Genetic Data Research Articles

First Report of Alternaria alternata Causing Leaf Spot on Smooth Bromegrass (Bromus inermis Leyss.) in China.

Smooth bromegrass (Bromus inermis Leyss.) is an important forage crop in northern China. In July 2021, leaf spot symptoms were observed on smooth bromegrass in Ewenki Banner, Hulunbuir, Inner Mongolia. In an area of approximately 0.12 hectares, 95% disease incidence was observed. Ten diseased plants were collected for pathogen isolation. Leaf tissues near the lesions were cut into 5 × 5 mm pieces, surface-disinfested in 75% ethanol for 3 min, and rinsed with sterile distilled water. The pieces were placed on water agar in petri plates and incubated at 25℃ for three days. The resulting colonies were flushed with sterile water and a spore suspension was serially diluted and plated on potato dextrose agar (PDA). A single-spore colony was obtained. Ten isolates were obtained and designated HE1 to HE10. The colony morphology was identical for all isolates, grayish white in color on the upper surface and light black on the underside. The mycelia were light gray and velvety. Conidia were light brown to brown in color and oblate, oblong or oval. The conidial dimensions were typically between 15 to 43 μm by 8 to 9 μm in size. The conidia possessed one to six transverse septa, with slight to distinct constrictions at each division, and zero to two longitudinal septa. These morphological characteristics resembled Alternaria alternata (Fr.) Keissl.. DNA was extracted from three isolates, HE3, HE4 and HE5, using the CTAB method. Polymerase chain reaction (PCR) was performed on the extracted DNA with a set of primers ITS1/ITS4, H31a/H31b, gpd1/gpd2, TEF1-728F/TEF1-986R, and RPB2-5F2/fRPB2-7cR. The amplicon sequences from the three isolates were analyzed using the BLAST in GenBank (https://www.ncbi.nlm.nih.gov/). The results showed a high sequence identity, ranging from 99 to 100%, with the A. alternata strain YTMZ-20-2 across all the genetic markers tested. The strong match reinforced the identification of the strains as A. alternata. The sequences were deposited in GenBank (Table S1). The three fungal isolates were identified as A. alternata based on their morphological and genetic data. To conduct Koch's postulates, the representative isolate HE4 was used. Smooth bromegrass seed was soaked in water for four days and sown in potting soil contained in plastic pots (10 cm diameter × 15 cm height, five seeds/pot) in a greenhouse under a 16-h photoperiod at temperatures between 20 to 25°C and 60% relative humidity. When the plants reached a height of approximately 20 cm, the plants in three pots (replicates) were sprayed with a spore suspension (106 conidial/ml) at 10 ml/pot, and three pots were sprayed with sterile water for control. Five days after inoculation, the plants exhibited leaf spot symptoms similar to those previously described, while the control plants remained unaffected. The causative fungus was successfully re-isolated from the diseased plants and confirmed morphologically and molecularly on its identity as described above. This experiment was independently conducted three times. This is the first report of A. alternata causing leaf spot on smooth bromegrass in China. Since there is risk that the disease could seriously reduce the yield of the forage crop smooth bromegrass, further research is needed.

Indirect tuning of a complementary orientation filter using velocity data and a genetic algorithm

In this paper, the accuracy of inertial sensor orientation relative to the level frame is improved through optimal tuning of a complementary filter by a genetic algorithm. While constant filter gains have been used elsewhere, these may introduce errors under dynamic motions when gyroscopes should be trusted more than accelerometers. Optimal gains are prescribed by a Mamdani fuzzy rule base whose membership functions are found using a genetic algorithm and experimental data. Furthermore, model fitness is not based directly on orientation but the error between estimated and ground truth velocities. This paper has three interrelated novel elements. The main novelty is the indirect tuning method, which is simple, low-cost and requires a single camera and inertial sensor. The method is shown to increase tracking accuracy compared with popular baseline filters. Secondary novel elements are the bespoke genetic algorithm and the time agnostic velocity error metric. The contributions from this work can help improve the localization accuracy of assets and human personnel. This research has a direct impact in command and control by improving situational awareness and the ability to direct assets to safe locations using safer routes. This results in increasing safety in applications such as firefighting and battlespace.

