget by combining water-harvesting mechanisms and morphophysiological traits. The Athel tamarisk (Tamarix aphylla) is a halophytic desert shrub that survives in arid, hypersaline conditions by excreting concentrated solutions of ions as droplets on its surface that crystallize into salt crystals and fall off the branches. Here, we describe the crystallization on the surface of the plant and explore the effects of external conditions such as diurnal changes in humidity and temperature. The salt mixtures contain at least ten common minerals, with NaCl and CaSO4·2H2O being the major products, SiO2 and CaCO3 main sand contaminants, and Li2SO4, CaSO4, KCl, K2Ca(SO4)2·H2O, CaMg(CO3)2 and AlNaSi3O8 present in smaller amounts. In natural conditions, the hanging or sitting droplets remain firmly attached to the surface, with an average adhesion force of 275 ± 3.5 µN measured for pure water. Rather than using morphological features of the surface, the droplets adhere by chemical interactions, predominantly by hydrogen bonding. Increasing ion concentration slightly increases the contact angle on the hydrophobic cuticle, thereby lowering surface wettability. Small amounts of lithium sulfate and possibly other hygroscopic salts result in strong hygroscopicity and propensity for deliquescence of the salt mixture overnight. Within a broader context, this natural mechanism for humidity harvesting that uses environmentally benign salts as moisture adsorbents could provide a bioinspired approach that complements the currently available water collection or cloud-seeding technologies.

Standardized Data Elements for Patients With Acute Pulmonary Embolism: A Consensus Report From the Pulmonary Embolism Research Collaborative.

Recent advances in therapy and the promulgation of multidisciplinary pulmonary embolism teams show great promise to improve management and outcomes of acute pulmonary embolism (PE). However, the absence of randomized evidence and lack of consensus leads to tremendous variations in treatment and compromises the wide implementation of new innovations. Moreover, the changing landscape of health care, where quality, cost, and accountability are increasingly relevant, dictates that a broad spectrum of outcomes of care must be routinely monitored to fully capture the impact of modern PE treatment. We set out to standardize data collection in patients with PE undergoing evaluation and treatment, and thus establish the foundation for an expanding evidence base that will address gaps in evidence and inform future care for acute PE. To do so, >100 international PE thought leaders convened in Washington, DC, in April 2022 to form the Pulmonary Embolism Research Collaborative. Participants included physician experts, key members of the US Food and Drug Administration, patient representatives, and industry leaders. Recognizing the multidisciplinary nature of PE care, the Pulmonary Embolism Research Collaborative was created with representative experts from stakeholder medical subspecialties, including cardiology, pulmonology, vascular medicine, critical care, hematology, cardiac surgery, emergency medicine, hospital medicine, and pharmacology. A list of critical evidence gaps was composed with a matching comprehensive set of standardized data elements; these data points will provide a foundation for productive research, knowledge enhancement, and advancement of clinical care within the field of acute PE, and contribute to answering urgent unmet needs in PE management. Evidence produced through the Pulmonary Embolism Research Collaborative, as it is applied to data collection, promises to provide crucial knowledge that will ultimately produce a robust evidence base that will lead to standardization and harmonization of PE management and improved outcomes.

The marine environmental microbiome mediates physiological outcomes in host nematodes.

