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PubMed2026

Research on multi-trait genome association study method based on Shannon information entropy.

BACKGROUND: Genetic analysis of complex traits is crucial for elucidating disease mechanisms and biological inheritance processes. However, traditional Genome-wide Association Study (GWAS) for single trait often fail to capture the synergistic effects of genetic loci on multiple traits. METHODS: This study proposes a method for analyzing the association between multiple traits and gene regions based on Shannon information entropy. Innovatively, Shannon information entropy is introduced to integrate gene region information as genetic entropy, thereby constructing an Inverse Shannon Entropy-Multi-Trait Association Analysis of Gene Region genetic model (InvSE-MTAGR). Furthermore, a partial regression test is applied to the model to establish the Inverse Partial Shannon Entropy-Multi-Trait Association Analysis of Gene Region method (InvPSE-MTAGR). When performing multi-trait analysis with InvSE-MTAGR, the method achieved statistical significance by accumulating minor effects, thereby enhancing the ability to identify pleiotropic gene regions. RESULTS: The simulation results showed that the proposed multi-trait gene region association analysis method performed well in terms of both Type I error rate control and statistical power. Leveraging tomato and sorghum datasets for validation, the proposed multi-trait gene region association analysis method based on Shannon information entropy accurately pinpointed most of the gene regions harboring candidate genes. CONCLUSION: The study reveals the advantage of multi-trait method in integrating weak-effect pleiotropic signals and capturing the correlation among traits, which provides an efficient theoretical tool for dynamic analysis of complex multi-trait genetic networks and multi-target collaborative breeding of crops.

Genome-Wide Association Study↗

PubMed2026

Genomic selection in timothy (Phleum pratense L.): a comprehensive evaluation of prediction models, multi-trait strategies, and forward validation across Norwegian environments.

This study presents a comprehensive evaluation of genomic selection (GS) in timothy (Phleum pratense L.), comparing nine prediction models across yield and quality traits at two Norwegian locations. Forward validation with independent full-sib (FS2) families revealed a substantial generalization gap, highlighting the need for realistic accuracy assessment in polyploid forage breeding. Timothy (Phleum pratense L.) is the most important forage grass in Northern Europe, yet genomic selection has not been systematically evaluated in this hexaploid species. We assessed 889 FS2-families originating from biparental crosses among 49 cultivars/populations. The FS2-families were genotyped with 30,698 SNP markers derived from genotyping-by-sequencing (GBS) and field tested for three harvest years at a highland and a lowland continental location in Southern Norway. Nine genomic prediction models were compared for six yield traits (dry matter yield per cut and total) and six quality traits (protein, digestibility, and fiber fractions) across three cuts/year. Within-training cross-validation accuracies were moderate to high (mean r = 0.62), with Random Forest and SVR consistently outperforming GBLUP. However, forward validation using 213 independent FS2-families revealed dramatically lower accuracies (mean r = 0.16), with only 16 of 30 trait-dataset combinations reaching statistical significance (p < 0.05). Genomic heritabilities (GREML), estimated across environments, ranged from near zero for the quality traits to 0.55 for the yield traits. Multi-trait models improved accuracy by 3-5% over single-trait approaches, while FS2 families-by-environment interaction models with Random Forest achieved the highest within-training accuracy (mean r = 0.71). Marker density analysis showed accuracy plateauing at approximately 15000 SNPs. Genetic correlations among the yield component traits were estimated by multi-trait REML; correlations among the quality traits could not be estimated reliably because their genomic heritabilities were low. A multi-trait selection index identified top-performing FS2-families for further crossing recommendations. These results provide a benchmark for GS implementation in hexaploid timothy and emphasize that cross-validation substantially overestimates prediction accuracy for truly independent material.

Norway↗

PubMed2026

Multi-trait polygenic scores for COPD and COPD exacerbations implicate druggable proteins.

