Biology subjects

Hirsch, C.

Publications and source records attributed to Hirsch, C..

15 recordsLinked to original sources

bioRxiv2026

Promoter Structural Variants are Drivers of Genome-Wide Differential Expression in Maize

BackgroundStructural variants (SVs) are large insertions or deletions of DNA sequences. While less numerous than single nucleotide polymorphisms, SVs often account for a greater proportion of nucleotide differences between genomes. Their size and frequent association with repetitive sequences has historically hindered their detection, which has limited the ability to associate this variation with molecular and phenotypic trait variation. While some SVs have been linked to observable traits, it remains unclear whether such effects are rare or broadly distributed across the genome. ResultsTo test for genome-wide relationships between SVs and gene expression, we analyzed genome assemblies and transcriptomic data from 10 tissues across 26 diverse maize inbred lines. We identified SVs amongst these lines and examined variants located within the 1kb promoter region upstream of genes. Thousands of genes showed expression differences associated with promoter SVs, often in a tissue-specific manner. One common feature of these SVs was the presence of transposable element sequences. LTR retrotransposons were enriched amongst promoter SVs associated with differential expression and often reduced expression of the nearby gene. Despite widespread expression changes, we found no enrichment for specific biological functions or pathways among affected genes. ConclusionsOur findings indicate that extant TE-mediated promoter SVs play a significant role in shaping gene expression patterns across the maize genome. However, their phenotypic effects appear limited or context-dependent, suggesting that many variants may have minimal impact outside specific developmental stages or environmental conditions.

genomics↗

bioRxiv2025

SWIF-TE: identifying novel transposable element insertions from short read data

Transposable element (TE) insertion polymorphisms (TIPs) are TEs not in the same location between individuals. TIPs have contributed to genomic and phenomic variation but have been historically difficult to study due to their repetitive nature. Here, we describe a fast and memory-efficient tool to identify novel TIPs from short read sequences. SWIF-TE was able to identify 1,438 insertions at a precision rate of 27% using 0.10 Gb of memory and 0.82 hours of runtime from 15x resequencing data of a non-reference maize inbred. SWIF-TE is a powerful tool for studying TE variation in species with TE-rich genomes.

bioinformatics↗

bioRxiv2025

Maize kernel composition and morphology influences pericarp retention during nixtamalization

Background and ObjectivesPericarp retention during nixtamalization directly influences masa quality, affecting texture, machinability, and nutritional content of staple foods such as tortillas and chips. Despite its industrial relevance, the underlying kernel traits that govern pericarp retention remain poorly characterized. This study aimed to assess an existing staining method to quantify pericarp retention based on visual evaluation, and identify the compositional and morphological characteristics of maize kernels that most strongly predict pericarp retention during nixtamalization. FindingsStain-based scoring of pericarp retention showed moderate correlation with directly measured pericarp mass, achieving a Pearsons correlation coefficient of 0.77 in the rapid cook test and 0.56 in the benchtop cook test. Pearson correlation coefficients of 18 compositional and morphological traits range from -0.294 to 0.538. Ground kernel ash content and initial pericarp quantity were the most correlated and predictive variables associated with pericarp retention. ConclusionsStain-based methods lack the resolution necessary for quantitative pericarp assessment. Initial pericarp quantity and ground kernel ash content are likely key determinants of nixtamalization pericarp retention. Significance and NoveltyThis study provides a first assessment on the impacts of morphological and compositional variation for pericarp retention, which can be built upon to develop improved varieties and cooking methods for product optimization.

plant biology↗

bioRxiv2024

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

Plant height can be an indicator of plant health across environments and used to identify superior genotypes or evaluate abiotic stress factors. Typically plant height is measured at a single time point when plants have reached terminal height for the season. Evaluating plant height using unoccupied aerial vehicles (UAVs) is faster, allowing for measurements throughout the growing season, which facilitates a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected weekly for a panel of [~]500 diverse maize inbred lines over four growing seasons. The variation in plant height throughout the season was significantly explained by genotype, year, and genotype-by-year interactions to varying extents throughout development. Genome-wide association studies revealed significant SNPs associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth that would not be identified by terminal height associations alone. When plant height growth rates were compared to growth rates estimated from canopy cover, greater Frechet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

plant biology↗

bioRxiv2024

Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions

Precision farming has been developing with the intention of identifying within field variability to adjust management strategies and maximize end of season yield and profitability and minimize negative environmental impacts. The development of quick, easy, and low cost methods to quantify field level variation is essential to successful implementation of precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield, which could facilitate mid-season management decisions. Image-based plant height data was collected weekly from commercial maize fields in three growing seasons to assess variation within fields and the relationship with grain yield variation. Plant height, growth rate, and grain yield had variable relationships depending on the time point and growth environment. Models developed using temporal traits predicted grain yield variation within a commercial field up to r = 0.7, though insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the model development. In the future, with more data from stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within field variations, and has application in making mid-season management decisions.

