Biology subjects

Poland, J.

Publications and source records attributed to Poland, J..

17 recordsLinked to original sources

bioRxiv2026

Evolutionary dynamics of the Timopheevii wheat lineage

Wheat, the most widely cultivated crop globally, includes multiple species with varying ploidy levels1. Globally cultivated species such as pasta and bread wheat belong to the Emmer lineage. In contrast, the Timopheevii lineage is of negligible agricultural significance2, yet it harbours genetic diversity of potential value for wheat improvement. Here, we present genomic resources for the Timopheevii wheat lineage. Our analyses uncovered extensive homoeologous exchange and genome instability in zhukovskys wheat, the hexaploid representative of the Timopheevii lineage. Population genomic analyses reveal a mosaic-like haplotype composition of domesticated tetraploid timopheevs wheat, suggesting it was once cultivated across a broader geographic range. This is further supported by the discovery of fossil haplotype segments in bread wheat landraces, likely derived from now-extinct domesticated timopheevs wheat populations. By examining the Timopheevii lineage, we provide new insights into the evolutionary dynamics of wheat, discovering previously underexplored and potentially useful genetic diversity for wheat improvement.

genomics↗

bioRxiv2025

Foundational genomic resources for date palm: A gap-free, telomere-to-telomere phased assembly of Ajwa and 19 high-quality genome assemblies of Phoenix dactylifera.

Phoenix dactylifera L. is an economically, nutritionally, and culturally important fruit crop in the arid and semi-arid regions of the Middle East and North Africa. Here, we present a gap-free, telomere-to-telomere reference genome of the variety Ajwa, along with 19 additional high-quality assemblies (18 female and 1 male). These assemblies reveal novel chromosomal structures validated through cytogenetics, Hi-C, optical mapping, and synteny analyses with other palm genomes. Chromosome names were revised based on average lengths across all sequenced genomes. The Sex Determination Region (SDR) on chromosome 14 was confirmed through male-specific k-mer analysis, spanning approximately 14.7 Mb. Nucleolar organizing regions (NORs) were localized on chromosome 10, where a large 45S rDNA locus displayed unique repeat spacer motifs containing transposon-like sequences. In some accessions, a second NOR was identified on the female sex chromosome. This collection of date palm assemblies, anchored by the Ajwa reference genome, provides a critical resource for advancing breeding strategies aimed at enhancing the genetic resilience and productivity of date palm.

genomics↗

bioRxiv2024

An extended omnigenic model explains genome-phenome relationships for complex traits in global sorghum diversity

A central finding of complex trait genetics is the geometric distribution of effect sizes, but the biological basis of this phenomena is not understood. The omnigenic model (OM) could explain this architecture, with oligogenic variation arising from direct regulatory genes (core genes) and polygenic variation from indirect regulators (peripheral trans regulators). Plant yield is a canonical complex trait and here we tested the OM using genome-phenome analysis of biomass yield in global sorghum diversity. We used field-based phenomics to characterize dynamic growth and yield formation traits, then decomposed oligogenic and polygenic components of variation using genome-wide association studies (GWAS), genome-wide prediction (GWP), and tissue-specific transcriptome analyses. We identified major dynamic QTL, including several persistent (Dw1, Dw3) or transient (ELF3) QTL at known genes, consistent with the oligogenic-core of the OM. Next, we evaluated a key prediction on the peripheral-polygenic component - a positive correlation between GWP marker loadings and gene expression in relevant tissues. GWP loadings are indeed correlated with gene expression in relevant tissues. However, correlations are often higher with non-relevant tissues from earlier growth stages or tissues, which is not predicted by within-tissue trans regulation for the peripheral-polygenic component (the "omnigenic in space" model). Therefore, these findings suggest that genes expressed in early growth stages, with indirect effects on later traits, are contributing to polygenic variation ("omnigenic in time"). Together, our findings suggest that an extended OM, with regulatory effects both in space and in time, could explain the ubiquitous geometric genetic architecture of complex traits.

genetics↗

bioRxiv2024

De novo annotation of the wheat pangenome reveals complexity and diversity of the hexaploid wheat pan-transcriptome