BACKGROUNDWe constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting.METHODSPRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic White participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), Mass General Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012).RESULTSPRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.CONCLUSIONSMulti-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management.TRIAL REGISTRATIONCOPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Humans↗

PubMed2026

Multi-trait GWAS identifies pleiotropic loci shared between early pregnancy bleeding and psychiatric traits.

INTRODUCTION: Early pregnancy bleeding is a common pregnancy complication, yet its genetic basis and potential links with psychiatric traits remain poorly understood. This study aimed to characterize the shared genetic architecture between early pregnancy bleeding and reproductive, psychiatric, and cardiometabolic traits. METHODS: We integrated linkage disequilibrium score regression (LDSC), local genetic correlation analysis (LAVA), and multi-trait genome-wide association analysis (MTAG). LDSC was used to estimate genome-wide genetic correlations, including sex-stratified analyses. LAVA was applied to identify genomic regions contributing to local genetic sharing. Guided by these correlation patterns, MTAG was performed to improve locus discovery, followed by cis-eQTL analysis using GTEx v8 to explore potential regulatory mechanisms. RESULTS: LDSC revealed significant positive genetic correlations between early pregnancy bleeding and reproductive traits, including endometriosis, miscarriage, and uterine fibroids. Strong positive correlations were also observed with several psychiatric disorders, including major depressive disorder, post-traumatic stress disorder, and attention deficit hyperactivity disorder. Sex-stratified analyses suggested stronger genetic correlations with emotional reactivity-related traits in females, whereas social and behavioral traits were more prominent in males. LAVA localized these shared signals to specific genomic regions and identified pleiotropic hotspots at 8q21 near RUNX1T1 and 9p21 near CDKN2A/B. MTAG identified two novel loci, 15q15.1 marked by rs45457497 and 11q13.1 marked by rs2452681. Cis-eQTL analysis showed that the lead variant at 15q15.1 regulates RMDN3 expression across multiple brain regions, while the 11q13.1 locus regulates PACS1, GAL3ST3, and SF3B2 expression in brain tissues and the pituitary. DISCUSSION: These findings position early pregnancy bleeding as a multifactorial trait shaped by shared reproductive, psychiatric, neuroendocrine, and stress-related biology. The implication of RMDN3, which encodes a mitochondrial outer membrane protein involved in ER-mitochondria tethering and calcium homeostasis, suggests a potential molecular link between neuroendocrine stress pathways, psychiatric susceptibility, and reproductive vulnerability.

RMDN3↗

PubMed2026

Genetic Analysis of Genomic and Methylomic Variation and Identification of Multi-Trait Mutants in Rice Carried on Chang'e-5.

Global food security is facing challenges from population growth to diminishing arable land. Space mutation breeding holds promise for overcoming the variation limitations in conventional breeding; however, the mutagenic effects of the deep-space environment on rice and the transgenerational inheritance patterns of induced variations remain unclear. In this study, rice seeds carried by the Chang'e-5 spacecraft were used as materials. Whole-genome sequencing and whole-genome bisulfite sequencing were performed on the first (SP1) and second generations (SP2) of space-mutagenized plants after their return to Earth. The results showed that the number of genomic variants in the SP2 generation increased significantly compared with SP1, and SNPs, homozygous sites, and variants in coding regions were more heritable. The genome-wide methylation level was elevated in the SP2 generation, and among differentially methylated cytosines, those in the CG context exhibited the highest heritability. Furthermore, large-scale screening for nitrogen efficiency, tolerance to PEG-induced stress, and germination-stage cold resistant mutants was conducted in the SP2 generation, and phenotypic validation was performed in the third generation (SP3). By integrating multi-omics analyses of representative mutants to mine candidate genes, a number of heritable elite mutants were obtained, and seven candidate genes for key traits were identified. This study systematically elucidates the transgenerational inheritance patterns of deep-space-induced variation in rice. The multi-trait mutants obtained provide valuable germplasm resources for gene cloning and breeding applications in rice.

