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PubMed2025

Identifying canopy wilting QTLs and evaluating remote sensing approaches for selecting drought-tolerant soybean.

Drought is the most damaging abiotic stress for soybean yield; cultivars with improved drought tolerance are needed to sustain and increase crop production. PI 603535 previously was identified as an ultra-slow canopy wilting (CW) line in a genome-wide association study but the quantitative trait loci (QTLs) underlying this phenotype have not been determined. In this study, a recombinant inbred line (RIL) population derived from Benning × PI 603535 was evaluated for three years under rain-fed conditions. CW was rated following extended periods of drought when CW variation was present. Aerial multispectral and thermal imagery was also captured in conjunction with visual ratings to explore the feasibility of implementing remote sensing to improve the efficiency and objectivity of drought evaluations. The normalized difference vegetation index (NDVI) and green-based NDVI (GNDVI) exhibited strong, significant correlations (|r|= 0.42-0.44) with CW across years. CW scores and the remote sensing traits were used as phenotypes for QTL mapping. Seven CW QTLs were identified across six chromosomes in the combined analysis, with NDVI and GNDVI QTLs generally colocalizing with the CW QTLs with the highest percentage of variation explained (PVE). The QTLs were not consistently identified among individual years, highlighting the complex genetics and gene expression of drought tolerance. The instability and low additive effect estimates of individual QTLs imply challenges of improving drought tolerance through the selection of a few QTLs. However, the slow CW RILs developed in this study can serve as valuable breeding stocks for future drought improvement breeding efforts and genetic studies.

Quantitative Trait Loci↗

PubMed2026

QTLs associated with phenolic acid accumulation and antioxidant activity in tropical maize.

Maize represents a significant source of phytochemicals, with phenolic acids standing out as one of the most extensively studied functional compound families. These bioactive molecules have gained attention for their potent antioxidant properties and potential contributions to human health improvement. To evaluate the segregation of phenolic compounds in maize and its genetic basis, this study was conducted to identify quantitative trait loci (QTLs) associated with major phenolic compounds and their antioxidant capacity. The mapping population comprised 100 recombinant inbred lines (RILs) derived from the cross between P84 and Kilima. Twelve traits were analyzed: free and cell wall-bound antioxidant capacity, total phenolic content, and contents of p-coumaric acid, ferulic acid, three isomers of di-ferulic acid, and three isomers of tri-ferulic acid. The RILs exhibited substantial diversity in phenolic compound profiles. In total, 19 QTLs were identified for nine traits, with the number of associated regions ranging from 1 to 5 and explaining between 2.95% and 37.48% of the phenotypic variation. This research provides substantial evidence for the co-localization of major QTLs for principal phenolic acids in maize with genomic regions harboring genes putatively related to their biosynthesis and biotic resistance. This is the first study to report QTLs associated with triferulic acids in maize. The identified regions co-localizing with biotic stress resistance genes represent targets for marker-assisted selection toward the improvement of phenolic acid accumulation in maize breeding programs.

QTLs↗

PubMed2026

Discovering common and population-specific QTLs for leaf rust resistance in different Barley populations.

Multi-population GWAS lead to identification of common and population-specific QTLs for leaf rust resistance in barley. Genome-wide association studies (GWAS) are a powerful tool for detecting genetic markers associated with traits of interest. However, these studies are typically restricted to a single population, and transferability of identified marker effects across populations is challenged by population differences in linkage, allele frequencies, epistatic effects, and environmental context. When comparing GWAS results between populations, a lack of overlapping signals is often interpreted as a lack of common quantitative trait loci (QTLs), although such discrepancies may result from differences in statistical power to detect signals. In barley (Hordeum vulgare L.), where genetic leaf rust resistance is rapidly overcome by evolving pathogens, identification of cross-population robust and potentially transferable resistance loci is a key task. Here, we present a mixed model approach for multi-population GWAS that estimates correlated marker effects in multiple populations and use this to test for significant effects across and within populations. Applying this model to four barley breeding populations revealed both common and population-specific QTL effects for leaf rust resistance, including loci colocalizing with known Rph genes and novel regions with plausible candidate genes. Multi-population GWAS increased power, revealing signals not detected by GWAS within populations. We categorized the reported QTLs into three groups based on marker-associated allele effects: (1) consistent effect direction across populations, (2) differing effect direction across populations, and (3) present in a single population. The study highlights the transferability and limitations of leaf rust resistance QTLs across different barley populations and provides a general statistical framework to support robust marker-assisted selection across populations.

