BackgroundThis research assessed the causal influence of atrial fibrillation (AF) on depression.MethodsA two-sample Mendelian randomization (MR) approach was utilized along with data from additional databases. A genome-wide association study (GWAS) involving 463,010 participants allowed for the exploration of genetic variations associated with AF. Another GWAS with 215,644 individuals offered insights into the relationship between gene variants and depression. Data on the correlation between gene variants and depression were obtained from another GWAS encompassing 449,414 individuals. Effect sizes were assessed utilizing the inverse-variance weighted technique. Sensitivity analysis was conducted by weighted median, outlier, MR pleiotropy residual sum, weighted mode, and MR-Egger. A meta-analysis of the inverse-variance weighted (IVW) results from the two datasets was conducted.ResultsA significant association was found between genetically predicted AF and increased incidence of depression using 15 single nucleotide polymorphisms (SNPs) as markers. No evidence of gene pleiotropy was detected, as indicated by MR-Egger. Sensitivity analyses employing alternative Mendelian randomization techniques consistently yielded robust results. The combined odds ratio for depression was estimated at 29.19 (95% CI = 4.43-192.13, P < 0.001).ConclusionThis study found a causal relationship between genetically predicted AF and a heightened risk of depression.

Soil salinity is a major abiotic stress limiting rice productivity, particularly in coastal and irrigated regions. Japonica rice, widely cultivated in temperate regions, is moderately sensitive to salt stress, especially during the seedling stage. To accelerate salt-tolerance improvement in Japonica backgrounds, we conducted large-scale multi-environment phenotyping of 225 diverse rice cultivars under controlled salt stress (0.7% NaCl) across two seasons (spring and summer 2024), followed by genome-wide association study (GWAS) and transcriptome profiling. Best linear unbiased predictors (BLUPs) derived from linear mixed-effects models effectively corrected seasonal, spatial, and environmental variations, revealing substantial genotypic differences in seedling-stage salt tolerance. GWAS identified five novel QTLs ( qSES3 , qSES5 , qSES6 , qSES7 , and qSES9 ), with qSES6 and qSES7 exhibiting strong synergistic effects. The transcriptome analysis of the highly tolerant cultivar ‘IR73571-3B-11-3-K2’ identified 834 DEGs, revealing enriched stress-responsive activities, such as oxidoreductase activity and phenylpropanoid biosynthesis. Integrating the GWAS and transcriptomic data highlighted Os07g0635500 (cytochrome P450) as a key candidate gene, and the haplotype analysis identifying two haplotypes, with Haplotype 2 conferring superior tolerance. However, favorable QTLs and haplotypes were predominantly found in Tongil-type cultivars, suggesting limited representation in Japonica cultivars. Therefore, targeted introgression and marker-assisted selection will be required to transfer salt tolerance traits into Japonica cultivars. Overall, this study dissected salt stress responses in rice and providing a multidimensional resource and practical insights for the molecular breeding of salt-tolerant Japonica rice.

Recent studies suggest that systemic sclerosis (SSc) may be associated with cognitive impairment and dementia. However, the causal relationship and its direction remain unclear. This study employed a two-sample bidirectional Mendelian randomization (MR) approach to systematically evaluate the genetic causal relationship between five types of dementia and SSc. Based on genome-wide association study (GWAS) summary data, we investigated the relationship between five types of dementia and SSc. The inverse variance weighted (IVW) method served as the primary analytical approach, supplemented by validation using the weighted median method, MR-Egger regression, simple mode, and weighted mode methods. Sensitivity analyses (leave-one-out, funnel plot, MR-PRESSO), pleiotropy tests (Egger intercept), and heterogeneity assessments (Cochran's Q) were conducted to ensure the robustness and reliability of the results. Additionally, reverse MR analysis was performed to further confirm the directionality of the causal relationship. Forward MR analysis revealed a significant negative association between Alzheimer's disease (AD) and the risk of SSc (IVW OR=0.530, 95% CI: 0.290-0.969, P =0.039). In contrast, no significant causal relationships were found between SSc and frontotemporal dementia, vascular dementia, dementia with Lewy bodies, or Parkinson's disease dementia. Reverse MR analysis did not identify any causal effects of SSc on the aforementioned types of dementia, further supporting the directionality of the causal relationship. Sensitivity analyses, pleiotropy tests, and heterogeneity assessments did not reveal significant horizontal pleiotropy or heterogeneity. Our study provides evidence of a potential causal relationship between AD and a reduced risk of SSc, highlighting the need for further research to explore the underlying mechanisms of this complex disease relationship.

