Architecture Of Traits Research Articles

161 Development, deployment, and calibration of genomic selection tools to improve performance traits for Canadian beef cattle

Abstract Genomic selection has the potential to accelerate the genetic improvement rate for difficult/expensive to measure traits such as feed efficiency and fertility traits in beef cattle. To develop genomic selection tools for Canadian beef cattle, we collected or sourced phenotypic data of feed efficiency along with other traits including carcass merit, female feed intake and fertility related traits, and their genotypes from both research and commercial herds. The phenotypic data were consolidated and recorded for contemporary groups, leading to the development of data sets including 11,292 beef cattle with residual feed intake, dry matter intake, average daily gain, metabolic body weight, 7,299 to 8,081 cattle with carcass merit traits, and 1,802 to 2,792 cows with feed intake and fertility traits after quality controls. Genotype data of the animals from various single nucleotide polymorphisms (SNP) panels were merged to the same allele format of 50K, and then imputed to 770K SNPs, and eventually to whole genome sequence variants. The refined Canadian beef cattle data sets have not only allowed us to investigate genetic architectures of the beef performance traits but also enabled the development of genomic selection tools including molecular breeding values and multiple trait selection indexes with a moderate to moderately high accuracy for the traits through a cross-validation of within reference data set. The genomic selection tools have been successfully deployed to over 10,415 breeding candidates submitted by Canadian beef producers through multiple demonstration projects. The average accuracy of the molecular breeding values and multiple trait selection index of the commercial beef cattle ranged from 0.38 to 0.49 for feed efficiency and carcass merit traits, and from 0.26 to 0.38 for female feed intake and fertility traits. Current research is focusing on calibrating the genomic selection tools with larger data sets and with more advanced genomic prediction methods.

Molecular Regulation of Shoot Architecture in Soybean.

Soybean (Glycine max [L.] Merr.) serves as a major source of protein and oil for humans and animals. Shoot architecture, the spatial arrangement of a plant's above-ground organs, strongly affects crop yield and is therefore a critical agronomic trait. Unlike wheat and rice crops that have greatly benefitted from the Green Revolution, soybean yield has not changed significantly in the past six decades owing to its unique shoot architecture. Soybean is a pod-bearing crop with pods adhered to the nodes, and variation in shoot architecture traits, such as plant height, node number, branch number and number of seeds per pod, directly affects the number of pods and seeds per plant, thereby determining yield. In this review, we summarize the relationship between soybean yield and these major components of shoot architecture. We also describe the latest advances in identifying the genes and molecular mechanisms underlying soybean shoot architecture and discuss possible directions and approaches for breeding new soybean varieties with ideal shoot architecture and improved yield.

The Importance of Regulatory Network Structure for Complex Trait Heritability and Evolution.

Complex traits are determined by many loci-mostly regulatory elements-that, through combinatorial interactions, can affect multiple traits. Such high levels of epistasis and pleiotropy have been proposed in the omnigenic model and may explain why such a large part of complex trait heritability is usually missed by genome-wide association studies while raising questions about the possibility for such traits to evolve in response to environmental constraints. To explore the molecular bases of complex traits and understand how they can adapt, we systematically analyzed the distribution of SNP heritability for ten traits across 29 tissue-specific Expression Quantitative Trait Locus (eQTL) networks. We find that heritability is clustered in a small number of tissue-specific, functionally relevant SNP-gene modules and that the greatest heritability occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. We confirm that this structure has allowed complex traits to evolve in response to environmental constraints, with the local "hubs" being the preferential targets of past and ongoing directional selection. Together, these results provide a conceptual framework for understanding complex trait architecture and evolution.