BACKGROUND: Nematodes are the most abundant metazoans in marine sediments, many of which are bacterivores; however, how habitat bacteria affect physiological outcomes in marine nematodes remains largely unknown.  RESULTS: Here, we used a Litoditis marina inbred line to assess how native bacteria modulate host nematode physiology. We characterized seasonal dynamic bacterial compositions in L. marina habitats and examined the impacts of 448 habitat bacteria isolates on L. marina development, then focused on HQbiome with 73 native bacteria, of which we generated 72 whole genomes sequences. Unexpectedly, we found that the effects of marine native bacteria on the development of L. marina and its terrestrial relative Caenorhabditis elegans were significantly positively correlated. Next, we reconstructed bacterial metabolic networks and identified several bacterial metabolic pathways positively correlated with L. marina development (e.g., ubiquinol and heme b biosynthesis), while pyridoxal 5'-phosphate biosynthesis pathway was negatively associated. Through single metabolite supplementation, we verified CoQ10, heme b, acetyl-CoA, and acetaldehyde promoted L. marina development, while vitamin B6 attenuated growth. Notably, we found that only four development correlated metabolic pathways were shared between L. marina and C. elegans. Furthermore, we identified two bacterial metabolic pathways correlated with L. marina lifespan, while a distinct one in C. elegans. Strikingly, we found that glycerol supplementation significantly extended L. marina but not C. elegans longevity. Moreover, we comparatively demonstrated the distinct gut microbiota characteristics and their effects on L. marina and C. elegans physiology. CONCLUSIONS: Given that both bacteria and marine nematodes are dominant taxa in sedimentary ecosystems, the resource presented here will provide novel insights to identify mechanisms underpinning how habitat bacteria affect nematode biology in a more natural context. Our integrative approach will provide a microbe-nematodes framework for microbiome mediated effects on host animal fitness.

Multi-biomimetic Double Interlaced Wetting Janus Surface for Efficient Fog Collection.

Various methods to solve water scarcity have attracted increasing attention. However, most existing water harvesting schemes have a high demand for preparation methods and costs. Here, a multi-biomimetic double interlaced wetting Janus surface (DIWJS) was prepared by laser for effective fog collection. The as-prepared surfaces are composed of superhydrophilic points/hydrophobic substrates on the A-side and superhydrophilic stripes/hydrophobic substrates on the B-side. The interlaced wettability and superhydrophilic points on the A side are conducive to capture and permeation of droplets. The superhydrophilic stripes and interlaced wettability on the B-side are conducive to transportation and shedding of droplets. Therefore, the overall fog collection process is accelerated. The proposal of smart farm model validates broad application prospects of DIWJS. This work provides an advanced and multi-biomimetic surface and provides important insights for green, low-cost, and versatile strategies to solve water scarcity issues.

Triboelectrically Empowered Biomimetic Heterogeneous Wettability Surface for Efficient Fog Collection.

Biomimetic engineering surfaces featuring heterogeneous wettability are vital for atmospheric water harvesting applications. Existing research predominantly focuses on the coordinated regulation of surface wettability through structural and chemical modifications, often overlooking the prevalent triboelectric charge effect at the liquid-solid interface. In this work, we designed a heterogeneous wettability surface by strategic masking and activated its latent triboelectric charge using triboelectric brushes, thereby enhancing the removal and renewal of surface droplets. By examining the dynamic evolution of droplets, the mechanism of triboelectric enhancement in the water collection efficiency is elucidated. Leveraging this inherent triboelectric charge interaction, fog collection capacity can be augmented by 29% by activating the system for 5 s every 60 s. Consequently, the advancement of triboelectric charge-enhanced fog collection technology holds both theoretical and practical significance for overcoming the limitations of traditional surface wettability regulation.

Cascade Enhancement and Efficient Collection of Single Photon Emission under Topological Protection.

High emission rate, high collection efficiency, and immunity to defects are the requirements of implementing on-chip single photon sources. Here, we theoretically demonstrate that both cascade enhancement and high collection efficiency of emitted photons from a single emitter can be achieved simultaneously in a topological photonic crystal containing a resonant dielectric nanodisk. The nanodisk excited by a magnetic emitter can be regarded as a large equivalent magnetic dipole. The near-field overlapping between this equivalent magnetic dipole and edge state enables achieving a cascade enhancement of single-photon emission with a Purcell factor exceeding 4 × 103. These emitted photons are guided into edge states with a collection efficiency of more than 90%, which is also corresponding to quantum yield due to topological antiscattering and the absence of absorption. The proposed mechanism under topological protection has potential applications in on-chip light-matter interactions, quantum light sources, and nanolasers.

Capillary blood self-collection for high-throughput proteomics.

