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PubMed2024

QTL mapping for seed vigor-related traits under artificial aging in common wheat in two introgression line (IL) populations.

BACKGROUND: Seed vigor recognized as a quantitative trait is of particular importance for agricultural production. However, limited knowledge is available for understanding genetic basis of wheat seed vigor. METHODS: The aim of this study was to identify quantitative trait loci (QTL) responsible for 10 seed vigor-related traits representing multiple aspects of seed-vigor dynamics during artificial aging with 6 different treatment times (0, 24, 36, 48, 60, and 72 h) under controlled conditions (48 °C, 95% humidity, and dark). The mapping populations were two wheat introgression lines (IL-1 and IL-2) derived from recipient parent (Lumai 14) and donor parent (Shaanhan 8675 or Jing 411). RESULTS: A total of 26 additive QTLs and 72 pairs of epistatic QTLs were detected for wheat seed-vigor traits. Importantly, chromosomes 1B and 7B contained several co-located QTLs, and chromosome 2A had a QTL-rich region near the marker Xwmc667, indicating that these QTLs may affect wheat seed vigor with pleiotropic effects. Furthermore, several possible consistent QTLs (hot-spot regions) were examined by comparison analysis of QTLs detected in this study and reported previously. Finally, a set of candidate genes for wheat seed vigor were predicted to be involved in transcription regulation, carbohydrate and lipid metabolism. CONCLUSION: The present findings lay new insights into the mechanism underlying wheat seed vigor, providing valuable information for wheat genetic improvement especially marker-assisted breeding to increase seed vigor and consequently achieve high grain yield despite of further investigation required.

Triticum↗

PubMed2026

Meta-QTL Analysis Reveals Consensus Genomic Regions and Candidate Genes for Resistance to Sudden Death Syndrome in Soybean.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, is one of the most economically important diseases limiting soybean production worldwide. Although numerous quantitative trait loci (QTL) associated with SDS resistance have been reported, inconsistencies among mapping populations, marker systems, and experimental conditions have hindered the identification of robust resistance loci for soybean improvement. In this study, a comprehensive meta-analysis was conducted to integrate published QTL and identify stable consensus genomic regions associated with SDS resistance. After a systematic literature survey and data curation, 153 QTL derived from 14 linkage-mapping studies were analyzed using a custom R-based workflow, resulting in the identification of 23 consensus meta-QTL (MQTL) distributed across 17 chromosomes. Several MQTL, particularly those located on chromosomes 6, 8, 18, and 20, were supported by multiple independent studies and represented major genomic hotspots for SDS resistance. Physical localization and functional annotation of these MQTL identified 217 candidate genes, including genes predicted to be involved in plant defense, signal transduction, transcriptional regulation, and secondary metabolism. Gene Ontology enrichment analysis identified response to salicylic acid as the only biological process that remained significant after FDR correction, whereas Kyoto Encyclopedia of Genes and Genomes pathway analysis did not identify significantly enriched pathways. Independent support using five published genome-wide association studies further supported several MQTL, especially those on chromosomes 6, 18, and 20, thereby increasing confidence in these genomic regions. The identified MQTL and prioritized candidate genes provide potential genomic resources for future marker development, improvement applications, and functional validation aimed at improving soybean resistance to SDS.

Fusarium virguliforme↗

PubMed2025

Dissecting seed composition QTL from wild soybean: fine-mapping, candidate gene identification, and evaluation of introgression effects on agronomic performance.

Seed composition QTL from wild soybean were confirmed and validated in two genetic backgrounds across multiple environments, candidate genes were identified, and agronomic performance of backcross introgression lines was evaluated. Through selection for soybean yield, breeders have inadvertently reduced seed protein content and increased oil due to phenotypic and genetic correlations between these three traits. Therefore, identifying alleles that increase protein without adversely affecting oil and yield is of interest for breeders and the entire soybean value chain. Previously, a G. max × G. soja population was used to map a protein-associated region to ~ 4.6 Mbp on chromosome (Chr) 14. The G. soja allele significantly increased protein 6.5-7.2 g kg-1, without significantly decreasing oil. Additionally, two oil quantitative trait loci (QTL) were reported on Chrs 8 and 14. In this study, we aimed to confirm the Chr 14 protein QTL, evaluate QTL effects on seed composition and agronomic performance, and further fine-map to identify candidate genes. We validated and fine-mapped the Chr 14 protein QTL to a 0.6 Mbp region in a different genetic background, where the G. soja allele significantly increased protein by 9.3 g kg-1. Further, we confirmed the Chr 14 oil QTL linked to the protein QTL and the Chr 8 oil QTL. Chr 14 protein QTL effects on agronomic traits were evaluated in a backcross population across eight environments. The QTL significantly increased protein content, without significantly impacting oil, maturity, or plant height. While the QTL impacted yield and lodging, its effect and significance varied within environments. The candidate genes identified for these three validated seed composition QTL, along with additional molecular markers developed, offer valuable resources for improving seed composition in soybean breeding programs.

