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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↗

PubMed1991

Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments.

As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mill.) and a closely-related wild species, L. cheesmanii. Using a RFLP map of the tomato genome, we compared the inheritance patterns of polymorphisms in 350 F2 individuals with phenotypes scored in three different ways: (1) from the F2 progeny themselves, grown near Davis, California; (2) from F3 families obtained by selfing each F2 individual, grown near Gilroy, California (F3-CA); and (3) from equivalent F3 families grown near Rehovot, Israel (F3-IS). Maximum likelihood methods were used to estimate the approximate chromosomal locations, phenotypic effects (both additive effects and dominance deviations), and gene action of QTLs underlying phenotypic variation in each of these three environments. A total of 29 putative QTLs were detected in the three environments. These QTLs were distributed over 11 of the 12 chromosomes, accounted for 4.7-42.0% of the phenotypic variance in a trait, and showed different types of gene action. Among these 29 QTLs, 4 were detected in all three environments, 10 in two environments, and 15 in only a single environment. The two California environments were most similar, sharing 11/25 (44%) QTLs, while the Israel environment was quite different, sharing 7/20 (35%) and 5/26 (19%) QTLs with the respective California environments. One major goal of QTL mapping is to predict, with maximum accuracy, which individuals will produce progeny showing particular phenotypes. Traditionally, the phenotype of an individual alone has been used to predict the phenotype of its progeny. Our results suggested that, for a trait with low heritability (soluble solids), the phenotype of F3 progeny could be predicted more accurately from the genotype of the F2 parent at QTLs than from the phenotype of the F2 individual. For a trait with intermediate heritability (fruit pH), QTL genotype and observed phenotype were about equally effective at predicting progeny phenotype. For a trait with high heritability (mass per fruit), knowing the QTL genotype of an individual added little if any predictive value, to simply knowing the phenotype. The QTLs mapped in the L. esculentum X L. cheesmanii F2 appear to be at similar locations to many of those mapped in a previous cross with a different wild tomato (L. chmielewskii).(ABSTRACT TRUNCATED AT 400 WORDS)

Biological Evolution↗

PubMed1992

Identification of genetic factors contributing to heterosis in a hybrid from two elite maize inbred lines using molecular markers.

The use of molecular markers to identify quantitative trait loci (QTLs) affecting agriculturally important traits has become a key approach in plant genetics-both for understanding the genetic basis of these traits and to help design novel plant improvement programs. In the study reported here, we mapped QTLs (and evaluated their phenotypic effects) associated with seven major traits (including grain yield) in a cross between two widely used elite maize inbred lines, B73 and Mo17, in order to explore two important phenomena in maize genetics-heterosis (hybrid vigor) and genotype-by-environment (G x E) interaction. We also compared two analytical approaches for identifying QTLs, the traditional single-marker method and the more recently described interval-mapping method. Phenotypic evaluations were made on 3168 plots (nearly 100,000 plants) grown in three states. Using 76 markers that represented 90-95% of the maize genome, both analytical methods showed virtually the same results in detecting QTLs affecting grain yield throughout the genome, except on chromosome 6. Fewer QTLs were detected for other quantitative traits measured. Whenever a QTL for grain yield was detected, the heterozygote had a higher phenotype than the respective homozygote (with only one exception) suggesting not only overdominance (or pseudooverdominance) but also that these detected QTLs play a significant role in heterosis. This conclusion was reinforced by a high correlation between grain yield and proportion of heterozygous markers. Although plant materials were grown and measured in six diverse environments (North Carolina, Iowa and Illinois) there was little evidence for G x E interaction for most QTLs.

Crosses, Genetic↗

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↗

PubMed1990

Fine mapping of quantitative trait loci using selected overlapping recombinant chromosomes, in an interspecies cross of tomato.

Quantitative trait loci (QTLs) have been mapped to small intervals along the chromosomes of tomato (Lycopersicon esculentum), by a method we call substitution mapping. The size of the interval to which a QTL can be mapped is determined primarily by the number and spacing of previously mapped genetic markers in the region surrounding the QTL. We demonstrate the method using tomato genotypes carrying chromosomal segments from Lycopersicon chmielewskii, a wild relative of tomato with high soluble solids concentration but small fruit and low yield. Different L. chmielewskii chromosomal segments carrying a common restriction fragment length polymorphism were identified, and their regions of overlap determined using all available genetic markers. The effect of these chromosomal segments on soluble solids concentration, fruit mass, yield, and pH, was determined in the field. Many overlapping chromosomal segments had very different phenotypic effects, indicating QTLs affecting the phenotype(s) to lie in intervals of as little as 3 cM by which the segments differed. Some associations between different traits were attributed to close linkage between two or more QTLs, rather than pleiotropic effects of a single QTL: in such cases, recombination should separate desirable QTLs from genes with undesirable effects. The prominence of such trait associations in wide crosses appears partly due to infrequent reciprocal recombination between heterozygous chromosomal segments flanked by homozygous regions. Substitution mapping is particularly applicable to gene introgression from wild to domestic species, and generally useful in narrowing the gap between linkage mapping and physical mapping of QTLs.

Alleles↗

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.↗

PubMed1992

Mapping quantitative trait loci for behavioral traits in the mouse.

After many years of studying various behavioral characters in the mouse, it is clear that most are heritable and are specified by complexes of genes or quantitative trait loci (QTLs). In order to attain a more complete understanding of the genetic causes of individual differences in behavior, the mechanism of action of these QTLs must be elucidated. The most straightforward approach to determining the mechanism of action of a particular QTL is to identify and molecularly clone the gene; this can be done by positional cloning, which depends on precise knowledge of the genetic map position. As the genetic data base for the mouse genome continues to develop, such strategies will become increasingly easy to perform. Here we suggest a multistage strategy for QTL mapping using recombinant-inbred strains of mice: (1) characterize genomic DNA from parental strains originally used to generate the RI strains; (2) characterize the RI strains for a quantitative character and for DNA markers that differ in the parental strains; and (3) assess the quantitative character in F2 mice derived from crosses between the parental strains, then determine the genotypes of extreme F2 mice for markers that account for at least 5% of the additive genetic variance. Data from these F2 crosses can be used to test hypotheses from the analysis of RI strains, i.e., that a QTL maps to a particular region. Using data from the mouse genome data base, this strategy should allow the molecular identification of the gene based on a candidate-gene approach. We illustrate the approach with examples from our work in mapping QTLs specifying neural sensitivity to the anesthetic effects of ethanol.

Alcoholism↗

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↗

PubMed1992

Evidence for orthologous seed weight genes in cowpea and mung bean based on RFLP mapping.

A well saturated genomic map is a necessity for a breeding program based on marker assisted selection. To this end, we are developing genomic maps for cowpea (Vigna unguiculata 2N = 22) and mung bean (Vigna radiata 2N = 22) based on restriction fragment length polymorphism (RFLP) markers. Using these maps, we have located major quantitative trait loci (QTLs) for seed weight in both species. Two unlinked genomic regions in cowpea contained QTLs accounting for 52.7% of the variation for seed weight. In mung bean there were four unlinked genomic regions accounting for 49.7% of the variation for seed weight. In both cowpea and mung bean the genomic region with the greatest effect on seed weight spanned the same RFLP markers in the same linkage order. This suggests that the QTLs in this genomic region have remained conserved through evolution. This inference is supported by the observation that a significant interaction (i.e., epistasis) was detected between the QTL(s) in the conserved region and an unlinked RFLP marker locus in both species.

Fabaceae↗

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↗