Biology subjects

Yates, S.

Publications and source records attributed to Yates, S..

13 recordsLinked to original sources

bioRxiv2026

Agronomic characterisation of a common buckwheat germplasm collection unlocks resources for genomics-assisted ideotype breeding

The mechanisation of agriculture has achieved major increases in labour productivity but has also led to an erosion of agrobiodiversity. Neglected and underutilized crops such as common buckwheat (Fagopyrum esculentum Moench) offer the potential to diversify food production systems but require major genetic improvement to compete with established crops. Although target traits for breeding such as more homogeneous ripening, reduced height, larger seeds and higher and more stable yields are well known, large-scale and field-based phenotyping data of diverse common buckwheat accessions across multiple environments are still scarce. Here, we tested the agronomic performance of a common buckwheat germplasm collection consisting of 170 accessions of diverse origin. The collection was tested at seven year-sites, and 19 agronomic traits related to phenology, growth habit and yield were assessed. Most traits showed high repeatability (0.31 < R2 < 0.98) and heritability (0.45 < H2 < 0.97), indicating genetic control of these traits. Coefficients of variation ranged between 6.19% and 46.15%, showing the high phenotypic diversity within the collection. By combining multiple traits, distinct ideotypes for different roles in the crop rotation were investigated, and accessions with promising agronomic profiles identified. Genome-wide association studies revealed marker-trait associations for 18 traits, allowing the identification of putative candidate genes influencing these traits such as a putative GRF-INTERACTING FACTOR associated with flowering time and biomass production. Our results show that the collection offers vast phenotypic diversity that can be exploited for the development of improved varieties tailored to meet specific agronomic purposes. The efficient utilization of these genetic resources should be supported by the application of genomics-based breeding approaches. The development of genetic markers and search for putative candidate genes mark important steps towards the establishment of efficient breeding schemes. Thus, these genomic resources provide a foundation for accelerating the genetic improvement of common buckwheat and developing agronomically competitive varieties.

plant biology↗

bioRxiv2026

Evolutionary signatures of self-incompatibility genes in grasses: high diversity, trans-generic polymorphism and co-evolution

In many flowering plants, self-incompatibility (SI) is a genetically controlled recognition system that prevents self-fertilization, thereby promoting outcrossing and maintaining genetic diversity within populations. The gametophytic SI system in grasses (Poaceae) is governed by two multiallelic loci, S and Z, each locus comprising of three genes governing the initial recognition between pollen and stigma. Identifying these genes establishes the molecular basis of SI in grasses, but how its unique two-locus architecture has shaped the evolution of the underlying loci and recognition genes remains largely unexplored. Here, we report targeted sequence capture of SI genes in 362 genotypes across nine forage grass species, revealing extensive allelic richness and sequence diversity. The SI genes exhibited elevated nucleotide diversity ({pi}) (0.134 < {pi} < 0.403) and great amino acid sequence divergence, ranging between 48.2-58.0% for the female determinants (sS and sZ) and 66.5-83.4% for the male determinants (SI-DUF247). The SI-DUF247 proteins also displayed trans-generic polymorphism, indicating that alleles predate speciation. Phylogenetic analysis of SI determinants showed congruence at both S- and Z-locus, consistent with co-evolution of recognition partners. Our results demonstrate that the unique two-locus SI system in grasses shares evolutionary characteristics with other SI systems, suggesting that their reproductive function is shaping their evolution. These findings also indicate that the grass SI loci are under long-term balancing selection to maintain recognition specificity while creating sufficient allelic diversity. Our study provides comprehensive sequence resources for SI genes and new insights into the evolution of the unique two-locus SI system of grasses.

plant biology↗

bioRxiv2026

An interactive 3D atlas of gene expression in the mouse brain

The Allen Mouse Brain Atlas provides gene expression data for over 20,000 genes and has been extensively used in neuroscience. However, its constrained by a resolution of 200 {micro}m3. We improved this to 25 {micro}m3 for 4,083 genes by re-registering 401,660 sections to atlas space. From this, we produced an atlas with regions defined by shared molecular signatures and built an interactive online platform allowing exploration of gene expression patterns.