Quantitative Trait Loci↗

PubMed2004

Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse.

Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.

Adenylyl Cyclases↗

PubMed2026

X chromosome-wide association studies for quantitative trait loci based on the mixture of general pedigrees and additional unrelated individuals.

Genome-wide association studies have successfully identified many genetic variants associated with complex traits. However, most existing methods target autosomes rather than X chromosome, and several existing X chromosome-wide association studies (XWAS) at quantitative trait loci (QTL) largely focus on unrelated individuals, with limited attention to general pedigrees or mixture of general pedigrees and additional unrelated individuals (called the mixed data for brevity). In this study, we propose nine novel methods for XWAS at QTL in the mixed data (${\mathrm{MQX}}_{\mathrm{cat}}$, ${\mathrm{MQZ}}_{\mathrm{max}}$, ${\mathrm{MT}}_{\mathrm{plinkw}}$, ${\mathrm{MT}}_{\mathrm{chenw}}$, $\mathrm{MwM}3\mathrm{VNA}$, ${\mathrm{MQMVX}}_{\mathrm{cat}}$, ${\mathrm{MQMVZ}}_{\mathrm{max}}$, $\mathrm{MpMV}$, and $\mathrm{McMV}$), also applicable to general pedigrees alone. The first four methods test for mean differences across genotypes; the latter four test for differences in both means and variances; $\mathrm{MwM}3\mathrm{VNA}$ tests for variance differences only. All mean-based and mean-variance-based methods incorporate X chromosome inactivation information, and all nine methods consider genetic relatedness in pedigrees. Simulation studies confirm well-controlled type I error rates, and inclusion of pedigrees significantly improves statistical power. Note that there has been no study focusing on X chromosome for the mixed data or general pedigrees from UK Biobank database, so we apply our proposed methods to this dataset, which identify five total cholesterol (TC)-associated and 13 low-density lipoprotein cholesterol (LDL-C)-associated single nucleotide polymorphisms (SNPs). Linkage disequilibrium (LD) analysis reveals that these SNPs fall into three distinct LD blocks. Functional annotation and gene ontology enrichment analysis reveal 16 and 28 enriched pathways for TC-associated and LDL-C-associated genes, respectively. These methods provide robust and powerful tools for XWAS at QTL in both mixed data and general pedigrees.

Quantitative Trait Loci↗

PubMed2026

Identifying Co-Expressed lncRNAs Correlated With Traits of Interest in an Animal Model for Metabolic Diseases in Humans.

Nutrigenomics investigates how nutrients modulate gene expression. Among them, fatty acids (FA) play important roles in regulating gene transcription, while long non-coding RNAs (lncRNAs) may be associated with gene regulation and metabolic diseases. This study aimed to analyze the hepatic transcriptome of pigs, a species frequently used as a model for nutrigenomic studies, to identify novel lncRNAs and their potential target genes in response to diets containing different sources of FA. Seventy-two pigs were fed four diets supplemented with 1.5% soybean oil (control), 3% canola oil, 3% fish oil, and 3% soybean oil. RNA sequencing of liver samples was performed to identify novel lncRNAs. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify modules associated with phenotypic traits related to lipid metabolism and inflammation. Functional enrichment analyses were then conducted to annotate genes within these modules using Gene Ontology (GO) terms and to assess overlap with Quantitative Trait Loci (QTL). The results revealed 106 novel lncRNAs potentially regulating genes associated with lipid metabolism and immune responses in pigs fed diets with different FA sources. These findings enhance understanding of the regulatory role of lncRNAs in pigs and reinforce their relevance as models for human metabolic diseases.