Genetic diversity and morphological characterization of different Adenium genotypes

The current study at Tamil Nadu Agricultural University in 2023–2024 aims to characterize the morphological features and genetic diversity of 20 Adenium genotypes. The research was performed with various vegetative and floral traits, revealing significant variations among genotypes. Notable differences were observed in plant height, branching habit, caudex circumference, leaf characteristics and floral features. Genetic analysis showed high genotypic and phenotypic coefficients of variation for traits like petal thickness, number of branches per plant and diameter of the corolla tube. These traits also exhibited high heritability and genetic advance, indicating potential for improvement through selective breeding. Other traits, such as plant height and leaf dimensions, showed moderate heritability and lower genetic advance, suggesting the influence of non-additive gene action and environmental factors. Principal component analysis identified seven principal components accounting for 85.178 % of the total variation. The first 2 components explained 40.59 % of the variability, with plant height, number of branches, flower diameter and length of anther appendages contributing significantly to PC1, while leaf width, length of corolla tube and flower weight were major contributors to PC2. This study provides valuable insight into the genetic architecture of Adenium traits, offering a foundation for future breeding efforts aimed at developing improved varieties for commercial and ornamental purposes. These findings highlight the potential for genetic improvement in Adenium through selective breeding, particularly for traits with high heritability and genetic advance.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Two grasses differ in their absorptive root physiological traits and rooting depth under drought in an alpine steppe.

Absorptive root traits play important roles in acquisition of water and nutrients from soil by plants. Despite numerous reports on the changes in species dominance under long-term drought in grassland community, few studies have specifically investigated absorptive root traits of these dominant species in grasslands, especially in the alpine grasslands. Here, two grass species (Leymus secalinus and Stipa purpurea) differing in their responses to drought were selected from an alpine steppe. A series of absorptive root traits were examined under drought in a 3-year glasshouse experiment. We found that drought had no effects on root morphological and architectural traits, whereas root physiological traits and rooting depth differed in their responses to drought. Specifically, drought significantly reduced root respiration and enhanced organ carbon (C) exudation rate, carboxylate exudation rate, acid phosphatase activity and rooting depth of L. secalinus. Particularly, L. secalinus released more citrate into the rhizosphere under drought than S. purpurea. In contrast, these root traits of S. purpurea remained relatively unchanged in response to the drought. These differential responses would render L. secalinus more competitive in acquisition of nutrients and water, thus contributing to its dominance in the community under drought. Moreover, root respiration was negatively correlated with organic C exudation rate, carboxylate exudation rate and acid phosphatase activity, indicating a tradeoff between root respiration and root exudates to acquire nutrients and water by optimizing C allocation under drought. Additionally, all root traits exhibited two independent dimensions in root economic space (RES) for both species under drought. These results indicate that the plant species with great capacity to acquire water and nutrients in soil by optimizing C allocation under drought will be dominant in the community of the alpine grasslands. These findings provide an important insight into species re-ordering under drought on the Tibetan Plateau.

An ultra-dense linkage map identified quantitative trait loci corresponding to fruit quality- and size-related traits in red goji berry.

Goji berries are a small-fruited shrub with industrial importance whose fruit considered beneficial in both fresh and dried forms. Current germplasms of goji berries include small fruits with a short shelf life, less sweet and bitter taste, and a lack of appropriate genetic information. This study aimed to employ whole genome resequencing to generate an ultra-dense bin linkage map and to elucidate the genetic basis of goji fruit quality and size using quantitative trait loci (QTL) mapping analysis in a cross-pollinated hybrid population. To achieve this goal, human sensory tests were carried out to determine the bitter taste (BT) and sweet taste (ST), and to quantify the soluble solid content (SSC), fruit firmness (FF), and fruit size-related traits of fresh goji fruits over three or four years. The results revealed that the goji bin linkage map based on resequencing spanned a total length of 966.42 cM and an average bin interval of 0.03 cM. Subsequent variant calling and ordering resulted in 3,058 bins containing 35,331 polymorphic markers across 12 chromosomes. A total of 99 QTLs, with individual loci in different environments explaining a phenotypic variance of 1.21-16.95% were identified for the studied traits. Ten major effects, including colocalized QTLs corresponding to different traits, were identified on chromosomes 1, 3, 5, 6, 7, and 8, with a maximum Logarithm of Odds (LOD) of 29.25 and 16.95% of explained phenotypic variance (PVE). In addition, four stable loci, one for FF, one for fruit weight (FW), and two for fruit shape index (FSI), were mainly mapped on chromosomes 5, 6, and 7, elucidating 2.10-16.95% PVE. These findings offer valuable insights into the genetic architecture of goji fruit traits along with identified specific loci and markers to further improve and develop sweeter, less bitter and larger fruited goji berry cultivars with extended shelf life.