Deep learning model for predicting genetic diseases using DNA sequence data

 The emergence of machine learning in the recent decade has excelled in determining new potential features and nonlinear relationships existing between the data derived from the DNA sequences of genetic diseases. Machine learning also enhances the process of handling data with maximum predicted variables compared to observations during the data mining process of prediction. In this context, our study presents a deep learning model for predicting Transcription Factor Binding Sites (TFBS) in DNA sequences, with a focus on features within genetic data associated with diseases. Transcription Factors (TFs) play a crucial role in modulating gene expression by binding to TFBS. The accurate prediction of TFBS is essential for understanding genome function and evolution. Thus, we develop an efficient deep learning model that considers TFBS prediction as a nucleotide-level binary classification task. In our proposed model, first we create an input matrix using the original DNA sequences. Next, we encode these DNA sequences using one-hot encoding, representing them as a sequence of numerical values. We then employ three convolutional layers, allowing our model to capture intricate patterns and motif features over a larger spatial range. To capture important features within the DNA sequence and to focus on them, we incorporate an attention layer. Finally, a dense layer, consisting of two fully connected layers and a dropout layer, calculates the probability of TF binding site occurrence based on the features learned by the proposed model. Our experimental results, using in-vivo datasets obtained from Chip-seq, demonstrate the superior performance of our proposed deep learning model in TFBS prediction compared to other existing state-of-the-art methods. The improvement in accuracy is due to additional layers of CNN and then an attention layer in the model. Thus, this result in a better performance of our approach in predicting the transcription factor binding sites and enhancing our understanding of gene regulation and genome function.

Multimodal cell atlas of the ageing human skeletal muscle.

Muscle atrophy and functional decline (sarcopenia) are common manifestations of frailty and are critical contributors to morbidity and mortality in older people1. Deciphering the molecular mechanisms underlying sarcopenia has major implications for understanding human ageing2. Yet, progress has been slow, partly due to the difficulties of characterizing skeletal muscle niche heterogeneity (whereby myofibres are the most abundant) and obtaining well-characterized human samples3,4. Here we generate a single-cell/single-nucleus transcriptomic and chromatin accessibility map of human limb skeletal muscles encompassing over 387,000 cells/nuclei from individuals aged 15 to 99 years with distinct fitness and frailty levels. We describe how cell populations change during ageing, including the emergence of new populations in older people, and the cell-specific and multicellular network features (at the transcriptomic and epigenetic levels) associated with these changes. On the basis of cross-comparison with genetic data, we also identify key elements of chromatin architecture that mark susceptibility to sarcopenia. Our study provides a basis for identifying targets in the skeletal muscle that are amenable to medical, pharmacological and lifestyle interventions in late life.

Mutations in the NUP93, NUP107 and NUP160 genes cause steroid-resistant nephrotic syndrome in Chinese children

BackgroundThe variants of nucleoporins are extremely rare in hereditary steroid-resistant nephrotic syndrome (SRNS). Most of the patients carrying such variants progress to end stage kidney disease (ESKD) in their childhood. More clinical and genetic data from these patients are needed to characterize their genotype–phenotype relationships and elucidate the role of nucleoporins in SRNS.MethodsFour patients of SRNS carrying biallelic variants in the NUP93, NUP107 and NUP160 genes were presented. The clinical and molecular genetic characteristics of these patients were summarized, and relevant literature was reviewed.ResultsAll four patients in this study were female and initially presented with SRNS. The median age at the onset of the disease was 5.08 years, ranging from 1 to 10.5 years. Among the four patients, three progressed to ESKD at a median age of 7 years, ranging from 1.5 to 10.5 years, while one patient reached stage 3 chronic kidney disease (CKD3). Kidney biopsies revealed focal segmental glomerulosclerosis in three patients. Biallelic variants were detected in NUP93 in one patient, NUP107 in two patients, as well as NUP160 in one patient respectively. Among these variants, five yielded single amino acid substitutions, one led to nonsense mutation causing premature termination of NUP107 translation, one caused a single nucleotide deletion resulting in frameshift and truncation of NUP107. Furthermore, one splicing donor mutation was observed in NUP160. None of these variants had been reported previously.ConclusionThis report indicates that biallelic variants in NUP93, NUP107 and NUP160 can cause severe early-onset SRNS, which rapidly progresses to ESKD. Moreover, these findings expand the spectrum of phenotypes and genotypes and highlight the importance of next-generation sequencing in elucidating the molecular basis of SRNS and allowing rational treatment for affected individuals.

