Search Results (2,271)

This study measured eight key phenotypic traits across 259 fresh maize inbred lines, including plant height and spike length. A total of 82 single nucleotide polymorphisms (SNPs) significantly associated with these phenotypes were identified by applying a mixed linear model to calculate the best linear unbiased prediction (BLUP) values and integrating genome-wide genotypic data through genome-wide association analysis (GWAS). A further analysis of significant SNPs contributed to the identification of 63 candidate genes with functional annotations. Notably, 11 major candidate genes were identified from multi-trait association loci, all of which exhibited highly significant P-values (<0.0001) and explained between 7.21% and 12.78% of phenotypic variation. These 11 genes, located on chromosomes 1, 3, 4, 5, 6, and 9, were functionally involved in signaling, metabolic regulation, structural maintenance, and stress response, and are likely to play crucial roles in the growth and physiological processes of fresh maize inbred lines. The functional genes identified in this study have significant implications for the development of molecular markers, the optimization of breeding strategies, and the enhancement of quality in fresh maize. Full article

Vegetable oils are essential for human nutrition and industrial applications. With growing global demand, increasing oil content in oilseed crops has become a top priority. This review synthesizes recent progress in understanding the genetic, environmental, and molecular mechanisms regulating oil content, and presents biotechnological strategies to enhance oil accumulation in major oilseed crops. Oil biosynthesis is governed by intricate genetic–environmental interactions. Environmental factors and agronomic practices significantly impact oil accumulation dynamics. Quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) have identified key loci and candidate genes involved in lipid biosynthesis pathways. Transcription factors and epigenetic regulators further fine-tune oil accumulation. Biotechnological approaches, including marker-assisted selection (MAS) and CRISPR/Cas9-mediated genome editing, have successfully generated high-oil-content variants. Future research should integrate multi-omics data, leverage AI-based predictive breeding, and apply precision genome editing to optimize oil yield while maintaining seed quality. This review provides critical references for the genetic improvement and breeding of high- and ultra-high-oil-content varieties in oilseed crops. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

Background: In recent years, comprehensive analyses using a genome-wide association study (GWAS) have been conducted to identify genetic factors related to athletic performance. In this study, we investigated the association between genetic variants and elite wrestling status across multiple ethnic groups using a genome-wide genotyping approach. Methods: This study included 168 elite wrestlers (64 Japanese, 67 Turkish, and 36 Russian), all of whom had competed in international tournaments, including the Olympic Games. Control groups consisted of 306 Japanese, 137 Turkish, and 173 Russian individuals without elite athletic backgrounds. We performed a GWAS comparing allele frequencies of single-nucleotide polymorphisms (SNPs) between elite wrestlers and controls in each ethnic cohort. Cross-population analysis comprised (1) identifying SNPs with nominal significance (p < 0.05) in all three groups, then (2) meta-analyzing overlapped SNPs to assess effect consistency and combined significance. Finally, we investigated whether the most significant SNPs were associated with gene expression in skeletal muscle in 23 physically active men. Results: The GWAS identified 328,388 (Japanese), 23,932 (Turkish), and 30,385 (Russian) SNPs reaching nominal significance. Meta-analysis revealed that the ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms were associated (p < 0.0001) with elite wrestling status across all three populations. Both variants are located in intronic regions and influence the expression of their respective genes in skeletal muscle. Conclusions: This is the first study to investigate gene polymorphisms associated with elite wrestling status in a multi-ethnic cohort. ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms may represent important genetic factors associated with achieving an elite status in wrestling, irrespective of ethnicity. Full article

