Explore the vast range of titles available.

Heterosis refers to the superiority of a hybrid over its parents. Existing heterosis theory has not sufficiently addressed the contribution of inbreeding at both population level and the level of individual lines within populations. The objectives of the present paper were to formalize theoretical extensions of heterosis theory to address inbreeding at multiple levels, to empirically test the theory in maize, and to provide greater clarity in the quantitative genetic interpretation of heterosis as a function of independent genetic principles of population structure and gene action. Existing heterosis theory for biparental crosses was extended by adding terms for inbreeding within panmictic parent populations. The theory was tested with an experiment in maize with a diverse set of panmictic and inbred parents. Extended theory demonstrated that both heterosis and inbreeding depression are linear functions of inbreeding, at the population level, and at the individual level, under a model of directional dominance. The model demonstrates that heterosis is expected to be negatively related to both midparent value and inbreeding depression within parent populations, i.e., heterosis increases as midparent value decreases and as inbreeding depression within parent populations decreases. Consistent with theoretical predictions we found that that for maize grain yield midparent value predicted 86% of heterosis in a set of crosses and parental inbreeding depression predicted 70% of variation in heterosis among crosses. Model extensions presented here illustrate the excess and transient nature of heterozygosity in the F generation that is partially responsible for the unique performance benefit of F hybrids. Mechanistically, the theory illustrates that heterosis is a function of two separate and independent mechanisms, population structure and gene action, both of which need to be considered in understanding the mechanisms of heterosis.

Inflorescence capacity plays a crucial role in reproductive fitness in plants, and in production of hybrid crops. Maize is a monoecious species bearing separate male and female flowers (tassel and ear, respectively). The switch from open-pollinated populations of maize to hybrid-based breeding schemes in the early 20th century was accompanied by a dramatic reduction in tassel size, and the trend has continued with modern breeding over the recent decades. The goal of this study was to identify selection signatures in genes that may underlie this dramatic transformation. Using a population of 942 diverse inbred maize accessions and a nested association mapping population comprising three 200-line biparental populations, we measured 15 tassel morphological characteristics by manual and image-based methods. Genome-wide association studies identified 242 single nucleotide polymorphisms significantly associated with measured traits. We compared 41 unselected lines from the Iowa Stiff Stalk Synthetic (BSSS) population to 21 highly selected lines developed by modern commercial breeding programs, and found that tassel size and weight were reduced significantly. We assayed genetic differences between the two groups using three selection statistics: cross population extended haplotype homozogysity, cross-population composite likelihood ratio, and fixation index. All three statistics show evidence of selection at genomic regions associated with tassel morphology relative to genome-wide null distributions. These results support the tremendous effect, both phenotypic and genotypic, that selection has had on maize male inflorescence morphology.

The interaction between nitrogen (N) rate, plant density, and hybrid on net return to seed and fertilizer cost remains unknown but important to be addressed given the multi‐input decision process corn growers make every year. We collected grain yield from a factorial experiment with five N rates (0–291 kg N ha−1), five plant densities (3.7–11.4 plants m−2), and four hybrids (old vs. new) over 2 years in Iowa. Data were sufficiently modeled with a quadratic plateau model with continuous covariate terms to include N × plant density interactions (R2 = 0.925, n = 387). We found a wide range of plant densities (6.7–8.4 plants m−2) and N rate (150–212 kg N ha−1) combinations to achieve 99% of the maximum net revenue. The economic optimum N rate increased with increasing plant density from 4 to 7 plants m−2. The fundamental relationships between grain yield response to N fertilizer and plant density were similar across old and new hybrids; however, the absolute values were different, with newer hybrids having 20% higher economic optimum N rate and 23% higher yield than the older hybrids. We concluded that by accounting for two inputs into the revenue equation, a larger range of combinations was found to achieve 99% of the maximum net revenue. Our study offers new insights into N rate by plant density by hybrid interaction to assist future research. Core Ideas The greatest economic risk occurs at high plant density and low N rates. A wide range of optimum N rate (150–210 kg N ha−1) and plant density (6.7–8.4 plants m−2) reached 99% of max revenue. New hybrids had 10% higher optimum plant density, 20% higher optimum N rate, and 23% higher yield than old hybrids. The optimum N rate varied the most and the optimum plant density the least across years and hybrids. The relationship between optimum yield and optimum N rate depends on the choice of the fitted regression model.

Because corn pollen can be carried great distances by wind, maintaining genetic purity of corn grain is challenging. The challenge is substantially reduced in popcorn, which carries the Ga1-s allele preventing pollination by ga1 plants, which include the vast majority of non-popcorn commercial maize varieties in the U.S.. Ga1-s can be transferred into dent corn but the effectiveness of the Ga1-s allele in popcorn and dent corn has never been compared, which is important because each are regulated differently regarding GMO contamination. We compared pollen exclusion of commercial popcorn hybrids, Ga1-s dent corn hybrids and normal dent corn hybrids for their ability to exclude ga1 pollen using a sensitive field-based assay. While both popcorn and Ga1-s dent corn had significantly better pollen exclusion than normal dent corn, popcorn was significantly better than Ga1 -s dent corn on average. Some  Ga1-s dent  hybrids excluded as well or better than some popcorn lines suggesting that identification of hybrids comparable to popcorn is possible. The information in this study will support revised gene purity regulations potentially decreasing costs and increasing genetic purity of organic corn.

