Architecture Of Traits Research Articles

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

How Close to the Mark Might Published Heritability Estimates Be?

The behavioural scientist who requires an estimate of narrow heritability, h2, will conduct a twin study, and input the resulting estimated covariance matrices into a particular mode of estimation, the latter derived under supposition of the standard biometric model (SBM). It is known that the standard biometric model can be expected to misrepresent the phenotypic (genetic) architecture of human traits. The impact of this misrepresentation on the accuracy of h2 estimation is unknown. We aimed to shed some light on this general issue, by undertaking three simulation studies. In each, we investigated the parameter recovery performance of five modes- Falconer’s coefficient and the SEM models, ACDE, ADE, ACE, and AE- when they encountered a constructed, non-SBM, architecture, under a particular informational input. In study 1, the architecture was single-locus with dominance effects and genetic-environment covariance, and the input was a set of population covariance matrices yielded under the four twin designs, monozygotic-reared together, monozygotic-reared apart, dizygotic-reared together, and dizygotic-reared apart; in study 2, the architecture was identical to that of study 1, but the informational input was monozygotic-reared together and dizygotic-reared together; and in study 3, the architecture was multi-locus with dominance effects, genetic-environment covariance, and epistatic interactions. The informational input was the same as in study 1. The results suggest that conclusions regarding the coverage of h2 must be drawn conditional on a) the general class of generating architecture in play; b) specifics of the architecture’s parametric instantiations; c) the informational input into a mode of estimation; and d) the particular mode of estimationemployed. The results showed that the more complicated the generating architecture, the poorer a mode’s h2 recovery performance. Random forest analyses furthermore revealed that, depending on the genetic architecture, h2, the dominance and locus additive parameter, and proportions of alleles were involved in complex interaction effects impacting on h2 parameter recovery performance of a mode of estimation. Data and materials: https://osf.io/aq9sx/

QTL analysis of divergent floral morphology traits between Gilia yorkii and G. capitata.

Speciation is a complex process typically accompanied by significant genetic and morphological differences between sister populations. In plants, divergent floral morphologies and pollinator differences can result in reproductive isolation between populations. Here, we explore floral trait differences between two recently diverged species, Gilia yorkii and G. capitata. The distributions of floral traits in parental, F1, and F2 populations are compared, and groups of correlated traits are identified. We describe the genetic architecture of floral traits through a quantitative trait locus analysis using an F2 population of 187 individuals. While all identified quantitative trait locus were of moderate (10-25%) effect, interestingly, most quantitative trait locus intervals were non-overlapping, suggesting that, in general, traits do not share a common genetic basis. Our results provide a framework for future identification of genes involved in the evolution of floral morphology.

Genetic control of root architectural traits under drought stress in spring barley (Hordeum vulgare L.).

Root architectural traits play pivotal roles in plant adaptation to drought stress, and hence they are considered promising targets in breeding programs. Here, we phenotyped eight root architecture traits in response to well-watered and drought stress conditions in 200 spring barley (Hordeum vulgare L.) inbred lines over two consecutive field seasons. Root architecture traits were less developed under drought in both seasons when compared with control treatments. Genetic variation in root architectural traits was dissected employing a genome-wide association study (GWAS) coupled with linkage disequilibrium mapping. GWAS uncovered a total of 186 significant single nucleotide polymorphism-trait associations for eight root traits under control, drought, and drought-related indices. Of these, a few loci for root traits were detected on chromosomes 3 and 5, which co-located with QTL identified in previous studies. Interestingly, 13 loci showed simultaneou associations with multiple root traits under drought and drought-related indices. These loci harbored candidate genes, which included a wide range of drought-responsive components such as transcription factors, binding proteins, protein kinases, nutrient and ion transporters, and stress signaling factors. For instance, two candidate genes, HORVU7Hr3G0713160 and HORVU6H r3G0626550, are orthologous to AtACX3 and AtVAMPs, which have reported functions in root length-mediated drought tolerance and as a key protein in abiotic stress tolerance, respectively. Interestingly, one of these loci underlying a high-confidence candidate gene NEW ENHANCER OF ROOT DWARFISM1 (NERD1) showed involvement with root development. An allelic variation of this locus in non-coding region was significantly associated with increased root length under drought. Collectively, these results offer promising multi-trait affecting loci and candidate genes underlying root phenotypic responses to drought stress, which may provide valuable resources for genetic improvement of drought tolerance in barley.