Uncovering novel regulatory variants in carbohydrate metabolism: a comprehensive multi-omics study of glycemic traits in the Indian population.

Clinical biomarkers such as fasting glucose, HbA1c, and fasting insulin, which gauge glycemic status in the body, are highly influenced by diet. Indians are genetically predisposed to type 2 diabetes and their carbohydrate-centric diet further elevates the disease risk. Despite the combined influence of genetic and environmental risk factors, Indians have been inadequately explored in the studies of glycemic traits. Addressing this gap, we investigate the genetic architecture of glycemic traits at genome-wide level in 4927 Indians (without diabetes). Our analysis revealed numerous variants of sub-genome-wide significance, and their credibility was thoroughly assessed by integrating data from various levels. This identified key effector genes, ZNF470, DPP6, GXYLT2, PITPNM3, BEND7, and LORICRIN-PGLYRP3. While these genes were weakly linked with carbohydrate intake or glycemia earlier in other populations, our findings demonstrated a much stronger association in the Indian population. Associated genetic variants within these genes served as expression quantitative trait loci (eQTLs) in various gut tissues essential for digestion. Additionally, majority of these gut eQTLs functioned as methylation quantitative trait loci (meth-QTLs) observed in peripheral blood samples from 223 Indians, elucidating the underlying mechanism of their regulation of target gene expression. Specific co-localized eQTLs-meth-QTLs altered the binding affinity of transcription factors targeting crucial genes involved in glucose metabolism. Our study identifies previously unreported genetic variants that strongly influence the diet-glycemia relationship. These findings set the stage for future research into personalized lifestyle interventions integrating genetic insights with tailored dietary strategies to mitigate disease risk based on individual genetic profiles.

Assessing the Limitations of Relief-Based Algorithms in Detecting Higher-Order Interactions.

The investigation of epistasis becomes increasingly complex as more loci are considered due to the exponential expansion of possible interactions. Consequently, selecting key features that influence epistatic interactions is crucial for effective downstream analyses. Recognizing this challenge, this study investigates the efficiency of Relief-Based Algorithms (RBAs) in detecting higher-order epistatic interactions, which may be critical for understanding the genetic architecture of complex traits. RBAs are uniquely non-exhaustive, eliminating the need to construct features for every possible interaction and thus improving computational tractability. Motivated by previous research indicating that some RBAs rank predictive features involved in higher-order epistasis as highly negative, we explore the utility of absolute value ranking of RBA feature weights as an alternative method to capture complex interactions. We evaluate ReliefF, MultiSURF, and MultiSURFstar on simulated genetic datasets that model various patterns of genotype-phenotype associations, including 2-way to 5-way genetic interactions, and compare their performance to two control methods: a random shuffle and mutual information. Our findings indicate that while RBAs effectively identify lower-order (2 to 3-way) interactions, their capability to detect higher-order interactions is significantly limited, primarily by large feature count but also by signal noise. Specifically, we observe that RBAs are successful in detecting fully penetrant 4-way XOR interactions using an absolute value ranking approach, but this is restricted to datasets with a minimal number of total features. These results highlight the inherent limitations of current RBAs and underscore the need for enhanced detection capabilities for the investigation of epistasis, particularly in datasets with large feature counts and complex higher-order interactions.

L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data

Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy structure of trees in natural forests and the presence of occlusion in TLS data pose significant challenges to achieving this goal. In this study, we present a novel algorithm, L1-Tree, for the construction of 3D tree models and the estimation of architectural traits from TLS data. This algorithm is grounded in the L1-Median algorithm and integrates a tree skeleton optimization procedure that considers the structural characteristics of tree branches. By comparing modeling results and manually derived branch traits for 24 trees of 24 species, we found that the L1-Tree algorithm achieved precision, recall, and F-score values of 0.94 for branch identification, coefficient of determination, root-mean-squared error, and normalized root-mean-squared error of 0.998, 0.068 m, and 0.3 % for branch length estimation, and a respective value of 0.958, 0.257 cm and 0.9 % for branch radius estimation. Additionally, the branch identification accuracy and accuracy in branch architectural trait estimation remained satisfactory across branch orders. Compared to established 3D tree model construction algorithms (e.g., TreeQSM), our L1-Tree algorithm demonstrated a superior capability in handling noisy environments and data gaps, making it a robust tool for TLS data-based tree architecture studies. Leaf-wood separation emerged as a crucial step influencing the performance of the L1-Tree algorithm. We observed significant drop in branch identification accuracy when using an automatic leaf-wood separation algorithm as input, highlighting the urgent need to develop effective leaf-wood separation algorithms to generate high-quality wood point clouds for tree architecture studies.