Quantitative Trait Loci↗

PubMed2025

Adaptation of the Cyst Nematode Globodera pallida to the Colinear Potato Resistant QTLs GpaVvrn and GpaVspl Involved Distinct Genomic Regions and Absence of Cross-Virulence.

The use of alternative methods to control cyst nematode populations has accelerated since the ban of chemical nematicides in Europe. The resistant QTL GpaVvrn, derived from the wild species Solanum vernei, is widely present in resistant European potato cultivars and provides strong protection against Globodera pallida populations although a risk of resistance breakdown has already been demonstrated in both experimental evolution studies and field populations. The wild relative S. sparsipilum, harbouring the resistant QTL GpaVspl, would be an interesting alternative source of resistance to control virulent G. pallida. The goal of the present study was to understand the genomics of adaptation of the nematode to these two colinear resistant QTLs. Starting with two natural populations, an experimental evolution approach allowed, after 10 generations on resistant potato genotypes, selecting independent nematode lineages adapted to each QTL. These virulent lineages were analysed through a combination of phenotyping and genome scans approaches. Phenotyping enabled the quantification of virulence levels and confirmed resistance breakdowns. Pool-Seq whole genome sequencing followed by genome scan analyses identified genomic regions under selection, potentially involved in the adaptive mechanisms to each resistance factor. Candidate genes within these regions provided insights into the genetic basis of adaptation, revealing effectors known to suppress plant immunity. As genome scans highlighted distinct genomic regions for the adaptation to both resistant factors, we were able to predict and phenotypically confirm the absence of cross-virulence between nematode lineages evolving on GpaVvrn and GpaVspl. These findings have significant implications for the design of effective and sustainable resistance management strategies.

Animals↗

PubMed2025

A novel high-dimensional model for identifying regional DNA methylation QTLs.

Varying coefficient models offer the flexibility to learn the dynamic changes of regression coefficients. Despite their good interpretability and diverse applications, in high-dimensional settings, existing estimation methods for such models have important limitations. For example, we routinely encounter the need for variable selection when faced with a large collection of covariates with nonlinear/varying effects on outcomes, and no ideal solutions exist. One illustration of this situation could be identifying a subset of genetic variants with local influence on methylation levels in a regulatory region. To address this problem, we propose a composite sparse penalty that encourages both sparsity and smoothness for the varying coefficients. We present an efficient proximal gradient descent algorithm that scales to high-dimensional predictor spaces, providing sparse solutions for the varying coefficients. A comprehensive simulation study has been conducted to evaluate the performance of our approach in terms of estimation, prediction and selection accuracy. We show that the inclusion of smoothness control yields much better results over sparsity-only approaches. An adaptive version of the penalty offers additional performance gains. We further demonstrate the utility of our method in identifying regional mQTLs from asymptomatic samples in the CARTaGENE cohort. The methodology is implemented in the R package sparseSOMNiBUS, available on GitHub.

Humans↗

PubMed2025

A spectral framework to map QTLs affecting joint differential networks of gene co-expression.

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Quantitative Trait Loci↗

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

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↗

PubMed2025

Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.