Dissecting root system architecture traits in durum wheat–goatgrass (Triticum durum–Aegilops speltoides) backcross introgression lines

AbstractA well‐developed root system is essential for efficient nutrient and water uptake. We phenotyped a set of 172 Triticum durum–Aegilops speltoides backcross introgression lines (BILs) for various root architecture traits during 2019–2020 and 2020–2021 cropping seasons. The roots were sampled at the maximum tillering stage, and data on various root architecture traits were recorded. The quantitative trait loci (QTL) mapping was carried out using 5672 polymorphic SNPs obtained from genotyping‐by‐sequencing. A total of 21 QTLs were detected for various root architecture traits on chromosomes 1A, 2A, 2B, 3B, 5A and 6B. Stable QTLs were detected for total root length, number of root tips and root dry weight over the two seasons. Candidate genes were identified by scanning the physical interval corresponding to the linkage disequilibrium (LD) decay flanking the SNPs linked to the stable QTLs. In silico expression studies of postulated candidate genes revealed root‐specific upregulation of some of the genes. These QTLs can be used in breeding programmes after the development and validation of suitable marker assays.

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Integrating meta‐analysis of genome‐wide association study with Pig Genotype‐Tissue Expression resources uncovers the genetic architecture for age at first farrowing in pigs

AbstractAge at first farrowing (AFF) is a reproductive trait with low heritability and high importance in the pig industry. To enhance the statistical power of genome‐wide association study (GWAS) and further explore the genetic nature of AFF, we first conducted GWAS meta‐analysis using three Yorkshire populations, and then integrated the Pig Genotype‐Tissue Expression (PigGTEx) resources to interpret their potential regulatory mechanism. Additionally, we compared the AFF in pig with the age at first birth (AFB) of human using GWAS summary statistics. We detected 18 independent variants in GWAS meta‐analysis and 8 genes in gene‐based association analysis significantly associated with AFF. By integrating the PigGTEx resource, we conducted transcriptome‐wide association study (TWAS) and colocalization analysis on 34 pig tissues. In TWAS, we detected 18 significant gene‐tissue pairs, such as DCAF6 in uterus and CREG1 in blood. In colocalization, we found 111 potential candidate tissue‐gene pairs, such as GJD4 and LYPLAL1. We found that the homologous gene, CHST10, might be the potential candidate gene between humans in AFB and pigs in AFF. In conclusion, integrating GWAS meta‐analysis and PigGTEx resources is a meaningful way to decipher the genetic architecture of complex traits. We found that DCAF6, CREG1, GJD4, and LYPLAL1 are candidate genes with high reliability for AFF in swine. The comparative analysis showed that CHST10 might play a potentially critical role in AFB/AFF across human and pigs.

PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics.

As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizing yield and quality. Particularly to expedite breeding methods like genomic selection, isolating core SNPs related to target traits from genome-wide data reduces irrelevant mutation noise, enhancing computational precision and efficiency. Thus, exploring efficient computational approaches to mine core SNPs is of great importance. This study introduces PlantMine, an innovative computational framework that integrates feature selection and machine learning techniques to effectively identify core SNPs critical for the improvement of rice traits. Utilizing the dataset from the 3000 Rice Genomes Project, we applied different algorithms for analysis. The findings underscore the effectiveness of combining feature selection with machine learning in accurately identifying core SNPs, offering a promising avenue to expedite rice breeding efforts and improve crop productivity and resilience to stress.

Gymnosperms demonstrate patterns of fine‐root trait coordination consistent with the global root economics space

Abstract Gymnosperms encompass a diverse group of mostly woody plants with high ecological and economic value, yet little is known about the scope and organization of fine‐root trait diversity among gymnosperms due to the undersampling of most gymnosperm families and the dominance of angiosperm groups in recent syntheses. New and existing data were compiled for morphological traits (root diameter, length, tissue density, specific root length [SRL] and specific root area [SRA]), the architectural trait branching ratio, root nitrogen content [N] and mycorrhizal colonization. We used phylogenetic least squares regression and principal component analysis to determine trait–trait relationships and coordination across 66 species, representing 11 of the 12 extant gymnosperm families from boreal, temperate, subtropical and tropical biomes. Finally, we compared the relationship between family divergence time and mean trait values to determine whether evolutionary history structured variation in fine‐root traits within the gymnosperm phylogeny. Wide variation in gymnosperm root traits could be largely captured by two primary axes of variation defined by SRL and diameter, and root tissue density and root nitrogen, respectively. However, individual root length and SRA each had significant correlations with traits defining both main axes of variation. Neither mycorrhizal colonization nor root branching ratio were closely related to other traits. We did not observe a directional evolution of mean trait values from older to more recently diverged gymnosperm families. Synthesis. Despite their unique evolutionary history, gymnosperms display a root economic space similar to that identified in angiosperms, likely reflecting common constraints on plants adapting to diverse environments in both groups. These findings provide greater confidence that patterns observed in broad syntheses justly capture patterns of trait diversity among multiple, distinct lineages. Additionally, independence between root architecture and other traits may support greater diversity in below‐ground resource acquisition strategies. Unlike angiosperms, there were no clear trends towards increasingly thin roots over evolutionary time, possibly because of lower diversification rates or unique biogeographic history among gymnosperms, though additional observations are needed to more richly test evolutionary trends among gymnosperms.