Animals↗

PubMed2001

The power to detect linkage disequilibrium with quantitative traits in selected samples.

Results from power studies for linkage detection have led to many ongoing and planned collections of phenotypically extreme nuclear families. Given the great expense of collecting these families and the imminent availability of a dense diallelic marker map, the families are likely to be used in allelic-association as well as linkage studies. However, optimal selection strategies for linkage may not be equally powerful for association. We examine the power to detect linkage disequilibrium for quantitative traits after phenotypic selection. The results encompass six selection strategies that are in widespread use, including single selection (two designs), affected sib pairs, concordant and discordant pairs, and the extreme-concordant and -discordant design. Selection of sibships on the basis of one extreme proband with high or low trait scores provides as much power as discordant sib pairs but requires the screening and phenotyping of substantially fewer initial families from which to select. Analysis of the role of allele frequencies within each selection design indicates that common trait alleles generally offer the most power, but similarities between the marker- and trait-allele frequencies are much more important than the trait-locus frequency alone. Some of the most widespread selection designs, such as single selection, yield power gains only when both the marker and quantitative trait loci (QTL) are relatively rare in the population. In contrast, discordant pairs and the extreme-proband design provide power for the broadest range of QTL-marker-allele frequency differences. Overall, proband selection from either tail provides the best balance of power, robustness, and simplicity of ascertainment for family-based association analysis.

Alleles↗

PubMed2025

Genomic prediction of agronomic traits in perennial ryegrass (Lolium perenne L.) and genotype x environment interactions at the limit of the species distribution.

KEY MESSAGE: Perennial ryegrass shows extensive genotype x environment interactions at the limit of its ecological niche. Accounting for GxE may improve prediction even when environmental and genetic samples are highly diverse. BACKGROUND: In breeding the aim is to identify and accumulate beneficial variants. However, detection of these variants may be challenging in the presence of extensive genotype x environment interactions (GxE). METHODS: The study assesses the performance of 264 diploid perennial ryegrass accessions in a multi-environment field trial. We investigate the extent of GxE, for yield (total dry matter) and persistence traits under environmental conditions experienced in Nordic and Baltic regions at the limit of the species distribution. Two different approaches to modelling GxE were tested and validated under three different breeding scenarios. RESULTS: Our analysis documented the presence of significant GxE for all traits. Validation showed improvements in prediction accuracy when accounting for GxE: up to 4% for yield when predicting in unobserved environments, and up to 22% and 9% for spring cover and winter kill, respectively, when predicting unobserved germplasm. Genome-wide-association-studies (GWAS) were utilized to detect genetic variants with marginal effects (environment-independent effect) and conditional effects (environment-dependent effects). Results showed the presence of large-effect genetic variants with marginal effects, in addition to few Quantitative Trait Loci (QTL) whose effects were adaptive under specific environmental conditions while neutral or deleterious under different environmental conditions. CONCLUSION: This study demonstrates the usefulness and limitations of genomic prediction models for predicting GxE in highly diverse samples and describes the extent of GxE at the limit of species distribution for perennial ryegrass. Our study points towards adaptive variation which may enhance persistence of perennial ryegrass populations in Nordic and Baltic growing conditions.

Lolium↗

PubMed2025

The genetic basis of chloride exclusion in grapevines.

Mediterranean regions are among the most important areas for global grape production, characterized by dry climates and frequent challenges associated with soil salinity. In these environments, chloride toxicity is a major factor limiting vine growth and fruit quality. Despite the critical role of chloride exclusion in salinity tolerance, the genetic mechanisms underlying this trait remain poorly understood. In this study, we analyzed natural variation in chloride exclusion using a diverse panel of 335 accessions representing 18 wild and cultivated Vitis species. This panel, comprising accessions from the southwestern United States and Mexico, captures a broad range of evolutionary adaptations to abiotic stress and provides a valuable genetic resource for breeding efforts aimed at introducing novel traits. Using genome-wide association and quantitative trait loci (QTL) mapping, we identified a major QTL on chromosome 8, now designated qClEx8.1, containing candidate genes encoding cation/H⁺ exchangers (CHXs), which are involved in ion transport and homeostasis. To validate these findings, we analyzed a mapping population derived from Vitis acerifolia longii 9018 and the commercial rootstock GRN3, confirming the chromosome 8 locus as a major determinant of chloride exclusion. Structural variant analysis revealed nonsynonymous substitutions within CHX genes that may influence protein function and salinity tolerance. Additionally, we discovered a novel QTL on chromosome 19 enriched with G-type lectin S-receptor-like serine/threonine-protein kinases, known regulators of stress signaling. By integrating phenotypic and genomic data across a diverse Vitis collection, this study advances our understanding of the genetic architecture underlying chloride exclusion and highlights candidate genes for breeding salt-tolerant rootstocks.