INTRODUCTION: Stem rust (SR), caused by Puccinia graminis f. sp. tritici (Pgt), remains a major threat to global barley production, particularly in regions with conducive environments and evolving pathogen populations. Despite progress in understanding seedling resistance, adult plant resistance (APR) to SR remains underexplored in diverse barley germplasm. This study aimed to dissect the genetic architecture of APR to SR in a panel of diverse origins of two-row spring barley using a genome-wide association study (GWAS). METHODS: A total of 273 barley accessions were evaluated for APR to SR in two distinct environments in Kazakhstan. Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. Transcriptomic data from 16 barley tissues were used to identify candidate genes within major QTL regions. Substantial phenotypic variation in SR severity was observed across environments. RESULTS: A total of 204 MTAs were identified, among which 96 were stable across models, resulting in 19 model-stable QTLs spanning all seven barley chromosomes. Six QTLs co-localized with known SR-resistance QTLs and genes, including Rpg1 and Rpg6. Q_rpg_7H.1 (coinciding with Rpg1) was one of the strongest and most consistent QTL, harboring 42 highly expressed candidate genes. A novel major-effect QTL on chromosome 5H, Q_rpg_5H.1 (3.5 - 9.9 Mb), not previously associated with known resistance loci, contained 10 highly expressed genes grouped into three co-expression clusters, including WRKY transcription factors and PR-5 proteins. CONCLUSION: This study provides new insights into the complex, multilayered genetic control of SR resistance in barley. The discovery of both known and novel QTLs offers valuable targets for marker-assisted selection and lays the foundation for breeding durable SR-resistant barley adapted to diverse agroecological conditions.

Hordeum vulgare L.↗

PubMed2025

Development of recombinant inbred lines and QTL analysis of plant height and fruit shape-related traits in Cucurbita pepo L.

UNLABELLED: Zucchini (Cucurbita pepo subsp. pepo) stands as an economically vital crop in China. In zucchini breeding, plant architectural patterns and fruit morphological characteristics serve as pivotal traits. In this study, we employed quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs) derived from two distinct inbred lines, JinGL (subsp. ovifera) and HM-S2 (subsp. pepo), in conjunction with a high-density genetic map. Our investigation focused on ten QTLs associated with six horticulturally significant traits, including hypocotyl length (HL), plant height (PH), and four fruit-related traits: fruit length (FL), fruit diameter (FD), fruit shape index (FSI), and fruit weight (FW). The QTLs governing HL and PH were mapped to Chr03/LG10 and named qhl3.1 and qph3.1, respectively. The candidate gene Cp4.1LG10g05910/CpDw for qph3.1 was successfully identified. Additionally, three novel QTLs related to fruit size and shape were discovered. Among them, qfsi8.1/qfl8.1, demarcated by Marker238258 and Marker240069 on Chromosome 08/Linkage group 17 (Chr08/LG17), is a new major QTL regulating the fruit shape of zucchini. Through genomic insertion-deletion (InDel) and qRT-PCR analyses, we predicted genes within the qfsi8.1/qfl8.1 candidate interval, uncovering Cp4.1LG17g02030/CpIAA12 and Cp4.1LG17g02010/CpCalB as potential candidate genes. We developed molecular markers tightly linked to qph3.1 and qfl8.1 and validated them in 171 and 224 Cucurbita pepo germplasms, achieving accuracy rates of 96% and 100%, respectively. This study deepens our understanding of the genetic basis of key traits and provides valuable references for molecular breeding in Cucurbita pepo. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11032-025-01592-y.

Cucurbita pepo↗

PubMed2025

Quantitative trait loci for Globodera pallida resistance derived from wild potato species Solanum gourlayi.

Globodera pallida is a major pest that is responsible for huge losses in potato yields worldwide. Expanding the gene pool of cultivated potatoes with clones resistant to this pest is made possible by searching for resistance genes in wild Solanum species. The aim of this study was to identify quantitative trait loci (QTLs) for potato resistance to Globodera pallida derived from Solanum gourlayi. A resistant diploid potato clone, Sg 2/7 (Solanum gourlayi, accession CGN17592), was crossed with a susceptible potato hybrid clone, DW 94-4235, to generate an F1 mapping population. All clones were tested for nematode resistance using G. pallida, pathotypes Pa2 and Pa3, in 2 or 3 years (2017-2019), respectively. Diversity Array Technology (DArTseq) was used for genotyping and genetic map construction. QTLs for nematode resistance were identified on potato chromosomes II, IV, V, VI, VII, X, XI, and XII, explaining from 10.1 to 21.5% of phenotypic variance. The most significant QTL for resistance to G. pallida pathotype Pa2 was identified on chromosome XII, explaining 20.9% of the phenotypic variance in the dataset from 2017. The most significant QTL for resistance to the G. pallida Pa3 pathotype was identified on chromosome VI, with a CAPS marker Exp928 in its peak, explaining 21.5% of the phenotypic variance in the dataset from 2017. The novel QTLs for resistance to S. gourlayi may be useful for breeding resistant potato cultivars, further studies of candidate genes, and host responses of potato to G. pallida infection.