DNA methylation↗

PubMed2026

Genome-wide cis-expression Quantitative Trait Loci (eQTL) and transcriptomic signals reveal distinct molecular regulation across correlated feed efficiency traits.

INTRODUCTION: Feed efficiency (FE) is a complex trait which determines livestock production profitability, yet the molecular mechanisms behind it remain unclear. This study investigated the blood transcriptomic profile of lambs, alongside genotype data with the aim to uncover the genetic basis of FE traits such as absolute dry matter intake (DMIabsolute), DMI adjusted for body size (DMIadjusted), average daily live weight gain (ADG), and residual feed intake (RFI). MATERIALS AND METHODS: Bulk RNA-Seq and genotype data were analysed using three complementary approaches: differential gene expression (DGE) analysis, weighted gene co-expression network analysis (WGCNA), and cis-expression Quantitative Trait Loci (cis-eQTL) mapping. These methods were used independently to identify genes and regulatory networks associated with FE traits and to investigate evidence supporting multi-trait candidate gene selection. RESULTS: DGE analysis revealed 2, 24, 85 and 4 differentially expressed genes for DMIabsolute, DMIadjusted, ADG, and RFI (Padjusted < 0.05), functionally enriched in sensory perception, ATP-dependent chromatin remodeling, Notch signaling and immune response pathways. 9 gene modules significantly associated with the FE traits (P ≤ 0.05) with correlations ranging from r = -0.56 to 0.49, were identified using WGCNA. Single nucleotide polymorphism (SNP)-level cis-eQTL analysis identified 93 eSNPs associated with 74 genes (false discovery rate (FDR) < 0.05), while permutation-derived gene level analysis identified 280 eGenes (FDR < 0.2, empirical P < 0.03). Across the three analyses, applying thresholds of DGE (Padjusted < 0.05), WGCNA (correlation, P ≤ 0.05), and cis-eQTL gene-level significance (empirical P < 0.05), multiple overlapping genes were identified including DNMT3A, KANSL1, NCOR1 for DMIadjusted, ACOX2, FANCF, CIMIP2B, LOC101115106, ARMH2, LOC132657496 for ADG, and LOC114114576 for RFI representing regulators of variations in FE. DISCUSSION: The integration of DGE, WGCNA, and cis-eQTL analyses identified key genes and regulatory mechanisms associated with variation in FE traits. These results highlight that integrated multi-trait candidate gene identification approaches can reveal key genes that lower feed intake while maintaining animal growth, supporting breeding strategies aimed at improving efficiency and long-term economic sustainability in sheep.

average daily gain (ADG)↗

PubMed2026

Multidimensional GWAS analyses on longitudinal phenotypes reveal candidate genes regulating multi-stage egg production traits in Wannan yellow chicken.

Egg production performance directly determines the economic viability of indigenous chicken breeding. However, the genetic regulation of multi-stage egg production traits remains difficult to characterize due to their complex and dynamic nature. Here, we integrated a multidimensional GWAS framework, including single-trait GWAS, multi-trait GWAS (MTAG), and longitudinal trajectory-based GWAS (TrajGWAS), to identify stage-specific and shared genetic effects underlying egg production traits in Wannan yellow chickens (WNY). Whole-genome sequencing of 354 WNY hens (10× depth) and quality control yielded 14,253,816 SNPs for analysis. Selective sweep analyses comparing red jungle fowl, commercial layers, and WNY identified a genomic region containing IGF1 under significant selection pressure. Single-trait GWAS identified SNPs 4_57990480 (BMPR1B) and 17_370912 (LOC112531479) associated with egg production across three laying stages (21-30, 31-40, and 21-40 weeks). MTAG further identified loci 8_4336468 (FASLG) and 21_654726 (CHD5) with shared effects across the laying period, whereas TrajGWAS revealed longitudinal associations involving PRKG1 and identified dynamic loci associated with clutch traits, including GRID1. For clutch traits, stage-specific loci were detected for average clutch size (ACS) and maximum clutch size (MCS), including SNP 8_8542036 at 21-30 weeks, PROK1 at 31-40 weeks, and CUL5, ALKBH8 across the entire laying period. These results demonstrate that integrating complementary GWAS strategies improves the resolution of genetic architecture underlying egg production traits by capturing trait-specific, shared, and stage-dependent genetic effects. The identified GWAS loci and selective-sweep candidate regions provide insights into the genetic architecture of egg production traits and breed differentiation.