Corn smut, caused by Ustilago maydis, significantly threatens maize production. This study evaluated 199 maize inbred lines at the seedling stage under greenhouse conditions for resistance to U. maydis, identifying 39 highly resistant lines. A genome-wide association study (GWAS) using the mrMLM model detected 19 significant single-nucleotide polymorphism (SNP) loci. Based on a linkage disequilibrium (LD) decay distance of 260 kb, 226 candidate genes were identified. Utilizing the significant loci chr1_244281660 and chr5_220156746, two kompetitive allele-specific PCR (KASP) markers were successfully developed. A PCR-based sequence-specific oligonucleotide probe hybridization technique applied to the 199 experimental lines and 60 validation lines confirmed polymorphism for both markers, with selection efficiencies of 48.12% and 43.33%, respectively. The tested materials were derived from foundational inbred lines of domestic and foreign origin. Analysis of 39 highly resistant lines showed that the advantageous alleles carrying thymine/cytosine (T/C) predominated at frequencies of 94.87% and 53.84%, respectively. The genotype TTCC conferred high resistance, while CCTT was highly susceptible. The resistance exhibited high heritability and significant gene-by-environment interaction. This work systematically dissects the genetic basis of common smut resistance in maize, identifies favorable alleles, and provides a novel KASP marker-based strategy for developing disease-resistant germplasm. Full article

Alzheimer’s disease (AD) and ischemic stroke (IS) are prevalent neurological disorders that frequently co-occur in the same individuals. Recent studies have demonstrated that AD and IS share several common risk factors and pathogenic elements, including an overlapping genomic architecture. However, the relationship between IS risk gene polymorphisms and AD has been less extensively studied. We aimed at determining whether IS risk gene polymorphisms were associated with the risk of AD and the severity of AD in AD patients. We utilized data of AD patients and normal controls (NCs) sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. IS risk single nucleotide polymorphisms (SNPs) were identified through the most recent and largest IS genome-wide association study (GWAS) meta-analysis. Subsequently, we conducted SNP-based association analysis of IS-risk SNPs with the risk of AD, along with amyloid, tau, and neuroimaging for AD. The generalized multifactor dimensionality reduction (GMDR) model was used to assess the interactions among IS-risk SNPs and apolipoprotein E (ApoE) ε4. Protein–protein interactions (PPIs) of the IS-risk genes product and APOE were explored using the STRING database. Seven IS-risk SNPs were involved in the study. Five SNPs were found to be associated with at least one measurement of cerebrospinal fluid (CSF) levels of amyloid-beta 1–42 (Aβ₄₂), total tau (t-tau), and phosphorylated tau 181 (p-tau₁₈₁), as well as the volumes of the hippocampus, whole brain, entorhinal cortex, and mid-temporal regions. After multiple testing corrections, we found that T allele of rs1487504 contributed to an increased risk of AD in non-ApoE ε4 carriers. The combination of rs1487504 and ApoE ε4 emerged as the optimal two-factor model, and its interaction was significantly related to the risk of AD. Additionally, C allele of rs880315 was significantly associated with elevated levels of CSF Aβ₄₂ in AD patients, and A allele of rs10774625 was significantly related to a reduction in the volume of the entorhinal cortex in AD patients. This study found that IS risk SNPs were associated with both the risk of AD and AD major indicators in the ADNI cohort. These findings elucidated the role of IS in AD from a genetic perspective and provided an innovative approach to predict AD through IS-risk SNPs. Full article

Osteoporosis is a multifactorial, polygenic disease characterized by reduced bone mineral density (BMD) and increased fracture risk. Genome-wide association studies (GWASs) have identified numerous loci associated with BMD and/or bone fractures, but functional characterization of these target genes is essential to understand the biological mechanisms underlying osteoporosis. This review focuses on current methodologies and key examples of successful functional studies aimed at evaluating gene function in osteoporosis research. Functional evaluation typically follows a multi-step approach. In silico analyses using omics datasets expression quantitative trait loci (eQTLs), protein quantitative trait loci (pQTLs), and DNA methylation quantitative trait loci (mQTLs) help prioritize candidate genes and predict relevant biological pathways. In vitro models, including immortalized bone-derived cell lines and primary mesenchymal stem cells (MSCs), are used to explore gene function in osteogenesis. Advanced three-dimensional culture systems provide additional physiological relevance for studying bone-related cellular processes. In situ analyses of patient-derived bone and muscle tissues offer validation in a disease-relevant context, while in vivo studies using mouse and zebrafish models enable comprehensive assessment of gene function in skeletal development and maintenance. Integration of these complementary methodologies helps translate GWAS findings into biological insights and supports the identification of novel therapeutic targets for osteoporosis. Full article

