Search Results (559)

Obesity is a multifaceted disorder influenced by various factors, with heredity being a significant contributor. Bariatric surgery is the most effective long-term intervention for morbid obesity and associated comorbidities, while outcomes vary significantly across individuals. Recent studies indicate that genetic and molecular determinants, particularly alterations in the leptin–melanocortin signalling pathway involving the fat mass and obesity-associated gene (FTO), pro-opiomelanocortin (POMC), melanocortin 4 receptor (MC4R), leptin (LEP), and leptin receptor (LEPR), influence the efficacy of weight loss and metabolic adaptations post-surgery. This narrative review consolidates evidence from peer-reviewed papers available in PubMed and Scopus until July 2025. The emphasis was on novel research and systematic reviews examining genetic polymorphisms, gene–environment interactions, and outcomes following bariatric procedures such as Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). Recent research emphasised the integration of genetic screening and precision medicine models into clinical bariatric workflows. Variants in FTO (e.g., rs9939609), MC4R (e.g., rs17782313), LEPR, and POMC are associated with diminished weight loss post-surgery, an increased likelihood of weight regain, and reduced metabolic enhancement. Patients with bi-allelic mutations in MC4R, POMC, or LEPR exhibited poor long-term outcomes despite receiving effective physical interventions. Furthermore, genes regulating mitochondrial metabolism (such as PGC1A), adipokine signalling (such as ADIPOQ), and glucose regulation (such as GLP1R) have been demonstrated to influence the body’s response to sugar and the extent of weight gain or loss. Two recent systematic reviews elucidate that candidate gene investigations are beneficial; however, larger genome-wide association studies (GWAS) and machine learning techniques are necessary to enhance predictive accuracy. Integrating genetic and molecular screening with bariatric surgery planning possesses significant therapeutic potential. Genotyping can assist in patient selection, procedural decisions, and medication additions, particularly for those with variants that influence appetite regulation or metabolic flexibility. Advancements in precision medicine, including the integration of polygenic risk scores, omics-based profiling, and artificial intelligence, will enhance the customisation of surgical interventions and extend the lifespan of individuals with severe obesity. The epigenetic regulators of energy balance DNA methylation, histone changes, and microRNAs that may affect individual differences in weight-loss patterns after bariatric surgery are also briefly contextualised. We discuss the concept that epigenetic modulation of gene expression, mediated by microRNAs in response to food and exercise, may account for variations in metabolic outcomes post-surgery. Full article

Modern crop breeding requires efficient tools for genetic identity and parentage verification to manage large-scale programs. To address this, we present GIPA (Genomic Identity and Parentage Analysis), a high-performance toolkit designed for these tasks. GIPA integrates key innovations: a sliding-window algorithm enhances accuracy by correcting genotyping errors, an intelligent system classifies samples by heterozygosity to streamline parentage analysis, and an integrated engine generates intuitive chromosome-level heatmaps. We demonstrate its utility in a soybean backcrossing scenario, where it identified a donor line with 98.02% genomic identity to the recipient, providing a strategy to significantly shorten the breeding program. In maize, its parentage module accurately identified the known parents of commercial hybrids with match scores exceeding 97%, validating its use for variety authentication and quality control. By transforming complex SNP data into clear, quantitative, and visual insights, GIPA provides a robust solution that accelerates data-driven decision-making in plant breeding. Full article

Background/Objectives: Accurate and rapid detection of Mycobacterium tuberculosis complex (MTBC) and drug resistance is essential for effective tuberculosis (TB) management, particularly in high-burden settings. The Cobas^® MTB and Cobas MTB/RIF-INH assays are moderate-complexity nucleic acid amplification tests that detect MTBC and resistance to rifampicin (RIF) and isoniazid (INH). Methods: This study evaluated the clinical diagnostic performance of the Cobas assays on sputum, using liquid culture as the reference standard and Xpert MTB/RIF Ultra (Xpert Ultra) for comparison. Diagnostic accuracy of the Cobas MTB assay on tongue swabs (TS) was also assessed. Results: In a study population (n = 354) with 56% HIV prevalence, the overall sensitivity and specificity of Cobas MTB on sputum was 93.8% (95% CI: 84.8–98.3) and 100% (95% CI: 98.7–100) compared with culture. The assay showed almost perfect agreement with Xpert Ultra (Cohen’s kappa = 0.904). Among HIV-positive participants, sensitivity was 88.2% (95% CI: 72.5–96.7). RIF resistance profiling by Cobas MTB/RIF-INH was fully concordant with culture and Xpert Ultra. Three INH-resistant cases were missed, likely due to genotypic–phenotypic discordance. Although specimen numbers were small, TS demonstrated better diagnostic accuracy when using a diluted (66%) microbial inactivation solution. Conclusions: The Cobas MTB and MTB/RIF-INH assays demonstrated high diagnostic accuracy compared to culture and Xpert Ultra on sputum. Findings support TS as an alternative specimen type for MTBC detection using an optimized protocol. These findings underscore the potential of the Cobas assays as reliable alternatives for TB and resistance diagnostics, particularly in settings where rapid, accurate detection of MTBC and RIF or INH resistance is crucial. Full article