Quantitative Trait Loci↗

PubMed2026

The potential of considering photosynthesis parameters in crop yield breeding by genomic prediction.

To meet the growing demand for agricultural products, optimizing photosynthesis is a promising strategy to improve crop yields. Phenotypic variance in photosynthesis has been observed within or between species. To explore the potential of integrating photosynthetic parameters into crop breeding programs, we explored the genetic variation in photosynthesis by assessing photosynthesis-related parameters across plant development in 631 barley recombinant inbred lines (RILs) from eight HvDRR subpopulations under field conditions. The genetic complexity of these parameters was resolved by analyses of bi-parental and multi-parental quantitative trait loci (QTLs). Finally, we examined the merit of integrating photosynthesis-related parameters in genomic prediction of yield and its components. Significant genotypic variations of the photosynthesis-related parameters were found among the RILs, with their heritability ranging from 0.38 to 0.54. The multiple QTLs and dynamic QTLs for photosynthesis observed across different developmental stages underlined the complexity of the genetics of photosynthesis in barley. The considerably higher percentage of phenotypic variance explained for genomic prediction than multi-parental QTL analysis illustrates that the photosynthesis-related parameters are inherited in a more complex way than classical agronomic traits. Notably, the prediction ability for yield was increased by integrating the photosynthesis-related parameters of some developmental stages into genomic prediction models. Thus, our results suggest a novel perspective on increasing the efficiency of crop breeding programs by integrating photosynthesis-related parameters into prediction models.

Photosynthesis↗

PubMed2026

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗

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↗

PubMed2024

Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior.

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

Animals↗

PubMed2026

Mitochondria-Related Pathogenic Genes in Paediatric Asthma: A Multi-Omics Mendelian Randomization Study.

Mitochondrial dysfunction is implicated in asthma pathogenesis, but causal roles of mitochondrial-related genes in paediatric asthma remain unclear. We performed a multi-omics Mendelian randomization study integrating GWAS data from paediatric asthma cohorts with blood-based methylation quantitative trait loci (mQTLs), expression QTLs (eQTLs) and protein QTLs (pQTLs) datasets. Causal inference was assessed using Summary-data-based Mendelian Randomization (SMR) and HEIDI testing, complemented by colocalization analysis. Findings were validated in independent cohorts and evaluated for tissue specificity using GTEx. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted. SMR analysis identified 80 methylation sites spanning 54 genes, 26 gene expressions, and three proteins significantly associated with paediatric asthma. Colocalization analysis confirmed strong evidence for 10 methylation sites (7 genes), the STX17 eQTL (PP.H4 = 0.98) and the UNG pQTL (PP.H4 = 0.84). Tissue-specific eQTL validation replicated the STX17 association. Multi-omics integration associated ALAS1 (cg13241645, cg15698299) and TXNRD1 (cg09884423) with asthma at both methylation and expression levels, with colocalization supporting both ALAS1 associations. Furthermore, integrated mQTL-eQTL analysis suggests that DNA methylation potentially regulates ALAS1 and TXNRD1 expression. Functional enrichment and network analyses revealed that these candidate genes converge on mitochondrial metabolic pathways and identified seven hub genes with potential regulatory significance (SDHB, MFN2, GLDC, PHB2, TXNRD1, ATP5MC1 and PHB). This study provides multi-omics evidence supporting a causal role for mitochondrial-related genes, particularly ALAS1 and TXNRD1, in paediatric asthma, offering new insights into pathogenesis and potential therapeutic targets.