Egg production↗

PubMed2026

Combining ability and gene action for grain yield and biofortification traits in pearl millet [Pennisetum glaucum (L.) R. Br.]: implications for breeding high-yielding biofortified hybrids in arid regions.

Hybrid RIB-9184 × RIB-15131 combines high yield (18.84 g plant⁻¹) with iron (46.16 mg kg⁻¹), zinc (38.86 mg kg⁻¹), and protein (11.91%); Fe-Zn correlation (rg = 0.82) permits simultaneous biofortification. Pearl millet [Pennisetum glaucum (L.) R. Br., syn. Cenchrus americanus (L.) Morrone] is a climate-resilient cereal with inherently high micronutrient levels, making it a priority crop for biofortification. Understanding gene action for yield and nutritional traits is essential for designing effective breeding strategies. Ten diverse inbred lines were crossed in a half-diallel design (Griffing's Method 2, Model 1), and the 55 entries (45 F1 hybrids + 10 parents) were evaluated across two sowing-date environments in a randomised complete block design with three replications at Jaipur, Rajasthan, India. Biofortification traits (Fe, Zn, protein) showed predominantly additive gene action (Baker's ratio 0.71-0.91) with high heritability (0.90-0.94). G×E interaction was significant for Fe and Zn but genotypic variance was substantially larger, maintaining high heritability; protein showed no G×E interaction. Grain yield was governed largely by non-additive effects (Baker's ratio 0.54) with significant G×E interaction, favouring hybrid breeding. Among parents, RIB-9205 had the highest GCA for Fe (6.65, P < 0.001), RIB-9184 for Zn (3.85, P < 0.001) and protein (0.78, P < 0.001), and RIB-9185 was a balanced combiner for yield (1.39, P < 0.001) and micronutrients. The hybrid RIB-9184 × RIB-15131 ranked first across all five weighting schemes of the multi-trait performance index (1.31), combining grain yield of 18.84 g plant⁻1 with Fe of 46.16 mg kg⁻1, Zn of 38.86 mg kg⁻1, and protein of 11.91%. The strong Fe-Zn correlation (rg = 0.82, P < 0.01) permits simultaneous micronutrient improvement. An integrated approach combining hybrid development for yield with population improvement for micronutrient density is recommended for biofortified pearl millet cultivars in arid regions.

Pennisetum↗

PubMed2026

Genetically-predicted placental gene expression links to uterine fibroids and endometriosis.

INTRODUCTION: Mother-to-child disease transmission begins in utero, with the placenta playing a critical role in pregnancy and offspring health. Uterine leiomyomata (fibroids, UFs) and endometriosis (ENDO) are common gynecologic diseases that have substantial overlaps in symptomology and risk factors, however drivers of disease risk remain unclear. The objective of this study was to investigate shared placental genetic associations across ENDO and UFs. METHODS: Genome-wide association study (GWAS) summary statistics were utilized from a published study of UFs (PMID: 40050615) and meta-analyzed for ENDO (24,092 cases and 548,255 controls). To improve our statistical power, we applied Multi-Trait Analysis of GWAS to the ENDO and UF GWAS. We estimated genetically predicted gene expression using S-PrediXcan across 49 tissues using GTEx v7 and a placental tissue expression model. RESULTS: We identified 54 and 14 genes where predicted expression in the placenta was significantly associated with UFs and ENDO, respectively. Twenty-one of these genes were shared between UFs and ENDO. Significant gene associations in placenta tissue were compared to the other 48 GTEx v7 tissue types to identify placenta specific associations. There were 40 and 13 significant gene-tissue associations specific to the placenta across UFs and ENDO, respectively. Eight of the placenta-specific genes were shared across UFs and ENDO. The strongest shared placenta-specific associations included PRKCI and HRH1. CONCLUSIONS: Our findings demonstrate a shared genetic relationship between UFs and ENDO in the placenta. The placenta specific associations suggest that dysregulation of early developmental pathways may contribute to a shared genetic origin of these diseases.

