Biology subjects

Bradbury, P.

Publications and source records attributed to Bradbury, P..

11 recordsLinked to original sources

bioRxiv2026

Inter-variety competition dynamics in US inbred and hybrid maize

Variety mixtures provide a potential avenue in US cropping systems to improve yield stability and disease resistance. However, implementation of variety mixtures requires an understanding of the competitive dynamics of the crop. In this study, we examine the effects of plant competition both between and within plots through five unique experiments: 1) 5,000 diverse inbred lines in single-row plots, 2) hybrids in two-row plots developed from the above inbred lines, 3) over 4,000 hybrids measured in 141 locations in two-row plots as part of Genomes to Fields, 4) mixtures of two hybrids within a two-row plot planted across two years and five locations, and 5) mixtures of up to twenty hybrids in four-row plots in three locations. Across all experiments, we find that competitive interactions are extremely limited. Within inbred lines, height of the neighboring plot accounts for 1.2% of the variance in focal plot height. Similarly, neighbor height explains 1.7% of the variance in focal plot yield in hybrids developed from the inbred lines. The genetics of neighboring plots explains 1.55% of the variation in yield across 141 location-year environments, reinforcing the generally modest impacts of neighbor competition. In evaluating mixtures of hybrids in both two and four-row plots, we observe no yield penalty compared to conventional single hybrid plots, even with large height differentials of the hybrids included in the mixture or in mixtures of up to 20 hybrids within a plot. Finally, we observe that mixtures have more yield stability compared to conventional plots, highlighting a new avenue for increased stability in higher risk environments. The lack of yield penalty and stability benefits are promising for future investigations of mixtures that may complement each other in disease resistance or abiotic stress tolerance and increase overall yield stability in the field.

plant biology↗

bioRxiv2022

Ectopic activation of the polar body extrusion pathway triggers cell fragmentation in preimplantation embryos

Cell fragmentation occurs during physiological processes, such as apoptosis, migration, or germ cell development. Fragmentation is also commonly observed during preimplantation development of human embryos and is associated with poor implantation prognosis during Assisted Reproductive Technology (ART) procedures. Despite its biological and clinical relevance, the mechanisms leading to cell fragmentation are unclear. Light sheet microscopy imaging of mouse embryos reveals that compromised spindle anchoring, due to Myo1c knockout or dynein inhibition, leads to fragmentation. We further show that defective spindle anchoring brings DNA in close proximity to the cell cortex, which, in stark contrast to previous reports in mitotic cells, locally triggers actomyosin contractility and pinches off cell fragments. The activation of actomyosin contractility by DNA in preimplantation embryos is reminiscent of the signals mediated by small GTPases throughout polar body extrusion (PBE) during meiosis. By interfering with the signals driving PBE, we find that this meiotic signaling pathway remains active during cleavage stages and is both required and sufficient to trigger fragmentation. Together, we find that fragmentation happens in mitosis after ectopic activation of actomyosin contractility by signals emanating from DNA, similar to those observed during meiosis. Our study uncovers the mechanisms underlying fragmentation in preimplantation embryos and, more generally, offers insight into the regulation of mitosis during the maternal-zygotic transition.

developmental biology↗

bioRxiv2022

Uncovering the roles of lignin-related genes in disease resistance in maize using mutant and association analysis

Brown midrib (BMR) maize harbors mutations that result in lower lignin levels and higher feed digestibility, making it a desirable silage market class for ruminant nutrition. Northern leaf blight (NLB) epidemics in upstate NY highlighted the disease susceptibility of commercially grown BMR maize hybrids. We found the bm1, bm2, bm3 and bm4 mutants in a W64A genetic background to be more susceptible to foliar fungal (NLB, gray leaf spot, and anthracnose leaf blight) and bacterial (Stewarts wilt) diseases. The bm1, bm2 and bm3 mutants showed enhanced susceptibility to anthracnose stalk rot, and the bm1 and bm3 mutants were more susceptible to Gibberella ear rot. Co-localization of quantitative trait loci and correlations between stalk strength and disease traits in recombinant inbred line families suggest possible pleiotropies. The role of lignin in plant defense was explored using high-resolution, genome-wide association analysis for resistance to NLB in the Goodman diversity panel. Association analysis identified 100 single and clustered SNP associations for resistance to NLB, but did not implicate natural functional variation at bm1-bm5. Strong associations implicated a suite of diverse candidate genes, including lignin-related genes such as a {beta}-glucosidase gene cluster, hct11, knox1, knox2, zim36, lbd35, CASP-like protein 8, and xat3. The candidate genes are targets for breeding quantitative resistance to NLB in maize for use in silage and non-silage purposes. Core IdeasO_LIBrown midrib mutants (bm1-bm4) in maize, characterized as mutations with reduced lignin content, are susceptible to a range of foliar pathogens. C_LIO_LISeveral brown midrib mutants in maize show enhanced susceptibility to stalk rot and ear rot pathogens. C_LIO_LIGenome wide association analysis identified a number of diverse candidate genes associated with resistance to northern leaf blight in maize, including lignin-related genes involved in the monolignol pathway, developmental genes and transcription factors. C_LIO_LIAssociation analysis for resistance to NLB implicated a {beta}-glucosidase gene cluster, including Zmbglu24 and Zmbglu25 known for targeting monolignols and contributing to [~]15% phenotypic variation. C_LIO_LIhct11, which plays an important role in the monolignol pathway, was implicated for resistance to northern leaf blight in maize with potential for multiple stress tolerance. C_LI