Vitis↗

PubMed2025

Indel mutation in transcription factor PabHLH2 regulates amygdalin accumulation and kernel bitterness in apricot.

Amygdalin, the phytochemical responsible for the characteristic bitterness of apricot (Prunus armeniaca L.) kernels, also exhibits significant bioactive properties and therapeutic potential. Genetic regulation of amygdalin content is therefore a key objective in apricot breeding programs aimed at quality improvement. In this study, we conducted quantitative trait loci (QTL) mapping to uncover the genetic basis of sweet-bitter differentiation in apricot kernels. We identified a 15-bp insertion/deletion (indel) polymorphism strongly related to kernel bitterness, with marker validation achieving 100% concordance across 601 apricot germplasm accessions. Notably, this polymorphic site is located within the helix-loop-helix (HLH) domain of the basic HLH (bHLH) transcription factor PabHLH2. Protein interaction analyses revealed that the 15-bp deletion variant impaired dimerization capacity, reducing transcriptional activation of downstream targets. Using yeast one-hybrid screening and dual-luciferase reporter assays, we identified PaCYP71AN24 and PaCYP79D16 as direct transcriptional targets of PabHLH2. Functional characterization further indicated that the PabHLH2a variant (harboring the 15-bp insertion) significantly enhanced the promoter activity of these cytochrome P450 genes compared with the deletion variant. Transient overexpression and silencing experiments in apricot kernels further confirmed that the 15-bp insertion positively regulates both PaCYP71AN24/PaCYP79D16 expression and prunasin accumulation, the immediate biosynthetic precursor of amygdalin. Overall, these findings provide mechanistic insights into the allelic variation underlying kernel bitterness and delineate the molecular cascade of amygdalin biosynthesis. The identified molecular markers and functional characterization establish a basis for marker-assisted breeding of low-amygdalin apricot cultivars, supporting the dual-purpose utilization of kernels in food and pharmaceutical industries.

Amygdalin↗

PubMed2025

The homeostasis of β-alanine is key for Arabidopsis reproductive growth and development.

β-Alanine, an abundant non-proteinogenic amino acid, acts as a precursor for coenzyme A and plays a role in various stress responses. However, a comprehensive understanding of its metabolism in plants remains incomplete. Previous metabolic genome-wide association studies (mGWAS) identified ALANINE:GLYOXYLATE AMINOTRANSFERASE2 (AGT2, AT4G39660) linked to β-alanine levels in Arabidopsis under normal conditions. In this study, we aimed to deepen our insights into β-alanine regulation by conducting mGWAS under two contrasting environmental conditions: control (12 h photoperiod, 21°C, 150 μmol m-2 sec-1) and stress (harvested after 1820 min at 32°C and darkness). We identified two highly significant quantitative trait loci (QTL) for β-alanine, including the AGT2 locus associated in both environments and ALDEHYDE DEHYDROGENASE6B2 (ALDH6B2, AT2G14170) associated only under stress conditions. A coexpression-correlation network revealed that the regulatory pathway involving β-alanine levels, AGT2, and ALDH6B2 connects the branched chained amino acid (BCAA) degradation through the propionate pathway. Metabolic profiles of AGT2 overexpression (OE) and knock-out (KO) lines (agt2) across various organs and developmental stages established the critical role of AGT2 in β-alanine metabolism. This work underscores the importance of β-alanine homeostasis for proper growth and development in Arabidopsis.

Arabidopsis↗

PubMed2026

Colocalization and functional analyses identify GBE1 as a gene linking muscle strength and cardiometabolic fitness.

