Biology subjects

Diepenbrock, C. H.

Publications and source records attributed to Diepenbrock, C. H..

12 recordsLinked to original sources

bioRxiv2026

Genetic mapping and genomic prediction for agronomic, grain compositional, and sensing-enabled traits in a cowpea MAGIC population along an environmental gradient

Cowpea (Vigna unguiculata [L.] Walp.) is a resilient grain legume and an important global source of dietary protein, yet the genetic and environmental basis of phenological and canopy development, as well as grain composition, remains incompletely characterized across production environments. In this study, we evaluated a cowpea multi-parent advanced generation intercross (MAGIC) population along an environmental gradient in California (with contrasting daylengths, temperatures, and soil types) using agronomic, grain compositional, and uncrewed aerial vehicle (UAV) and rover-enabled phenotyping. Near-infrared spectroscopy (NIRS) enabled assessment of grain compositional traits, while sensing-enabled time-series imaging captured canopy and reproductive dynamics. Quantitative trait locus (QTL) mapping identified 267 QTL, and genome-wide association studies (GWAS) detected 1,973 marker-trait associations. Integrating QTL mapping and GWAS results identified two major genomic hotspots affecting multiple traits. A chromosome 9 hotspot (5.8-6.0 Mb) was associated with flowering time and co-localized with sensing-enabled measures of flower and pod counts, plant height, and vegetation fraction, indicating broad effects on phenological and canopy development. A chromosome 8 hotspot (37.3-37.9 Mb) contained co-localized signals for seed weight, protein, starch, phytate, and moisture. A total of 22 prioritized candidate genes were identified within these and other loci with multi-environment QTL and GWAS support. Genomic predictive abilities were moderate to high for most traits and scenarios, with multi-trait MegaLMM outperforming RR-BLUP. Together, these results define major genomic regions controlling cowpea phenology, canopy development, and grain composition, and provide targets and strategies for breeding cowpea cultivars with favorable and environmentally resilient productivity and grain composition. Significance StatementTo dissect the genetic basis of cowpea productivity, adaptation, and grain composition, and how performance for these traits varies and can be predicted across environments, we combined multi-environment phenotyping, including sensing of canopy and reproductive traits, with quantitative genetic analyses in a multi-parental population. We identified genomic hotspots for seed size/composition and reproductive phenology and an across-environment predictive advantage for multi-trait vs. single-trait genomic prediction. Overall, these findings support the comprehensive improvement of cowpea.

genetics↗

bioRxiv2026

Genotypic and multi-environment phenotypic evaluation of the lima bean USDA National Plant Germplasm System collection

Lima bean (Phaseolus lunatus L.) is an economically and agronomically important grain legume. Lima beans (or limas) show a range of climatic adaptations with independent domestications in the Andes (large-seeded) and Mesoamerica (small- or medium-seeded). We generated and integrated genotypic and comprehensive field- and laboratory-based phenotypic information for the available accessions in the USDA National Plant Germplasm System collection across multiple environments to inform germplasm utilization in breeding. A total of 810 accessions were genotyped using short-read, low-coverage sequencing. Accession geographic origin and domestication explained population structure. A partially overlapping subset of the panel (n=141-308) was field-evaluated across two years in each of Davis, CA, Central Ferry, WA, and Coachella Valley, CA (the latter was fall-planted for evaluation of photoperiod-sensitive accessions) to assess trait performance in contrasting environments. Agronomic traits such as determinacy and flowering time, and seed traits such as seed coat color and hundred-seed weight, were scored. Macronutrient traits (protein, starch, fat, and ash content) were measured on dry (mature) harvested grain via near-infrared spectroscopy. Genome-wide association analyses identified loci significantly associated with descriptive, agronomic, and seed traits, including orthologs of known genes in common bean and novel candidate regions. Genomic predictive abilities were moderate to high for key traits. Finally, we established a conditional core collection that was constrained to include 211 extensively phenotyped accessions and for which 91 supplemental accessions were selected to maximize genetic diversity from among the genotyped accessions. Overall, these resources provide a foundation to support genomics-assisted breeding of limas.

