Search Results (528)

Reproductive inefficiencies, such as delayed age at first calving (AFC) and a prolonged calving interval (CI), hinder the productivity of Thai swamp buffaloes. This study aimed to improve the genetic evaluation of these traits by integrating genomic selection (GS) and genome-wide association studies (GWASs). Reproductive records (n = 1034) and genotypes (n = 474) from swamp buffaloes across Thailand were analyzed. Variance components were estimated using pedigree data, and genomic predictions were performed via weighted single-step genomic best linear unbiased prediction. AFC heritability was moderate (0.36 and 0.45), whereas CI heritability was low (0.051 and 0.043). The positive genetic correlation ($r_g$ = 0.495 and 0.517) between AFC and CI suggested potential for the simultaneous genetic improvement of both traits through selection. The WssGBLUP method showed greater effectiveness, with the prediction accuracy increasing by up to 41% for CI and 28% for AFC when compared to the ABLUP method. The GWAS revealed 20 associated single-nucleotide polymorphisms (SNPs) on chromosomes 3, 4, 14, 15, and 25. Candidate genes COLEC10, TNFRSF11B, PDZRN4, and MACROD2 were linked to immune function, hormonal regulation, and reproductive tissue development. Pleiotropic SNPs affecting both traits were identified, indicating shared genetic control mechanisms. These findings support the application of tailored GS programs to improve reproduction in swamp buffaloes under tropical smallholder systems. Full article

Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010–2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework—spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity—to provide a coherent comparative lens across diverse DT implementations. Hybrid edge–cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15–20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management—an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming. Full article

Helicobacter pylori is a well-established causative agent of gastritis, peptic ulcers, gastric adenocarcinoma, and primary gastric lymphoma. It colonizes the human stomach and expresses numerous virulent factors that influence disease progression. Among these factors is the cytotoxin vacA gene, which encodes the vacuolating capacity of the cytotoxin and plays a key role in the bacterium’s pathogenic potential. This study investigated the allelic diversity of the vacA among H. pylori strains infecting patients in Jordan with various gastric conditions and examined potential associations between vacA s-and m- genotypes, histopathological and endoscopic findings, and the development of gastric diseases. Gastric biopsies were collected from 106 patients at two hospitals in Jordan who underwent endoscopic examination. The collected biopsies for each patient were subjected to histopathological assessment, urease detection using the Rapid Urease Test (RUT), a diagnostic test for H. pylori, and molecular detection of the vacA gene and its s and m alleles. The histopathology reports indicated that 83 of 106 patients exhibited gastric disorders, of which 81 samples showed features associated with H. pylori infection. The RUT was positive in 76 of 106 with an accuracy of 93.8%. Real-time polymerase chain reaction (RT-PCR) targeting the 16S rRNA gene confirmed the presence of H. pylori in 79 of 81 histologically diagnosed cases as infected (97.5%), while the vacA gene was detected only in 75 samples (~95%). To explore genetic diversity, PCR-amplified fragments underwent sequence analysis of the vacA gene. The m-allele was detected in 58 samples (73%), the s-allele was detected in 45 (57%), while both alleles were not detected in 13% of samples. The predominant genotype combination among Jordanians was vacA s2/m2 (50%), significantly linked to mild chronic gastritis, followed by s1/m2 (35%) and s1/m1 (11.8%) which are linked to severe gastric conditions including malignancies. Age-and gender-related differences in vacA genotype were observed with less virulent s2m2 and s1m2 genotypes predominating in younger adults specially males, while the more virulent m1 genotypes were found exclusively in females and middle-aged patients. Genomic sequencing revealed extensive diversity within H. pylori, likely reflecting its long-standing co-evolution with human hosts in Jordan. This genetic variability plays a key role in modulating virulence and influencing clinical outcomes. Comprehensive characterization of vacA genotypic variations through whole-genome sequencing is essential to enhance diagnostic precision, strengthen epidemiological surveillance, and inform targeted therapeutic strategies. While this study highlights the significance of the vacA m and s alleles, future research is recommended in order to investigate the other vacA allelic variations, such as the i, d, and c alleles, to achieve a more comprehensive understanding of H. pylori pathogenicity and associated disease severity across different strains. These investigations will be crucial for improving diagnostic accuracy and guiding the development of targeted therapeutic strategies. Full article