Dollar spot is a severe and widespread turfgrass disease. Ultraviolet-C (UV-C) light treatment offers a promising management strategy, and its integration into autonomous mowers could reduce fungicide use, promoting sustainable and efficient turfgrass management. To ensure effectiveness and optimize intervention timing, monitoring is essential and hyperspectral sensing could represent a valuable resource. This study aimed to develop an innovative approach for the early detection and integrated management of dollar spot in bermudagrass by evaluating (i) the efficacy of an autonomous mower equipped with UV-C lamps in mitigating infections, and (ii) the potential of full-range hyperspectral sensing (350–2500 nm) for disease detection and monitoring. The autonomous mower enabled UV-C treatment with a field capacity of 0.04 ha h⁻¹, requiring 1.3 machines to treat 1 ha day⁻¹, and a primary energy consumption of 55.06 kWh ha⁻¹ for a complete weekly treatment. Full-range canopy hyperspectral data (400–2400 nm) enabled rapid, non-destructive field detection. Permutational multivariate analysis of variance (PERMANOVA) detected significant effects of Clarireedia jacksonii (Cj; dollar spot pathogen) and the Cj × UV-C interaction. Partial least-squares discriminant analysis (PLS-DA) separated Cj+/UV+ and Cj+/UV− plots (Accuracy validation ≈ 0.73; K ≈ 0.69). Investigated spectral indices confirmed Cj × UV-C interactions. Future research should explore how to optimize UV-C application regimes, improve system scalability, and enhance the robustness of hyperspectral models across diverse turfgrass genotypes, growth stages, and environmental conditions. Full article

The accuracy of genomic selection has a significant impact on the selection of superior individuals in livestock. Studies have reported that integrating GWAS information can improve the accuracy of genomic prediction. In this study, phenotypic data, systematic environmental data, and genotypic data of important economic traits (cashmere yield, cashmere diameter, body weight, and cashmere length) of Inner Mongolia cashmere goats were utilized. Based on the results of a previous genome-wide association study that considered additive and dominance effects, the top 5%, top 10%, top 15%, and top 20% of loci were extracted as prior marker information. The genomic breeding values for each trait were estimated using the GBLUP–GA method based on GWAS prior information, and the accuracy of genomic prediction was further evaluated using a five-fold cross-validation method. The results showed that the contribution of significant loci to the genetic variance of each trait gradually increased with an increase of the number of integrated loci. The genetic variance contribution rates of significant loci to cashmere yield, cashmere diameter, body weight, and cashmere length were 64–71%, 47–57%, 76–82%, and 66–80%, respectively. The additive heritability estimates for cashmere yield, cashmere diameter, body weight, and cashmere length using GWAS prior information were 0.252–0.266, 0.297–0.580, 0.305–0.330, and 0.107–0.117, respectively. These values were higher than those obtained using the traditional G matrix constructed from all loci, with increases of 0.052–0.066, 0.007–0.29, 0.134–0.159, and 0.015–0.025, respectively. The results of genomic prediction accuracy showed that when 5% of the GWAS prior information was integrated, the highest genomic prediction accuracy was achieved for cashmere yield (0.8156), body weight (0.8361), and cashmere length (0.7571). When 20% of the GWAS prior information was integrated, the genomic prediction accuracy for cashmere diameter was 0.8074, which was significantly higher than that at other levels. Additionally, it was found that the dominance heritability for cashmere diameter, body weight, and cashmere length was very small and could be ignored when integrating GWAS prior information. Therefore, when integrating prior information for genomic selection of these traits, the influence of dominance effects can be disregarded. Full article