GWAS advancements to investigate disease associations and biological mechanisms.

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

Multi-trait genetic analysis identifies novel pleiotropic loci for stroke and hematological traits or risk factors

Accumulating evidence suggests a correlation between stroke and hematological traits along with their associated risk factors, yet the precise shared genetic basis remains elusive. We collected GWAS summary statistics of stroke and potentially related hematological traits or risk factors from the GWAS Catalog and used LDSC to estimate genetic correlations and heritabilities. For trait pairs with significant correlations, we performed a comprehensive multi-trait GWAS analysis to identify pleiotropic loci shared by stroke and genetically correlated hematological traits or risk factors in both Europeans and East Asians. Our MTAG analysis identified 59 pleiotropic loci associated with both stroke and hypertension in Europeans, along with 17 such loci in East Asians. Among these loci, 33 were previously unknown in Europeans, and six were previously unknown in East Asians, suggesting potential new insights into the genetic basis of these traits. In the MTAG analysis of ischemic stroke and venous thromboembolism, we found six known loci and two novel loci. Notably, the 10q22.1 locus (rs12242391) was identified as an eQTL for the nearby gene TSPAN15 in multiple tissues, including whole blood. Furthermore, the 12q13.13 locus (rs4759076), which had not been reported in association with ischemic stroke or venous thromboembolism previously, exhibited a strong cis-eQTL effect with the nearby gene NFE2, a crucial regulator of megakaryocyte biogenesis and platelet production. Our study provided valuable insights into the shared genetic architecture of stroke and hematological traits or risk factors, highlighting potential avenues for future research and clinical applications.

Toward telomere-to-telomere cat genomes for precision medicine and conservation biology.

Genomic data from species of the cat family Felidae promise to stimulate veterinary and human medical advances, and clarify the coherence of genome organization. We describe how interspecies hybrids have been instrumental in the genetic analysis of cats, from the first genetic maps to propelling cat genomes toward the T2T standard set by the human genome project. Genotype-to-phenotype mapping in cat models has revealed dozens of health-related genetic variants, the molecular basis for mammalian pigmentation and patterning, and species-specific adaptations. Improved genomic surveillance of natural and captive populations across the cat family tree will increase our understanding of the genetic architecture of traits, population dynamics, and guide a future of genome-enabled biodiversity conservation.

Genome-wide association study and genomic prediction of resistance to summer mortality in Pacific oyster (Crassostrea gigas) using whole genome resequencing

The Pacific oyster Crassostrea gigas is one of the most important farmed marine shellfish worldwide. However, massive mortality during the summer months caused significant economic losses to the C. gigas farming industry. Understanding the genetic architecture of resistance traits has been an ongoing research issue, and the incorporation of genomic information into breeding programs is expected to accelerate the process of genetic improvement. Genomic selection (GS), as a new approach of genetic improvement, has great potential for breeding new lines with adaptive advantages. In this study, we conducted a genome-wide association study (GWAS) for summer mortality resistance and estimated the accuracy of genomic predictions using different methods. The heritability of the C. gigas summer mortality resistance was low, with heritability of 0.108 ± 0.036, 0.161 ± 0.102 and 0.134 ± 0.043 for PBLUP, GBLUP and ssGBLUP, respectively. In addition, we detected 9,783,674 SNPs and used a mixed linear model to identify 18 significant SNPs associated with summer mortality resistance. The phenotypic variance explained (PVE) for these SNPs ranged from 8.25% to 10.14%. Based on these significantly related SNPs, nine significant candidate genes were identified (TLR4, HEX, SLC22A8, PDE2A, HUWE1, SLMAP, RAD52, TBK1 and RAPH1). The prediction accuracy using PBLUP, GBLUP, ssGBLUP, and weight ssGBLUP (WssGBLUP) was 0.549 ± 0.026, 0.381 ± 0.074, 0.544 ± 0.026, and 0.869 ± 0.018, respectively. Therefore, WssGBLUP models are more suitable for genomic prediction of summer mortality resistance in C. gigas. Our results suggest a polygenic genetic architecture that provide new perspectives for studying candidate genes for resistance to summer mortality, which may facilitate the genetic improvement for resistance lines in C. gigas.

