Architecture Of Traits Research Articles

Genetic architecture underlying the parallel evolution of leaf ecomorphs in Viburnum

Abstract In a neotropical lineage of the plant clade Viburnum (Adoxaceae) several leaf ecomorphs evolved independently and repeatedly as the group radiated through cloud forests of North and South America. Here, we focus on one pair of co-occurring sister species within this radiation with strongly contrasting leaf morphotypes and document the presence of phenotypically diverse and genetically admixed hybrid individuals in multiple hybrid swarms. Hybrid phenotypes are generally intermediate in form, but sometimes show parental or entirely novel and transgressive combinations of leaf traits, suggesting that parental leaf ecomorphs can be functionally and genetically dissociated. We used admixture mapping within hybrid swarms to investigate the genetic architecture of key traits comprising these leaf ecomorphs and uncovered loci proximal to known genes implicated in leaf development, including some that may alter multiple leaf traits simultaneously, potentially facilitating the emergence of leaf syndromes. We conclude that the shared genetic architecture underlying some traits, such as leaf size and marginal teeth, could promote the repeated evolution of these traits in concert, while low levels of genetic linkage between other leaf traits supports the hypothesis that selection promoted the repeated assembly of particular combinations of leaf traits as Viburnum radiated throughout the neotropics.

Generation Mean Analysis in Physio-morphic Traits of Aerobic Rice in Assam, India

An experiment was carried out to study the genetic architecture of few physio-morphic quantitative traits of rice under aerobic condition. Generation mean analysis was carried out in three selected crosses viz.Banglami/Luit, Koimurali/Luit and Guni/Gopinath, involving six generations (P­1, P2, F1, F2, BC1 and BC2) at the experimental farm of SCS college of Agriculture, Assam Agricultural University, Dhubri, Assam, India during 2017 and 2018. The individual scaling tests were used to test the adequacy of additive dominance model. The gene effects were estimated using three parameter models (joint scaling test) suggested by Cavalli (1952) and six parameter model suggested by Haymen (1958). The analysis of variance among different generations of different crosses revealed significant variation for most of the characters indicating considerable variability in the material studied. All the characters except height growth rate in cross II, recorded significance for at least one of the four individual scaling tests in all the 3 crosses. Estimation of different gene effects and their interactions were done in OPSTAT software. Significant positive additive effects were recorded for plant height and height growth rate for in all the three crosses. Preponderance of dominance effect in the expression of height growth rate, spikelet fertility, grain yield, harvest index, root weight and root shoot ratio was evident from the significant positive dominance (h) effect in all the three crosses. With respect to interaction effects, all the characters except height growth rate in cross II, exhibited significant interaction effect for one or more epistatic interactions i.e.[i], [j] or [l] in all the three crosses studied. It is evident that for all the yield and other adaptive traits, additive, dominance and interaction effects were present indicating the complex inheritance of the traits under aerobic condition. Hence, focus should be on improving individual characters separately based on the nature of gene action.

Computational Identification of Milk Trait Regulation Through Transcription Factor Cooperation in Murciano-Granadina Goats

The Murciano-Granadina goat (MUG) is a renowned dairy breed, known for its adaptability and resilience, as well as for its exceptional milk traits characterized by high protein and fat content, along with low somatic cell counts. These traits are governed by complex biological processes, crucial in shaping phenotypic diversity. Thus, it is imperative to explore the factors regulating milk production and lactation for this breed. In this study, we investigated the genetic architecture of seven milk traits in MUGs, employing a two-step computational analysis to examine genotype–phenotype associations. Initially, a random forest algorithm identified the relative importance of each single-nucleotide polymorphism (SNP) in determining the traits of interest. The second step applied an information theory-based approach to exploring the complex genetic architecture of quantitative milk traits, focusing on epistatic interactions that may have been overlooked in the first step. These approaches allowed us to identify an almost distinct set of candidate genes for each trait. In contrast, by analyzing the promoter regions of these genes, we revealed common regulatory networks among the milk traits under study. These findings are crucial for understanding the molecular mechanisms underlying gene regulation, and they highlight the pivotal role of transcription factors (TFs) and their preferential interactions in the development of these traits. Notably, TFs such as DBP, HAND1E47, HOXA4, PPARA, and THAP1 were consistently identified for all traits, highlighting their important roles in immunity within the mammary gland and milk production during lactation.