plant biology↗

bioRxiv2026

Genotypic and environmental effects on seed coat patterning and nutritional composition in common bean (Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) is the leading grain legume consumed directly by humans and a primary source of nutrients in many communities. This study utilized common bean genotypes with diverse seed coat phenotypes to investigate genotypic and environmental effects on pigmented seed coat area and seed macronutrient (protein, starch, fat, ash, moisture), anti-nutrient (phytate), and mineral nutrient (iron, zinc, calcium, phosphorus, magnesium, potassium, sodium) profiles. Recombinant inbred lines (RILs) that comprise six phenotypic classes for seed coat patterning and nine commercial cultivars were field-evaluated for multiple years across inland, coastal, and intermountain environments in California. A custom near-infrared spectroscopy calibration improved macronutrient prediction accuracy relative to a pre-existing calibration. Environmental effects on macronutrients were pronounced; the 2022 coastal growing environment was the most distinct, characterized by significantly higher starch and moisture content and significantly lower protein content in the RILs relative to any other environments. Across growing years in the RILs, greater consistency was observed at the inland site, where only protein was significantly different; all macronutrient traits significantly differed within the intermountain site. Certain commercial cultivars largely maintained their relative rank for protein content across environments, indicating consistency of genotypic performance, and Black Nightfall ranked among the highest for iron, zinc, phosphorus, and magnesium. Percent pigmented seed coat area was significantly negatively correlated with both calcium and magnesium concentrations. These results underscore the importance of genotype-by-environment field trials for seed coat patterning, seed nutritional composition, and their interplay, to support breeding of common bean among other grain legumes. HighlightsO_LICustom near-infrared spectroscopy (NIRS) calibration improved prediction accuracies C_LIO_LIEnvironmental effects significantly influenced common bean macronutrient composition C_LIO_LICertain cultivars ranked consistently for macronutrient traits across environments C_LIO_LISeed coat pattern was significantly associated with mineral nutrient concentrations C_LI

plant biology↗

bioRxiv2024

An empirical evaluation of simulated gastrointestinal digestion platforms for use in plant breeding, using common bean (Phaseolus vulgaris L.) as a model

There remains a disconnect in plant breeding between increasing nutrient levels in crops at time of harvest and increasing bioaccessible levels of those nutrients during digestion. This study aims to develop and compare simulated digestion models for use in plant breeding and examine bioaccessible nutrient levels in common bean samples with differing seed coat coloration and patterning. The highest trait values (starch and protein hydrolysis, total phenolics, and antioxidant power) were observed from more dynamic digestion models, but even simple dynamic models showed higher trait values than a commonly used static digestion model. The use of these models provided insight on nutrient bioaccessibility; e.g., differences were observed during digestion between common bean genotypes for protein hydrolysis and between growing environments for both total phenolics and protein hydrolysis. Together, these results inform potential future pathways for applying simulated digestion models in plant breeding to improve bioaccessible nutrient levels in crops.

plant biology↗

bioRxiv2023

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)--an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. This project assesses the accuracy of several genomic prediction (GP) strategies for these traits ({beta}-carotene, {beta}-cryptoxanthin, provitamin A, lutein, and zeaxanthin) within and between four environments in Mexico. Methods employing Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, or Reproducing Kernel Hilbert Spaces had high accuracy in predicting all tested provitamin A carotenoid traits and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, prediction accuracies were higher when using genome-wide markers rather than only the markers proximal to two previously identified carotenoid-related genes that have been used in marker-assisted selection, suggesting that GP is worthwhile for these traits, even though key genes have already been identified. Prediction accuracy was maintained for all traits (except lutein) in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of GP methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

plant biology↗

bioRxiv2023

Sequence characterization of T, Bip, and Phbw demonstrates the role of MYB-bHLH-WD40 complexes and temperature in common bean seed color pattern formation