Glutathione peroxidase 8(GPX8) is a key member of the glutathione peroxidase family. Genome-wide association studies (GWAS) have indicated that GPX8 is highly associated with growth and carcass traits of pigs. Using porcine skeletal muscle satellite cells, this study explored the effects of GPX8 on myogenic differentiation and myofiber type switching through GPX8 knockdown and overexpression, combined with analyses involving immunofluorescence staining, qRT-PCR, and Western blotting. The results demonstrated that GPX8 knockdown significantly increased the myogenic differentiation index (P<0.01) and promoted both mRNA and protein levels of the myogenic marker genes MyHC and MyoG (P<0.05). Conversely, GPX8 overexpression exhibited the opposite effects. GPX8 knockdown significantly reduced the mRNA level of MYH7 (P<0.01) and protein level of slow-twitch MyHC (slow-MyHC)(P<0.05), while suppressing mitochondrial biogenesis. In contrast, GPX8 overexpression exhibited opposing results. Integrated multi-omics data from GWAS analyses were employed to identify expression quantitative trait locus (eQTLs) regulating GPX8 expression. The effects of candidate SNPs on GPX8 promoter activity were further validated using dual-luciferase reporter assays. Five candidate SNPs (rs335618489, rs325233940, rs32989756, rs322106839, and rs701033890) were identified within the GPX8 promoter region. Among these, rs335618489-T, rs325233940-G, rs32989756-T, and rs322106839-G significantly upregulated GPX8 expression level by altering promoter activity (P<0.01), thereby influencing porcine muscle development traits. In summary, this study demonstrates that GPX8 inhibits the myogenic differentiation of porcine skeletal muscle satellite cells and promotes the transition from fast-twitch to slow-twitch myofibers. Functional SNPs in the GPX8 promoter region influence porcine muscle development by modulating GPX8 expression, thereby providing valuable breeding targets for improving pork production.

Abstract The decline in prostate cancer screening in recent years has led to a 6% annual increase in patients diagnosed with distant prostate cancer, where the 5-year survival rate is only 38%, demonstrating the urgent need to improve prostate cancer screening practices. Prostate cancer is highly heritable and presents a unique opportunity to implement risk-stratified screening. Accordingly, we have identified many strong genetic risk factors of prostate cancer and developed a polygenic risk score (PRS) that is highly predictive of prostate cancer risk across diverse populations, which could inform the decision to initiate screening, along with the optimal age and frequency of screening. Several of these genetic factors have implications beyond screening—for instance, among patients diagnosed with low-risk prostate cancer being monitored on active surveillance, the PRS was predictive of risk of upgrading or extreme upgrading (i.e., to grade group 3 or higher) along with a higher percentage of cancerous biopsy cores. In recent work, we demonstrated that it is possible to improve the ability of the PRS to distinguish risk of advanced from indolent prostate cancer. This has launched an effort to characterize the common genetic architecture of aggressive prostate cancer by undertaking a large-scale multi-ancestry genome-wide association study (GWAS) focused on prostate cancer aggressiveness. This work is anticipated to identify new genetic mechanisms contributing to the etiology of lethal prostate cancer while also advancing PRS and clinical models to inform prostate cancer screening and treatment decisions. Citation Format: Burcu F. Darst. Advances in using germline genetics to inform prostate cancer risk assessment and disease progression [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr IA003.