Impact of Polyploidy on Crop Improvement and Plant Breeding Strategies: A Review

Polyploidy, the condition of possessing multiple sets of chromosomes, is a widespread phenomenon in plants that has had a profound impact on crop evolution and improvement. This review explores the role of polyploidy in plant breeding strategies, emphasizing its contributions to enhancing agronomic traits, overcoming genetic barriers, and expanding genetic diversity. Polyploid crops, such as wheat, cotton, and Brassica species, exhibit superior yield, increased biomass, and enhanced stress tolerance compared to their diploid counterparts. Polyploidy can arise naturally or be induced artificially, providing breeders with opportunities to create novel crop varieties through synthetic polyploidization. The complexity of polyploid genomes poses significant challenges, including fertility issues, meiotic instability, and difficulties in genome management. Advances in genomic and biotechnological tools, such as genomic selection and CRISPR-based genome editing, are beginning to address these obstacles, allowing for precise manipulation of polyploid genomes and improved breeding outcomes. Additionally, polyploidy plays a crucial role in overcoming reproductive barriers in interspecific hybrids, facilitating the transfer of desirable traits between species that would otherwise be genetically incompatible. The potential of polyploidy for developing climate-resilient crops is particularly noteworthy, as polyploid plants often exhibit greater tolerance to drought, salinity, and extreme temperatures. This makes polyploid breeding a promising approach for addressing the challenges of climate change and ensuring food security. Future research should focus on understanding the genetic and epigenetic mechanisms underlying polyploid genome stability and trait expression, as well as integrating advanced computational tools to predict and manipulate polyploid gene function. Emerging areas such as synthetic biology and multi-omics integration will further enhance our ability to engineer polyploid crops with complex trait architectures. Polyploidy remains a powerful and versatile tool for plant breeders, offering immense potential for crop improvement, genetic innovation, and the development of resilient agricultural systems. As the understanding and technological capabilities in polyploid breeding continue to advance, polyploid crops will play a central role in sustainable agricultural development and global food production.

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 role in a given allele frequency change. Predicting allele frequency changes under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. 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. We derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x to y in time t) 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.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

QTL mapping reveals predominantly nonadditive genetic architecture of heterotic growth traits in yearling Pacific oysters Crassostrea gigas

QTL mapping reveals predominantly nonadditive genetic architecture of heterotic growth traits in yearling Pacific oysters Crassostrea gigas

Leaf architecture and functional traits for 122 species at the University of California Botanical Garden at Berkeley.

The dataset contains leaf venation architecture and functional traits for a phylogenetically diverse set of 122 plant species (including ferns, basal angiosperms, monocots, basal eudicots, asterids, and rosids) collected from the living collections of the University of California Botanical Garden at Berkeley (37.87° N, 122.23° W; CA, USA) from February to September 2021. The sampled species originated from all continents, except Antarctica, and are distributed in different growth forms (aquatic, herb, climbing, tree, shrub). The functional dataset comprises 31 traits (mechanical, hydraulic, anatomical, physiological, economical, and chemical) and describes six main leaf functional axes (hydraulic conductance, resistance and resilience to damages caused by drought and herbivory, mechanical support, and construction cost). It also describes how architecture features vary across venation networks. Our trait dataset is suitable for (1) functional and architectural characterization of plant species; (2) identification of venation architecture-function trade-offs; (3) investigation of evolutionary trends in leaf venation networks; and (4) mechanistic modeling of leaf function. Data are made available under the Open Data Commons Attribution License.

Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs, although sensitivity analyses indicated that other structures were plausible. The multivariate GWASs of the GIBNs identified 74 genome-wide significant (GWS) loci (p < 5 × 10−8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed between attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). CT GIBNs displayed a negative genetic correlation with alcohol dependence. Even though we observed model instability in our application of genomic SEM to high-dimensional data, jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across neuroimaging phenotypes offers new insights into the genetics of cortical structure and relationships to psychopathology.

Genomic prediction of cereal crop architectural traits using models informed by gene regulatory circuitries from maize.

Plant architecture is a major determinant of planting density, which enhances productivity potential for crops per unit area. Genomic prediction is well positioned to expedite genetic gain of plant architectural traits since they are typically highly heritable. Additionally, the adaptation of genomic prediction models to query predictive abilities of markers tagging certain genomic regions could shed light on the genetic architecture of these traits. Here, we leveraged transcriptional networks from a prior study that contextually described developmental progression during tassel and leaf organogenesis in maize (Zea mays) to inform genomic prediction models for architectural traits. Since these developmental processes underlie tassel branching and leaf angle, 2 important agronomic architectural traits, we tested whether genes prioritized from these networks quantitatively contribute to the genetic architecture of these traits. We used genomic prediction models to evaluate the ability of markers in the vicinity of prioritized network genes to predict breeding values of tassel branching and leaf angle traits for 2 diversity panels in maize and diversity panels from sorghum (Sorghum bicolor) and rice (Oryza sativa). Predictive abilities of markers near these prioritized network genes were similar to those using whole-genome marker sets. Notably, markers near highly connected transcription factors from core network motifs in maize yielded predictive abilities that were significantly greater than expected by chance in not only maize but also closely related sorghum. We expect that these highly connected regulators are key drivers of architectural variation that are conserved across closely related cereal crop species.

Identifying and quantifying the contribution of maize plant traits to nitrogen uptake and use through plant modelling

Abstract Breeding for high nitrogen use efficient crops can contribute to maintaining or even increasing yield with less nitrogen. Nitrogen use is co-determined by N uptake and physiological use efficiency (PE, biomass per unit of N taken up), to which soil processes as well as plant architectural, physiological and developmental traits contribute. The relative contribution of these crop traits to N use is not well known but relevant to identify breeding targets in important crop species like maize. To quantify the contribution of component plant traits to maize N uptake and use, we used a functional-structural plant model. We evaluated the effect of varying both shoot and root traits on crop N uptake across a range of nitrogen levels. Root architectural traits were found to play a more important role in root N uptake than physiological traits. Phyllochron determined the structure of the shoot through changes in source: sink ratio over time which, in interaction with light and temperature, resulted in a significant effect on PE and N uptake. Photosynthesis traits were more relevant to biomass accumulation rather than yield, especially under high nitrogen conditions. The traits identified in this study are potential targets in maize breeding for improved crop N uptake and use.

Genome-Wide Association study for root system architecture traits in field soybean [Glycine max (L.) Merr.]

Roots play a crucial role in plant development, serving to absorb water and nutrients from the soil while also providing structural stability. However, the impacts of global warming can impede root growth by altering soil conditions that hinder overall plant growth. To address this challenge, there is a need to screen and identify plant genotypes with superior Root System Architecture traits (RSA), that can be used for future breeding efforts in enhancing their resilience to these environmental changes. In this project, 500 mid to late-maturity soybean accessions were grown on blue blotting papers hydroponically with six replicates and assessed seven RSA traits. Genome-Wide Association Studies (GWAS) were carried out with root phenotypic data and SNP data from the SoySNP50K iSelect SNP BeadChip, using both the TASSEL 5.0 and FarmCPU techniques. A total of 26 significant SNP-trait correlations were discovered, with 11 SNPs on chromosome 13. After SNP selection, we identified 14 candidate genes within 100-kb regions flanking the SNPs, which are related to root architecture. Notably, Glyma.17G258700, which exhibited substantial differential expression in root tips and its Arabidopsis homolog, AT4G24190 (GRP94) is involved in the regulation of meristem size and organization. Other candidate genes includes Glyma.03G023000 and Glyma.13G273500 that are also play a key role in lateral root initiation and root meristem growth, respectively. These findings significantly contribute to the discovery of key genes associated with root system architecture, facilitating the breeding of resilient cultivars adaptable to changing climates.