Female↗

PubMed2026

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs.

MOTIVATION: Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. RESULTS: We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Genome-Wide Association Study↗

PubMed2026

Genetic evidence that advanced COVID-19 accelerates longitudinal brain atrophy: A Mendelian randomization study.

Coronavirus disease 2019 (COVID-19) was reported to persist long-term in the brain and leave several long-term neurologic sequelae. However, the causal relationship between COVID-19 and brain aging is still unknown. The genome-wide association study (GWAS) data on COVID-19 phenotypes (susceptibility, hospitalization, and severity), involving a total of 5,779,391 participants, were collected from the COVID-19 Host Genetics Initiative. In addition, GWAS data on longitudinal changes in 15 brain structures, assessed via magnetic resonance imaging across the lifespan, were sourced from the ENIGMA Consortium and involved 15,640 participants. Two-sample Mendelian randomization was conducted to infer the causal relationship between COVID-19 and longitudinal brain changes. Multi-trait GWAS meta-analysis, colocalization, and fine-mapping analyses were performed to identify shared genetic etiologies. H3K27me3 ChIP-seq was used to evaluate the regulatory effect of colocalized loci. Two-step Mendelian randomization was applied to explore potential mediating mechanisms across multi-omics layers, including proteomics, metabolomics, and immunomics. Our results showed that COVID-19 hospitalization (β = -262.405, P = .041) and severity (β = -177.676, P = .049) were genetically associated with atrophied volume of total brain during longitudinal change. This suggests that individuals with advanced COVID-19 may be more susceptible to accelerated global brain aging. Caudate was genetically affected by all COVID-19 phenotypes. Seven variants were shared between advanced COVID-19 and global brain aging. rs117169628 was colocalized between advanced COVID-19 and global brain aging, and exerted an inhibitory effect on CDH15 expression, further strengthening the causality. Six metabolites, 1 protein, and 1 immune trait were identified as potential mediators. Our study indicates that advanced COVID-19 might be genetically associated with accelerated brain aging. Brain health should be paid more attention in long COVID-19.

Humans↗

PubMed2026

Hypertrophic cardiomyopathy: a genome-wide association meta-analysis and polygenic risk score.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a heritable trait with marked variability in expression and outcomes. Our aims were to discover new genetic loci associated with HCM and to test the effect of a new polygenic risk score (PRS) on incidence, phenotype and outcomes stratified by genotype status. METHODS: A discovery genome-wide association study (GWAS) was performed on 2284 HCM cases and 4525 controls. Two fixed-effects meta-analyses combined our discovery GWAS with single-trait and multi-trait results from a published study. Discovered loci underwent comprehensive bioinformatic analysis including functional and druggability annotations. A PRS using loci from the two meta-analyses was evaluated for association with HCM diagnosis in 411 213 individuals from UK Biobank (UKBB); imaging phenotypes in individuals without HCM; a composite endpoint (including all-cause mortality and transplantation); and sudden cardiac death (SCD) in 1756 HCM cases. PRS analyses were stratified by genotype status. RESULTS: Three loci were found in the discovery GWAS (BAG3, FHOD3 and novel locus PPP1R3A). In the meta-analyses, 70 unique loci were identified, four novel (MYPN, YWHAE, NOS1AP and OBSCN). Bioinformatic analyses identified NOS1AP as a candidate HCM gene. A new PRS was significantly associated with HCM diagnosis (HR=3.19, 95% CI 2.46 to 4.14 for top 5% vs lower 95%; HR=1.88, 95% CI 1.72 to 2.06 per SD increase). Significant associations were found between PRS and greater left ventricular (LV) wall thickness and higher LV ejection fraction in UKBB participants without HCM. Genotype-negative HCM cases in the top 20% of the PRS distribution had an increased risk of SCD (HR=2.72, 95% CI 1.03 to 7.17). CONCLUSIONS: We report novel HCM loci. A new PRS predicted the risk of HCM development and associated imaging characteristics in the UKBB and outcomes in an HCM cohort.