Handgrip strength is a proxy for muscular fitness, an indicator for general health status, and is associated with cardiometabolic health. The mechanisms connecting handgrip strength to skeletal muscle function are incompletely understood. We applied integrated linkage-disequilibrium-adjusted colocalization analysis of genome-wide association study summary statistics for handgrip strength, combined with expression and splicing quantitative trait loci from skeletal muscle, and identified glycogen branching enzyme 1 (GBE1) as a candidate gene for handgrip strength. CRISPR-interference knockdown of GBE1 in immortalized human skeletal muscle cells (HMCL-7304) demonstrated decreased glycogen content and accumulation of polyglucosan bodies. Knockdown of GBE1 led to increased oxygen consumption rate, oxidative stress, and changes in mitochondrial morphology. Transcriptomic profiling of GBE1 knockdown cells identified upregulation of the human superoxide dismutase 2 and enrichment of pathways related to muscle contraction and oxidative stress responses. These functional genomic analyses prioritize GBE1 as a muscle-relevant candidate gene for handgrip strength and provide mechanistic insights to muscle fitness.NEW & NOTEWORTHY Colocalization of genome-wide association study (GWAS) loci with quantitative trait loci (QTL) in skeletal muscle tissue identified GBE1 as a candidate for handgrip strength. Cellular phenotypes with GBE1 knockdown in immortalized human skeletal muscle cells include decreased glycogen content, accumulation of polyglucosan bodies, changes in mitochondrial function and morphology, and increased expression of reactive oxygen species (ROS) scavengers. Transcriptomic changes suggest a role for GBE1 in muscle contraction and oxidative stress-mediated responses.

Humans↗

PubMed2025

Genome wide association study unveils the genetic basis of Orobanche crenata resistance in pea.

GWAS using DArTseq markers identified novel resistance sources against parasitic broomrape in pea, elucidating candidate genes for marker-selected breeding as leverage for cultivar development and efficient disease control to enhance food security. Crenate broomrape (Orobanche crenata) is an important obligate root parasitic weed that causes severe yield losses in pea (Pisum sativum) production. O. crenata is difficult to eradicate in pea fields due to its high resilience and prolific seed boom capable of hibernating in soils for decades. Existing control strategies are not cost effective in low input legumes like pea. The most efficient ecofriendly mode of control is using resistant cultivars. Quantitative trait loci (QTL) studies based on bi-parental mapping has guided O. crenata resistance discovery, albeit their deployment in pea breeding is hindered by low marker resolution and large genetic distance. This study presents the first genome-wide association study (GWAS) on O. crenata resistance in pea, utilizing 324 diverse accessions and 26,045 diversity array technology sequence (DArTseq) markers. Phenotyping was performed over four seasons under field conditions using alpha lattice design. Results showed a strong phenotypic variation with an environmental influence on O. crenata infection. Novel resistance sources were identified mainly within the wild Pisum fulvum and P. sativum subsp. elatius. GWAS with two models yielded a total of 73 marker-trait associations with Chromosome 5 as major hotspot. Interestingly, some linked markers were detected in close proximity to four previous O. crenata resistance QTL. DArTseq markers identified 24 putative candidate genes participating in different cellular processes, including vesicle trafficking and transports, deoxyribonucleic acid transcription regulation, and defense including some leucine rich repeat receptor-like kinases. These results provide a valuable genetic resource for O. crenata resistance and a step toward its effective sustainable management-to enhance genetic diversity and cultivar improvement for food security.

Pisum sativum↗

PubMed2026

Multi-omics Mendelian Randomization Prioritizes Neutrophil Extracellular Trap-related Genes Associated with Atrial Fibrillation Risk.