Wheat is the most widely cultivated crop in the world with over 215 million hectares grown annually. However, to meet the demands of a growing global population, breeders face the challenge of increasing wheat production by approximately 60% within the next 40 years. The 10+ Wheat Genomes Project recently sequenced and assembled to chromosome level the genomes of nine wheat cultivars to develop our understanding of genetic diversity and selection within the pan-genome of wheat. Here, we provide a wheat pan-transcriptome with de novo annotation and differential expression analysis for these wheat cultivars, across multiple different tissues and whole seedlings sampled at dusk/dawn. Analysis of these de novo annotations facilitated the discovery of genes absent from the Chinese Spring reference, identified genes specific to particular cultivars and defined the core and dispensable genomes. Expression analysis across cultivars and tissues revealed conservation in expression between a large core set of homeologous genes, but also widespread changes in subgenome homeolog expression bias between cultivars. Co-expression network analysis revealed the impact of divergence of sub-genome homeolog expression and identified cultivar-specific expression profiles. In a case study utilising both the newly constructed wheat pan-genome and pan-transcriptome we demonstrate prevalent variation in the prolamin superfamily and immune-reactive proteins across the pan-cultivars.In summary, this work provides both a valuable resource for the wider wheat community and reveals diversity in gene content and expression patterns between global wheat cultivars.

genomics↗

bioRxiv2023

Genomic Characterization and Gene Bank Curation of Aegilops: The Wild Relatives of Wheat

Genetic diversity found in crop wild relatives is critical to preserve and utilize for crop improvement to achieve sustainable food production amid climate change and increased demand. We genetically characterized a large collection of 1,041 Aegilops accessions distributed among 23 different species using more than 45K single nucleotide polymorphisms identified by genotyping-by-sequencing (GBS). The Wheat Genetics Resource Center (WGRC) Aegilops germplasm collection was curated through the identification of misclassified and redundant accessions. There were 49 misclassified and 28 sets of redundant accessions within the four diploid species. The curated germplasm sets now have improved utility for genetic studies and wheat improvement. We constructed a phylogenetic tree and PCA cluster for all Aegilops species together, giving one of the most comprehensive views of Aegilops. The Sitopsis section and the U genome Aegilops clade were further scrutinized with in-depth population analysis. The genetic relatedness among the pair of Aegilops species provided strong evidence for the species evolution, speciation and diversification. We inferred genome symbols for two species Ae. neglecta and Ae. columnaris based on the sequence read mapping and the presence of segregating loci on the pertinent genomes as well as genetic clustering. The high genetic diversity observed among Aegilops species indicated that the genus could play an even greater role in providing the critical need for untapped genetic diversity for future wheat breeding and improvement. To fully characterize these Aegilops species, there is an urgent need to generate reference assemblies for these wild wheats, especially for the polyploid Aegilops. One-sentence summaryGenotyping Aegilops species, the wild relatives of wheat, has revealed high genetic diversity and unique evolutionary relationships among the Aegilops and with wheat, giving insight into the effective use of these germplasms for bread wheat improvement.

genetics↗

bioRxiv2022

Genomics and Phenomics Enabled Prebreeding Improved Early-Season Chilling Tolerance in Sorghum

In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index (NDVI) values from the chilling nested association mapping population detected CT QTL that colocialized with manual phenotyping CT QTL. Two of the four first-generation KASP molecular markers, generated using the peak QTL SNPs, failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic FST analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from two independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.

genetics↗

bioRxiv2022

Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat

Barley yellow dwarf (BYD) is one of the major viral diseases of cereals. Phenotyping BYD in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to, i) evaluate the use of high-throughput phenotyping (HTP) from unmanned aerial systems to improve BYD assessment and selection, ii) identify genomic regions associated with BYD resistance, and iii) evaluate genomic prediction models ability to predict BYD resistance. Up to 107 wheat lines were phenotyped during each of five field seasons under both insecticide treated and untreated plots. Across all seasons, BYD severity was lower with the insecticide treatment and plant height (PTHTM) and grain yield (GY) showed increased values relative to untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying resistance gene Bdv2 on chromosome 7DL. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a potentially novel genomic region for resistance on chromosome 5AS. Given the variable heritability of the trait (0.211 - 0.806), we obtained relatively good predictive ability for BYD severity ranging between 0.06 - 0.26. Including Bdv2 on the predictive model had a large effect for predicting BYD but almost no effect for PTHTM and GY. This study was the first attempt to characterize BYD using field-HTP and apply GS to predict the disease severity. These methods have the potential to improve BYD characterization and identifying new sources of resistance will be crucial for delivering BYD resistant germplasm.