genetics↗

bioRxiv2021

The Practical Haplotype Graph, a platform for storing and using pangenomes for imputation

MotivationPangenomes provide novel insights for population and quantitative genetics, genomics, and breeding not available from studying a single reference genome. Instead, a species is better represented by a pangenome or collection of genomes. Unfortunately, managing and using pangenomes for genomically diverse species is computationally and practically challenging. We developed a trellis graph representation anchored to the reference genome that represents most pangenomes well and can be used to impute complete genomes from low density sequence or variant data. ResultsThe Practical Haplotype Graph (PHG) is a pangenome pipeline, database (PostGRES & SQLite), data model (Java, Kotlin, or R), and Breeding API (BrAPI) web service. The PHG has already been able to accurately represent diversity in four major crops including maize, one of the most genomically diverse species, with up to 1000-fold data compression. Using simulated data, we show that, at even 0.1X coverage, with appropriate reads and sequence alignment, imputation results in extremely accurate haplotype reconstruction. The PHG is a platform and environment for the understanding and application of genomic diversity. AvailabilityAll resources listed here are freely available. The PHG Docker used to generate the simulation results is https://hub.docker.com/ as maizegenetics/phg:0.0.27. PHG source code is at https://bitbucket.org/bucklerlab/practicalhaplotypegraph/src/master/. The code used for the analysis of simulated data is at https://bitbucket.org/bucklerlab/phg-manuscript/src/master/. The PHG database of NAM parent haplotypes is in the CyVerse data store (https://de.cyverse.org/de/) and named /iplant/home/shared/panzea/panGenome/PHG_db_maize/phg_v5Assemblies_20200608.db. Contactpjb39@cornell.edu

bioinformatics↗

bioRxiv2021

Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 93% imputation accuracy with 0.01x sequence coverage, which was only slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.9%). Compared to Beagle, on average, PHG imputation was ~4% (p-value = 0.00027) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. The reduced accuracy of imputation with GBS data (90.4%) is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in our reference panel. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

genomics↗

bioRxiv2020

A Maize Practical Haplotype Graph Leverages Diverse NAM Assemblies

As a result of millions of years of transposon activity, multiple rounds of ancient polyploidization, and large populations that preserve diversity, maize has an extremely structurally diverse genome, evidenced by high-quality genome assemblies that capture substantial levels of both tropical and temperate diversity. We generated a pangenome representation (the Practical Haplotype Graph, PHG) of these assemblies in a database, representing the pangenome haplotype diversity and providing an initial estimate of structural diversity. We leveraged the pangenome to accurately impute haplotypes and genotypes of taxa using various kinds of sequence data, ranging from WGS to extremely-low coverage GBS. We imputed the genotypes of the recombinant inbred lines of the NAM population with over 99% mean accuracy, while unrelated germplasm attained a mean imputation accuracy of 92 or 95% when using GBS or WGS data, respectively. Most of the imputation errors occur in haplotypes within European or tropical germplasm, which have yet to be represented in the maize PHG database. Also, the PHG stores the imputation data in a 30,000-fold more space-efficient manner than a standard genotype file, which is a key improvement when dealing with large scale data.

genomics↗

bioRxiv2019

Maize nested introgression library provides evidence for the involvement of liguleless1 in resistance to northern leaf blight