Seed colors and color patterns are critical for the survival of wild plants and the consumer appeal of crops. In common bean, a major global staple, these patterns are also critical for determining market classes, yet the genetic and environmental control of many pigmentation patterns remains unresolved. In this study, we genetically mapped variation for three important seed pattern loci, T, Bip, and phbw, which co-segregated completely with PvTTG1, PvMYC1, and PvTT8, respectively. Proteins encoded by these genes are predicted to work together in MYB-bHLH-WD40 (MBW) complexes, propagating flavonoid biosynthesis across the seed coat. Whole-genome sequencing of 37 diverse accessions identified putative mutations in each gene, including seven unique parallel mutations in T (PvTTG1) and a non-synonymous SNP in a conserved residue in bipana (PvMYC1). A 612 bp intron deletion in phbw (PvTT8) eliminated motifs conserved since the origins of the Papilionoidea and corresponded to a 20-fold reduction in transcript abundance. Mutations in MBW candidate genes for Z (PvTT2) and Sellatus (WDR) were also identified. In multi-location field trials with seven varieties with partial seed coat patterning, pigmented seed coat area was highly unstable and correlated with temperature, with up to 11-fold differences in pigmented area between the warmest and the coolest environments. In controlled growth chamber conditions, an increase of 4 {degrees}C was sufficient to cause pigmentation on an additional 21% of the seed coat area. Our results shed light on the fundamental activation of flavonoid biosynthesis in common bean and will be instrumental for maximizing consumer appeal in this nutritious staple crop. Summary- Seed colors and patterns are critical for the survival of wild plants, and are important in differentiating crop market classes, but the genetic control of these in the staple crop common bean (Phaseolus vulgaris) is largely unknown. - The genetic, transcriptional, and environmental basis of common bean seed color patterning was explored through QTL mapping, whole-genome sequencing, RT-qPCR, and automated pigmentation quantification of seed grown in multi-location field trials and growth chamber environments. - MYB-bHLH-WD40 complex-forming genes PvTTG1, PvMYC1, and PvTT8 co-segregated completely with the color patterning genes T, Bip, and phbw. Mutations were identified in each gene, including seven unique parallel mutations in T (PvTTG1), a non-synonymous SNP in a conserved residue in bipana (PvMYC1), and an intron deletion in phbw (PvTT8) eliminating highly conserved motifs and corresponding to 20-fold lower PvTT8 transcript abundance. Mutations in MBW candidate genes Z (PvTT2) and Sellatus (WDR) were also identified. In multi-location field trials, pigmented seed coat area was highly unstable and corresponded to temperature. In growth chamber conditions, an increase of 4 {degrees}C caused pigmentation on an additional 21% of the seed coat area. - Our results highlight the critical interaction between MYB-bHLH-WD40 complex components and temperature in establishing seed pattern diversity.

plant biology↗

bioRxiv2023

Root system growth and function response to soil temperature in maize (Zea mays L.)

Crop adaptation to the mixture of environments that defines the target population of environments is the result from a balanced resource allocation between roots, shoots and reproductive organs. Root growth places a critical role in the determination of this balance. Root growth and function responses to temperature can determine the strength of roots as sinks but also influence the crops ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize since the middle of the last century, and the genetic determinants are unknown. Low temperatures often recorded in deep soil layers limit root growth and soil exploration and may constitute a bottleneck towards increasing drought tolerance, nitrogen recovery, sequestration of carbon and productivity in maize. High throughput phenotyping (HTP) systems were developed to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here we show that there is: 1) genetic variation of root growth under low temperature and below 10{degrees}C, and 2) genotypic variation in water transport under low temperature. Using simulation, we demonstrate that the measured variation for both traits contribute to drought tolerance and explain important components of yield variation in the US corn-belt. The trait set examined herein and HTP platform developed for its characterization reveal a unique opportunity to remove a major bottleneck for crop improvement, and adaptation to climate change.