genetics↗

bioRxiv2021

Genetic Characterization and Curation of Diploid A-Genome Wheat Species

The A-genome diploid wheats represent the earliest domesticated and cultivated wheat species in the Fertile Crescent and the donor of the wheat A sub-genome. The A-genome species encompass the cultivated einkorn (Triticum. monococcum L. subsp. monococcum), wild einkorn (T. monococcum L. subsp. aegilopoides (Link) Thell.) and T. urartu. We evaluated the collection of 930 accessions in the Wheat Genetics Resource Center (WGRC), using genotyping-by-sequencing (GBS) and identified 13,089 curated SNPs. Genomic analysis detected misclassified and duplicated accessions with most duplicates originated from the same or a nearby locations. About 56% (n = 520) of the WGRC A-genome species collections were duplicates supporting the need for genomic characterization for effective curation and maintenance of these collections. Population structure analysis confirmed the morphology-based classifications of the accessions and reflected the species geographic distributions. We also showed that the T. urartu as the closest A-genome diploid to wheat through phylogenetic analysis. Population analysis within the wild einkorn group showed three genetically distinct clusters, which corresponded with wild einkorn races , {beta}, and {gamma} described previously. The T. monococcum genome-wide FST scan identified candidate genomic regions harboring domestication selection signature (Btr1) on the short arm of chromosome 3Am at ~ 70 Mb. We established A-genome core set (79 accessions) based on allelic diversity, geographical distribution, and available phenotypic data. The individual species core set maintained at least 80% of allelic variants in the A-genome collection and constitute a valuable genetic resource to improve wheat and domesticated einkorn in breeding programs. One-sentence summaryGenotyping of gene bank collections of diploid A-genome relatives of wheat uncovered relatively higher genetic diversity and unique evolutionary relationships which gives insight to the effective use of these germplasm for wheat improvement.

genetics↗

bioRxiv2020

The gap-free rice genomes provide insights for centromere structure and function exploration and graph-based pan-genome construction

Rice (Oryza sativa), a major staple throughout the world and a model system for plant genomics and breeding, was the first crop genome completed almost two decades ago. However, all sequenced genomes to date contain gaps and missing sequences. Here, we report, for the first time, the assembly and analyses of two gap-free reference genome sequences of the elite O. sativa xian/indica rice varieties Zhenshan 97 (ZS97) and Minghui 63 (MH63) that are being used as a model system to study heterosis. Gap-free reference genomes also provide global insights into the structure and function of centromeres. All rice centromeric regions share conserved centromere-specific satellite motifs but with different copy numbers and structures. Importantly, we demonstrate that >1,500 genes are located in centromere regions, of which ~15.6% are actively transcribed. The generation and release of both the ZS97 and MH63 gap-free genomes lays a solid foundation for the comprehensive study of genome structure and function in plants and breed climate resilient varieties for the 21st century.

genomics↗

bioRxiv2020

Breeding Program Optimization for Genomic Selection in Winter Wheat

Developing methodologies in the fields of phenomics and genomic prediction have the potential to increase the production of crop species by accelerating germplasm improvement. The integration of these technologies into germplasm improvement and breeding programs requires evidence that there will be a direct economic benefit to the program. We determined a basic set of parameters, such as prediction accuracy greater than 0.3, the ability to genotype over 7 lines for the cost of one phenotypic evaluation, and heritability levels below 0.4, at which the use of genomic selection would be of economic benefit in terms of genetic gain and operational costs to the Kansas State University (KSU) winter wheat breeding program. The breeding program was then examined to determine whether the parameters benefitting genomic selection were observed or achievable in a practical sense. Our results show that the KSU winter wheat breeding program is at a decision point with regards to their primary means of selection. A few operational changes to increase prediction accuracy would place the program in the parameter space where genomic selection is expected to outpace the current phenotypic selection methodology at a parity of the operation cost and would be of greatest benefit to the program.