Ujimqin sheep, known for its distinctive multi-vertebrae phenotypes (T13L7, T14L6, and T14L7) and economic value, has garnered significant attention. However, conventional phenotypic detection methods suffer from low efficiency and high costs. In this study, based on a key SNP locus (ABCD4 gene, Chr7:89393414, C > T) identified through a genome-wide association study (GWAS), a TaqMan-MGB (minor groove binder) genotyping system was developed. the objective was to establish a high-throughput and efficient molecular marker-assisted selection (MAS) tool. Specific primers and dual fluorescent probes were designed to optimize the reaction system. Standard plasmids were adopted to validate genotyping accuracy. A total of 152 Ujimqin sheep were subjected to TaqMan-MGB genotyping, digital radiography (DR) imaging, and Sanger sequencing. the results showed complete concordance between TaqMan-MGB and Sanger sequencing, with an overall agreement rate of 83.6% with DR imaging. For individuals with T/T genotypes (127/139), the detection accuracy reached 91.4%. This method demonstrated high specificity, simplicity, and cost-efficiency, significantly reducing the time and financial burden associated with traditional imaging-based approaches. the findings indicate that the TaqMan-MGB technique can accurately identify the T/T genotype at the SNP site and its strong association with the multi-vertebrae phenotypes, offering an effective and reliable tool for molecular breeding of Ujimqin sheep. Full article

Cervical cancer remains a major health burden among women in sub-Saharan Africa, where screening is often limited. Persistent high-risk human papillomavirus (HR-HPV) infection is the principal cause, highlighting the need for accurate molecular diagnostics. This cross-sectional study evaluated the analytical performance of one mRNA assay, APTIMA^® HPV assay (APTIMA mRNA), and two DNA-based assays, the Abbott RealTime High Risk HPV assay (Abbott DNA) and Seegene Allplex™ II HPV28 assay (Seegene DNA), in 527 cervical samples from a South African tertiary hospital, focusing on 14 shared HR-HPV genotypes. Seegene DNA yielded the highest detection rate (53.7%), followed by Abbott DNA (48.2%) and APTIMA mRNA (45.2%). APTIMA mRNA showed a strong agreement with Abbott DNA (87.9%, κ = 0.80), 89.9% sensitivity, 91.2% NPV, and the highest accuracy (AUC = 0.8804 vs. 0.8681). The agreement between APTIMA mRNA and Seegene DNA was moderate (83.4%, κ = 0.70), reflecting target differences. Many DNA-positive/mRNA-negative cases likely represent transient infections, though some may be latent with reactivation potential, warranting a follow-up. In resource-constrained settings, prioritizing transcriptionally active infections through mRNA testing may enhance screening efficiency and reduce burden. Scalable, cost-effective assays with strong clinical utility are essential for broadening access and improving cervical cancer prevention. Further studies should assess the integration of mRNA testing into longitudinal screening algorithms. Full article

Hepatocellular carcinoma (HCC) is the most prevalent subtype of liver cancer with an increasing incidence worldwide. Single nucleotide polymorphisms (SNPs) may influence disease risk and serve as predictive markers. This study aimed to evaluate the association of PNPLA3 (rs738409 and rs2294918), GCKR (rs780094), MBOAT7 (rs641738), NCAN (rs2228603), and TM6SF2 (rs58542926) SNPs with the risk of developing HCC in a Mexican population. A case-control study was conducted in unrelated Mexican individuals. Cases were 173 adults with biopsy-confirmed HCC and 346 were healthy controls. Genotyping was performed using TaqMan allelic discrimination assay. Logistic regression was applied to evaluate associations under codominant, dominant, and recessive inheritance models. p-values were corrected using the Bonferroni test (pC). Haplotype and gene–gene interaction were also analyzed. The GG homozygous of rs738409 and rs2294918 of PNPLA3, TT, and TC genotypes of GCKR, as well as the TT genotype of MBOAT7, were associated with a significant increased risk to HCC under different inheritance models (~Two folds in all cases). The genotypes of NCAN and TM6SF2 did not show differences. The haplotype G-G of rs738409 and rs2294918 of PNPLA3 was associated with an increased risk of HCC [OR (95% CI) = 2.2 (1.7–2.9)]. There was a significant gene–gene interaction between PNPLA3 (rs738409), GCKR (rs780094), and MBOAT7 (rs641738) (Cross-validation consistency (CVC): 10/10; Testing accuracy = 0.6084). This study demonstrates for the first time that PNPLA3 (rs738409 and rs2294918), GCKR (rs780094), and MBOAT7 (rs641738) are associated with an increased risk of developing HCC from multiple etiologies in Mexican patients. Full article