Single nucleotide polymorphism (SNP) involves plenty of genetic disorders in organisms that can be passed down to the next generation or cause the stimulant signal that leads to early mortality in infants, especially within humankind. In medical field, real-time polymerase chain reaction (RT-PCR) is the most popular method for disease diagnosis. The investigation of genetic maps for the prediction of inherited illnesses needs the collaboration of sequencing technique and genome analysis. Although these methods are popular now, the cost for each test is quite high. Moreover, there is the requirement of extra machines and skillful technician or specialist level. Among these popular methods, the allele-specific polymerase chain reaction (AS-PCR), allele-specific loop isothermal mediated amplification (AS-LAMP), and allele-specific recombinase polymerase amplification (AS-RPA) are brought up for screening the nucleotide differences in the genetic sequence which will be noticed in this review as their availability, novelty, and potential for quick distinguishing of disease caused by SNP. Point-of-care testing (POCT) is a system built in a portable size but can perform the entire process of SNP recognition. Along with that, the POCT is intersected with the mentioned amplification methods and the genetic material preparation steps to become a united framework for higher efficiency and accuracy and lower cost. According to that, this review will focus on three common amplification techniques and their combination with POCT in the upstream and downstream process to genotype SNP related to human diseases. Full article

Accurate differentiation between Coilia brachygnathus and Coilia nasus is imperative for the effective management of fisheries, the conservation of aquatic ecosystems, and the mitigation of commercial fraud. Current morphological identification remains challenging due to their high morphological similarity—particularly for processed samples—while conventional molecular methods often lack the speed or specificity required for field applications or high-throughput screening. In this study, a novel integrated approach was developed and validated, combining TaqMan quantitative real-time PCR (qPCR). for precise genotyping of C. brachygnathus and C. nasus with Recombinase Polymerase Amplification coupled with Lateral Flow Dipstick (RPA-LFD) for rapid on-site screening. First, species-specific RPA-LFD assays were designed to target the mitochondrial COI gene sequence. This enabled visual detection within 10 min at 37 °C, with a sensitivity of 10² copies/μL, and required no complex equipment. A dual TaqMan MGB qPCR assay was further developed by validating stable differentiating SNPs (chr21:3798155, C/T) between C. brachygnathus and C. nasus, using FAM/VIC dual-labeled MGB probes. Results showed that this assay could distinguish the two species in a single tube: for C. brachygnathus, Ct values in the FAM channel were significantly earlier than those in the VIC channel (ΔCt ≥ 1), with a FAM detection limit of 125 copies/reaction; for C. nasus, only VIC channel amplification was observed, with a detection limit as low as 12.5 copies/reaction. Validation with 171 known tissue samples demonstrated 100% concordance with expected species identities. This integrated approach effectively combines the high accuracy and quantitative capacity of TaqMan qPCR for confirmatory laboratory genotyping with the speed, simplicity, and portability of RPA-LFD for initial field or point-of-need screening. This reliable, efficient, and user-friendly technique provides a powerful tool for resource management, biodiversity monitoring, and ensuring the authenticity of high-quality C. brachygnathus and C. nasus. Full article

Background: The accurate and rapid genotyping of ABO (chromosome 9q34.2) blood types is critical for clinical diagnostics and transfusion medicine, particularly in scenarios where serological methods yield uncertain results, such as in neonatal testing or with rare ABO subtypes. Methods: This study describes a loop-mediated isothermal amplification (LAMP)-based method for ABO genotyping that offers a faster and more cost-effective alternative to conventional PCR-based techniques. Results: The method targets four key single nucleotide polymorphisms (SNPs) at positions 261, 297, 703, and 930, allowing for the differentiation of common A, B, and O blood types, as well as the rare AB subtype B(A)01. The detection of the B(A)01 subtype is clinically important for preventing transfusion mismatches where serology may be inconclusive. Operating at a constant temperature, the assay can be completed in under an hour without the need for a thermocycler, offering significant time and cost benefits over qPCR. The method demonstrated high specificity, demonstrating detection down to 10 copies across all assays. When validated against a gold-standard method on clinical blood samples, the LAMP assay showed high accuracy (95% C value calculated via binomial exact method): 97.4% for type O, 98.7% for type A, 98.7% for type B, and 100% for the B(A)01 subtype. To enhance usability for point-of-care applications, freeze-dried reagents were developed that permit direct loading of lysed blood samples while maintaining high performance. Conclusions: This simplified and robust format positions the LAMP assay as a promising tool for rapid and reliable ABO genotyping in diverse clinical settings. Full article