MPH: fast REML for large-scale genome partitioning of quantitative genetic variation.

Genome partitioning of quantitative genetic variation is useful for dissecting the genetic architecture of complex traits. However, existing methods, such as Haseman-Elston regression and linkage disequilibrium score regression, often face limitations when handling extensive farm animal datasets, as demonstrated in this study. To overcome this challenge, we present MPH, a novel software tool designed for efficient genome partitioning analyses using restricted maximum likelihood. The computational efficiency of MPH primarily stems from two key factors: the utilization of stochastic trace estimators and the comprehensive implementation of parallel computation. Evaluations with simulated and real datasets demonstrate that MPH achieves comparable accuracy and significantly enhances convergence, speed, and memory efficiency compared to widely used tools like GCTA and LDAK. These advancements facilitate large-scale, comprehensive analyses of complex genetic architectures in farm animals. The MPH software is available at https://jiang18.github.io/mph/.

A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs.

Genomic variation in weedy and cultivated broomcorn millet accessions uncovers the genetic architecture of agronomic traits.

Large-scale genomic variations are fundamental resources for crop genetics and breeding. Here we sequenced 1,904 genomes of broomcorn millet to an average of 40× sequencing depth and constructed a comprehensive variation map of weedy and cultivated accessions. Being one of the oldest cultivated crops, broomcorn millet has extremely low nucleotide diversity and remarkably rapid decay of linkage disequilibrium. Genome-wide association studies identified 186 loci for 12 agronomic traits. Many causative candidate genes, such as PmGW8 for grain size and PmLG1 for panicle shape, showed strong selection signatures during domestication. Weedy accessions contained many beneficial variations for the grain traits that are largely lost in cultivated accessions. Weedy and cultivated broomcorn millet have adopted different loci controlling flowering time for regional adaptation in parallel. Our study uncovers the unique population genomic features of broomcorn millet and provides an agronomically important resource for cereal crops.

Genome-wide association analyses reveal genotype-by-environment interactions of growth and organ weights in gilthead seabream (Sparus aurata)

Gilthead seabream is one of the most important fish species for Mediterranean aquaculture. More than 80% of the genetically improved fingerlings of this species originate from a single country, Greece. A previous study revealed moderate genotype-by-environment (G x E) interactions for growth traits and organ weights between commercial production sites in Greece and Spain. The main objectives of this study were to identify QTLs and genes associated with growth traits and organ weights in gilthead seabream and to identify the biological processes and pathways controlling these traits under different temperature environments. For growth traits, we found fourteen genome-wide suggestive SNP effects in Spain. A strong peak between 1.97 Mb to 4.69 Mb on chromosome (chr) 22 was shared among three growth traits (harvest weight, fillet weight and thermal growth coefficient). Fourteen genome-wide suggestive SNP effects were also identified in Greece. However, none overlap with those found in Spain. Two SNPs on chr5 (chr5:5729959, chr5:2336903) and two SNPs on chr6 (chr6:1957732, chr6:3659811) were associated with all three growth traits. For organ weights, a total of 15 SNPs were associated above the suggestive threshold in Spain. Four SNPs (chr22:1966940, chr22:6094383, chr22:67838, chr15:16798259) are shared between viscera weight and liver weight, while one SNP (chr22:67838) is shared among viscera, liver and heart weight. Interestingly, three of these four SNPs (chr22:1966940, chr22:67838, and chr15:16798259) also overlap with significant SNPs associated with growth traits. None of the SNPs associated with viscera weight, liver weight and heart weight are shared with Greece. GO and KEGG functional enrichment analyses showed that light absorption and ECM-receptor interaction are significantly enriched with growth traits QTLs in Spain but not in Greece. For organ weights, response to stimulus is the most prominent process in Spain, while cell adhesion, and regulation of phosphorylation are more prominent in Greece. Genomic architectures for growth traits and organ weights differ between environments. These findings not only explain part of the G x E interactions, but also give insight into how genetics determines differences in how environmental conditions affect growth and organ weights.