Background: Accurate and rapid diagnosis of drug-resistant tuberculosis is essential for initiating appropriate treatment and preventing the transmission of these strains. This study compares phenotypic and genotypic methods of drug susceptibility testing for Mycobacterium tuberculosis (M. tuberculosis). Methods: Resistance to first-line drugs, as well as resistance to second-line drugs (fluoroquinolones and aminoglycosides), was assessed using the Löwenstein–Jensen medium phenotypic method and the GenoType MTBDRplus genotypic method and analyzed. Results: The phenotypic resistance rate was 84.85% for INH (n = 56), 46.97% for RIF (n = 31), 48.48% for STR (n = 32), and 30.30% for EMB (n = 20). Of the MDR-TB isolates (n = 29), 41.37% were resistant to fluoroquinolones (n = 12) and 31.03% were resistant to both fluoroquinolones and injectable aminoglycosides, being classified as XDR-TB (n = 9). In addition, 22.73% of the MDR-TB isolates were resistant to all four first-line drugs (n = 15). The overall concordance between the line probe assay method and phenotypic testing was 94.74% for RIF and 95.16% for INH. Discordances were identified in three cases for RIF and two cases for INH, where isolates were reported as susceptible by GenoType MTBDRplus, but phenotypically resistant. Conclusions: Genotypic testing using GenoType MTBDRplus provides rapid and accurate results, but some cases of phenotypic resistance are not detected by this method. The results highlight the importance of using combined phenotypic and genotypic methods for accurate diagnosis of MDR-TB, as well as the need to integrate genomic sequencing to improve diagnostic accuracy. Full article

Background/Objectives: Improving sparse testing is essential for enhancing the efficiency of genomic prediction (GP). Accordingly, new strategies are being explored to refine genomic selection (GS) methods under sparse testing conditions. Methods: In this study, a sparse testing approach was evaluated, specifically in the context of predicting performance for tested lines in untested environments. Sparse testing is particularly practical in large-scale breeding programs because it reduces the cost and logistical burden of evaluating every genotype in every environment, while still enabling accurate prediction through strategic data use. To achieve this, we used training data from CIMMYT (Obregon, Mexico), along with partial data from India, to predict line performance in India using observations from Mexico. Results: Our results show that incorporating data from Obregon into the training set improved prediction accuracy, with greater effectiveness when the data were temporally closer. Across environments, Pearson’s correlation improved by at least 219% (in a testing proportion of 50%), while gains in the percentage of matching in top 10% and 20% of top lines were 18.42% and 20.79%, respectively (also in a testing proportion of 50%). Conclusions: These findings emphasize that enriching training data with relevant, temporally proximate information is key to enhancing genomic prediction performance; conversely, incorporating unrelated data can reduce prediction accuracy. Full article

Brassica napus is one of the most important oil crops. Rapid breeding of excellent genotypes is an important aspect of breeding. As a cutting-edge and reliable technique, genome-wide selection (GS) has been widely used and is influenced by many factors. In this study, ten phenotypic traits of two populations were studied, including oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), glucosinolate (GSL), seed oil content (SOC), and seed protein content (SPC), silique length (SL), days to initial flowering (DIF), days to final flowering (DFF), and the flowering period (FP). The effects of different GS models, marker densities, population designs, and the inclusion of nonadditive effects, trait-specific SNPs, and deleterious mutations on GS were evaluated. The results highlight the superior prediction accuracy (PA) under the RF model. Among the ten traits, the PA of glucosinolate was the highest, and that of linolenic acid was the lowest. At the same time, 5000 markers and a population of 400 samples, or a training population three times the size of an applied breeding population, can achieve optimal performance for most traits. The application of nonadditive effects and deleterious mutations had a weak effect on the improvement of traits with high PA but was effective for traits with low PA. The use of trait-specific SNPs can effectively improve the PA, especially when using markers with p-values less than 0.1. In addition, we found that the PA of traits was significantly and positively correlated with the number of markers, according to p-values less than 0.01. In general, based on the associated population, a GS system suitable for B. napus was established in this study, which can provide a reference for the improvement of GS and is conducive to the subsequent application of GS in B. napus, especially in the aspects of model selection of GS, the application of markers, and the setting of population sizes. Full article

Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. This study investigates genotype-by-environment (G×E) interactions in cotton, using data from 250 recombinant inbred lines (CCRI70 RILs) cultivated across 14 diverse environments in China’s major cotton cultivation areas. Our findings reveal that environmental effects predominantly influenced yield-related traits (boll weight, lint percentage, and the seed index), contributing to 34.7% to 55.7% of their variance. In contrast fiber quality traits showed lower environmental sensitivity (12.3–27.0%), with notable phenotypic plasticity observed in the boll weight, lint percentage, and fiber micronaire. Employing six machine learning models, Random Forest demonstrated superior predictive ability (R² = 0.40–0.72; predictive Pearson correlation = 0.63–0.86). Through SHAP-based interpretation and sliding-window regression, we identified key environmental drivers primarily active during mid-to-late growth stages. This approach effectively reduced the number of influential input variables to just 0.1–2.4% of the original dataset, spanning 2–9 critical time windows per trait. Incorporating these identified drivers significantly improved cross-environment predictions, enhancing Random Forest accuracy by 0.02–0.15. These results underscore the strong potential of machine learning to uncover critical temporal environmental factors underlying G×E interactions and to substantially improve predictive modeling in cotton breeding programs, ultimately contributing to more resilient and productive cotton cultivation. Full article

Genome-wide association studies (GWASs) play a central role in precision medicine, powering a range of clinical applications from pharmacogenomics to disease risk prediction. A critical component of GWASs is genotype imputation, a computational method used to infer untyped genetic variants. While imputation increases variant coverage by estimating genotypes at untyped loci, this expanded coverage can enhance the ability to detect genetic associations in some cases. However, imputation also introduces biases, particularly for rare variants and underrepresented populations, which may compromise clinical accuracy. This review examines the challenges and clinical implications of genotype imputation errors, including their impact on therapeutic decisions and predictive models, like polygenic risk scores (PRSs). In particular, the sources of imputation errors have been deeply explored, emphasizing the disparities in performance across ancestral populations and downstream effects on healthcare equity and addressing ethical considerations surrounding the access to equitable genomic resources. Based on the above, we propose evidence-based best practices for clinical GWAS implementation, including the direct genotyping of clinically actionable variants, the cross-population validation of imputation models, the transparent reporting of imputation quality metrics, and the use of ancestry-matched reference panels. As genomic data becomes increasingly adopted in healthcare systems worldwide, ensuring the accuracy and inclusivity of GWAS-derived insights is paramount. Here, we suggest a framework for the responsible clinical integration of imputed genetic data, paving the way for more reliable and equitable personalized medicine. Full article

Although numerous rice plant height-related genes have been cloned and functionally characterized in recent years, a gap between the identified genes and their utilization in breeding still exists. Here, we developed a linear regression model by pyramiding plant height-related alleles to predict rice plant height and confirmed that it can be used in rice breeding. In our study, we firstly identified 22 plant height-associated molecular markers from 218 markers in an association mapping population which consisted of 273 rice varieties. Linear regression analysis revealed a positive correlation between rice plant height and the number of plant height-increasing alleles derived from these 22 molecular markers. Subsequently, linear regression models were developed using 2–10 loci based on the genotype and phenotype data of the association mapping population. The predictive accuracy of the model was tested using a recombinant inbred line (RIL) population consisting of 219 lines, and it revealed the trend that predictive accuracy increased with more loci in a certain range of less than five loci. If the prediction model was built based on 5–10 loci, it yielded an average absolute error from 11.05 to 11.96 cm, which was smaller than absolute error induced by environmental factors (5.72 cm to 12.79 cm). The reliable prediction of rice plant height by this model highlights its value as a practical tool for optimizing rice breeding strategies. Additionally, the linear regression model developed in this study not only can facilitate plant height manipulation but also will inspire other design breeding techniques in other crops or other traits. Full article