Causal inference and GWAS: Rubin, Pearl, and Mendelian randomization.

Although Genome Wide Analysis (GWAS) have been widely used to understand the genetic architecture of complex quantitative traits, interpreting their results in terms of the biological processes that determine those traits has been difficult or even lacking, because of the variability in responses to the tests of hypotheses within a trait, species, and breed or cross, and the lack of follow-up studies. It is then essential employing appropriate statistical tests that point out to the causal genes responsible of the relevant fraction of the genetic variability observed. We briefly review the main theoretical aspects of the two schools of causal inference (Rubin's Causal Model, RCM, and Pearl's causal inference, PCI). RCM approachs the hypothesis testing from a randomization perspective by considering a wider space of the observation, i.e. the "potential outcomes", rather than the narrower space that results from defining "treatment" effects after observing the data. Next, we discuss the assumptions involved to meet the requirements of randomization for RCM with observational data (non-designed experiments) with special emphasis on the Stable Unit Treatment Analysis (SUTVA). Due to the presence of "confounders" (i.e. systematic fixed effects, environmental permanent effects, interaction among genes, etc.), causal average treatment effects are viewed through the familiar lens of normal linear (or mixed) models. To overcome the difficulties of association analyses, a tests of causal effects is introduced using independent predicted residual breeding values from animal models of genetic evaluation that avoids the effects of population structure and confounder effects. An independent section discusses the issue of whether the additive effects defined at the "gene" level by R. A. Fisher and popularized in D. S. Falconer's textbook of quantitative genetics can be termed causal from either RCM or PCI.

CBCT Analysis of Radix Entomolaris: Anatomical and Endodontic Implication

This paper aims to provide examples of mandibular first molars that have an extra distolingual root and discuss how to manage them with the right tools and methods. Mandibular molars occasionally exhibit radix entomolaris, a variant in which the tooth has an additional root fixed to its lingual aspect. If this extra root is misdiagnosed or treated improperly, it could make endodontic management of the tooth more difficult. Thus, Understanding the variances in tooth/canal architecture and distinguishing traits is essential since it can help with canal location, negotiation, and management. Mandibular molars with this root shape have a different prognosis depending on the precise diagnosis and appropriate use of clinical endodontic competence.

Assessing the opportunity for selection to impact morphological traits in crosses between two Solanum species.

Within biology, there have been long-standing goals to understand how traits impact fitness, determine the degree of adaptation, and predict responses to selection. One key step in answering these questions is to study the mode of gene action or genetic architecture of traits. The genetic architecture underlying a trait will ultimately determine whether selection can lead to a change in the phenotype. Theoretical and empirical research have shown that additive architectures are most responsive to selection. The genus Solanum offers a unique system to quantify the genetic architecture of traits. Crosses between Solanum pennellii and S. lycopersicum, which have evolved unique adaptive traits for very different environments, offer an opportunity to investigate the genetic architecture of a variety of morphological traits that often are not variable within species. We generated cohorts between strains of these two Solanum species and collected phenotypic data for eight morphological traits. The genetic architectures underlying these traits were estimated using an information-theoretic approach to line cross analysis. By estimating the genetic architectures of these traits, we were able to show a key role for maternal and epistatic effects and infer the accessibility of these traits to selection.

TopoRoot+: computing whorl and soil line traits of field-excavated maize roots from CT imaging

BackgroundThe use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739–57. https://doi.org/10.1093/plphys/kiab311.). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties.ResultsWe developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z.) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:40{0}^{3}$$\\end{document} on a desktop workstation. Our software and test dataset are freely distributed on Github.ConclusionsTopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping.