Evolutionary history and biological adaptation of Han Chinese people on the Mongolian Plateau

Complex demographic processes and natural selection pressures are critical to resolving patterns of the molecular genetic basis of adaptive traits or complex diseases. Recent ancient genome data allow us to trace how key traits evolved in different human populations over time, connecting ancient population history with disease susceptibility in western Eurasians. To fill this gap in eastern Eurasians and provide deep insights into the evolutionary history of the population-specific genetic basis of complex biological traits, we explored the evolutionary basis of adaptive traits in one integrative modern and ancient genomic database, including 225 out of 5583 genomes first reported here. We comprehensively characterized the demographic history and biological adaptation of Han Chinese individuals on the Mongolian Plateau based on the allele frequency spectrum and haplotype-resolved fragments. We found strong genetic homogeneity among geographically different Han populations from Inner Mongolia (IMH). We reconstructed their admixture models and demographic events, revealing that IMH had a close genetic relationship with ancient millet farmers and obtained additional gene flow from Altaic-speaking populations. The enrichment of selected candidate genes suggested that essential metabolism-related genes promoted the rapid adaptation of eastern Eurasians to ancient environmental shifts and dietary changes during agricultural innovations. Evolutionary trajectory reconstruction of metabolism-related genes of methylenetetrahydrofolate reductase (MTHFR) and fatty acid desaturase 1 (FADS1) suggested that ancient dietary shifts during the Neolithic transition period promoted the differentiated metabolic rate of folate and fatty acid. We revealed the polygenicity of biological adaptation traits and the pleiotropy of adaptive genes, indicating that complex recent polygenic adaptations, genetic interactions, and genotype–phenotype correlations have contributed to the genetic architecture of complex traits in eastern Eurasians.

C-terminally encoded peptide-like genes are associated with the development of primary root at qRL16.1 in soybean.

Root architecture traits are belowground traits that harness moisture and nutrients from the soil and are equally important to above-ground traits in crop improvement. In soybean, the root length locus qRL16.1 was previously mapped on chromosome 16. The qRL16.1 has been characterized by transcriptome analysis of roots in near-isogenic lines (NILs), gene expression analysis in a pair of lines contrasting with alleles of qRL16.1, and differential gene expression analysis in germplasm accessions contrasting with root length. Two candidate genes, Glyma.16g108500 and Glyma.16g108700, have shown relatively higher expression in longer root accessions than in shorter rooting accessions. The C-terminal domain of Glyma.16g108500 and Glyma.16g108700 is similar to the conserved domain of C-terminally encoded peptides (CEPs) that regulate root length and nutrient response in Arabidopsis. Two polymorphisms upstream of Glyma.16g108500 showed a significant association with primary root length and total root length traits in a germplasm set. Synthetic peptide assay with predicted CEP variants of Glyma.16g108500 and Glyma.16g108700 demonstrated their positive effect on primary root length. The two genes are root-specific in the early stage of soybean growth and showed differential expression only in the primary root. These genes will be useful for improving soybean to develop a deep and robust root system to withstand low moisture and nutrient regimes.

Evaluation of variation in seedling root architectural traits and their potential association with nitrogen fixation and agronomic traits in field pea accessions

AbstractRoot system architecture (RSA) plays a central role in water and nutrient acquisition in plants. Plasticity and genetic variation in RSA can be used as an adaptive strategy to optimize plant performance under variable environments. We quantified phenotypic variation for seedling RSA among 44 diverse pea (Pisum sativum L.) genotypes, including breeding lines and germplasm accessions, grown under controlled conditions for 14 days using two‐dimensional hydroponic root imaging. Root image analysis revealed significant genotypic variability among the lines for all root traits, namely root length (RL), root diameter (RD), root volume, root surface area, number of tips, network width (NW), network depth (ND), and network convex area. Significant positive correlations were observed among the evaluated root traits, ranging from 0.5 to 0.9. Pea lines were ranked based on estimated means for root traits, with lines E20, F1, and F8 showing high rankings, while E4 and F5 received low rankings for most traits. To associate root traits with nitrogen (N) fixation and field agronomic performance, we performed redundancy analysis (RDA). The quantified root traits accounted for significant variation in the agronomic traits (R2 = ∼30%, p < 0.001). RDA showed a positive association between lodging susceptibility and root system NW and between plant height and root system ND. RD was positively associated with grain yield and N fixation. N fixation was positively associated with the number of lateral roots. The findings of this study indicate that variation for seedling root traits in pea could aid selection for N fixation and other important agronomic traits.