genomics↗

bioRxiv2020

Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

Breeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

genetics↗

bioRxiv2019

Chromosome-scale genome assembly provides insights into rye biology, evolution, and agronomic potential

We present a chromosome-scale annotated assembly of the rye (Secale cereale L. inbred line Lo7) genome, which we use to explore Triticeae genomic evolution, and ryes superior disease and stress tolerance. The rye genome shares chromosome-level organization with other Triticeae cereals, but exhibits unique retrotransposon dynamics and structural features. Crop improvement in rye, as well as in wheat and triticale, will profit from investigations of rye gene families implicated in pathogen resistance, low temperature tolerance, and fertility control systems for hybrid breeding. We show that rye introgressions in wheat breeding panels can be characterised in high-throughput to predict the yield effects and trade-offs of rye chromatin.

genomics↗

bioRxiv2019

Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativa L.)

Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.

genetics↗

bioRxiv2019

High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat

BackgroundPrecise measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties. ResultsHere we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly score plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this breeder-trained network to directly score wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and extremely high accuracy against the ground-truth values from visual scoring. Using the traits scored by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control. ConclusionsWe demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly score morphological and developmental phenotypes in genetic populations. Most powerfully, the deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping as it can potentially score any trait in any plant species through leveraging expert knowledge from breeders, geneticist, pathologists and physiologists.

genetics↗

bioRxiv2018

Efficient curation of genebanks using next-generation sequencing reveals substantial duplication of germplasm accessions

Genebanks are valuable resources for crop improvement through the acquisition, ex-situ conservation and sharing of unique germplasm among plant breeders and geneticists. With over seven million existing accessions and increasing storage demands and costs, genebanks need efficient characterization and curation to make them more accessible and usable and to reduce operating costs, so that the crop improvement community can most effectively leverage this vast resource of untapped novel genetic diversity. However, the sharing and inconsistent documentation of germplasm often results in unintentionally duplicated collections with poor characterization and many identical accessions that can be hard or impossible to identify without passport information and unmatched accession identifiers. Here we demonstrate the use of genotypic information from these accessions using a cost-effective next generation sequencing platform to find and remove duplications. We identify and characterize over 50% duplicated accessions both within and across genebank collections of Aegilops tauschii, an important wild relative of wheat and source of genetic diversity for wheat improvement. We present a pipeline to identify and remove identical accessions within and among genebanks and curate globally unique accessions. We also show how this approach can also be applied to future collection efforts to avoid the accumulation of identical material. When coordinated across global genebanks, this approach will ultimately allow for cost effective and efficient management of germplasm and better stewarding of these valuable resources.

genomics↗

bioRxiv2018

Use of Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype x environment (G x E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregon, Mexico over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.

genomics↗

bioRxiv2018

Resistance gene discovery and cloning by sequence capture and association genetics

Genetic resistance is the most economic and environmentally sustainable approach for crop disease protection. Disease resistance (R) genes from wild relatives are a valuable resource for breeding resistant crops. However, introgression of R genes into crops is a lengthy process often associated with co-integration of deleterious linked genes1, 2 and pathogens can rapidly evolve to overcome R genes when deployed singly3. Introducing multiple cloned R genes into crops as a stack would avoid linkage drag and delay emergence of resistance-breaking pathogen races4. However, current R gene cloning methods require segregating or mutant progenies5-10, which are difficult to generate for many wild relatives due to poor agronomic traits. We exploited natural pan-genome variation in a wild diploid wheat by combining association genetics with R gene enrichment sequencing (AgRenSeq) to clone four stem rust resistance genes in <6 months. RenSeq combined with diversity panels is therefore a major advance in isolating R genes for engineering broad-spectrum resistance in crops.

genomics↗