Research Note: Genetic parameters estimation of egg quality traits in Rhode Island Red and White Leghorn chickens

The continuous increasing demand for egg quality and quantity, and the expanding market share have enabled the egg industry to achieve significant benefits through genetic improvement. This study aims to estimate the genetic parameters and explore selectable breeding traits in the purebred Rhode Island Red (RIR) and White Leghorn (WL), which are 2 high-yielding layer breeds, and better understand their underlying genetic basis and accelerate genetic progress. The DMU software was utilized to analyze 12 egg quality traits, including egg length (EL), egg width (EW), egg shape index (ESI), egg weight (EWT), albumen height (AH), yolk color (YC), Haugh unit (HU), yolk weight (YW), albumen weight (AW), albumen-to-egg weight ratio (AWR), yolk-to-albumen ratio (YAR), and yolk-to-egg weight ratio (YWR). In RIR, the heritability of egg quality traits ranged from 0.196 to 0.427, while the repeatability ranged from 0.395 to 0.668. In WL, the heritability of egg quality traits ranged from 0.203 to 0.347, and the repeatability ranged from 0.424 to 0.656. In both RIR and WL, highly strong genetic correlations were observed between AW and EW, as well as between AW and EWT. The genetic correlations for AW and EW were 0.902 in RIR and 0.864 in WL, while the genetic correlations for AW and EWT were 0.981 in RIR and 0.960 in WL. The egg quality traits in both breeds showed moderate heritability, indicating great genetic potential for improvement through selective breeding. This can help breeders meet the increasingly diverse egg preferences of consumers through genetic selection. Additionally, there is a highly strong correlation between egg width/egg weight, and albumen weight in both breeds. In practical production, it is feasible to estimate albumen weight by measuring egg width and egg weight, which can simplify the method for measuring albumen weight. In conclusions, our finding provided valuable insights into the genetic architecture of egg quality traits in RIR and WL chickens. They help our understanding of the potential for genetic improvement of these traits through selective breeding programs.

Genome wide detection of CNV and their association with body size in Danzhou chickens

Copy number variation (CNV) is a crucial component of genetic diversity in the genome, serving as the foundation for the genetic architecture and phenotypic variability of complex traits. In this study, we examined CNVs in the Danzhou (DZ) chicken, an indigenous breed exclusive to Hainan Province, China. By employing whole-genome resequencing data from 200 DZ chickens, we conducted a comprehensive genome-wide analysis of CNVs using CNVpytor and performed CNV-based genome-wide association studies (GWAS) on six body size traits, including body slope length (BSL), keel length (KeL), tibial length (TiL), tibial circumference (TiC), chest width (ChW), and chest depth (ChD) utilizing linear mixed model methods considering a genomic relationship matrix. We identified a total of 144,265 autosomal CNVs among the 200 individuals, comprising 67,818 deletions and 76,447 duplications. After merging these variants together, we obtained 4,824 distinct copy number variant regions, which accounted for approximately 20% of the chicken autosomal genome. Furthermore, we discovered several significantly associated CNV segments with body size traits located proximal to genes such as IHH, WNT6, WNT10A, LPR4, FZD2, WNT7B, and GNAS that have been extensively implicated in skeletal development and growth processes. These findings enhance our understanding of CNVs in chickens and their potential impact on body size traits by revealing candidate genes involved in the regulation of these traits. This establishes a solid framework for future studies and may prove particularly beneficial for exploring genetic structural variation in chickens.

Leaf venation network architecture coordinates functional trade-offs across vein spatial scales: evidence for multiple alternative designs.

Variation in leaf venation network architecture may reflect trade-offs among multiple functions including efficiency, resilience, support, cost, and resistance to drought and herbivory. However, our knowledge about architecture-function trade-offs is mostly based on studies examining a small number of functional axes, so we still lack a more integrative picture of multidimensional trade-offs. Here, we measured architecture and functional traits on 122 ferns and angiosperms species to describe how trade-offs vary across phylogenetic groups and vein spatial scales (small, medium, and large vein width) and determine whether architecture traits at each scale have independent or integrated effects on each function. We found that generalized architecture-function trade-offs are weak. Architecture strongly predicts leaf support and damage resistance axes but weakly predicts efficiency and resilience axes. Architecture traits at different spatial scales contribute to different functional axes, allowing plants to independently modulate different functions by varying network properties at each scale. This independence of vein architecture traits within and across spatial scales may enable evolution of multiple alternative leaf network designs with similar functioning.