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements. Breeding has increased crop productivity, but whether it has also changed phenotypic plasticity is unclear. Here, the authors find maize genomic regions selected for high productivity show reduced contribution to genotype by environment variation and provide evidence for regulatory control of phenotypic stability.

Abstract Single-cross hybrids have been critical to the improvement of maize (Zea mays L.), but the characterization of their genetic architectures remains challenging. Previous studies of hybrid maize have shown the contribution of within-locus complementation effects (dominance) and their differential importance across functional classes of loci. However, they have generally considered panels of limited genetic diversity, and have shown little benefit from genomic prediction based on dominance or functional enrichments. This study investigates the relevance of dominance and functional classes of variants in genomic models for agronomic traits in diverse populations of hybrid maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the U.S. (1106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1640 hybrids). We investigated 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 suggest that dominance and gene annotations improve genomic prediction across diverse populations in hybrid maize.

Objectives Phenotyping plants in a field environment can involve a variety of methods including the use of automated instruments and labor-intensive manual measurement and scoring. Researchers also collect language-based phenotypic descriptions and use controlled vocabularies and structures such as ontologies to enable computation on descriptive phenotype data, including methods to determine phenotypic similarities. In this study, spoken descriptions of plants were collected and observers were instructed to use their own vocabulary to describe plant features that were present and visible. Further, these plants were measured and scored manually as part of a larger study to investigate whether spoken plant descriptions can be used to recover known biological phenomena. Data description Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants’ (whose personal data are protected for ethical reasons) and volunteers’ observation transcripts, volunteers’ audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected phenotypes (e.g., plant height, ear and tassel features, etc.; measurements and scores). Data were collected during the summer of 2021 at Iowa State University’s Agricultural Engineering and Agronomy Research Farms.

Modern maize breeding relies upon selection in inbreeding populations to improve performance in cross-population hybrids. The United States Department of Agriculture - Agricultural Research Service (USDA-ARS) reciprocal recurrent selection experiment between the BSSS and BSCB1 populations represents one of the longest standing models of selection for this type of hybrid performance. To investigate the genomic impact of this selection program, we used the Illumina MaizeSNP50 high-density SNP array to determine genotypes of progenitor lines and over 600 individuals across multiple cycles of selection. Consistent with previous research, we found that genetic diversity within each population steadily decreases, with a corresponding increase in population structure. High marker density also enabled the first view of haplotype ancestry, fixation and recombination within this historical maize experiment. Extensive regions of haplotype fixation within each population are visible in the pericentromeric regions, where large blocks trace back to single founder inbreds. Simulation attributes most of the observed reduction in genetic diversity to genetic drift. Signatures of selection were difficult to observe in the background of this strong genetic drift, but heterozygosity in each population has fallen more than expected. As observed previously, the regions most likely targeted by selection do not overlap between the two populations. We discuss how this pattern is likely to occur during selection for hybrid performance, and how it poses challenges for dissecting the impacts of modern breeding and selection on the maize genome.

Molecular characterization of a given set of maize germplasm could be useful for understanding the use of the assembled germplasm for further improvement in a breeding program, such as analyzing genetic diversity, selecting a parental line, assigning heterotic groups, creating a core set of germplasm and/or performing association analysis for traits of interest. In this study, we used single nucleotide polymorphism (SNP) markers to assess the genetic variability in a set of doubled haploid (DH) lines derived from the unselected Iowa Stiff Stalk Synthetic (BSSS) maize population, denoted as C0 (BSSS(R)C0), the seventeenth cycle of reciprocal recurrent selection in BSSS (BSSS(R)C17), denoted as C17 and the cross between BSSS(R)C0 and BSSS(R)C17 denoted as C0/C17. With the aim to explore if we have potentially lost diversity from C0 to C17 derived DH lines and observe whether useful genetic variation in C0 was left behind during the selection process since C0 could be a reservoir of genetic diversity that could be untapped using DH technology. Additionally, we quantify the contribution of the BSSS progenitors in each set of DH lines. The molecular characterization analysis confirmed the apparent separation and the loss of genetic variability from C0 to C17 through the recurrent selection process. Which was observed by the degree of differentiation between the C0_DHL versus C17_DHL groups by Wright’s F-statistics (FST). Similarly for the population structure based on principal component analysis (PCA) revealed a clear separation among groups of DH lines. Some of the progenitors had a higher genetic contribution in C0 compared with C0/C17 and C17 derived DH lines. Although genetic drift can explain most of the genetic structure genome-wide, phenotypic data provide evidence that selection has altered favorable allele frequencies in the BSSS maize population through the reciprocal recurrent selection program.

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield—those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.