The architecture of rice tillers plays a pivotal role in yield potential, yet conventional phenotyping methods have struggled to capture these intricate three-dimensional (3D) structures with high fidelity. In this study, a 3D model reconstruction method was developed specifically for rice tillers to overcome the challenges posed by their slender, feature-poor morphology in multi-view stereo-based 3D reconstruction. By applying strategically designed colorful reference markers, high-resolution 3D tiller models of 231 rice landraces were reconstructed. Accurate phenotyping was achieved by introducing ScaleCalculator, a software tool that integrated depth images from a depth camera to calibrate the physical sizes of the 3D models. The high efficiency of the 3D model-based phenotyping pipeline was demonstrated by extracting the following seven key agronomic traits: flag leaf length, panicle length, first internode length below the panicle, stem length, flag leaf angle, second leaf angle from the panicle, and third leaf angle. Genome-wide association studies (GWAS) performed with these 3D traits identified numerous candidate genes, nine of which had been previously confirmed in the literature. This work provides a 3D phenomics solution tailored for slender organs and offers novel insights into the genetic regulation of complex morphological traits in rice. Full article

Background/Objectives: Astaxanthin, a xanthophyll carotenoid, has garnered significant interest due to its benefits with regard to dyslipidemia. This multifaceted functional food ingredient modulates several key enzymes associated with lipid regulation, including HMG-CoA reductase, CPT1, ACCβ, and acyl-CoA oxidase. It influences key antioxidant molecular pathways like the Nrf2, limiting dyslipidemia occurrence and regulating liver cholesterol uptake through the modulation of liver lipid receptors. Due to the current lack of systematic reviews and meta-analyses assessing moderate to high dosages (6–24 mg/d) of astaxanthin supplementation on lipid dysregulation, the present manuscript aims to fill this gap in the literature. Methods: Following the PRISMA guidelines, we included eight studies comprising eleven results from the PubMed, Springer Link, Science Direct, Cochrane, and Google Scholar databases. The Jamovi (Version 2.6.26, Solid) software was utilized for statistics. Our primary objective was to assess in detail the effects of astaxanthin on LDL-C, HDL-C, triglyceride, and total cholesterol levels. Results: The meta-analysis concludes positive effects of astaxanthin (6–20 mg/d) on HDL-C (0.4200; 95% CI: 0.1081 to 0.7319) and triglyceride (−0.3058; 95% CI: −0.5138 to −0.0978) levels. Unfortunately, astaxanthin (6–20 mg/d) does not appear to significantly influence LDL-C (−0.0725; 95% CI: −0.3070 to 0.1620) and total cholesterol (−0.0448; 95% CI: −0.3369 to 0.2473) levels. Regarding HDL-C, improvements were observed from 55 ± 8 mg/dL (pre-intervention) to 63 ± 8 mg/dL (post-intervention) (p < 0.01) in the 12 mg/d of astaxanthin groups. In the assessment of triglyceride levels, results show a decrease from 151 ± 26 mg/dL (pre-intervention) to 112 ± 40 mg/dL (post-intervention) (p < 0.01) for 18 mg/d astaxanthin supplementation. Conclusions: Further research is necessary to fully harness the potential of astaxanthin, which includes assessing astaxanthin in different subsets of patients, using a GWAS, and in combination with other nutraceuticals to understand the compound’s effectiveness with regard to varying health conditions, genetic and epigenetic factors, and synergistic effects with other compounds. Full article