Paired fins in vertebrate evolution and ontogeny.

The origin of paired appendages became one of the most important adaptations of vertebrates, allowing them to lead active lifestyles and explore a wide range of ecological niches. The basic form of paired appendages in evolution is the fins of fishes. The problem of paired appendages has attracted the attention of researchers for more than 150 years. During this time, a number of theories have been proposed, mainly based on morphological data, two of which, the Balfour-Thacher-Mivart lateral fold theory and Gegenbaur's gill arch theory, have not lost their relevance. So far, however, none of the proposed ideas has been supported by decisive evidence. The study of the evolutionary history of the appearance and development of paired appendages lies at the intersection of several disciplines and involves the synthesis of paleontological, morphological, embryological, and genetic data. In this review, we attempt to summarize and discuss the results accumulated in these fields and to analyze the theories put forward regarding the prerequisites and mechanisms that gave rise to paired fins and limbs in vertebrates.

Screening and surveillance recommendations for central nervous system hemangioblastomas in pediatric patients with Von Hippel-Lindau disease.

Von Hippel-Lindau (VHL) disease is an autosomal-dominantly inherited tumor predisposition syndrome. One of the most common tumors are central nervous system (CNS) hemangioblastomas. Recommendations on the initiation and continuation of the screening and surveillance program for CNS tumors in pediatric VHL patients are based on small case series and thus low evidence level. To derive more robust screening recommendations, we report on the largest monocentric pediatric cohort of VHL patients. We performed a retrospective analysis on a pediatric cohort of 99 VHL patients consulted at our VHL center from 1992 to 2023. Clinical, surgical, genetic, and imaging data were collected and statistically analyzed. 42 patients (50% male) developed CNS hemangioblastomas, of whom 18 patients (56% male) underwent hemangioblastoma surgery (mean age at first surgery: 14.9 ± 1.9 years; range 10.2-17). The first asymptomatic patient was operated on at the age of 13.2 years due to tumor progress. Truncating VHL mutation carriers had a significantly higher manifestation rate (HR = 3.7, 95% CI: 1.9-7.4, p < 0.0001) and surgery rate (HR = 3.3, 95% CI: 1.2-8.9, p = 0.02) compared with missense mutation carriers. We recommend starting MRI imaging at the age of 12 years with examination intervals every (1-) 2 years depending on CNS involvement. Special attention should be paid to patients with truncating variants. Affected families should be educated regularly on potential tumor-associated symptoms to enable timely MRI imaging and eventually intervention, as CNS hemangioblastoma may develop before screening begins. DRKS00029553, date of registration 08/16/2022, retrospectively registered.

Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.

Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.

Tasmanian devil (Sarcophilus harrisii) gene flow and source-sink dynamics

Increased access to genetic data has substantially improved how we manage threatened species. The Tasmanian devil (Sarcophilus harrisii) is listed as endangered due to the ongoing threat of a highly contagious cancer, devil facial tumour disease (DFTD), causing more than 80% population reductions. To assist future management interventions (e.g. releases into wild sites) we expanded upon previous studies of gene flow for the devil by assessing more recent and broad-scale patterns. We use genome-wide single nucleotide polymorphisms generated via DArTSeq across 21 devil sites to delineate source-sink dynamics across the species’ range. Our findings revealed gene flow is stronger on the northeast and central regions of Tasmania, with high rates of bidirectional gene flow among central sites. The northwest exhibits weaker connectivity relative to other regions of Tasmania, while gene flow appears to be non-existent between the southwest and other areas. Northeast coastal sites tend to serve as ‘sources’ for inland central sites, whereas gene flow appears restricted to the coastline in the northwest. These results are consistent with genetic structure of devil sites and spatial spread of DFTD, which has yet to arrive in the southwest region of Tasmania. Southwest isolation is probably due to mountain ranges and lack of roadways. Interestingly, some waterbodies did not appear to restrict devil movement among sites. Conversely, areas of high elevation act as apparent barriers, as evidenced by limited gene flow observed between eastern and western sites. Integrating source-sink dynamics into conservation management planning will be crucial in developing effective strategies to safeguard the Tasmanian devil and other threatened species facing similar threats (i.e. disease, habitat loss).