Fertility is key for calf production. Direct selection for female fertility under field conditions is hindered by low accuracy and selection response. An alternative widely implemented is selection for scrotal circumference (SC), genetically correlated with daughter fertility. This study performed a genome-wide association study (GWAS) to identify genomic regions and candidate loci linked to SC and female fertility in Retinta cattle. A multivariate ssGBLUP was applied using SC records from 1061 bulls, fertility-related traits from 59,254 females and genotypes from 1230 animals using the Axiom™ Bovine Genotyping v3 Array (65k). The ssGWAS revealed 23 1-Mb windows explaining >1% of additive genetic variance for SC, one on chromosome 2 and 22 on chromosome 3. Within these windows, 198 regions spanning 118 protein-coding genes and 80 RNA genes were identified. Several genes, including GSTM3, SPATA1, HFM1, and MSH4, were previously associated with male fertility. Six regions overlapped across male and female traits, containing two protein-coding genes (THSD7B and ENSBTAG00000021755). Identification of genomic markers linked to both female fertility and male SC enables selection of superior animals, improving reproductive efficiency and advancing knowledge of the genomic basis of male–female fertility relationships. Full article

Acipenser dabryanus, once abundant in China’s freshwater ecosystems, is now extinct in the wild. Effective genetic tools are urgently needed to support conservation efforts under the Yangtze River Protection Law and the 10-year fishing ban. Traditional molecular markers (e.g., COI, SSR, SNP) often lack sufficient resolution for fine-scale population assessment. Here, we developed a high-resolution Multiple-Nucleotide Polymorphism (MNP) system for A. dabryanus, comprising 424 newly developed, highly polymorphic markers optimized for multiplex PCR and high-throughput sequencing. The MNP system demonstrated excellent performance in individual fin tissue samples, successfully distinguishing Acipenser sinensis and Acipenser ruthenus individuals from the A. dabryanus population. In addition, 41 characteristic alleles specific to A. dabryanus were further identified. Across samples, it achieved >90% MNP locus detection rate, with an average of 7.48 alleles per locus, 66.5% heterozygosity, >98% reproducibility, and 99% accuracy. A strong correlation was observed between DNA concentration and spike-in-based copy numbers (R² > 0.99), and sensitivity analysis confirmed reliable detection at ~1 copy/reaction. Application of the system across 97 samples, including 51 A. dabryanus tissue samples and 46 water environmental samples, revealed clear population structure with an average genetic differentiation of 70.45%, highlighting substantial genetic diversity within the sampled populations. Based on the above experimental results, the high-resolution MNP system has the potential to enable construction of population-specific allelic genotypes to distinguish wild individuals from released ones and, when applied to tissue and eDNA samples, to facilitate monitoring of migration pathways and habitat connectivity. Such applications could provide essential genetic information to evaluate release programs, guide conservation strategies, and inform habitat restoration for the recovery of A. dabryanus. Full article

In recent years, Machine Learning (ML) has garnered increasing attention for its applications in genomic prediction. ML effectively processes high-dimensional genomic data and establishes nonlinear models. Compared to traditional Genomic Selection (GS) methods, ML algorithms enhance computational efficiency and offer higher prediction accuracy. Therefore, this study strives to achieve the optimal machine learning algorithm for genome-wide selection of cashmere traits in Inner Mongolian cashmere goats. This study compared the genomic prediction accuracy of cashmere traits using four machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting Tree (XGBoost), Gradient Boosting Decision Tree (GBDT), and LightGBM—based on genotype data and cashmere trait phenotypic data from 2299 Inner Mongolian cashmere goats. The results showed that after parameter optimization, LightGBM achieved the highest selection accuracy for fiber length (56.4%), RF achieved the highest selection accuracy for cashmere production (35.2%), and GBDT achieved the highest selection accuracy for cashmere diameter (40.4%), compared with GBLUP, the accuracy improved by 0.8–2.7%. Among the three traits, XGBoost exhibited the lowest prediction accuracy, at 0.541, 0.309, and 0.387. Additionally, following parameter optimization, the prediction accuracy of the four machine learning methods for cashmere fineness, cashmere yield, and fiber length improved by an average of 2.9%, 2.7%, and 3.8%, respectively. The mean squared error (MSE) and mean absolute error (MAE) for all machine learning methods also decreased, indicating that hyperparameter tuning can enhance prediction accuracy in ML algorithms. Full article

Simulation-based studies can support breeders’ decisions inexpensively, since there is no need to perform a new procedure. The objective was to assess the efficiency of recurrent genomic selection in panmictic population under additive–dominance and additive–dominance with epistasis models. We assumed two broiler chicken populations with contrasting linkage disequilibrium (LD) levels, 38,500 SNPs, and 1000 genes controlling feed conversion ratio. We applied recurrent genomic selection over seven cycles. The genomic selection efficacy, expressed as realized total genetic gain, was proportional to the LD level and genotypic variance. Genomic selection required model updating to achieve a higher efficacy. The training set size required by genomic selection can be as low as 10%/generation. Under this low-cost scenario, the genomic selection efficacy was slightly lower than the maximum efficacy. There is no difference between genetic evaluation methods regarding the decrease in the genotypic variance due to selection. In general, additive value prediction accuracies and realized genetic gains were highly correlated. The accumulated inbreeding level was not high due to avoidance of sib cross. The genomic inbreeding coefficient over generations was close to zero. Except for dominant epistasis, the efficacy of genomic selection was 4.1 to 46.2% lower than the efficacy under no epistasis. Full article