neuroscience↗

bioRxiv2025

The first nested association mapping (NAM) population for outbreeding Italian ryegrass reveals candidate genes for seed shattering and related traits

Nested association mapping (NAM) populations are a powerful tool for investigating the genetic control of agronomically important traits and have been successfully used in many inbreeding crops. Here, we present the first NAM population established in an outcrossing forage grass species, Italian ryegrass (Lolium multiflorum Lam.), to dissect the genetic control of seed shattering. The NAM population was based on 23 diverse and one common founder plants and consisted of 708 F2 individuals. Reduced-representation sequencing (ddRAD) of the 708 F2 individuals, combined with whole genome sequencing data of the 24 founder plants, yielded a total of 3,199,253 SNPs that were used for population structure analysis, parentship analysis and genome-wide association studies. Phenotypic data for seed shattering and seed yield-related traits, collected in three year x location environments, showed high phenotypic variance within the NAM population. A total of seven QTL were identified for seed shattering, seed yield, spike length, flag leaf length and flowering time. Within these QTL regions, one candidate gene for seed shattering and three candidate genes for flowering time were identified. For seed shattering, the significant SNP association within the gene chr7.26897, known to be in involved in ripening-related pathways, explained 10.03% of the phenotypic variance. These candidate genes identified provide valuable targets for functional validation and demonstrate the effectiveness of NAM populations for elucidating the genetic architecture of complex traits in outcrossing forage grasses.

plant biology↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

genetics↗

bioRxiv2024

Integrative multi-environmental genomic prediction in apple

Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, non-additive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard G-BLUP. Including non-additive effects slightly improved predictive ability by up to 0.03 for two traits, but enviromic-based effects resulted in no improvement. The deep learning approach achieved the highest predictive ability for three traits with simpler genetic architectures, outperforming the benchmark by up to 0.10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.

plant biology↗

bioRxiv2023

Field phenotyping reveals the importance of including cultivar-specific per se temperature response in phenology modeling

Predicting plant development, a longstanding goal in plant physiology, involves two interwoven components: continuous growth and the progression of growth stages (phenology). Current models, like thermal time, assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses significantly affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific non-linear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.

physiology↗

bioRxiv2023

ChromaX: a fast and scalable breeding program simulator

SummaryChromaX is a Python library that enables the simulation of genetic recombination, genomic estimated breeding value calculations, and selection processes. By utilizing GPU processing, it can perform these simulations up to two orders of magnitude faster than existing tools with standard hardware. This offers breeders and scientists new opportunities to simulate genetic gain and optimize breeding schemes. Availability and ImplementationThe documentation is available at https://chromax.readthedocs.io The code is available at https://github.com/kora-labs/chromax Contactomargallalaly.younis@inf.ethz.ch, luca.corinzia@inf.ethz.ch, matteo.turchetta@inf.ethz.ch

bioinformatics↗

bioRxiv2022

Fine-mapping and comparative genomic analysis reveal the gene composition at the S and Z self-incompatibility loci in grasses

Self-incompatibility (SI) is a genetic mechanism of hermaphroditic plants to prevent inbreeding after self-pollination. Allogamous Poaceae species exhibit a unique gametophytic SI system controlled by two multi-allelic and independent loci, S and Z. Despite intense research efforts in the last decades, the genes that determine the initial recognition mechanism are yet to be identified. Here, we report the fine-mapping of the Z-locus in perennial ryegrass (Lolium perenne L.) and provide evidence that the pollen and stigma components are determined by two genes encoding DUF247 domain proteins (ZDUF247-I and ZDUF247-II) and the gene sZ, respectively. The pollen and stigma determinants are located side-by-side and were genetically linked in 10,245 individuals of two independent mapping populations segregating for Z. Moreover, they exhibited high allelic diversity as well as tissue-specific gene expression, matching expected characteristics of SI determinants known from other systems. Revisiting the S-locus using the latest high-quality whole-genome assemblies revealed a similar gene composition and structure as found for Z, supporting the hypothesis of a duplicated origin of the two-locus SI system of grasses. Ultimately, comparative genomic analyses across a wide range of self-compatible and self-incompatible Poaceae species revealed that the absence of a functional copy of at least one of the six putative SI determinants is accompanied by a self-compatible phenotype. Our study provides new insights into the origin and evolution of the unique gametophytic SI system in one of the largest and economically most important plant families.

plant biology↗

bioRxiv2021

Identification of candidate genes for self-compatibility in perennial ryegrass (Lolium perenne L.)