Aplastic anemia (AA) is an immune-mediated bone marrow failure syndrome marked by pancytopenia and depletion of hematopoietic stem cells. Although autoreactive T cells and inflammatory cytokines are involved in its pathogenesis, their causal relationships remain unclear. We conducted a bidirectional two-sample Mendelian randomization using large-scale genome-wide association study datasets to explore the causal roles of 731 immune cell traits and 91 inflammatory cytokines in AA. The primary analysis used inverse-variance weighting, with additional sensitivity analyses including MR-Egger regression and MR-PRESSO. Mediation analysis was performed to determine whether cytokines mediate the effects of immune cells on disease risk. We identified 12 immune cell traits conferring protection against aplastic anemia (AA). The strongest protective association was observed for TD CD4 + AC (OR = 0.890, 95% CI: 0.817-0.968, P = 0.007). Conversely, 24 immune cell traits demonstrated positive associations with increased AA risk, with the strongest association found for CD127 - CD8br %T cell (OR = 1.135, 95% CI: 1.032-1.247, P = 0.009). Elevated levels of leukemia inhibitory factor (LIF) and its receptor were associated with reduced AA risk. Mediation analysis indicated partial mediation by LIF of the associations linking dendritic cells, HLA-DR + + monocytes, and AA risk. Sensitivity analyses supported the robustness of these findings. This study established causal relationships between specific immune cell phenotypes, inflammatory cytokines, and AA. Importantly, LIF partially mediated the effects of immune cells on AA, suggesting the LIF-LIFR axis as a potential therapeutic target.

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F2 population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (&gt;99%) and Precision (&gt;90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation.

Transposable elements (TEs) are abundant and evolutionarily important components of plant genomes, yet the population-scale landscape of TE insertion polymorphisms (TIPs) and their regulatory roles in gene expression and trait variation remain insufficiently understood. In this study, genomic resequencing, RNA-seq, and agronomic trait data from a panel of 381 Brassica napus accessions were integrated to characterize population-level TIP dynamics and assess their impacts on gene regulation, ecotype differentiation, and phenotypic innovation. Using a developed computational pipeline, a robust pan-TE library was constructed based on 28 diverse reference genomes, and 77,603 TIP loci were profiled by mapping resequencing data from 381 accessions. Most TE insertions were found to be dispensable and weakly linked to neighboring SNPs, suggesting that they represent recent or ecotype-specific variants that serve as independent sources of regulatory and adaptive diversity in B. napus. The regulatory roles of TEs were examined through two complementary strategies (direct-effect analyses and TIP-based eQTL mapping), which together revealed that TEs modulate gene expression via both cis- and long-range trans-effects. Notably, TE-mediated trans-regulation, rarely investigated in previous studies, was found to be widespread, with trans-effects predominating and displaying strong tissue specificity, emphasizing the extensive regulatory influence of TEs on the plant transcriptome. Furthermore, selective sweep analyses identified ecotype-specific TIPs associated with adaptive divergence, particularly those contributing to semi-winter type diversification. TIP-based genome-wide association studies (GWAS) revealed 1,102 candidate insertions significantly associated with key agronomic traits, including flowering time, fatty acid composition, and glucosinolate content, some of which were not detected by SNP-based analyses. This study provides the population-scale atlas of TE insertions in B. napus, uncovers their extensive regulatory roles, and demonstrates their contribution to adaptation and trait variation, offering valuable resources for breeding and functional genomics.

Erythrocyte transfusion is the cornerstone of clinical practice, playing a vital role in treating various conditions such as severe blood loss, anemia, and certain hematological disorders. However, worries regarding the possible elevated hazard of transfusion-related immunomodulation (TRIM) persist. We attempt to identify TRIM-associated erythrocyte characteristics and evaluate their genetic influence on TRIM based on a Mendelian randomization (MR) study. A multivariate MR method was used to screen out the potential exposure factors. Gene variants related to the 12 erythrocyte characteristics were acquired from a genome-wide association study of hematologic characteristics, and these variants were utilized in the UK Biobank. Univariate MR methods were employed for sensitivity analyses. Hemoglobin was identified to be a significant erythrocyte trait in the context of TRIM. Based on the inverse variance-weighted (IVW) method, hemoglobin predicted by genetics was found to be favorably correlated with TRIM. MR-Egger, weighted median, and MR pleiotropy residual sum and outlier tests also provided robust estimation. When incorporating all available gene variants for the erythrocyte characteristics, the exposure factor most associated with TRIM, based on marginal inclusion probability (MIP), was hemoglobin (MIP=0.90), while all other erythrocyte characteristics had an MIP < 0.24. This indicates that hemoglobin stands out prominently among the various erythrocyte characteristics in its association with TRIM. The high MIP value for hemoglobin strongly suggests that it is a key determinant in the development of TRIM, while the much lower MIP values for other traits imply their relatively minor roles in this context. Sensitivity analyses showed that even when eliminating the highly correlated hematocrit, hemoglobin was still chosen with the greatest MIP. Additionally, univariate MR using IVW validated that hemoglobin was uniformly favorably correlated with TRIM. Hemoglobin is the key erythrocyte characteristic underlying TRIM, which shows that increasing hemoglobin via blood transfusion increases the risk of TRIM. Hence, the benefits of blood transfusion that raise hemoglobin need to be weighed against the side effects.

Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.

To explore the causal relationship between several possible behavioral factors and high myopia (HM) using multivariable Mendelian randomization (MVMR) approach and to find the mediators among them with mediation analysis. The causal effects of several behavioral factors, including screen time, education time, time spent outdoors, and physical activity, on the risk of HM using univariable Mendelian randomization (MR) and MVMR analyses were first assessed. Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM. MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM. Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM. Additionally, MR analyses identified 13 serum metabolites significantly associated with HM, of which 12 were lipids and one was an amino acid derivative. Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM, with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC (p-16:0; 30.83%). This study suggests that in addition to outdoor time, moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity. These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.

BackgroundAs a relatively common respiratory disease, chronic obstructive pulmonary disease (COPD) has a high incidence and mortality rate. Mitochondrial dysfunction has been implicated in COPD pathogenesis, but the causal genes and underlying molecular mechanisms remain unclear.MethodsWe performed a summary data-based Mendelian Randomization (SMR) study integrating summary data from genome-wide association studies (GWAS) with blood-based methylation (mQTL), expression (eQTL), and protein quantitative trait loci (pQTL) to identify mitochondrial-related genes causally associated with COPD. Significant findings were validated using two-sample MR, independent lung tissue transcriptomic data (GSE76925), and weighted gene co-expression network analysis (WGCNA) to assess transcriptional consistency and functional convergence.ResultsOur integrative SMR and colocalization analyses identified 77 mitochondrial genes linked to COPD risk, including GPX1, TUFM, COQ5, BPHL, and NAGS. A methylation-to-expression regulatory cascade was observed, with hypermethylation at GPX1 cg24011261 associated with increased gene expression and higher COPD risk—despite its role as an antioxidant enzyme. Two-sample MR confirmed robust causal effects of GPX1, BPHL, TUFM and COQ5 expression on COPD. These findings were replicated in lung tissue: GPX1, COQ5, and TUFM were significantly upregulated in COPD patients (GSE76925). WGCNA revealed that these genes reside within a highly interconnected turquoise module strongly correlated with COPD status (r = 0.43, p < 0.001) and enriched in oxidative phosphorylation and mitochondrial energy metabolism pathways.ConclusionThis study provides systematic genetic and transcriptomic evidence that mitochondrial-related genes, particularly GPX1 and TUFM, exert causal effects on COPD risk through regulatory cascades and coordinated network dysregulation. The convergence of genetic, epigenetic, and co-expression evidence underscores mitochondrial dysfunction as a central mechanism in COPD pathogenesis and highlights potential targets for future therapeutic development.

Lung cancer is a leading cause of cancer-related mortality globally, yet its association with physical exercise remains incompletely understood. This study employed Mendelian randomization (MR) and bioinformatics to investigate the causal link between physical exercise and lung cancer,further validating potential molecular mediators through experimental analysis. We applied a two-sample MR framework, using genetic variants as instrumental variables, to examine the causal associations between five distinct exercise modalities-recreational walking, strenuous sports, miscellaneous activities, light do-it-yourself (DIY) activities, and heavy DIY activities-and lung cancer risk. This analysis was conducted using data from the UK Biobank and the Genome-Wide Association Studies (GWAS) consortia. To ensure the robustness of our results, we performed sensitivity analyses including MR-Egger, weighted median, and MR-PRESSO. Furthermore, we investigated potential mediators such as lipids, immune cells, inflammatory cytokines, and metabolites through MR. Bioinformatics analyses, specifically GEPIA, were employed to identify candidate genes and key molecules, and the expression level of the key molecule SULT1A1 was further validated by Qpcr, Western blot and immunofluorescence. The findings indicated that participation in activities such as swimming, cycling, fitness training, bowling (ebi-a-GCST90018875: P = 0.017, OR = 0.086;ieu-b-4954: P = 0.004, OR = 0.972; ieu-b-4955: P = 0.003, OR = 0.972), and walking (ebi-a-GCST90018875: P = 0.021, OR = 0.170; ieu-b-4954, P = 0.025, OR = 0.980; ieu-b-4955: P = 0.016, OR = 0.978) was associated with a decreased risk of lung cancer. Our analysis found significant causal relationships between lung cancer and 21 lipids, 7 immune cell subtypes, 4 inflammatory markers, and 16 metabolites, but no statistically significant regulatory effect of physical activity on them. Bioinformatics analysis revealed that SULT1A1 expression was significantly lower in lung tumors, specifically in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), and was correlated with improved prognosis (hazard ratio [HR] = 0.67 for LUAD, P = 0.0083). In murine models, exercise led to a reduction in tumor growth, volume, and Ki-67 + cell proliferation, while concurrently increasing SULT1A1 mRNA and protein expression levels (P < 0.05). This study offers causal evidence supporting the protective effects of exercise against lung cancer, potentially through pathways dependent on SULT1A1.