The impact of artificial intelligence on medical diagnostics: A letter to the editor

Madam, Artificial intelligence (AI) describes the generation of intelligent machines that are capable of carrying out activities that usually call for human intellect. (1) It involves creating algorithms and models that enable computers to learn from and analyze vast amounts of data, recognize patterns, make decisions, and even engage in natural language processing.  (2) AI has become increasingly important in various fields due to its potential to automate complex processes, enhance efficiency, and drive innovation. It can transform industries, improve decision-making, and revolutionize how we live and work. In medical diagnostics, AI has had a profound impact on improving accuracy, efficiency, and patient outcomes. By leveraging machine learning algorithms, AI systems can process and analyze large volumes of medical data, including patient records, laboratory results, medical images, and genomic data. This allows for more accurate and timely diagnoses, as AI algorithms can detect subtle patterns and anomalies that may not be visible to human observers. AI-powered diagnostics can assist healthcare professionals in detecting diseases, predicting patient outcomes, and designing personalized treatment plans. (3) Furthermore, AI can contribute to the early detection of diseases, thereby enabling timely interventions and improving patient survival rates. For example, AI algorithms analyze medical imaging data, such as X-rays, MRIs, and CT scans, to detect abnormalities and potential signs of diseases like cancer. This helps radiologists and clinicians identify and diagnose conditions early when treatment options are more effective. (4) Additionally, AI aids in interpreting genetic data, allowing for the identification of genetic markers associated with specific diseases or drug responses. This information guides clinicians in selecting the most suitable treatment options for individual patients, leading to improved therapeutic outcomes. (5) AI in medical diagnostics also faces hurdles with data privacy and accessibility, as patient information must be handled securely. Ensuring AI algorithms are unbiased, and fair is a challenge to avoid disparities in healthcare outcomes. The interpretability of AI models is vital to building trust with healthcare professionals and facilitating their adoption. Regulatory compliance and ethical considerations are also significant factors that need to be addressed for the responsible and effective integration of AI in the medical field. In conclusion, Artificial Intelligence (AI) is a transformative technology with the potential to revolutionize various industries, including medical diagnostics. Its ability to analyze vast amounts of data, recognize patterns, and make intelligent decisions has proven invaluable in improving diagnostic processes accuracy, efficiency, and effectiveness. ---Continue

Association between sleep traits and sarcopenia-related traits: A two-sample bidirectional Mendelian randomization study.

Despite limited evidence regarding the impact of sleep quality on sarcopenia, it is widely recognized as being associated with various diseases. This study aimed to explore the causal relationship between sleep traits and sarcopenia-related traits. This study utilized a two-sample bidirectional Mendelian randomization analysis. Genetic genome-wide summary data of sleep quality indicators, including chronotype, morning wake-up time, sleep duration, daytime napping, insomnia and daytime dozing, were used. Data on sarcopenia-related traits, such as appendicular lean mass, grip strength of both hands, walking pace and waist circumference, were collected from a large cohort study. The primary method used was the inverse-variance weighted analysis. A causal association was found between chronotype and appendicular lean mass (odds ratio [OR] 1.019, 95% confidence interval [CI] 1.016-1.211, P = 0.021). Napping during the day was connected with walking pace (OR 0.879, 95% CI 0.834-0.928, P = 2.289 × 10-6) and waist circumference (OR 1.234, 95% CI 1.081-1.408, P = 0.002). Insomnia was related to lower grip strength of the right hand (OR 0.844, 95% CI 0.747-0.954, P = 0.007), left hand (OR 0.836, 95% CI 0.742-0.943, P = 0.003), as well as walking pace (OR 0.871, 95% CI 0.798-0.951, P = 0.002). Furthermore, the reverse Mendelian randomization analysis showed associations between certain sarcopenia-related traits and poor sleep quality. Some sleep traits were associated with the occurrence of sarcopenia. These findings emphasized the significance of prioritizing sleep quality as a preventive measure against sarcopenia. Geriatr Gerontol Int 2024; ••: ••-••.