Plant disease resistance is largely governed by complex genetic architecture. In maize, few disease resistance loci have been characterized. Near-isogenic lines (NILs) are a powerful genetic tool to dissect quantitative trait loci (QTL). We analyzed an introgression library of maize near-isogenic lines (NILs), termed a nested NIL (nNIL) library for resistance to northern leaf blight (NLB) caused by the fungal pathogen Setosphaeria turcica. The nNIL library was comprised of 412 BC5F4 NILs that originated from 18 diverse donor parents and a common recurrent parent, B73. Single nucleotide polymorphisms identified through genotyping by sequencing (GBS) were used to define introgressions and for association analysis. NILs that conferred resistance and susceptibility to NLB were comprised of introgressions that overlapped known NLB QTL. Genome-wide association analysis and stepwise regression further resolved five QTL regions, and implicated several candidate genes, including Liguleless1 (Lg1), a key determinant of leaf architecture in cereals. Two independently-derived mutant alleles of lg1 inoculated with S. turcica showed enhanced susceptibility to NLB. In the maize nested association mapping population, leaf angle was positively correlated with NLB in five recombinant inbred line (RIL) populations, and negatively correlated with NLB in four RIL populations. This study demonstrates the power of a nNIL library combined with high density SNP coverage to resolve QTLs. Furthermore, the role of lg1 in leaf architecture and in resistance to NLB has important applications in crop improvement.\n\nSignificance Statement (120 words)Understanding the genetic basis of disease resistance is important for crop improvement. We analyzed response to northern leaf blight (NLB) in a maize population consisting of 412 near-isogenic lines (NILs) derived from 18 diverse donor parents backcrossed to a recurrent parent, B73. NILs were genotyped by sequencing to detect introgressed segments. We identified NILs with greater resistance or susceptibility to NLB than B73. Genome-wide association analysis, coupled with stepwise regression, identified 5 candidate loci for NLB resistance, including the liguleless1 gene. The LIGULELESS1 transcription factor is critical in development of the leaf ligular region and influences leaf angle. We found that liguleless1 mutants are significantly more susceptible to NLB, uncovering a pleiotropic role for liguleless1 in development and disease resistance.

plant biology↗

bioRxiv2019

A sorghum Practical Haplotype Graph facilitates genome-wide imputation and cost-effective genomic prediction

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage - only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

genomics↗

bioRxiv2019

The relevance of dominance and functional annotations to predict agronomic traits in hybrid maize

Heterosis has been key to the development of maize breeding but describing its genetic basis has been challenging. Previous studies of heterosis have shown the contribution of within-locus complementation effects (dominance) and their differential importance across genomic regions. However, they have generally considered panels of limited genetic diversity and have shown little benefit to including dominance effects for predicting genotypic value in breeding populations. This study examined within-locus complementation and enrichment of genetic effects by functional classes in maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the United States (1,106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1,640 hybrids). We assayed three agronomic traits: days to silking (DTS), plant height (PH) and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results point to mechanisms by which heterosis arises through local complementation in proximal gene regions and suggest the relevance of dominance and gene annotations for genomic prediction in maize.

genomics↗

bioRxiv2018

Regional protein expression in human Alzheimer’s brain correlates with disease severity

Alzheimers disease (AD) is a progressive neurodegenerative disorder that currently affects 36 million people worldwide with no effective treatment available. Development of AD follows a distinctive pattern in the brain and is poorly modelled in animals. Therefore, it is vital to widen both the spatial scope of the study of AD and prioritise the study of human brains. Here we show that functionally distinct human brain regions show varying and region-specific changes in protein expression. These changes provide novel insights into the progression of disease, novel AD-related pathways, the presence of a gradient of protein expression change from less to more affected regions, and the presence of a protective protein expression profile in the cerebellum. This spatial proteomics analysis provides a framework which can underpin current research and opens new avenues of interest to enhance our understanding of molecular pathophysiology of AD, provides new targets for intervention and broadens the conceptual frameworks for future AD research.

neuroscience↗

bioRxiv2017

Comparison of one-stage and two-stage genome-wide association studies

Linear mixed models are widely used in humans, animals, and plants to conduct genome-wide association studies (GWAS). A characteristic of experimental designs for plants is that experimental units are typically multiple-plant plots of families or lines that are replicated across environments. This structure can present computational challenges to conducting a genome scan on raw (plot-level) data. Two-stage methods have been proposed to reduce the complexity and increase the computational speed of whole-genome scans. The first stage of the analysis fits raw data to a model including environment and line effects, but no individual marker effects. The second stage involves the whole genome scan of marker tests using summary values for each line as the dependent variable. Missing data and unbalanced experimental designs can result in biased estimates of marker association effects from two-stage analyses. In this study, we developed a weighted two-stage analysis to reduce bias and improve power of GWAS while maintaining the computational efficiency of two-stage analyses. Simulation based on real marker data of a diverse panel of maize inbred lines was used to compare power and false discovery rate of the new weighted two-stage method to single-stage and other two-stage analyses and to compare different two-stage models. In the case of severely unbalanced data, only the weighted two-stage GWAS has power and false discovery rate similar to the one-stage analysis. The weighted GWAS method has been implemented in the open-source software TASSEL.

genetics↗