plant biology↗

bioRxiv2022

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of [~]1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including five that had not been previously associated with maize grain tocochromanols: four biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for co-localized eQTL and GWAS association signals. Our multi-omics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.

genetics↗

bioRxiv2021

Simultaneous Dissection of Grain Carotenoid Levels and Kernel Color in Biparental Maize Populations with Yellow-to-Orange Grain

Maize enriched in provitamin A carotenoids could be key in combatting vitamin A deficiency in human populations relying on maize as a food staple. Consumer studies indicate that orange maize may be regarded as novel and preferred. This study identifies genes of relevance for grain carotenoid concentrations and kernel color, through simultaneous dissection of these traits in 10 families of the U.S. maize nested association mapping population that have yellow to orange grain. Quantitative trait loci (QTL) were identified via joint-linkage analysis, with phenotypic variation explained for individual kernel color QTL ranging from 2.4 to 17.5%. These QTL were cross-analyzed with significant marker-trait associations in a genome-wide association study that utilized [~]27 million variants. Nine genes were identified: four encoding activities upstream of the core carotenoid pathway, one at the pathway branchpoint, three within the - or {beta}-pathway branches, and one encoding a carotenoid cleavage dioxygenase. Of these, three exhibited significant pleiotropy between kernel color and one or more carotenoid traits. Kernel color exhibited moderate positive correlations with {beta}-branch and total carotenoids and negligible correlations with -branch carotenoids. These findings can be leveraged to simultaneously achieve desirable kernel color phenotypes and increase concentrations of provitamin A and other priority carotenoids.

plant biology↗

bioRxiv2021

Can we harness digital technologies and physiology to hasten genetic gain in U.S. maize breeding?

Genetic gain in breeding programs depends on the predictive skill of genotype-to-phenotype algorithms and precision of phenotyping, both integrated with well-defined breeding objectives for a target population of environments (TPE). The integration of physiology and genomics could improve predictive skill by capturing additive and non-additive interaction effects of genotype (G), environment (E), and management (M). Precision phenotyping at managed stress environments (MSEs) can elicit physiological expression of processes that differentiate germplasm for performance in target environments, thus enabling algorithm training. Gap analysis methodology enables design of GxM technologies for target environments by assessing the difference between current and attainable yields within physiological limits. Harnessing digital technologies such as crop growth model-whole genome prediction (CGM-WGP) and gap analysis, and MSEs, can hasten genetic gain by improving predictive skill and definition of breeding goals in the U.S. maize production TPE. A half-diallel maize experiment resulting from crossing 9 elite maize inbreds was conducted at 17 locations in the TPE and 6 locations at MSEs between 2017 and 2019. Analyses over 35 families represented by 2367 hybrids demonstrated that CGM-WGP offered a predictive advantage (y) compared to WGP that increased with occurrence of drought as measured by decreasing whole-season evapotranspiration (ET; log(y) = 0.80({+/-}0.6) - 0.006({+/-}0.001) x ET; r2 = 0.59; df = 21). Predictions of unobserved physiological traits using the CGM, akin to digital phenotyping, were stable. This understanding of germplasm response to ET enables predictive design of opportunities to close productivity gaps. We conclude that enabling physiology through digital methods can hasten genetic gain by improving predictive skill and defining breeding objectives bounded by physiological realities.

plant biology↗

bioRxiv2020

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea mays L.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr-1, which is higher than rates (0.7% yr-1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models. HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

plant biology↗

bioRxiv2020

Eleven biosynthetic genes explain the majority of natural variation for carotenoid levels in maize grain

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize is a food staple. Extensive natural variation exists for carotenoids in maize grain; to understand its genetic basis, we conducted a joint linkage and genome-wide association study in the U.S. maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percent phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide the most comprehensive genome-level understanding of the genetic and molecular control of carotenoids in any plant system, and a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.

genetics↗