Nonlinear models based on leaf architecture traits explain the variability of mesophyll conductance across plant species.

Mesophyll conductance ( ) describes the efficiency with which moves from substomatal cavities to chloroplasts. Despite the stipulated importance of leaf architecture in affecting , there remains a considerable ambiguity about how and whether leaf anatomy influences . Here, we employed nonlinear machine-learning models to assess the relationship between 10 leaf architecture traits and . These models used leaf architecture traits as predictors and achieved excellent predictability of . Dissection of the importance of leaf architecture traits in the models indicated that cell wall thickness and chloroplast area exposed to internal airspace have a large impact on interspecific variation in . Additionally, other leaf architecture traits, such asleaf thickness, leaf densityand chloroplast thickness, emerged as important predictors of . We also found significant differences in the predictability between models trained on different plant functional types. Therefore, by moving beyond simple linear and exponential models, our analyses demonstrated that a larger suite of leaf architecture traits drive differences in than has been previously acknowledged. These findings pave the way for modulating by strategies that modify its leaf architecture determinants.

Genome-Wide Association Analysis Identifies Important Haplotypes and Candidate Gene XKR4 for Body Size Traits in Pekin Ducks.

Body size is an important growth indicator in ducks and is a primary selection criterion for physical improvement. An excessively rapid growth rate in meat ducks can result in excessive body size, which may hinder subsequent processing and slaughter operations. However, only a few molecular markers related to body size have been studied in meat ducks. In this study, we performed a genome-wide association study (GWAS) to identify candidate genes and QTLs affecting body length (BL), keel bone length (KBL), neck length (NL), and breast width (BrW) in Pekin ducks (Anas platyrhynchos domestica). Our results indicate the significant SNP for NL is located within a pseudogene, whereas the significant SNP for BrW is located in an intergenic region. More importantly, our analysis identified a haplotype that was significantly associated with both BL and KBL. This haplotype, containing 48 single-nucleotide polymorphisms (SNPs), is localized within the XKR4 gene. The identification of this haplotype suggests that XKR4 may be a key candidate gene influencing BL and KBL in Pekin ducks. These findings have important implications for the breeding and genetic improvement of Pekin ducks, and provide valuable insights into the genetic architecture of body size traits in this species.

A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology.

Personality is influenced by both genetic and environmental factors and is associated with other psychiatric traits such as anxiety and depression. The 'big five' personality traits, which include neuroticism, extraversion, agreeableness, conscientiousness and openness, are a widely accepted and influential framework for understanding and describing human personality. Of the big five personality traits, neuroticism has most often been the focus of genetic studies and is linked to various mental illnesses, including depression, anxiety and schizophrenia. Our knowledge of the genetic architecture of the other four personality traits is more limited. Here, utilizing the Million Veteran Program cohort, we conducted a genome-wide association study in individuals of European and African ancestry. Adding other published data, we performed genome-wide association study meta-analysis for each of the five personality traits with sample sizes ranging from 237,390 to 682,688. We identified 208, 14, 3, 2 and 7 independent genome-wide significant loci associated with neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively. These findings represent 62 novel loci for neuroticism, as well as the first genome-wide significant loci discovered for agreeableness. Gene-based association testing revealed 254 genes showing significant association with at least one of the five personality traits. Transcriptome-wide and proteome-wide analysis identified altered expression of genes and proteins such as CRHR1, SLC12A5, MAPT and STX4. Pathway enrichment and drug perturbation analyses identified complex biology underlying human personality traits. We also studied the inter-relationship of personality traits with 1,437 other traits in a phenome-wide genetic correlation analysis, identifying new associations. Mendelian randomization showed positive bidirectional effects between neuroticism and depression and anxiety, while a negative bidirectional effect was observed for agreeableness and these psychiatric traits. This study improves our comprehensive understanding of the genetic architecture underlying personality traits and their relationship to other complex human traits.