Genetic architecture of phosphorus-use-efficiency across diverse environmental conditions: Insights from maize elite and landrace lines.

Phosphorus is an essential nutrient for all crops. Thus, a better understanding of the genetic control of phosphorus-use-efficiency reflected in physiological, developmental, and morphological traits and its environmental plasticity is required to establish the basis for maintaining or enhancing yield while making agriculture more sustainable. In this study, we utilized a diverse panel of maize (Zea mays L.), including 398 elite and landrace lines, phenotyped across three environments and two phosphorus fertilization treatments. We performed genome-wide association mapping for 13 traits, including phosphorus uptake and allocation, that showed a strong environment-dependency in their expression. Our results highlight the complex genetic architecture of phosphorus-use-efficiency as well as the substantial differences between the evaluated genetic backgrounds. Despite harboring more of the identified QTL, almost all of the favourable alleles from landraces were found to be present in at least one of the two elite heterotic groups. Notably, we also observed trait-specific genetic control even among biologically related characteristics, as well as a substantial plasticity of the genetic architecture of several traits in response to the environment and P fertilization. Collectively, our work illustrates the difficulties in improving phosphorus-use-efficiency but also presents possible solutions for the future contribution of plant breeding to improve the phosphorus cycle.

Genetic Dissection of Flowering and Plant Architectural Traits to Develop Early Maturing Compact Upland Cotton Genotypes for High-Density Planting

Genetic Dissection of Flowering and Plant Architectural Traits to Develop Early Maturing Compact Upland Cotton Genotypes for High-Density Planting

A Review on Balancing Genomic Selection Efforts for Allogamous Plant Breeding

Genomic selection (GS) represents a paradigm shift in allogamous plant breeding, leveraging genome-wide marker data to predict the genetic potential of breeding candidates with unprecedented accuracy. This comprehensive review explores the evolution, principles, and applications of GS, highlighting its transformative impact on breeding efficiency and crop improvement. GS uses dense SNP markers to capture the genetic architecture of complex traits, surpassing traditional marker-assisted selection in predictive power. The integration of GS in breeding programs for crops like maize and perennial ryegrass has demonstrated significant gains in traits such as yield, disease resistance, and stress tolerance. Despite its advantages, GS faces challenges including high genotyping costs, the need for large training populations, and the complexity of genetic architectures. Economic constraints and ethical concerns about genetic diversity and data access also pose barriers. Emerging technologies such as AI, machine learning, high-throughput phenotyping, and genome editing hold promise for enhancing GS's accuracy and efficiency. Future strategies should focus on optimizing resource allocation, integrating GS with conventional breeding methods, and fostering collaborative efforts for data sharing and capacity building. The long-term impact of GS is profound, potentially accelerating breeding cycles, enhancing genetic diversity, and developing climate-resilient crops. Collaborative initiatives and open-access genomic resources will be crucial in overcoming current limitations and ensuring that GS benefits global agriculture. As GS continues to evolve, it promises to drive sustainable agricultural practices and improve food security, meeting the challenges posed by climate change and a growing global population. This review underscores the critical role of GS in modern plant breeding and its potential to revolutionize crop improvement.

Genetic architecture of meat traits in Large White sows

Currently, genome-wide association analysis is a modern and reliable method for analyzing genomic information about animals, as well as determining the “genotype — phenotype” relationship. This study aims to use the GWAS method to identify significant SNPs located within or linked to genes for meat traits in Large White pigs — backfat thickness over the 6–7th and 10–12th vertebrae, and loin muscle depth. The conducted GWA analysis revealed 60 genes, of which 17 are associated with biological functionality, annotated using the DAVID program. Three genes were found to have codification in the Pig QTL database. The genes were divided into 10 groups based on gene ontology (GO). Of all the genes, the AUTS2 gene, located on chromosome 3 and predicting the number of corpora lutea in sows, is of greatest interest. The results of this scientific work will contribute to the development of a genetic evaluation system and improvement of meat qualities in pigs.

