Architecture Of Traits Research Articles

Evaluation of heritability partitioning approaches in livestock populations

BackgroundHeritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.ResultsHere, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).ConclusionsHeritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

Terrestrial laser scanning and low magnetic field digitization yield similar architectural coarse root traits for 32-year-old Pinus ponderosa trees

BackgroundUnderstanding how trees develop their root systems is crucial for the comprehension of how wildland and urban forest ecosystems plastically respond to disturbances such as harvest, fire, and climate change. The interplay between the endogenously determined root traits and the response to environmental stimuli results in tree adaptations to biotic and abiotic factors, influencing stability, carbon allocation, and nutrient uptake. Combining the three-dimensional structure of the root system, with root morphological trait information promotes a robust understanding of root function and adaptation plasticity. Low Magnetic Field Digitization coupled with AMAPmod (botAnique et Modelisation de l’Architecture des Plantes) software has been the best-performing method for describing root system architecture and providing reliable measurements of coarse root traits, but the pace and scale of data collection remain difficult. Instrumentation and applications related to Terrestrial Laser Scanning (TLS) have advanced appreciably, and when coupled with Quantitative Structure Models (QSM), have shown some potential toward robust measurements of tree root systems. Here we compare, we believe for the first time, these two methodologies by analyzing the root system of 32-year-old Pinus ponderosa trees.ResultsIn general, at the total root system level and by root-order class, both methods yielded comparable values for the root traits volume, length, and number. QSM for each root trait was highly sensitive to the root size (i.e., input parameter PatchDiam) and models were optimized when discrete PatchDiam ranges were specified for each trait. When examining roots in the four cardinal direction sectors, we observed differences between methodologies for length and number depending on root order but not volume.ConclusionsWe believe that TLS and QSM could facilitate rapid data collection, perhaps in situ, while providing quantitative accuracy, especially at the total root system level. If more detailed measures of root system architecture are desired, a TLS method would benefit from additional scans at differing perspectives, avoiding gravitational displacement to the extent possible, while subsampling roots by hand to calibrate and validate QSM models. Despite some unresolved logistical challenges, our results suggest that future use of TLS may hold promise for quantifying tree root system architecture in a rapid, replicable manner.

Enhanced recombination empowers the detection and mapping of Quantitative Trait Loci

Modern plant breeding, such as genomic selection and gene editing, is based on the knowledge of the genetic architecture of desired traits. Quantitative trait loci (QTL) analysis, which combines high throughput phenotyping and genotyping of segregating populations, is a powerful tool to identify these genetic determinants and to decipher the underlying mechanisms. However, meiotic recombination, which shuffles genetic information between generations, is limited: Typically only one to two exchange points, called crossovers, occur between a pair of homologous chromosomes. Here we test the effect on QTL analysis of boosting recombination, by mutating the anti-crossover factors RECQ4 and FIGL1 in Arabidopsis thaliana full hybrids and lines in which a single chromosome is hybrid. We show that increasing recombination ~6-fold empowers the detection and resolution of QTLs, reaching the gene scale with only a few hundred plants. Further, enhanced recombination unmasks some secondary QTLs undetected under normal recombination. These results show the benefits of enhanced recombination to decipher the genetic bases of traits.

Development and optimization of expected cross value for mate selection problems

In this study, we address the mate selection problem in the hybridization stage of a breeding pipeline, which constitutes the multi-objective breeding goal key to the performance of a variety development program. The solution framework we formulate seeks to ensure that individuals with the most desirable genomic characteristics are selected to cross in order to maximize the likelihood of the inheritance of desirable genetic materials to the progeny. Unlike approaches that use phenotypic values for parental selection and evaluate individuals separately, we use a criterion that relies on the genetic architecture of traits and evaluates combinations of genomic information of the pairs of individuals. We introduce the expected cross value (ECV) criterion that measures the expected number of desirable alleles for gametes produced by pairs of individuals sampled from a population of potential parents. We use the ECV criterion to develop an integer linear programming formulation for the parental selection problem. The formulation is capable of controlling the inbreeding level between selected mates. We evaluate the approach or two applications: (i) improving multiple target traits simultaneously, and (ii) finding a multi-parental solution to design crossing blocks. We evaluate the performance of the ECV criterion using a simulation study. Finally, we discuss how the ECV criterion and the proposed integer linear programming techniques can be applied to improve breeding efficiency while maintaining genetic diversity in a breeding program.