Humans↗

PubMed2026

Wheat breeding during and after the "green revolution" contributed to the reduced use of elite nitrogen metabolism alleles linked to nitrogen use efficiency.

The wheat "Green Revolution (GR)" that occurred from the 1960s to the 1970s significantly enhanced the harvest index and resistance to lodging, thereby increasing grain production, but at the cost of reduced nitrogen (N) use efficiency (NUE) in wheat. The NUE of wheat is mainly regulated by N metabolism genes (NMGs). However, the evolutionary process of NMGs during GR and post-GR wheat breeding, as well as which of them affect NUE, remains unclear. Here, we collected 265 wheat varieties that were released before, during, and after the GR and investigated grain yield per plant and 24 other traits under different N supply conditions. Next, we identified the genotypes of these wheat varieties using a 100 K targeted sequencing array. Then, we systematically analyzed the signatures in the genomes of GR and post-GR released varieties compared with pre-GR released varieties through population divergence (Fst) and nucleotide diversity (π) ratio analyses, and found that 41 NMGs were located within the selective sweep regions during the GR and post-GR breeding. We further identified 118 quantitative trait loci (QTLs) involved in regulating NUE through genome-wide association studies (GWAS). Four NMGs-NRT1 AND PEPTIDE TRANSPORTER FAMILY 2.7-D (TaNPF2.7-D), TaNPF2.3-D, TaNPF2.7 L-D, and QUASIMODO2-B (TaQUA2-B)-were located within overlapping regions of selective sweeps and NUE-related QTLs. Notably, the elite haplotypes of these genes for NUE are less utilized in GR and post-GR released cultivars. Furthermore, we found that TaNPF2.7-D positively regulates nitrate exudation as well as the wheat development. Collectively, our findings uncover an important reason for the reduction in NUE in modern cultivars and provide a valuable resource for improving wheat NUE.

Triticum↗

PubMed2025

Genetic diversity, population structure in a historical panel of Brazilian soybean cultivars.

Soybean [Glycine max (L.) Merrill] is one of the most widely grown legumes in the world, with Brazil being its largest producer and exporter. Breeding programs in Brazil have resulted from multiple cycles of selection and recombination starting from a small number of USA cultivar ancestors in the 1950s and 1960s years. This process has led to the successful adaptation of this crop to tropical conditions, a phenomenon known as tropicalization. Many studies describe a narrow genetic background in Brazilian soybean cultivars. Various factors can affect the genetic diversity in species, especially in cultivated crops, such as the reproduction type, artificial selection, and the number and sources of variability in the breeding programs. In turns, the genetic diversity can affect the linkage disequilibrium blocks (LD) patterns and, consequently, molecular breeding strategies for selection of target loci for agronomic traits. We used high-throughput genotyping with SoySNP50K Illumina SNP markers to assess a collection of 370 Brazilian soybean accessions covering more than 60 years of soybean breeding in Brazil. Our goal was to investigate population structure and genetic diversity in the Brazilian germplasm, detect patterns of LD blocks, and identify regions presenting signals of selective swaps linked with quantitative trait loci (QTLs) of agronomic interest. Population structure analysis revealed two major groups among all genotypes, primarily differentiated by the year of release, separating old and new cultivars (before and after 2000´s years), and by growth habit (stem termination type-SST). The group I comprises about 75% of the panel and includes cultivars release before 2000`s years, including the oldest cultivars released in Brazil, most of which exhibit a determinate growth habit and maturity groups VI and VII. Group II includes only 83 materials, but shows higher levels of diversity than group I, representing most recent introductions in Brazilian germplasm. Further analysis of substructure within Group I, identified seven subgroups with no clear trend for segregation based on maturity group, STT or year of release. Instead, these subgroups were based on the contribution of key donors of disease resistance and adaptability, as soybean cultivation expanded from the South to Central region of Brazil. This finding is consistent with the history of soybean expansion in Brazil. We identified 123 genomic regions under selection among the groups of Brazilian cultivars associated with 440 quantitative trait loci (QTLs), revealing regions fixed across the breeding process associated with yield, disease resistance, water efficiency use, and others.

Glycine max↗