plant biology↗

bioRxiv2023

Importance of genetic architecture in marker selection decisions for genomic prediction

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program. Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

plant biology↗

bioRxiv2021

Predicting Moisture Content During Maize Nixtamalization Using Machine Learning with NIR Spectroscopy

Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearmans rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait. Key MessageMoisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.

plant biology↗

bioRxiv2021

Genetic Architecture of Kernel Compositional Variation in a Maize Diversity Panel

Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which overtime has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants. However, much of the underlying genetic architecture controlling these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g. carbohydrates, protein, starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g. dent, sweetcorn, popcorn) were explored and significant differences and correlations were detected. In total, 22.9-71.1% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 70 statistically significant loci for 12 compositional traits. This study provides valuable insights in the phenotypic variation and genetic architecture underlying compositional traits that can be used in breeding programs for improving maize grain quality. Core IdeasO_LIUnderstanding kernel compositional variation is important for food grade corn improvement. C_LIO_LIGenetic and environmental factors account for most of the variation in compositional traits. C_LIO_LIA broad range in trait heritabilities was observed across compositional traits. C_LIO_LICompositional trade-offs will be important to consider when conducting multitrait breeding. C_LIO_LICompositional traits are mostly controlled by a large number of small effect loci. C_LI

plant biology↗

bioRxiv2021

Whole Genome Assembly and Annotation of Northern Wild Rice, Zizania palustris L., Supports a Whole Genome Duplication in the Zizania Genus

Northern Wild Rice (NWR; Zizania palustris L.) is an aquatic grass native to North America that is notable for its nutritious grain. This is an important species with ecological, cultural, and agricultural significance, specifically in the Great Lakes region of the United States. Using long- and short-range sequencing, Hi-C scaffolding, and RNA-seq data from eight tissues, we generated an annotated whole genome de novo assembly of NWR. The assembly is 1.29 Gb, highly repetitive ([~]76.0%), and contains 46,421 putative protein-coding genes. The expansion of retrotransposons within the genome and a whole genome duplication prior to the Zizania-Oryza speciation event have both led to an increase in genome size of NWR in comparison with O. sativa and Z. latifolia. Both events depict a genome rapidly undergoing change over a short evolutionary time. Comparative analyses revealed conservation of large syntenic blocks with Oryza sativa L., which were used to identify putative seed shattering genes. Estimates of divergence times revealed the Zizania genus diverged from Oryza [~]26-30 million years ago (MYA), while NWR and Zizania latifolia diverged from one another [~]6-8 MYA. Comparative genomics confirmed evidence of a whole genome duplication in the Zizania genus and provided support that the event was prior to the NWR-Z. latifolia speciation event. This high-quality genome assembly and annotation provides a valuable resource for comparative genomics in the Oryzeae tribe and provides an important resource for future conservation and breeding efforts of NWR.

genomics↗

bioRxiv2020

Population Level Variation of Transposable Elements in a Maize Diversity Panel

Intact transposable elements (TEs) account for 65% of the maize genome and can impact gene function and regulation. Although TEs comprise the majority of the maize genome and affect important phenotypes, genome wide patterns of TE polymorphisms in maize have only been studied in a handful of maize genotypes, due to the challenging nature of assessing highly repetitive sequences. We implemented a method to use short read sequencing data from 509 diverse inbred lines to classify the presence/absence of 445,418 non-redundant TEs that were previously annotated in four genome assemblies including B73, Mo17, PH207, and W22. Different orders of TEs (i.e. LTRs, Helitrons, TIRs) had different frequency distributions within the population. LTRs with lower LTR similarity were generally more frequent in the population than LTRs with higher LTR similarity, though high frequency insertions with very high LTR similarity were observed. LTR similarity and frequency estimates of nested elements and the outer elements in which they insert revealed that most nesting events occurred very near the timing of the outer element insertion. TEs within genes were at higher frequency than those that were outside of genes and this is particularly true for those not inserted into introns. Many TE insertional polymorphisms observed in this population were tagged by SNP markers. However, there were also 19.9% of the TE polymorphisms that were not well tagged by SNPs (R2 < 0.5) that potentially represent information that has not been well captured in previous SNP based marker-trait association studies. This study provides a population scale genome-wide assessment of TE variation in maize, and provides valuable insight on variation in TEs in maize and factors that contribute to this variation.