Differential spatial plasticity response in common bean (Phaseolus vulgaris L.) root architecture under water stress is driven by increased root diameter, surface area and volume at deeper layers

Root plasticity enables plants to adapt to spatial and temporal changes in soil resources. In this study, 40 common bean genotypes evaluated for two root and shoot traits under irrigated and water stress. Three genotypes WB-216, WB-N-2, and WB-966 with contrasting plasticity responses were used for in-depth study. Highest positive plasticity for most root traits was found in case of WB-N2 and WB-216, whereas, WB-966 had negative plasticity for all the traits recorded. In terms of spatial plasticity for root traits in three root length sections, WB-216 was positively plastic for root diameter with progressive decrease from top to bottom sections. WB-N2 had positive plasticity values for root diameter, root surface area and root volume. WB-966 had negative plasticity for all the traits. For WB-216, the root diameter increased under drought in S1 but was almost same in bottom sections. In case of WB-N2, there was increase in root diameter in S2 and S3, but for WB-966, root diameter decreased in all sections. Similar trend was observed in all three genotypes for root surface area and volume. We report that major drivers of spatial plasticity of root architectural traits are increased root diameter, surface area and volume at deeper layers.

Biometric variability of inflorescence and flower traits among ex situ accessions of the neotropical oilseed palm Acrocomia Mart.

The oilseed palm genus Acrocomia is suitable for sustainable oil production in South America. The high phenotypic diversity of wild populations poses a challenge for the delimitation of the genus. Comparing the inflorescence architecture, a first-order panicle, and staminate and pistillate flower traits could be a valuable tool in resolving the taxonomic disarray. Thus, this study aims to characterize the differences in the inflorescence architecture and floral structures of three common and economically significant Acrocomia species: A. aculeata, A. totai, and A. intumescens. Biometric traits of the inflorescence architecture and floral structures of various Acrocomia accessions in an ex situ germplasm collection in Brazil were assessed. The unweighted pair group method with arithmetic mean (UPGMA) cluster analysis based on the Gower distance was used to measure dissimilarities between the individual plants of the accessions. To our best knowledge, this study provides the first evidence of the presence of second-order rachillae in the genus Acrocomia. Evaluated traits showed a high level of variation within and between accessions, emphasizing the phenotypic diversity of the genus. The accessions of A. totai were distinguishable from those of the other two species by their inflorescence architecture and flower traits. The dissimilarities between A. aculeata and A. intumescens were not sufficient to differentiate both. In conclusion, the quantitative assessment of inflorescence and floral traits is a valuable tool for taxonomic resolution of the genus.

Genetic dissection of minerals and phytate content in pearl millet germplasm panel using genome‐wide association study

AbstractBillions of people around the world suffer from malnutrition, leading to severe adverse health effects. Pearl millet (Pennisetum glaucum) is a multifaceted versatile crop with excellent nutritional profile which can help to combat nutritional disorders and climate change. In this study, we evaluated the global pearl millet germplasm panel known as PMiGAP for natural variation and genetic maker trait associations for important minerals, that is, iron, zinc, calcium, magnesium, potassium and sodium along with phytate. The genotypes IP‐15947, IP‐5121, IP‐4020, IP‐12768, IP‐5695, IP‐8786 and IP‐11310 were found to be superior for majority of minerals examined but had lower phytate‐to‐zinc ratio. Phytate/mineral molar ratios are typically used to predict the bioavailability of iron and calcium contents in grains, and surprisingly none of the PMiGAP genotypes showed such ratios below threshold indicating PMiGAP entries studied in this study seriously suffer from bioavailability issues of these minerals. On the other hand, 73 genotypes had lower zinc/phytate ratio than the threshold in the germplasm panel. Iron and zinc content had significant positive association among them but phytate content in general was not significantly correlated with minerals except for magnesium and potassium. A genome‐wide association study using 456 K SNPs identified 74 significant marker–trait associations and 59 candidate genes around 50 Kb distance near the significant SNPs. Ten significant SNPs were found within the candidate genes. The associated markers and the candidate genes provide new insights into the genetic architecture of the mineral traits studied and will facilitate marker‐assisted selection to accelerate breeding of such minerals in future varieties to combat rising malnutrition problem via diet.