Climate change, rising global food demand, and shrinking resources require transformative innovations in crop breeding. This review outlines recent advances in new breeding technologies (NBTs), including molecular markers, genome-wide association studies (GWAS), genomic selection (GS), next-generation sequencing (NGS), and gene editing (GE) tools such as the clustered regularly interspaced short palindromic repeat (CRISPR/Cas), base editing, and prime editing. These methods enable the accurate improvement of traits, thereby accelerating the development of crops resistant to both abiotic and biotic stresses. The integration of multi-omics platforms, including genomics, transcriptomics, proteomics, metabolomics, and phenomics, provides a comprehensive framework for deciphering and manipulating complex trait architectures. Artificial intelligence (AI) and machine learning (ML) enhance precision breeding by providing data-driven insights and enabling the forecasting of traits. Emphasis is also placed on combining gene editing with other strategies, such as speed breeding, to accelerate the development of traits. This review underscores the importance of an integrated systems biology approach that combines multi-omics, gene editing, AI, and speed breeding to accelerate the development of climate-resilient, high-yielding, and nutritionally enhanced crops. The integration of these innovative technologies holds great promise for addressing global food security, environmental sustainability, and agricultural resilience in the face of climate change. A strategic framework for the future of plant breeding is outlined, emphasizing the importance of interdisciplinary collaboration in building a sustainable agricultural future.

Causal evidence linking chronic pain genetics to late-onset asthma via the nervous system.

Since the molecular mechanisms underlying sex determination in Procambarus clarkii are still unclear, it is important to investigate the genetic basis of sex determination in crustaceans. Currently, the molecular mechanisms of sex determination and the gender-specific markers in this species remain poorly understood. In this study, a total of 14,046,984 SNPs and 2,160,652 InDels were identified through genome-wide resequencing of 89 individuals (45 females and 44 males). Further analysis confirmed that the candidate chromosome was Chr38, the sex determination system was identified as XY, and the sex determination region was located at Chr38: 6,000,000–21,100,000 bp. A pair of sex-specific molecular markers has been identified based on a 21 bp female-specific insertion within the candidate sex-determining region. Additionally, SOAT, NPC1, PTGS2, FANCD1, and VAlRS were identified as candidate sex-determining genes through the screening of candidate genes and RT-qPCR validation analysis. These findings provide a robust foundation for investigating sex-determining mechanisms in crustaceans. Through the integration of genome-wide association studies (GWAS), selection signals, and transcriptome analysis, we identified, for the first time, genes associated with sex determination, growth, and immunity. These genes represent promising candidates for further functional studies and genetic improvement in Procambarus clarkii.

Crohn's disease (CD) is a chronic inflammatory bowel disease with a complex etiology involving genetic, immune, microbial, and environmental factors. Despite advances in understanding its pathogenesis, accurately predicting CD risk remains challenging, particularly in East Asian populations. In this study, we evaluated the performance of a polygenic risk score (PRS) model to predict CD risk in a Chinese cohort comprising whole-exome sequencing data of 76 CD patients and 552 healthy controls. We calculated PRS by applying causal genetic effect estimated from summary statistics of a public large-scale multi-ethnic genome-wide association study. Our results demonstrated that the PRS model effectively distinguished CD patients from healthy controls, achieving an area under the receiver opperating characteristiccurve of 0.75 and an odds ratio of 13 for individuals with a PRS above 2.3. The model also showed consistent performance in independent control data sets of Chinese, East Asian, European, and American ancestries. These findings highlight the potential of PRS derived from multi-ethnic causal effect as a non-invasive tool for CD risk prediction in East Asian populations. However, the moderate predictive accuracy and unexplained variance emphasize the need for larger studies and the integration of additional genetic and environmental factors to refine PRS model further.