In this study, we sought to compare protein concentrations obtained from a high-throughput proteomics platform (Olink) on samples collected using capillary blood self-collection (with the Tasso+ device) versus standard venipuncture (control). Blood collection was performed on 20 volunteers, including one sample obtained via venipuncture and two via capillary blood using the Tasso+ device. Tasso+ samples were stored at 2°C-8°C for 24-hs (Tasso-24) or 48-h (Tasso-48) prior to processing to simulate shipping times from a study participant's home. Proteomics were analyzed using Olink (384 Inflammatory Panel). Tasso+ blood collection was successful in 37/40 attempts. Of 230 proteins included in our analysis, Pearson correlations (r) and mean coefficient of variation (CV) between Tasso-24 or Tasso-48 versus venipuncture were variable. In the Tasso-24 analysis, 34 proteins (14.8%) had both a correlation r > 0.5 and CV < 0.20. In the Tasso-48 analysis, 68 proteins (29.6%) had a correlation r > 0.5 and CV < 0.20. Combining the Tasso-24 and Tasso-48 analyses, 26 (11.3%) proteins met these thresholds. We concluded that protein concentrations from Tasso+ samples processed 24-48 h after collection demonstrated wide technical variability and variable correlation with a venipuncture gold-standard. Use of home capillary blood self-collection for large-scale proteomics should be limited to select proteins with good agreement with venipuncture.

Multi-environment association study highlights candidate genes for robust agronomic quantitative trait loci in a novel worldwide Capsicum core collection.

Investigating crop diversity through genome-wide association studies (GWAS) on core collections helps in deciphering the genetic determinants of complex quantitative traits. Using the G2P-SOL project world collection of 10 038 wild and cultivated Capsicum accessions from 10 major genebanks, we assembled a core collection of 423 accessions representing the known genetic diversity. Since complex traits are often highly dependent upon environmental variables and genotype-by-environment (G × E) interactions, multi-environment GWAS with a 10 195-marker genotypic matrix were conducted on a highly diverse subset of 350 Capsicum annuum accessions, extensively phenotyped in up to six independent trials from five climatically differing countries. Environment-specific and multi-environment quantitative trait loci (QTLs) were detected for 23 diverse agronomic traits. We identified 97 candidate genes potentially implicated in 53 of the most robust and high-confidence QTLs for fruit flavor, color, size, and shape traits, and for plant productivity, vigor, and earliness traits. Investigating the genetic architecture of agronomic traits in this way will assist the development of genetic markers and pave the way for marker-assisted selection. The G2P-SOL pepper core collection will be available upon request as a unique and universal resource for further exploitation in future gene discovery and marker-assisted breeding efforts by the pepper community.

2024 AHA/ASA Performance and Quality Measures for Spontaneous Intracerebral Hemorrhage: A Report From the American Heart Association/American Stroke Association.

The American Heart Association/American Stroke Association released a revised spontaneous intracerebral hemorrhage guideline in 2022. A working group of stroke experts reviewed this guideline and identified a subset of recommendations that were deemed suitable for creating performance measures. These 15 performance measures encompass a wide spectrum of intracerebral hemorrhage patient care, from prehospital to posthospital settings, highlighting the importance of timely interventions. The measures also include 5 quality measures and address potential challenges in data collection, with the aim of future improvements.

Genetic diversity and population structure of wheat landraces in Southern Winter Wheat Region of China.

BACKGROUND: Wheat landraces are considered a valuable source of genetic diversity for breeding programs. It is useful to evaluate the genetic diversity in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection. In addition, constructing a core germplasm set that represents the genetic diversity of the entire variety set is of great significance for the efficient conservation and utilization of wheat landrace germplasms. RESULTS: To understand the genetic diversity in wheat landrace, 2,023 accessions in the Jiangsu Provincial Crop Germplasm Resource Bank were used to explore the molecular diversity and population structure using the Illumina 15 K single nucleotide polymorphism (SNP) chip. These accessions were divided into five subpopulations based on population structure, principal coordinate and kinship analysis. A significant variation was found within and among the subpopulations based on the molecular variance analysis (AMOVA). Subpopulation 3 showed more genetic variability based on the different allelic patterns (Na, Ne and I). The M strategy as implemented in MStratv 4.1 software was used to construct the representative core collection. A core collection with a total of 311 accessions (15.37%) was selected from the entire landrace germplasm based on genotype and 12 different phenotypic traits. Compared to the initial landrace collections, the core collection displayed higher gene diversity (0.31) and polymorphism information content (PIC) (0.25), and represented almost all phenotypic variation. CONCLUSIONS: A core collection comprising 311 accessions containing 100% of the genetic variation in the initial population was developed. This collection provides a germplasm base for effective management, conservation, and utilization of the variation in the original set.