Seminal root angle is associated with root system architecture in durum wheat

AbstractOptimal root system architecture (RSA) is critical for efficient resource capture in soils, hence being an interest in crop breeding. Seminal root angle (SRA) at the seedling stage in durum wheat has been suggested to be a good indicator of RSA. However, research on correlating such laboratory‐based seedling root phenotyping to RSA at later phases of plant growth is limited, resulting in the importance of root trait variation seen in seedlings often being overstated. To explore the role of SRA in modifying RSA at later phases of plant growth, we assessed 11 genotypes contrasting in SRA (wide and narrow), grown in a rhizobox designed for phenotyping root systems of plants during late‐tillering. Aboveground traits and root dry mass in different soil depths and across the entire soil volume were measured manually, while root architectural traits were extracted using image analysis and summarised by multiple factor analysis to describe RSA. When comparing the wide and narrow genotypes, no differences were detected for aboveground traits and total root dry mass. However, differences were observed in the allocation of root dry mass at different depths. The wide and narrow genotypes showed distinct RSAs, particularly in the upper soil (0–30 cm). The wide genotypes exhibited a ‘spread‐out’ root system with dense and thin roots, whereas the narrow genotypes had a compact root system with fewer but thicker roots. Our study demonstrated a clear difference in RSA between the wide and narrow genotypes, highlighting the association between SRA on the direction and distribution of root growth in plants at later growth stages.

Construction of a high-density linkage map and QTL detection for growth traits in South African abalone (Haliotis midae).

Haliotis midae is one of the most important molluscs in South African commercial aquaculture. In this study, a high-resolution integrated linkage map was constructed, and QTL identified using 2b-RADseq for genotyping SNPs in three families. The final integrated linkage map was composed by merging the individual family maps, resulting in 3290 informative SNPs mapping to 18 linkage groups, conforming to the known haploid chromosome number for H. midae. The total map spanned 1798.25 cM with an average marker interval of 0.55 cM, representing a genome coverage of 98.76%. QTL analysis, across all three families, resulted in a total of five QTL identified for growth-related traits, shell width, shell length, and total body weight. For shell width and total body weight, one QTL was identified for each trait respectively, whilst three QTL were identified for shell length. The identified QTL respectively explained between 7.20% and 11.40% of the observed phenotypic variance. All three traits were significantly correlated (r = 0.862-0.970; p < 0.01) and shared overlapping QTL. The QTL for growth traits were mapped back to the H. midae draft genome and BLAST searches revealed the identity of candidate genes, such as egf-1, megf10, megf6, tnx, sevp1, kcp, notch1, and scube2 with possible functional roles in H. midae growth. The constructed high-density linkage map and mapped QTL have given valuable insights regarding the genetic architecture of growth-related traits and will be important genetic resources for marker-assisted selection. It remains, however, important to validate causal variants through linkage disequilibrium fine mapping in future.

Unraveling the ability of wheat to endure drought stress by analyzing physio-biochemical, stomatal and root architectural traits

Unraveling the ability of wheat to endure drought stress by analyzing physio-biochemical, stomatal and root architectural traits

Root hairs: an underexplored target for sustainable cereal crop production.