Fusarium root rot, caused by Fusarium equiseti, poses a significant threat to soybean production. This study aimed to explore the genetic basis of resistance to Fusarium equiseti root rot (FERR) by evaluating the resistance phenotype of 346 soybean germplasms and conducting a genome-wide association study (GWAS) using 698,949 SNP markers obtained from soybean germplasm resequencing data. GWAS analysis identified 101 SNPs significantly associated with FERR resistance, distributed across nine chromosomes, with the highest number of SNPs on chromosomes 13 and 20. Further gene-based association and allele variation analyses identified candidate genes whose mutations are closely related to FERR resistance. To accelerate soybean FERR resistance breeding screening, we developed CAPS markers S13_14464319-CAPS1 and S15_9215524-CAPS2, targeting these SNP sites, and KASP markers based on the S15_9205620-G/A, providing an effective tool for marker-assisted selection (MAS). This study offers a valuable theoretical foundation and molecular marker resources for the functional validation of FERR resistance genes and soybean disease resistance breeding. Full article

Enhancing quality traits is a primary objective in silage maize breeding programs. The use of genome-wide association studies (GWAS) for quality traits, in combination with the integration of genetic resources, presents an opportunity to identify crucial genomic regions and candidate genes influencing silage maize quality. In this study, a GWAS was conducted on 580 inbred lines of silage maize, and a meta-analysis was performed on 477 quantitative trait loci (QTLs) from 34 studies. The analysis identified 27 significant single nucleotide polymorphisms (SNPs) and 87 consensus QTLs (cQTLs), with 7 cQTLs associated with multiple quality traits. By integrating the SNPs identified through association mapping, one SNP was found to overlap with the cQTL interval related to crude protein, neutral detergent fiber, and starch content. Furthermore, enrichment analysis predicted 300 and 5669 candidate genes through GWAS and meta-analysis, respectively, highlighting pathways such as cellular metabolism, the biosynthesis of secondary metabolites, ribosome function, carbon metabolism, protein processing in the endoplasmic reticulum, and amino acid biosynthesis. The examination of 13 candidate genes from three co-located regions revealed Zm00001d050977 as a cytochrome P450 family gene, while the other 2 genes primarily encode proteins involved in stress responses and other biological pathways. In conclusion, this research presents a methodology combining GWAS and meta-analysis to identify genomic regions and potential genes influencing quality traits in silage maize. These findings serve as a foundation for the identification of significant QTLs and candidate genes crucial for improving silage maize quality. Full article

Common carp (Cyprinus carpio) is one of the most significant fish species worldwide, with its natural distribution spanning Europe and Asia. To conduct a genome-wide association study (GWAS) and compare the prediction accuracy of genomic selection (GS) models for the growth traits of common carp in spring and autumn at 2 years of age, a total of 325 carp individuals were re-sequenced and phenotypic measurements were taken. Three GWAS methods (FarmCPU, GEMMA, and GLM) were applied and their performance was evaluated in conjunction with various GS models, using significance levels based on p-values. GWAS analyses were performed on eight traits (including the body length, body weight, fat content of fillet, and condition factor) for both spring and autumn seasons. Eleven different GS models (such as Bayes A, Bayes B, and SVR-linear) were combined to evaluate their performance in genomic selection. The results demonstrate that the FarmCPU method consistently exhibits superior stability and predictive accuracy across most traits, particularly under higher SNP densities (e.g., 5K), where prediction accuracies frequently exceed 0.8. Notably, when integrated with Bayesian approaches, FarmCPU achieves a substantial performance boost, with the prediction accuracy reaching as high as 0.95 for the autumn body weight, highlighting its potential for high-resolution genomic prediction. In contrast, GEMMA and GLM exhibited a more variable performance at lower SNP densities. Overall, the integration of FarmCPU with genomic selection (GS) models offers one of the most reliable and efficient frameworks for trait prediction, particularly for complex traits with substantial genetic variation. This approach proves especially powerful when coupled with Bayesian methodologies, further enhancing its applicability in advanced breeding programs. Full article