Transcriptome-based prediction for polygenic traits in rice using different gene subsets.

BACKGROUND: Transcriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait. RESULTS: Using the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R2 = 0.59 when using all genes; R2 = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as "gibberellic acid sensitivity" showed higher predictive ability than using all genes for root dry weight. CONCLUSIONS: Our results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes.

High performance of the DNA methylation-based WID-qEC test for detecting uterine cancers independent of sampling modalities.

Endometrial cancer (EC) is the most prevalent gynaecological cancer in high-income countries and its incidence is continuing to rise sharply. Simple and objective tools to reliably detect women with EC are urgently needed. We recently developed and validated the DNA methylation (DNAme)-based women's cancer risk identification-quantitative polymerase chain reaction test for endometrial cancer (WID-qEC) test that could address this need. Here, we demonstrate that the stability of the WID-qEC test remains consistent regardless of: (i) the cervicovaginal collection device and sample media used (Cervex brush and PreservCyt or FLOQSwab and eNAT), (ii) the collector of the specimen (gynaecologist- or patient-based), and (iii) the precise sampling site (cervical, cervicovaginal and vaginal). Furthermore, we demonstrate sample stability in eNAT medium for 7 days at room temperature, greatly facilitating the implementation of the test into diagnostic laboratory workflows. When applying FLOQSwabs (Copan) in combination with the eNAT (Copan) sample collection media, the sensitivity and specificity of the WID-qEC test to detect uterine (i.e., endometrial and cervical) cancers in gynaecologist-taken samples was 92.9% (95% confidence interval [CI] = 75.0%-98.8%) and 98.6% (95% CI = 91.7%-99.9%), respectively, whilst the sensitivity and specificity in patient collected self-samples was 75.0% (95% CI = 47.4%-91.7%) and 100.0% (95% CI = 93.9%-100.0%), respectively. Taken together these data confirm the robustness and clinical potential of the WID-qEC test.

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research.

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Deep Learning - Methods to Amplify Epidemiological Data Collection and Analyses.

Deep learning is a subfield of artificial intelligence and machine learning based mostly on neural networks and often combined with attention algorithms that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 0000;000(00):0000-0000) present a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high dimensional data. The tools for implementing deep learning methods are not quite yet as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, healthcare providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiological principles of assessing bias, study design, interpretation and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.

Invited Commentary: Race, Ethnicity, and Racism in Epidemiologic Research-Perspectives from Public Health Critical Race Praxis (PHCRP).

In 2023, Martinez et al. examined trends in the inclusion, conceptualization, operationalization and analysis of race and ethnicity among studies published in US epidemiology journals. Based on a random sample of papers (N=1,050) published from 1995-2018, the authors describe the treatment of race, ethnicity, and ethnorace in the analytic sample (N=414, 39% of baseline sample) over time. Between 32% and 19% of studies in each time stratum lacked race data; 61% to 34% lacked ethnicity data. The review supplies stark evidence of the routine omission and variability of measures of race and ethnicity in epidemiologic research. Informed by public health critical race praxis (PHCRP), this commentary discusses the implications of four problems the findings suggest pervade epidemiology: 1) a general lack of clarity about what race and ethnicity are; 2) the limited use of critical race or other theory; 3) an ironic lack of rigor in measuring race and ethnicity; and, 4) the ordinariness of racism and white supremacy in epidemiology. The identified practices reflect neither current publication guidelines nor the state of the knowledge on race, ethnicity and racism; therefore, we conclude by offering recommendations to move epidemiology toward more rigorous research in an increasingly diverse society.