To meet the demands of a rising human population, plant breeders will need to develop improved crop varieties that maximize yield in the face of increasing pressure on crop production. Historically, the optimization of crop root architecture has represented a challenging breeding target due to the inaccessibility of the root systems. Root hairs, single cell projections from the root epidermis, are perhaps the most overlooked component of root architecture traits. Root hairs play a central role in facilitating water, nutrient uptake, and soil cohesion. Current root hair architectures may be suboptimal under future agricultural production regimes, coupled with an increasingly variable climate. Here, we review the genetic control of root hair development in the world's three most important crops-rice, maize, and wheat-and highlight conservation of gene function between monocots and the model dicot species Arabidopsis. Advances in genomic techniques including gene editing combined with traditional plant breeding methods have the potential to overcome many inherent issues associated with the design of improved root hair architectures. Ultimately, this will enable detailed characterization of the effects of contrasting root hair morphology strategies on crop yield and resilience, and the development of new varieties better adapted to deliver future food security.

Genetic mapping of regions associated with root system architecture in rice using MutMap QTL-seq

The root system architecture is an important complex trait in rice. With changing climatic conditions and soil nutrient deficiencies, there is an immediate need to breed nutrient-use-efficient rice varieties with robust root system architectural (RSA) traits. To map the genomic regions associated with crucial component traits of RSA viz. root length and root volume, a biparental F2 mapping population was developed using TI-128, an Ethyl Methane Sulphonate (EMS) mutant of a mega variety BPT-5204 having high root length (RL) and root volume (RV) with wild type BPT-5204. Extreme bulks having high RL and RV and low RL and RV were the whole genome re-sequenced along with parents. Genetic mapping using the MutMap QTL-Seq approach elucidated two genomic intervals on Chr.12 (3.14–3.74 Mb, 18.11–20.85 Mb), and on Chr.2 (23.18–23.68 Mb) as potential regions associated with both RL and RV. The Kompetitive Allele Specific PCR (KASP) assays for SNPs with delta SNP index near 1 were associated with higher RL and RV in the panel of sixty-two genotypes varying in root length and volume. The KASP_SNPs viz. Chr12_S4 (C→T; Chr12:3243938), located in the 3’ UTR region of LOC_Os12g06670 encoding a protein kinase domain-containing protein and Chr2_S6 (C→T; Chr2:23181622) present upstream in the regulator of chromosomal condensation protein LOC_Os2g38350. Validation of these genes using qRT-PCR and in-silico studies using various online tools and databases revealed higher expression in TI-128 as compared to BPT- 5204 at the seedling and panicle initiation stages implying the functional role in enhancing RL and RV.

Multi-trait association mapping for phosphorous efficiency reveals flexible root architectures in sorghum

BackgroundOn tropical regions, phosphorus (P) fixation onto aluminum and iron oxides in soil clays restricts P diffusion from the soil to the root surface, limiting crop yields. While increased root surface area favors P uptake under low-P availability, the relationship between the three-dimensional arrangement of the root system and P efficiency remains elusive. Here, we simultaneously assessed allelic effects of loci associated with a variety of root and P efficiency traits, in addition to grain yield under low-P availability, using multi-trait genome-wide association. We also set out to establish the relationship between root architectural traits assessed in hydroponics and in a low-P soil. Our goal was to better understand the influence of root morphology and architecture in sorghum performance under low-P availability.ResultIn general, the same alleles of associated SNPs increased root and P efficiency traits including grain yield in a low-P soil. We found that sorghum P efficiency relies on pleiotropic loci affecting root traits, which enhance grain yield under low-P availability. Root systems with enhanced surface area stemming from lateral root proliferation mostly up to 40 cm soil depth are important for sorghum adaptation to low-P soils, indicating that differences in root morphology leading to enhanced P uptake occur exactly in the soil layer where P is found at the highest concentration.ConclusionIntegrated QTLs detected in different mapping populations now provide a comprehensive molecular genetic framework for P efficiency studies in sorghum. This indicated extensive conservation of P efficiency QTL across populations and emphasized the terminal portion of chromosome 3 as an important region for P efficiency in sorghum. Increases in root surface area via enhancement of lateral root development is a relevant trait for sorghum low-P soil adaptation, impacting the overall architecture of the sorghum root system. In turn, particularly concerning the critical trait for water and nutrient uptake, root surface area, root system development in deeper soil layers does not occur at the expense of shallow rooting, which may be a key reason leading to the distinctive sorghum adaptation to tropical soils with multiple abiotic stresses including low P availability and drought.