Genomic prediction has emerged as a powerful tool in aquaculture breeding, but its effectiveness depends on the careful selection of informative single nucleotide polymorphisms (SNPs) and the application of appropriate prediction models. This study aimed to enhance genomic prediction accuracy in Russian sturgeon (Acipenser gueldenstaedtii) by optimizing SNP selection strategies and exploring the performance of linear and machine learning models. Three economically important traits—caviar yield, caviar color, and body weight—were selected due to their direct relevance to breeding goals and market value. Whole-genome sequencing (WGS) data were obtained from 971 individuals with an average sequencing depth of 13.52×. To reduce marker density and eliminate redundancy, three SNP selection strategies were applied: (1) genome-wide association study (GWAS)-based prioritization to select trait-associated SNPs; (2) linkage disequilibrium (LD) pruning to retain independent markers; and (3) random sampling as a control. Genomic prediction was conducted using both linear (e.g., GBLUP) and machine learning models (e.g., random forest) across varying SNP densities (1 K to 50 K). Results showed that GWAS-based SNP selection consistently outperformed other strategies, especially at moderate densities (≥10 K), improving prediction accuracy by up to 3.4% compared to the full WGS dataset. LD-based selection at higher densities (30 K and 50 K) achieved comparable performance to full WGS. Notably, machine learning models, particularly random forest, exceeded the performance of linear models, yielding an additional 2.0% increase in accuracy when combined with GWAS-selected SNPs. In conclusion, integrating WGS data with GWAS-informed SNP selection and advanced machine learning models offers a promising framework for improving genomic prediction in sturgeon and holds promise for broader applications in aquaculture breeding programs. Full article

The yak is a vital livestock resource on the Qinghai–Tibet Plateau, renowned for its strong disease resistance and high-quality meat. However, various diseases pose significant threats to its health and lead to substantial economic losses. Current feeding management practices, along with available drugs and vaccines, have demonstrated limited effectiveness in preventing and controlling infectious diseases. Additionally, challenges such as drug resistance and the safety of animal products persist. Therefore, enhancing the disease-resistant breeding capacity of yaks is crucial. In this study, we examined 192 yaks by measuring the concentrations of 10 immune indicators in serum by using the ELISA method and conducting whole-genome resequencing, which identified 19,182,942 SNP loci. Through genome-wide association analysis, we detected 323 significant SNPs located near or within 125 candidate genes, most of which are associated with disease and significantly enriched in the TGF-β signaling pathway. Overall, our study identified a series of novel variants and candidate genes associated with disease resistance traits in yaks, providing important information for the molecular breeding of disease resistance in yaks. These results not only contribute to a deeper understanding of the function of disease resistance genes in yaks but also hold great potential for accelerating precision disease resistance breeding in yaks. Full article

Data center virtualization has grown rapidly alongside the expansion of application-based services but continues to face significant challenges, such as downtime caused by suboptimal hardware selection, load balancing, power management, incident response, and resource allocation. To address these challenges, this study proposes a combined machine learning method that uses an MDP to choose which VMs to move, the RF method to sort the VMs according to load, and NSGA-III to achieve multiple optimization objectives, such as reducing downtime, improving SLA, and increasing energy efficiency. For this model, the GWA-Bitbrains dataset was used, on which it had a classification accuracy of 98.77%, a MAPE of 7.69% in predicting migration duration, and an energy efficiency improvement of 90.80%. The results of real-world experiments show that the hybrid machine learning strategy could significantly reduce the data center workload, increase the total migration time, and decrease the downtime. The results of hybrid machine learning affirm the effectiveness of integrating the MDP, RF method, and NSGA-III for providing holistic solutions in VM placement strategies for large-scale data centers. Full article