Transforming the oncology data paradigm by creating, capturing, and retrieving structured cancer data at the point of care: A Mayo Clinic pilot.

INTRODUCTION: Structured data capture requires defined languages such as minimal Common Oncology Data Elements (mCODE). This pilot assessed the feasibility of capturing 5 mCODE categories (stage, disease status, performance status (PS), intent of therapy and intent to change therapy). METHODS: A tool (SmartPhrase) using existing and custom structured data elements was Built to capture 4 data categories (disease status, PS, intent of therapy and intent to change therapy) typically documented as free-text within notes. Existing functionality for stage was supported by the Build. Participant survey data, presence of data (per encounter), and time in chart were collected prior to go-live and repeat timepoints. The anticipated outcome was capture of >50% sustained over time without undue burden. RESULTS: Pre-intervention (5-weeks before go-live), participants had 1390 encounters (1207 patients). The median percent capture across all participants was 32% for stage; no structured data was available for other categories pre-intervention. During a 6-month pilot with 14 participants across three sites, 4995 encounters (3071 patients) occurred. The median percent capture across all participants and all post-intervention months increased to 64% for stage and 81%-82% for the other data categories post-intervention. No increase in participant time in chart was noted. Participants reported that data were meaningful to capture. CONCLUSIONS: Structured data can be captured (1) in real-time, (2) sustained over time without (3) undue provider burden using note-based tools. Our system is expanding the pilot, with integration of these data into clinical decision support, practice dashboards and potential for clinical trial matching.

Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.).

BACKGROUND: Genomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively. RESULTS: Large phenotypic variations for the fruit traits were found in these collections and the 51K Axiom™ SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594-0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780-0.835), fruit width (0.791-0.865), and pericarp thickness (0.643-0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768-12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets. CONCLUSION: Our study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars.

Genetic diversity and population structure of superior shea trees (Vitellaria paradoxa subsp. paradoxa) using SNP markers for the establishment of a core collection in Côte d'Ivoire.

BACKGROUND: The shea tree is a well-known carbon sink in Africa that requires a sustainable conservation of its gene pool. However, the genetic structure of its population is not well studied, especially in Côte d'Ivoire. In this study, 333 superior shea tree genotypes conserved in situ in Côte d'Ivoire were collected and genotyped with the aim of investigating its genetic diversity and population structure to facilitate suitable conservation and support future breeding efforts to adapt to climate change effects. RESULTS: A total of 7,559 filtered high-quality single nucleotide polymorphisms (SNPs) were identified using the genotyping by sequencing technology. The gene diversity (HE) ranged between 0.1 to 0.5 with an average of 0.26, while the polymorphism information content (PIC) value ranged between 0.1 to 0.5 with an average of 0.24, indicating a moderate genetic diversity among the studied genotypes. The population structure model classified the 333 genotypes into three genetic groups (GP1, GP2, and GP3). GP1 contained shea trees that mainly originated from the Poro, Tchologo, and Hambol districts, while GP2 and GP3 contained shea trees collected from the Bagoué district. Analysis of molecular variance (AMOVA) identified 55% variance within populations and 45% variance within individuals, indicating a very low genetic differentiation (or very high gene exchange) between these three groups (FST = 0.004, gene flow Nm = 59.02). Morphologically, GP1 displayed spreading tree growth habit, oval nut shape, higher mean nut weight (10.62 g), wide leaf (limb width = 4.63 cm), and small trunk size (trunk circumference = 133.4 cm). Meanwhile, GP2 and GP3 showed similar morphological characteristics: erect and spreading tree growth habit, ovoid nut shape, lower mean nut weight (GP2: 8.89 g; GP3: 8.36 g), thin leaf (limb width = 4.45 cm), and large trunk size ( GP2: 160.5 cm, GP3: 149.1 cm). A core set of 100 superior shea trees, representing 30% of the original population size and including individuals from all four study districts, was proposed using the "maximum length sub-tree function" in DARwin v. 6.0.21. CONCLUSION: These findings provide new knowledge of the genetic diversity and population structure of Ivorian shea tree genetic resources for the design of effective collection and conservation strategies for the efficient use of inbreeding.