Cardiomyopathies↗

PubMed2026

Decoding Primary Open-Angle Glaucoma: A Multi-Omics Approach to Identify Druggable Effector Genes.

PURPOSE: Genomewide association studies (GWAS) have identified numerous primary open angle glaucoma (POAG) risk loci, yet most reside in non-coding regions with unclear function. Mapping these loci to effector genes can elucidate disease mechanisms, identify functionally conserved variants, improve cross-ancestry risk prediction by reducing population-specific noise, and uncover shared therapeutic targets. METHODS: Here, we integrate European POAG GWAS with six types of multi-omics molecular Quantitative Trait Locis (xQTLs) using multi-trait colocalization to identify candidate effector variants and evaluate their cross-population relevance using genetic risk score (GRS) analysis, and their therapeutic potential through drug target prioritization. RESULTS: We identified 25 POAG effector variants colocalized with at least one xQTLs. In non-European populations, effector variants showed stronger effect size correlations with Europeans than non-colocalized variants (Pearson r2 = African 0.85 vs. 0.71; East Asian 0.81 vs. 0.69; and Latin American 0.91 vs. 0.75). Effector variants also had smaller allele frequency variations across populations (average interquartile range [IQR] = 0.15 vs. 0.20). The genetic risk score based on effector variants performed comparably to the genome-wide significant single-nucleotide polymorphism (SNP)-based GRS in non-European populations. Drug prioritization identified zinc, copper, sunitinib, probucol, and astemizole as potential common therapeutic agents for POAG and its subtypes. CONCLUSIONS: Our findings offer deeper insight into the molecular mechanisms underlying glaucoma and effector variants for developing more robust GRS models and broadly effective therapeutic strategies for POAG.

Humans↗

PubMed2026

Integrated functional genomics and safety assessment of plant-growth-promoting Caryophanales from post-maize-cultivation soils.

This study aimed to evaluate six environmental bacterial strains isolated from post-maize cultivation soils as candidates for agricultural biopreparation development, using an integrated functional genomic and safety assessment framework. Building on experimental validation of plant-growth-promoting activities, the analysis included: plant-growth-promoting traits (PGPT-Pred) using PLABase; carbohydrate-active enzymes (CAZymes) relevant for lignocellulosic crop residue degradation (dbCAN3); secondary metabolite profiles (antiSMASH); and screening for virulence factors and antibiotic resistance genes (ABRicate, BTyper3).All analyzed strains possess 1,449-1,617 predicted PGPT-encoding genes (24.1-35.9% of total genes), which are strongly shaped by taxonomic relatedness, as confirmed by congruence testing against ANI-based genomic divergence. Paenibacillus amylolyticus 5mez and Priestia megaterium 7psych showed distinct functional profiles compared to Bacillus spp., while Bacillus subtilis sensu lato strains were most similar to each other. Genomic predictions suggest involvement in nutrient acquisition (N, P, K, Fe) and stress mitigation. Secondary metabolite analysis revealed high biosynthetic potential, with non-Bacillus species harbouring a large proportion of unknown gene clusters, indicating underexplored metabolite diversity. CAZyme profiling identified P. amylolyticus 5mez as the most enzyme-rich strain, while B. cereus s.s. zielonkawy showed ligninolytic potential despite low overall CAZyme abundance. The safety assessment identified B. cereus s.s. zielonkawy as toxigenic and unsuitable for use. Of the remaining strains, P. amylolyticus 5mez and Pr. megaterium 7psych demonstrated the most favourable safety profiles, exhibiting no detectable virulence factors or antibiotic resistance genes, justifying their priority use in agricultural biopreparations, pending phenotypic validation. Given the high-dimensional, low-sample-size nature of multi-trait datasets in applied microbial genomics, tailored statistical approaches, including noise-reduction-validated PCA and distance-based congruence testing, were applied; their rationale and limitations are discussed.