Idiopathic short stature (ISS) represents one of the most frequent yet enigmatic conditions in pediatric endocrinology. Traditionally defined by auxological parameters in the absence of identifiable causes, ISS has long served as a diagnosis of exclusion. However, with the advent of next-generation sequencing, our understanding of the etiological landscape has significantly evolved. Recent studies have revealed that many children previously labeled as idiopathic actually harbor monogenic variants in genes related to the growth hormone–insulin-like growth factor axis, extracellular matrix components, or growth plate signaling pathways. This review integrates auxological assessment with current knowledge on molecular diagnostics to propose a more accurate and individualized approach to short stature. We examine emerging genotype–phenotype correlations, criteria for selecting candidates for genetic testing, and implications for recombinant human growth hormone therapy. Additionally, we advocate for a shift in clinical mindset: from a descriptive to a biologically grounded framework. ISS should be regarded as a transitional label pending further endocrine and genetic clarification. Recognizing this paradigm shift will improve diagnostic accuracy, personalize treatment strategies, and ultimately enhance care for children with growth failure in the genomic era. Full article

Background: Research concerning the genetic risk for dementia has recently been headed towards new directions. Novel findings from genome-wide association studies have highlighted the association of Alzheimer’s disease incidence with many gene polymorphisms, apart from the Apolipoprotein-E genotype. The identification of additional genetic risk factors has led to the construction of specific genetic risk scores for dementia, considering many different genetic factors and specific biological pathways related to Alzheimer’s disease. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis method, summarizing existing data regarding genetic risk scores for Alzheimer’s disease and dementia, in order to improve the current understanding of the genetic underpinnings of dementia. In specific, five databases (PubMed/MEDLINE, Embase, Scopus, Web of science, and Cochrane Central) were searched using the keywords “genetic risk score”, “Alzheimer’s disease”, and “dementia” with specific inclusion and exclusion criteria. Results: From the 552 articles identified, we finally included 20 studies for the qualitative analysis. These reports were classified in three different categories of genetic scores: “polygenic risk scores (PRSs)” (including 11 studies), “pathway specific polygenic risk scores (p-PRSs)” (5 studies), and “complex genetic risk scores” (4 studies). Conclusions: Existing genetic risk scores have contributed to better dementia prediction and a better understanding of the underlying pathology. Novel approaches integrating multiple polygenic risk scores might ameliorate the accuracy of genetic risk scores. The combination of polygenic risk scores that are specific to related biological pathways or relevant biomarkers is of utmost importance to achieve a better predictive ability. Full article

Background/Objectives: Pathogenic variants in the PRPH2 gene are implicated in a wide spectrum of Inherited Retinal Dystrophies (IRDs), which show significant phenotypic heterogeneity. This study combines genomic, clinical, and instrumental data, including BCVA, OCT, ERG, and visual field testing, using a multimodal approach to identify known and novel PRPH2 variants, with the aim of refine genotype–phenotype correlations and improving the diagnosis of IRDs. Methods: A total of 830 Italian subjects diagnosed with IRDs by the multimodal clinical approach underwent WES on the Illumina^® Next-Seq 550 system. Genetic variants were evaluated by considering type, frequency, and pathogenicity using dedicated databases and bioinformatics tools. Results: WES analysis led to the identification of three novel PRPH2 variants (c.653C>G, c.700T>C, c.121del) and seven previously reported variants (c.424C>T, c.458A>G, c.461_463del, c.493T>C, c.499G>A, c.612C>G, c.734dup) documented in public databases and the scientific literature. Conclusions: Our data confirm the wide spectrum of IRDs associated with PRPH2 genetic variants and highlight the importance of integrating genetic, clinical, and instrumental data. This strategy enhances diagnostic accuracy and strengthens genotype–phenotype correlations, ultimately improving clinical decision-making and personalized patient management. Full article