Feasibility studies on plant architecture traits influence on seed cotton yield of Gossypium hirsutum zeromonopodial genotypes and assessment of ecological suitability to machine harvesting

Feasibility studies on plant architecture traits influence on seed cotton yield of Gossypium hirsutum zeromonopodial genotypes and assessment of ecological suitability to machine harvesting

Genome-wide association testing beyond SNPs.

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Unveiling the Genetic Architecture of Semen Traits in Thai Native Roosters: A Comprehensive Analysis Using Random Regression and Spline Function Models.

Improving reproductive traits, particularly semen quality and quantity, is crucial for optimizing poultry production and addressing the current limitations in native chicken reproduction. The aim of this study was to develop a genetic model to estimate genetic parameters guiding the selection of individual Thai native roosters. Using data collected from 3475 records of 242 Thai native grandparent roosters aged 1-4 years, we evaluated semen traits (mass movement, semen volume, and sperm concentration) over 54 weeks. A random regression test-day model incorporating five covariance functions, including a linear spline function with four, five, six, and eight knots (SP4, SP5, SP6, and SP8) and second-order Legendre polynomial function (LG2), was used to estimate genetic parameters. The results showed that the SP8 model consistently outperformed the other models across all traits, with the lowest mean square error, highest coefficient of determination, and superior predictive ability. Heritability estimates for mass movement, semen volume, and sperm concentration ranged from 0.10 to 0.25, 0.22 to 0.25, and 0.11 to 0.24, respectively, indicating moderate genetic influence on these traits. Genetic correlations between semen volume and sperm concentration were highest in the SP8 model, highlighting a strong genetic association between these traits. The SP8 model also revealed a high genetic correlation between mass movement and semen volume, supporting the potential for selecting mass movement as a predictor of semen volume. In conclusion, this study highlights the effectiveness of random regression models with linear spline functions to evaluate the genetic parameters of semen traits in native Thai roosters. The SP8 model is a robust tool for breeders to enhance the reproductive performance of native Thai chickens, contributing to sustainable poultry production systems.

Genome-wide association mapping and genomic prediction of water deficit stress tolerance indices in spring bread wheat

Genome-wide association mapping (GWAM) is crucial for identifying the genetic architecture of quantitative traits, such as drought tolerance indices in bread wheat. This study aims to identify Marker-Trait Associations (MTAs) and genes related to drought tolerance indices in wheat. Seven drought tolerance indices were calculated based on grain weight per spike under field drought stress (FDS) and field non-stress (FNS) conditions. These indices included mean productivity (MP), geometric mean productivity (GMP), relative drought index (RDI), stress tolerance index (STI), tolerance index (TOL), stress susceptibility index (SSI), and yield stability index (YSI). Genotyping of the samples was performed using single nucleotide polymorphism (SNP) markers. A total of 96 MTAs were identified for the studied indices and conditions FNS and FDS in this experiment, with a threshold of -log10p ≥ 3.0. These included FNS, FDS, GMP, MP, STI, SSI, RDI, and YSI, with 15, 11, 16, 16, 20, 6, 6, and 6 MTAs, respectively. The MTAs identified for the drought tolerance indices GMP, MP, and STI were located on chromosomes 2 A, 3B, and 6 A, respectively. Moreover, this study identified four genes related to the indices, namely “TraesCS2B02G000100,” “TraesCS3A02G000100,” “TraesCS2B02G000200,” and “TraesCS4B02G000100”. These genes play a crucial role in drought tolerance and can be utilized for marker-assisted selection to enhance drought tolerance wheat genotypes.