plant biology↗

bioRxiv2020

Gapless assembly of maize chromosomes using long read technologies

Creating gapless telomere-to-telomere assemblies of complex genomes is one of the ultimate challenges in genomics. We used long read technologies and an optical map based approach to produce a maize genome assembly composed of only 63 contigs. The B73-Ab10 genome includes gapless assemblies of chromosome 3 (236 Mb) and chromosome 9 (162 Mb), multiple highly repetitive centromeres and heterochromatic knobs, and 53 Mb of the Ab10 meiotic drive haplotype.

bioinformatics↗

bioRxiv2019

Effect of Sequence Depth and Length in Long-read Assembly of the Maize Inbred NC358

Recent improvements in the quality and yield of long-read data and scaffolding technology have made it possible to rapidly generate reference-quality assemblies for complex genomes. Still, generating these assemblies is costly, and an assessment of critical sequence depth and read length to obtain high-quality assemblies is important for allocating limited resources. To this end, we have generated eight independent assemblies for the complex genome of the maize inbred line NC358 using PacBio datasets ranging from 20-75x genomic depth and N50 read lengths of 11-21 kb. Assemblies with 30x or less depth and N50 read length of 11 kb were highly fragmented, with even the low-copy genic fraction of the genome showing degradation at 20x depth. Distinct sequence-quality thresholds were observed for complete assembly of genes, transposable elements, and highly repetitive genomic features such as telomeres, heterochromatic knobs and centromeres. This study provides a useful resource allocation reference to the community as long-read technologies continue to mature.

genomics↗

bioRxiv2019

Emergence of the Ug99 lineage of the wheat stem rust pathogen through somatic hybridisation

Parasexuality contributes to diversity and adaptive evolution of haploid (monokaryotic) fungi. However non-sexual genetic exchange mechanisms are not defined in dikaryotic fungi (containing two distinct haploid nuclei). Newly emerged strains of the wheat stem rust pathogen, Puccinia graminis f. sp. tritici (Pgt), such as Ug99, are a major threat to global food security. Here we show that Ug99 arose by somatic hybridisation and nuclear exchange between dikaryons. Fully haplotype-resolved genome assembly and DNA proximity analysis revealed that Ug99 shares one haploid nucleus genotype with a much older African lineage of Pgt, with no recombination or reassortment. Generation of genetic variation by nuclear exchange may favour the evolution of dikaryotism by providing an advantage over diploidy.

genetics↗

bioRxiv2019

Highly Genotype- and Tissue-specific Single-Parent Expression Drives Dynamic Gene Expression Complementation in Maize Hybrids

Maize exhibits tremendous gene expression variation between different lines. Complementation of diverse gene expression patterns in hybrids could play an important role in the manifestation of heterosis. In this study, we used transcriptome data of five different tissues from 33 maize inbreds and 89 hybrids (430 samples in total) to survey the global gene expression landscape of F1-hybrids relative to their inbred parents. Analysis of this data set revealed that single parent expression (SPE), which is defined as gene expression in only one of the two parents, while commonly observed, is highly genotype- and tissue-specific. Genes that have SPE in at least one pair of inbreds also tend to be tissue-specific. Genes with SPE caused by genomic presence/absence variation (PAV SPE) are much more frequently expressed in hybrids than genes that are present in the genome of both inbreds, but expressed in only a single-parent (non-PAV SPE) (74.7% vs. 59.7%). For non-PAV SPE genes, allele specific expression was used to investigate whether parental alleles not expressed in the inbred line (\"silent allele\") can be actively transcribed in the hybrid. We found that expression of the silent allele in the hybrid is relatively rare ([~]6.3% of non-PAV SPE genes), but is observed in almost all hybrids and tissues. Non-PAV SPE genes with expression of the silent allele in the hybrid are more likely to exhibit above high-parent expression level in the hybrid than those that do not express the silent allele. Finally, both PAV SPE and non-PAV SPE genes are highly enriched for being classified as non-syntenic, but depleted for curated genes with experimentally determined functions. This study provides a more comprehensive understanding of the potential role of non-PAV SPE and PAV SPE genes in heterosis.

genomics↗

bioRxiv2017

Tandem Duplicate Genes in Maize are Abundant and Date to Two Distinct Periods of Time

Tandem duplicate genes are proximally duplicated and as such occur in the same genomic neighborhood. Using the maize B73 and PH207 de novo genome assemblies, we identified thousands of tandem gene duplicates that account for ~10% of the genes. These tandem duplicates have a bimodal distribution of estimated ages corresponding to known periods of genomic instability. Tandem duplicates had a number of associated features that suggest origins in nonhomologous recombination based on smaller size distribution and higher rate of containing LTRs than non-tandem duplicates. Within relatively recent tandem duplicate genes, ~26% appear to be undergoing degeneration or divergence in function from the ancestral copy. Our results show that tandem duplicates are abundant in maize, arose in bursts throughout maize evolutionary history under multiple potential mechanisms, and may provide a substrate for novel phenotypic variation.

genomics↗