A Path Integral Approach for Allele Frequency Dynamics Under Polygenic Selection.

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a roll in a given allele frequency change. Predicting how much allele frequencies change under drift and selection had remained an open problem well into the 21st century, even those contributing to simple, monogenic traits. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. In particular, we derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from , to in time ) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis.

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

Treegraph: tree architecture from terrestrial laser scanning point clouds

AbstractAccurate quantification of tree architecture is critical to interpreting the growth, health and functioning of trees and forests. Terrestrial laser scanning (TLS) offers millimetre‐level point cloud data, but current approaches to 3D tree reconstruction from TLS point clouds primarily focus on retrieving total volume at tree scale for aboveground biomass (AGB) estimation. Few methods have been designed specifically to provide tree architectural properties, including branch‐level morphology and topology, rather than AGB; derived topological traits have tended to be a compromise, and of secondary importance to volume. We present Treegraph, a new approach explicitly designed to retrieve the architectural traits of trees at multiple scales, from the whole tree scale down to individual branches and internodes, using TLS data with limited assumptions about tree form. It provides morphological traits such as branch length and diameter, alongside topological traits including parent–daughter connections of branches and internodes, furcation (branching) number and branch order. We compare Treegraph‐derived morphological and topological traits with manual measurements of branches from eight destructively harvested trees, yielding RMSE values of 0.60 m (5.96%) for branch length, 2.99 cm (33.45%) for branch diameter, 0.46 (19.38%) for furcation number and 0.08 m (33.16%) for internode length, respectively. In a broader application to 603 trees from tropical, temperate and urban forests, we demonstrate that the derived morphological and topological traits support testing of structure‐related metabolic scaling theories. Testing branches over 10 cm in diameter across 18 657 branching nodes shows that Treegraph‐derived branch‐level scaling exponents deviate from WBE predictions, exhibiting area‐preserving behaviour while displaying asymmetry in length and diameter of daughter branches. Available as open‐source Python software, Treegraph provides fine‐level branching network information, promoting improved insights into tree structure and function. This data‐driven approach reduces the need for empirical heuristic parameters, which has the potential for advancing large‐scale ecological studies on tree architecture.

Genotype × environment interactions in gene regulation and complex traits.

Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.

CRISPR/Cas9-mediated mutagenesis of FT/TFL1 in petunia improves plant architecture and early flowering

Petunias are renowned ornamental species widely cultivated as pot plants for their aesthetic appeal both indoors and outdoors. The preference for pot plants depends on their compact growth habit and abundant flowering. While genome editing has gained significant popularity in many crop plants in addressing growth and development and abiotic and biotic stress factors, relatively less emphasis has been placed on its application in ornamental plant species. Genome editing in ornamental plants opens up possibilities for enhancing their aesthetic qualities, offering innovative opportunities for manipulating plant architecture and visual appeal through precise genetic modifications. In this study, we aimed to optimize the procedure for an efficient genome editing system in petunia plants using the highly efficient multiplexed CRISPR/Cas9 system. Specifically, we targeted a total of six genes in Petunia which are associated with plant architecture traits, two paralogous of FLOWERING LOCUS T (PhFT) and four TERMINAL FLOWER-LIKE1 (PhTFL1) paralogous genes separately in two constructs. We successfully induced homogeneous and heterogeneous indels in the targeted genes through precise genome editing, resulting in significant phenotypic alterations in petunia. Notably, the plants harboring edited PhTFL1 and PhFT exhibited a conspicuously early flowering time in comparison to the wild-type counterparts. Furthermore, mutants with alterations in the PhTFL1 demonstrated shorter internodes than wild-type, likely by downregulating the gibberellic acid pathway genes PhGAI, creating a more compact and aesthetically appealing phenotype. This study represents the first successful endeavor to produce compact petunia plants with increased flower abundance through genome editing. Our approach holds immense promise to improve economically important potting plants like petunia and serve as a potential foundation for further improvements in similar ornamental plant species.