Mendelian randomization: causal inference leveraging genetic data.

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Exploring causal correlations between inflammatory cytokines and knee osteoarthritis: a two-sample Mendelian randomization.

Knee osteoarthritis (KOA) and certain inflammatory cytokines (such as interleukin 1 [IL-1] and tumor necrosis factor alpha [TNF-a]) are related; however, the causal relationship remains unclear. Here, we aimed to assess the causal relationship between 41 inflammatory cytokines and KOA using Mendelian randomization (MR). Two-sample bidirectional MR was performed using genetic variation data for 41 inflammatory cytokines that were obtained from European Genome-Wide Association Study (GWAS) data (n=8293). KOA-related genetic association data were also obtained from European GWAS data (n=40,3124). Inverse variance weighting (IVW), MR, heterogeneity, sensitivity, and multiple validation analyses were performed. Granulocyte colony-stimulating factor (G-CSF) or colony-stimulating factor 3 (CSF-3) levels were negatively associated with the risk of developing KOA (OR: 0.93, 95%CI:0.89-0.99, P=0.015). Additionally, macrophage inflammatory protein-1 alpha (MIP-1A/CCL3) was a consequence of KOA (OR: 0.72, 95%CI:0.54-0.97, P=0.032). No causal relationship was evident between other inflammatory cytokines and KOA development. This study suggests that certain inflammatory cytokines may be associated with KOA etiology. G-CSF exerts an upstream influence on KOA development, whereas MIP-1A (CCL-3) acts as a downstream factor.

Cohort profile: Worldwide Collaboration on OsteoArthritis prediCtion for the Hip (World COACH) – an international consortium of prospective cohort studies with individual participant data on hip osteoarthritis

PurposeHip osteoarthritis (OA) is a major cause of pain and disability worldwide. Lack of effective therapies may reflect poor knowledge on its aetiology and risk factors, and result in the...

Spectrum of ERCC6-Related Cockayne Syndrome (Type B): From Mild to Severe Forms.

(1) Background: Cockayne syndrome (CS) is an ultra-rare multisystem disorder, classically subdivided into three forms and characterized by a clinical spectrum without a clear genotype-phenotype correlation for both the two causative genes ERCC6 (CS type B) and ERCC8 (CS type A). We assessed this, presenting a series of patients with genetically confirmed CSB. (2) Materials and Methods: We retrospectively collected demographic, clinical, genetic, neuroimaging, and serum neurofilament light-chain (sNFL) data about CSB patients; diagnostic and severity scores were also determined. (3) Results: Data of eight ERCC6/CSB patients are presented. Four patients had CS I, three patients CS II, and one patient CS III. Various degrees of ataxia and spasticity were cardinal neurologic features, with variably combined systemic characteristics. Mean age at diagnosis was lower in the type II form, in which classic CS signs were more evident. Interestingly, sNFL determination appeared to reflect clinical classification. Two novel premature stop codon and one novel missense variants were identified. All CS I subjects harbored the p.Arg735Ter variant; the milder CS III subject carried the p.Leu764Ser missense change. (4) Conclusion: Our work confirms clinical variability also in the ERCC6/CSB type, where manifestations may range from severe involvement with prenatal or neonatal onset to normal psychomotor development followed by progressive ataxia. We propose, for the first time in CS, sNFL as a useful peripheral biomarker, with increased levels compared to currently available reference values and with the potential ability to reflect disease severity.

Causal effects of neuroticism on postpartum depression: a bidirectional mendelian randomization study.