Embryo genomic selection (EGS) is a contemporary breeding strategy that combines genomic selection (GS) methodology with embryo biotechnology. By conducting genotyping and genomic prediction at the pre-implantation stage, embryos with superior breeding value can be identified for transfer, markedly increasing breeding efficiency while reducing the uncertainty and temporal expenditure associated with conventional GS. This study establishes a reliable embryo biopsy-based GS pipeline for sheep, incorporating optimized whole-genome amplification and microcell genotyping techniques. We developed a high-efficiency in vitro sheep embryo production platform compatible with embryo biopsy. Systematic comparison of Multiple Displacement Amplification (MDA) and Multiple Annealing and Looping Based Amplification Cycles (MALBAC) whole-genome amplification systems yielded high-quality genotypes from biopsy samples of embryos containing as few as 10 cells. Imputation using 10× whole-genome sequencing data significantly increased both genotype call rates and accuracy. High concordance was observed between embryo and lamb genotypes, and genomic estimated breeding values (GEBVs) for key growth traits exhibited strong correlations (R²: 0.91–0.98). This system enables accurate preimplantation genomic evaluation and provides an efficient strategy to accelerate genetic improvement in sheep breeding programs. Full article

The objective of this study was to evaluate the impact of blending, tuning, and scaling adjustments in ssGBLUP on the accuracy of genomic estimated breeding values (GEBVs) for South African Holstein cattle. The edited dataset included pedigree information for 541,325 animals, 696,413 phenotypic records (milk, protein, and fat yields), and genotypes for 1221 Holstein cattle. The accuracy of GEBVs was evaluated based on different parameter settings for blending (β = 0.05, 0.10, 0.20, 0.30, and 0.40), tuning (τ), and scaling (τ and ω), ranging from 0.60 to 1.00. The results show that ssGBLUP outperformed the traditional pedigree-based approach (ABLUP), with realized accuracies increasing from 0.01 to 0.23 for milk yield, 0.03 to 0.29 for protein yield, and 0.03 to 0.30 for fat yield. Blending with β = 0.30–0.40 slightly increased the accuracy, while tuning adjustments showed limited influence on the prediction results. Scaling factors had a significant influence on accuracy, with ω = 0.60 yielding the highest values (0.26 for milk, 0.32 for protein, and 0.34 for fat). The results of this study show the importance of optimizing the integration of pedigree and genomic information in ssGBLUP to improve the accuracy of genomic predictions, ultimately enhancing selection decisions and genetic progress in South African Holstein cattle. Full article

Abdominal fat is a key indicator of chicken meat quality. Excessive deposition not only reduces meat quality but also decreases feed conversion efficiency, making the breeding of low-abdominal-fat strains economically important. Genomic selection (GS) uses information from genome-wide association studies (GWASs) and high-throughput sequencing data. It estimates genomic breeding values (GEBVs) from genotypes, which enables early and precise selection. Given that abdominal fat is a polygenic trait controlled by numerous small-effect loci, this study combined population genetic analyses with machine learning (ML)-based feature selection. Relevant single-nucleotide polymorphisms (SNPs) were first identified using a combined GWAS and linkage disequilibrium (LD) approach, followed by a two-stage feature selection process—Lasso for dimensionality reduction and recursive feature elimination (RFE) for refinement—to generate the model input set. We evaluated multiple machine learning models for predicting genomic estimated breeding values (GEBVs). The results showed that linear models and certain nonlinear models achieved higher accuracy and were well suited as base learners for ensemble methods. Building on these findings, we developed a Dynamic Adaptive Weighted Stacking Ensemble Learning Framework (DAWSELF), which applies dynamic weighting and voting to heterogeneous base learners and integrates them layer by layer, with Ridge serving as the meta-learner. In three independent validation populations, DAWSELF consistently outperformed individual models and conventional stacking frameworks in prediction accuracy. This work establishes an efficient GEBV prediction framework for complex traits such as chicken abdominal fat and provides a reusable SNP feature selection strategy, offering practical value for enhancing the precision of poultry breeding and improving product quality. Full article