Soil Microbiology↗

PubMed2024

Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice.

Over half of the world's arable land is acidic, which constrains cereal production. In South America, different rice-growing regions (Cerrado in Brazil and Llanos in Colombia and Venezuela) are particularly affected due to high aluminum toxicity levels. For this reason, efforts have been made to breed for tolerance to aluminum toxicity using synthetic populations. The breeding program of CIAT-CIRAD is a good example of the use of recurrent selection to increase productivity for the Llanos in Colombia. In this study, we evaluated the performance of genomic prediction models to optimize the breeding scheme by hastening the development of an improved synthetic population and elite lines. We characterized 334 families at the S0:4 generation in two conditions. One condition was the control, managed with liming, while the other had high aluminum toxicity. Four traits were considered: days to flowering (FL), plant height (PH), grain yield (YLD), and zinc concentration in the polished grain (ZN). The population presented a high tolerance to aluminum toxicity, with more than 72% of the families showing a higher yield under aluminum conditions. The performance of the families under the aluminum toxicity condition was predicted using four different models: a single-environment model and three multi-environment models. The multi-environment models differed in the way they integrated genotype-by-environment interactions. The best predictive abilities were achieved using multi-environment models: 0.67 for FL, 0.60 for PH, 0.53 for YLD, and 0.65 for ZN. The gain of multi-environment over single-environment models ranged from 71% for YLD to 430% for FL. The selection of the best-performing families based on multi-trait indices, including the four traits mentioned above, facilitated the identification of suitable families for recombination. This information will be used to develop a new cycle of recurrent selection through genomic selection.

Oryza↗

PubMed2026

Genomic dimensions deconstruct the clinical heterogeneity of bipolar disorder.

Bipolar disorder's (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder-and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic-GABAergic gradient along the psychotic factor. BD's genetic architecture appears hierarchical-a general liability resolving into dimensions of course and comorbidity, beyond subtypes.

Journal Article↗

PubMed2026

Accounting for voluntary waiting period changes in US dairy herds: Adjusting daughter pregnancy rate and introducing first-service to conception.