BACKGROUND: Neutrophil extracellular traps (NETs) participate in thrombosis, inflammation, and cardiovascular remodeling, yet whether NET-related genes (NRGs) are associated with atrial fibrillation (AF) risk across multiple molecular layers remains unclear. This study used a multiomics Mendelian randomization framework to prioritize NRGs supported by methylation, expression, and protein quantitative trait loci (QTL) data. METHODS: Genome-wide significant cis instruments (P < 5 × 10-8) were obtained for 90 methylation QTLs (mQTLs), 100 expression QTLs (eQTLs), and 38 protein QTLs (pQTLs) mapped to 137 literature- curated NRG entries. Summary-data-based Mendelian randomization (SMR) coupled with the heterogeneity in dependent instruments (HEIDI) test was applied using whole-blood mQTL data (n = 1,980), eQTLGen blood eQTL data (n = 31,684), and deCODE plasma pQTL data (n = 35,559). AF outcome data were obtained from a meta-analysis including 60,620 cases and 970,216 controls of European ancestry. RESULTS: At the methylation level, 21 CpG-feature associations across 13 genes remained significant after HEIDI filtering and false discovery rate (FDR) correction. Expression-level analysis identified eight significant gene-AF associations, whereas protein-level analysis identified seven significant features representing five unique proteins. Cross-omics integration prioritized C3, MAPK3, and STAT3 as Tier 1 genes, CTSC, LPAR3, and THBD as Tier 2 genes, and fourteen additional genes as Tier 3 candidates. C3 showed risk-increasing protein-level associations together with multiple significant CpG signals, whereas MAPK3 and STAT3 showed directionally protective expression/protein or methylation/protein patterns. DISCUSSION: The cross-omics convergence on C3, MAPK3, and STAT3 is consistent with complement activation, immune-fibrotic signaling, and cytokine-regulatory pathways implicated in AF biology, but the findings should be interpreted as genetic prioritization rather than definitive intervention-ready causality. CpG-level heterogeneity at the C3 locus and the blood/plasma origin of the QTL resources further support a cautious interpretation. Modest colocalization support and the unresolved possibility of pQTL sample overlap further support this cautious, hypothesis-generating interpretation. CONCLUSION: Multi-omics SMR prioritizes C3, MAPK3, and STAT3 as the most consistently supported NET-related genes associated with AF risk. These findings provide a framework for atrialtissue replication and mechanistic validation of NET-related pathways in AF.

Atrial fibrillation↗

PubMed2026

Pervasive context-dependent effects in the genetic architecture of complex and quantitative traits revealed by a powerful multiparent mapping population in yeast.

The genetic dissection of complex traits remains a major challenge in basic and biomedical research, but is essential for understanding the molecular pathways that shape phenotypic variation and for developing predictive models of trait and disease susceptibility. Here, we leverage a novel multiparent mapping population of budding yeast, CYClones, comprising 9,344 haploid strains derived from eight genetically diverse founders (~270,000 SNVs, ~ 1 per 44 bp, capturing 56% of common variants and 32% of all variants with a minor allele frequency greater than 0.005 in the global population), to identify quantitative trait loci (QTL) and systematically investigate the genetic architecture of growth rates across ten environmental conditions. In total, we identified 349 QTL (ranging from 18 to 49 QTL per growth condition) that explained between 60% and 100% of narrow sense heritability across traits. The high power and resolution of CYClones revealed that growth traits exhibited distinct, condition-specific genetic architectures with extensive allelic heterogeneity, where a QTL was the result of multiple tightly linked causal variants. We also observed pleiotropy among QTL with complex, trait-dependent allele effects that are also consistent with allelic heterogeneity. Genetic complexity varied widely, with some traits showing nearly Mendelian architectures, while others were highly polygenic. Introgressed loci played a prominent role in the landscape of growth rate QTL, including a QTL localized to a 2.4 kb interval in the PCA1 cadmium transporter that explains 72% of variation in cadmium resistance and is largely driven by an introgression, and a non-additive interaction between the GAL3 regulator and introgressed GAL1/7/10 alleles, extending a previously described three-locus GAL-pathway incompatibility to a four-locus interaction. In both cadmium and galactose conditions, we show that allelic variation at a small number of loci stratifies the population into regulatory or physiological subgroups, each with distinct genetic architectures, a specific manifestation of epistasis we term allele-dependent stratification. Collectively, our results provide novel insights into the genetics of growth rates in budding yeast, the architectural features of genetic complexity, and demonstrate that CYClones is a powerful platform for revealing the molecular basis of complex trait variation.

Quantitative Trait Loci↗

PubMed2026

Multi-omics identification of therapeutic targets of compound sappan decoction in hepatocellular carcinoma.