Bread wheat (Triticum aestivum L.) is one of the most important staple crops globally. Grain number per spikelet (GPS) is an important yield component in wheat. It is influenced by floret number per spikelet (FPS) and floret fertility. Through three consecutive years of observation of FPS, GPS, and floret fertility per spikelet (FFPS) in a globally collected hexaploid wheat core collection and using an improved genotyping dataset, we identified 42 marker-trait associations (MTA) for spikelet-related traits through the single-nucleotide polymorphism and the k-mer-based genome-wide association studies. Those MTA explain 4.45%-24.45% phenotypic variations for the FFPS and GPS traits, indicating a substantial effect on spikelet-related traits. We found significant negative associations between days to heading (DTH) and spikelet-related traits, suggesting that DTH and spikelet-related traits are subject to a certain degree of common genetic control. We identified several potential candidate gene models in the associated regions, including TraesCS2A03G0815500, which encodes a protein with a sterile alpha motif domain, and TraesCS2A03G0780600, which encodes a plastid-targeted lipoxygenase. These genes may facilitate wheat breeding and help elucidate the mechanisms of wheat grain set.

The wheat streak mosaic (WSM) complex, primarily caused by wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV), results in significant annual yield losses in the northern plains of the United States. Wheat wild relatives, including Aegilops tauschii , represent valuable resources of genetic diversity, including resistance to pathogens. In this study, we report the first comprehensive phenotypic assessment and genome-wide association study (GWAS) of a geographically diverse panel of 250 Ae. tauschii accessions for WSMV tolerance in single and mixed infections with TriMV. Phenotyping for WSMV symptom severity and quantitative polymerase chain reaction (qPCR)-derived viral titers identified 124 tolerant genotypes in single infections. In double-infection assays, 22 of 39 tested accessions, including both WSMV-tolerant and susceptible genotypes, exhibited tolerance to both viruses. The GWAS revealed that 12 genomic loci were significantly associated with WSMV severity and 8 loci were associated with the viral titer in single infections. Notably, a large effect locus for symptom severity mapped to the long arm of chromosome 5D within lineage two (L2) at 432 Mb. Additional loci in the same region, also identified by the BLINK model, were detected at 430 Mb and 529 Mb. These regions harbor multiple previously reported disease resistance-related genes. These findings suggest that tolerance to WSMV in Ae. tauschii is controlled by multiple quantitative trait loci (QTL), highlighting the need for further validation and functional characterization. The WSMV-tolerant germplasm identified in this study constitutes a valuable genetic resource for incorporation into wheat improvement programs. This work lays the foundation for the functional characterization of WSMV tolerance loci in Ae. tauschii and provides a framework for leveraging genetic diversity for improving virus resistance in wheat through marker-assisted breeding strategies.

A complete telomere-to-telomere (T2T) pig genome serves as a high-precision reference for functional genomics and structural variation studies due to its high level of completeness and minimal error rate. Here we present a comprehensive framework for genomic research aimed at the effective utilization of Neijiang pig genetic resources. The highly repetitive centromeric regions of the Neijiang pig are identified, and their characteristic centromeric landscapes are constructed using chromosomal landmark identification combined with centromeric repeat monomer localization strategies. Genome-wide association studies (GWAS) based on the T2T reference genome identify key genetic loci associated with reproductive traits, offering potential molecular targets for genetic improvement. Gene family analysis and genetic investigation into head morphology in Neijiang pigs reveal selection signals within olfactory receptor (OR) genes that are associated with head shape differentiation, highlighting the potential role of environmental adaptation in the phenotypic domestication of Chinese indigenous pig breeds. The Neijiang pig T2T genome (NJP-T2T) assembly provides a critical reference resource and foundational dataset for genetic improvement and functional genomic studies in indigenous pig breeds.