Self-incompatibility (SI) is a genetic mechanism preventing self-pollination in approximately 40% of plant species. Two multiallelic loci, called S and Z, control the gametophytic SI system of the grass family (Poaceae), which contains all major forage grasses. Loci independent from S and Z have been reported to disrupt SI and lead to self-compatibility (SC). A locus causing SC in perennial ryegrass (Lolium perenne L.) was previously mapped on linkage group (LG) 5 in a F2 population segregating for SC. Using a subset of the same population (n=68), we first performed low-resolution quantitative trait locus (QTL) mapping to exclude the presence of additional, previously undetected contributors to SC. The previously reported QTL on LG 5 explained 38.4% of the phenotypic variation, and no significant contribution from other genomic regions was found. This was verified by the presence of significantly distorted markers in the region overlapping with the QTL. Second, we fine mapped the QTL to 0.26 cM using additional 2,056 plants and 23 novel sequence-based markers. Using an Italian ryegrass (Lolium multiflorum Lam.) genome assembly as reference, the markers flanking SC were estimated to span a ~3 Mb region encoding for 57 predicted genes. Among these, seven genes were proposed as relevant candidate genes based on their annotation and function described in previous studies. Our work is a step forward to identify SC genes in forage grasses and provides diagnostic markers for marker-assisted introgression of SC into elite germplasm. Key messageA previously reported QTL conferring self-compatibility to perennial ryegrass was fine mapped to a 0.26 cM locus, corresponding to an estimated ~ 3 Mb physical region containing 57 predicted protein-encoding genes.

plant biology↗

bioRxiv2019

Temperature response of wheat affects final height and the timing of key developmental stages under field conditions

In wheat, temperature affects the timing and intensity of stem elongation (SE). Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during SE and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between SE and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable (H2 = 0.81) and positively related to a later start and end of SE as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTL). Furthermore, putative candidate genes for temperature-response QTL were frequently related to the flowering pathway in A. thaliana, whereas temperature-irresponsive QTLs corresponded with growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of SE accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops. HighlightWe measured ambient temperature response of stem elongation in wheat grown under field conditions. The results indicate that temperature response is highly heritable and linked to the flowering pathway.

plant biology↗

bioRxiv2018

Precision phenotyping reveals novel loci for quantitative resistance to septoria tritici blotch in European winter wheat

O_LIAccurate, high-throughput phenotyping for quantitative traits is the limiting factor for progress in plant breeding. We developed automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance.\nC_LIO_LI335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform a GWAS.\nC_LIO_LIWe identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four resistance traits. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance.\nC_LIO_LIOur experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes.\nC_LI

plant biology↗

bioRxiv2018

Crop Information Engine and Research Assistant (CIERA) for managing genealogy, phenotypic and genotypic data for breeding programs

BackgroundWith the advent of next-generation marker platforms and phenomics in crop breeding programs, the volume of both the genotypic and phenotypic data produced has increased exponentially. Often the data remain underutilized if not properly collated, managed and accessed. Effective management of the data is paramount to making sound and timely decision on cross planning in order to accelerate genetic gain ({Delta}G) in crops for disease resistance, agronomic and end-use quality traits.\n\nResultsTo address the challenges in managing and efficient utilization of the sheer volume of data generated in a crop breeding program, we developed an electronic information system called the Crop Information Engine and Research Assistant (CIERA). The CIERA, written in Visual Basic, runs on the Microsoft Windows operating system and requires the .Net Framework 4.7 as well as the MySQL Community Server 5.7. The highly intuitive graphical user interface of CIERA includes user-friendly query tools to facilitate the collation of data across relevant phenotypic environments from its phenotypic data management database and can combine that information with the genealogy and genetic data from its genealogy management and genetic data management databases, respectively.\n\nConclusionsUsing CIERA, breeders can build a comprehensive profile of germplasm, within a few minutes, to assist them in planning crosses for enhancing genetic gain by selecting superior lines for crosses.

bioinformatics↗