The genetic evaluation of female fertility traits in dairy cattle in the United States has progressed over the past 2 decades, with 4 additional traits integrated into the national evaluation system since the introduction of daughter pregnancy rate (DPR) in 2004. However, concerns have arisen in the dairy sector, with reports of producers extending the voluntary waiting period (VWP), which is the time of initial breeding after calving, due to more persistent lactation yields. Since its inception, DPR calculations in the US evaluation have assumed a fixed 50-d VWP, which may not reflect modern reproductive strategies. Furthermore, producers may determine that some of their cows should have their VWP extended while others may follow the standard time. A re-evaluation of the current female fertility evaluation is necessary to ensure traits align with current management practices. Therefore, this study explores the addition of a potential new trait, First Service to Conception (FSC), along with a revised DPR formula that permits more flexibility with VWP. About 32 million records from the National Cooperator Database, covering 5 major dairy breeds (2003-2023), were used for this study. Data for cows calving before 2003 were unavailable because insemination records were not recorded prior. The findings suggest FSC enhances female fertility evaluations, providing a more comprehensive reproductive assessment independent of VWP. Another approach was an adjustment to the DPR calculation (DPRadj) to account for herd VWP on a herd-year and lactation group basis. The mean DPR value was 45.06%, while DPRadj increased the mean value to 52.58%. The mean FSC was 62.64 d. In an analysis using first lactation cows for cow traits and heifers for heifer traits, genetic correlations of FSC with other fertility traits were 0.97 with Cow Conception Rate (CCR) and DPR, 0.98 with DPRadj, 0.40 with Heifer Conception Rate (HCR), and 0.32 with Early First Calving (EFC). Predicted transmitting abilities (PTA) were calculated to assess the implications of adding FSC to, or replacing DPR with DPRadj within the CDCB multi-trait evaluation index, Net Merit $ (NM$). The highest correlation among the top 10% of bulls occurred when replacing DPR with DPRadj (0.99). Adding FSC to the original model reduced the latter correlation to 0.88, which indicates slight re-ranking of top bulls. Among animals with PTA reliability above 50%, the lowest correlation was between the original index and the index replacing DPR with FSC (0.95). This work advances genetic evaluation in dairy cattle, improving reproductive efficiency and productivity.

daughter pregnancy rate↗

PubMed2026

Dissecting the genetics of forage quality traits in soft red winter wheat in the U.S. southeast region.

Winter wheat plays a viable role in agriculture, not only as a primary grain crop but also as a valuable forage source that bridges fall-spring forage gaps in many regions, including the southeastern (SE) U.S. Despite its nutritive potential, genetic basis of forage-quality traits remains insufficiently understood, limiting breeding efforts for dual-purpose cultivars. This study aimed to dissect the genetic architecture of forage quality in 182 soft red winter wheat (SRWW) genotypes adapted to the SE U.S. using genome-wide association study (GWAS). Field experiments were carried out in randomized complete block design across three Georgia locations over two growing seasons (2023-2025), with forage sampled at the end of tillering and evaluated using near-infrared reflectance spectroscopy. Significant phenotypic variation was observed for dry matter (DM), crude protein (CP), acid and neutral detergent fiber (ADF, NDF), acid detergent lignin (ADL), total digestible nutrients (TDN), sugars (SUG), and relative forage quality (RFQ). Heritability estimates ranged from low-to-moderate in combined environments and from low-to-high within individual locations. Correlation analysis revealed strong positive associations among fiber-related traits and negative associations with TDN, RFQ, and SUG, while CP declined with increasing fiber. Genome-wide association analysis identified 282 significant marker-trait associations (P < 1×10-4) across 19 chromosomes, which were consolidated into 121 QTLs, including 27 major-effect QTLs. Three QTLs QRfq.uga-3B.1, QRfq.uga-3B.2 (RFQ) and QDm/Sug.uga-7A (DM, SUG) were stable across locations while QAdf/Adl.uga-2A (ADF, ADL) and QDm/Sug.uga-7A (DM, SUG) indicated multi-trait control. Notably, 25 of the 27 major QTLs were putatively novel, highlighting substantial untapped allelic diversity for forage-quality improvement in SE SRWW. Favorable allele accumulation resulted in an overall improvement in forage quality, increasing desirable nutritive traits (DM, RFQ, SUG, CP) while reducing undesirable traits (ADF, ADL). Candidate gene analysis linked six major QTLs with genes implicated in abiotic stress response, plant development, and metabolic regulation, supporting their functional relevance in forage-quality determination. Incorporating these loci into breeding programs provides a robust genetic framework for marker-assisted selection, enabling the development of dual-purpose wheat cultivars with enhanced forage quality, thereby strengthening wheat's utility as a reliable forage resource during periods of seasonal feed scarcity in SE production systems.

GWAS↗