Polyploidization Impact on Plant Architecture of Watermelon (Citrullus lanatus)

Plant architecture includes traits such as plant height, stem diameter, and branching pattern, which have significant impacts on yield and fruit quality. Polyploidization can bring changes in plant architectural traits in different crops along with other agronomic and biochemical attributes; however, the specific physiological and biochemical mechanisms are still unclear. In this study, we utilized five watermelon lines: ‘91E7’, ‘Zhengzhou No. 3’, ‘Fanzu No. 1’, ‘Shenlong’, and ‘Houlv’, along with their corresponding autopolyploid derivatives (diploid, autotriploid, and autotetraploid) to compare plant architecture differences in different polyploidy watermelon plants. The results showed that the growth habits of diploid, triploid, and tetraploid watermelon plants were noticeably different. Triploid and tetraploid watermelon plants had greater stem diameters and larger leaf sizes. The leaf angle was also larger in polyploid watermelons than in their diploid ancestor lines. Although vine length was significantly higher in diploid watermelon, there was no significant difference in node number, indicating that the short vine length was due to the short internodal length. The major differences between diploid and polyploid watermelon plants were found in the branching pattern, as diploid watermelon lines have more branching compared to their polyploid sister lines. Furthermore, we examined the phytohormone content of diploid, triploid, and tetraploid ‘Fanzu No. 1’. The reasons for the selection of this material are its robust growth and profuse branching habit, which cause visible differences among the ploidy levels. Hormone analysis showed distinct variations in abscisic acid in the nodal and stem regions, gibberellin in the auxiliary bud regions, and brassinosteroids in the apical meristematic regions. The correlation coefficient also strongly correlated these hormones with architecture-related traits. Our findings indicated that gibberellin, ABA, and brassinosteroids might be associated with variations in plant architectural traits like branching, vine length, internodal length, stem thickness, and leaf angle among different ploidy levels of watermelon. The exogenous application of GA3 showed a positive effect on branching, whereas ABA showed a negative effect on branching. The application of brassinosteroid at the apical meristem demonstrated its effect on leaf angle, leaf size, and internodal length. The results of this study can provide a theoretical reference and valuable insights into the link between plant architecture and ploidy levels.

Genome-wide association studies of root system architecture traits in a broad collection of Brassica genotypes.

The root systems of Brassica species are complex. Eight root system architecture (RSA) traits, including total root length, total root surface area, root average diameter, number of tips, total primary root length, total lateral root length, total tertiary root length, and basal link length, were phenotyped across 379 accessions representing six Brassica species (B. napus, B. juncea, B. carinata, B. oleracea, B. nigra, and B. rapa) using a semi-hydroponic system and image analysis software. The results suggest that, among the assessed species, B. napus and B. oleracea had the most intricate and largest root systems, while B. nigra exhibited the smallest roots. The two species B. juncea and B. carinata shared comparable root system complexity and had root systems with larger root diameters. In addition, 313 of the Brassica accessions were genotyped using a 19K Brassica single nucleotide polymorphism (SNP) array. After filtering by TASSEL 5.0, 6,213 SNP markers, comprising 5,103 markers on the A-genome (covering 302,504 kb) and 1,110 markers on the C-genome (covering 452,764 kb), were selected for genome-wide association studies (GWAS). Two general linear models were tested to identify the genomic regions and SNPs associated with the RSA traits. GWAS identified 79 significant SNP markers associated with the eight RSA traits investigated. These markers were distributed across the 18 chromosomes of B. napus, except for chromosome C06. Sixty-five markers were located on the A-genome, and 14 on the C-genome. Furthermore, the major marker-trait associations (MTAs)/quantitative trait loci (QTLs) associated with root traits were located on chromosomes A02, A03, and A06. Brassica accessions with distinct RSA traits were identified, which could hold functional, adaptive, evolutionary, environmental, pathological, and breeding significance.