Postpartum depression (PPD) brings adverse and serious consequences to both new parents and newborns. Neuroticism affects PPD, which remains controversial for confounding factors and reverse causality in cross-sectional research. Therefore, mendelian randomization (MR) study has been adopted to investigate their causal relationship. This study utilized large-scale genome-wide association study genetic pooled data from three major databases: the United Kingdom Biobank, the European Bioinformatics Institute, and the FinnGen databases. The causal analysis methods used inverse variance weighting (IVW). The weighted median, MR-Egger method, MR-PRESSO test, and the leave-one-out sensitivity test have been used to examine the results' robustness, heterogeneity, and horizontal pleiotropy. The fixed effect model yielded the results of meta-analysis. In the IVW model, a meta-analysis of the MR study showed that neuroticism increased the risk of PPD (OR, 1.17; 95% CI, 1.11-1.25, p < 0.01). Reverse analysis showed that PPD could not genetically predict neuroticism. There was no significant heterogeneity or horizontal pleiotropy bias in this result. Our study suggests neuroticism is the risk factor for PPD from a gene perspective and PPD is not the risk factor for neuroticism. This finding may provide new insights into prevention and intervention strategies for PPD according to early detection of neuroticism.

Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation.

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a gene's downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2L2)-related factor 2 (NRF2) and c-Myc. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-Myc or NRF2 transcriptional pathway activities.

Molecular profiling of Greek native germplasm collection of Rosa canina L. for enhanced fruit extract production: a comprehensive approach utilizing neutral, gene, and exon-based markers

AbstractThe genus Rosa L. is globally distributed and Rosa canina L. is a distinguished member of multifaceted interest. Apart from the traditional uses of R. canina in folk medicine, food and cosmetic industries, or its ornamental applications, its rose hips are renowned for their functional bioactive components. Thus, identifying the genetic diversity within this species is crucial for any plant breeding project. This study employed three molecular markers namely inter simple sequence repeats; (ISSRs), start codon-targeted (SCoTs), and exon-based amplified polymorphisms; (EBAPs) to conduct the first comprehensive genetic analysis of 12 R. canina genotypes. DNA extraction, marker selection, and sequences’ amplification were performed following established protocols. The resulting genetic data were analyzed for polymorphism, diversity indices, and population structure using various statistical methods including principal component analysis (PCA), unweighted pair group method with arithmetic mean (UPGMA) clustering, and STRUCTURE analysis. The ISSR analysis revealed a high level of polymorphism (81.82%) and identified two major clusters in the UPGMA dendrogram. SCoT and EBAP markers also exhibited substantial polymorphism (74.56% and 82.11%, respectively) and formed three distinct clusters. PCA indicated a consistent pattern across markers suggesting reliable genetic grouping. STRUCTURE analysis supported the presence of three genetically uniform subpopulations (K = 3) within the studied R. canina germplasm collection. This study provided a comprehensive genetic characterization of the Greek native R. canina genebank collection. The observed genetic diversity and population structure offered valuable insights for future breeding programs targeting specific R. canina genetic clusters.

Exploration of Imaging Genetic Biomarkers of Alzheimer's Disease Based on a Machine Learning Method.

Alzheimer's disease (AD) is an irreversible primary brain disease with insidious onset. The rise of imaging genetics research has led numerous researchers to examine the complex association between genes and brain phenotypes from the perspective of computational biology. Given that most previous studies have assumed that imaging data and genetic data are linearly related and are therefore unable to explore their nonlinear relationship, our study applied a joint depth semi-supervised nonnegative matrix decomposition (JDSNMF) algorithm to solve this problem. The JDSNMF algorithm jointly decomposed multimodal imaging genetics data into both a standard basis matrix and multiple feature matrices. During the decomposition process, the coefficient matrix A multilayer nonlinear transformation was performed using a neural network to capture nonlinear features. The results using a real dataset demonstrated that the algorithm can fully exploit the association between strongly correlated image genetics data and effectively detect biomarkers of AD. Our results might provide a reference for identifying biologically significant imaging genetic correlations, and help to elucidate disease-related mechanisms. The diagnostic model constructed by the top features of the three modality data sets mined by the algorithm has high accuracy, and these features are expected to become new therapeutic targets for AD.

Genetic risk of type 2 diabetes modifies the association between lifestyle and glycemic health at 5 years postpartum among high-risk women

IntroductionLifestyle interventions are effective in preventing type 2 diabetes, but genetic background may influence the individual response. In the Finnish gestational diabetes prevention study, RADIEL, lifestyle intervention during pregnancy and...