BACKGROUND: Compound sappan decoction (CSD) is a multi-herbal traditional Chinese medicine formulation with clinical relevance in hepatocellular carcinoma (HCC). However, its therapeutic mechanisms remain unclear. METHODS: Bioactive compounds of CSD were identified and standardized using pharmacological and chemical databases. Potential targets were predicted via multiple target inference platforms. HCC-related genes were curated from comprehensive disease databases. Summary-data-based Mendelian randomization (SMR) was conducted to infer causal relationships between compound targets and HCC risk using large-scale quantitative trait loci (QTL) datasets and HCC genome-wide association study data. Colocalization analysis, protein-protein interaction (PPI) network construction, and GO/KEGG enrichment were performed on SMR-identified targets. Molecular docking evaluated binding affinities of representative compounds to prioritized targets. RESULTS: A total of 784 overlapping genes between predicted CSD targets and HCC-related genes were subjected to SMR analysis. Among these, 22 targets were significantly associated with HCC risk based on transcriptomic or proteomic QTLs and showed colocalization evidence. Notably, four targets (ADRB2, APOE, SYK, and PGF) were supported by both replication in an independent cohort and strong colocalization. These 22 targets were enriched in apoptosis, PI3K-Akt signaling, redox metabolism, and detoxification pathways. PPI analysis revealed central hubs including MMP9, BCL2, CASP1, and MCL1. Molecular docking demonstrated strong binding of APOE to quercetin, PGF to luteolin-7-olate, and SYK to kaempferol. CONCLUSIONS: CSD may exert therapeutic effects on HCC through modulation of genetically validated targets involved in tumor progression, inflammation, and metabolic reprogramming, supporting its potential clinical utility as an adjunctive treatment strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s12672-026-04740-8.

Caesalpinia↗

PubMed2023

Utilizing evolutionary conservation to detect deleterious mutations and improve genomic prediction in cassava.

INTRODUCTION: Cassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication ~10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression. METHODS: To locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava. RESULTS: Simulations showed a sizable increase of prediction accuracy when incorporating functional variants in the model when the trait was determined by<100 quantitative trait loci (QTL). Utilizing deleterious mutations and functional weights informed through evolutionary conservation, we saw improvements in genomic prediction accuracy that were dependent on trait and prediction. CONCLUSION: We showed the potential for using evolutionary information to track functional variation across the genome, in order to improve whole genome trait prediction. We anticipate that continued work to improve genotype accuracy and deleterious mutation assessment will lead to improved genomic assessments of cassava clones.

cassava (Manihot esculenta)↗

PubMed2025

Smarter stomata: emergent technologies unlocking yield potential in a changing climate.

Stomata, the gatekeepers of leaf gas exchange, regulate carbon dioxide uptake and water loss, functions increasingly critical as crops face more frequent, intense heat and drought. Under dry conditions, stomatal conductance (g s) typically decreases, limiting carbon assimilation and yield. Heat stress, in contrast, elicits variable g S responses: sometimes increasing to facilitate transpirational cooling, while at other times decreasing, especially when combined with drought. Heat and drought also induce complex, context-dependent shifts in stomatal anatomy. Smaller, denser stomata improve drought resilience in some cases, while reduced density confers greater tolerance in others. The optimal stomatal ideotype remains unknown, and different or even opposing traits may confer resilience dependent on the environmental scenario. Substantial genotypic variation in g s and stomatal anatomy, high heritability and co-localized quantitative trait loci for stomatal traits and yield highlight their untapped potential as breeding targets for climate-resilient crops. However, stomatal traits remain largely absent from breeding pipelines due to challenges of phenotyping at scale. This is changing rapidly. Advances in deep learning, porometry, digital microscopy, and remote sensing now enable high-throughput measurement of stomatal physiology and anatomy. Next-generation breeding technologies including clustered regularly interspaced short palindromic repeats (CRISPR), multi-omics approaches, and artificial intelligence-driven ideotype selection models could revolutionize breeding, allowing precise engineering of stomatal traits for resilience to environmental stress. The time has come to move beyond characterizing stomatal traits and start actively incorporating them into breeding strategies. By leveraging these technologies, stomatal traits can become high value targets, unlocking their potential to enhance crop performance in a hotter, drier future.

abiotic stress↗