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90 results for “maize genomics”
Combined analysis of transposable elements and structural variation in maize genomes reveals genome contraction outpaces expansion
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Genomic Prediction for the Germplasm Enhancement of Maize Project
<p>The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public and private sector maize breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multi-environment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on six years of topcross evaluations from the two GEM programs in Raleigh, NC and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross-validated within program and heterotic group. Predicted genetic gain from genomic selection ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within-program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm. </p>
Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data
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Data from: Genome-wide diversity in lowland and highland maize landraces from southern South America: Population genetics insights to assist conservation
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Dataset for genome-wide association study of maize phosphorus efficiency
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Phenome-to-genome insights for evaluating root system architecture in field studies of maize
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Genomic prediction applied to multiple traits and environments in second season maize hybrids
<p>Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these data sets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in four years of second season field trials for the traits grain yield, number of ears and grain moisture. Genotypes of these hybrids were inferred <i>in silico</i> based on their parental inbred lines using Single Nucleotide Polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the <b>H</b> matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.</p>
Data from: Integrated genome-scale analysis identifies novel genes and networks underlying senescence in maize
Premature senescence in annual crops reduces yield while delayed senescence, termed stay-green, is known to impose both positive and negative impact on yield and nutrition quality. Despite the importance, scant information is available on the genetic architecture of senescence in maize (Zea mays L.) and other cereals. We combined a systematic characterization of natural diversity for senescence in maize and co-expression networks derived from transcriptome analysis of normally senescing and stay-green lines. Sixty-four candidate genes were identified by GWAS, and 14 of these are supported by additional evidence for involvement in senescence-related processes including proteolysis, sugar transport and signaling, and sink activity. Eight of the GWAS candidates, independently supported by a co-expression network underlying stay-green, include a trehalose-6-phosphate synthase, a NAC transcription factor, and two xylan biosynthetic enzymes. Source-sink communication and the activity of cell walls as a secondary sink emerge as key determinants of stay-green. Mutant analysis supports the role of a candidate encoding cysteine protease in stay-green in Arabidopsis (Arabidopsis thaliana), and analysis of natural alleles suggest a similar role in maize. This study provides a foundation for enhanced understanding and manipulation of senescence for increasing carbon yield, nutritional quality, and stress tolerance of maize and other cereals.
Dataset for "Majority of the highly variable NLRs in maize share genomic location and contain additional target-binding domains"
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Genomic variation within the maize Stiff Stalk heterotic germplasm pool
<p>The Stiff Stalk heterotic group is an important source of inbreds used in U.S. commercial hybrid production. Founder inbreds B14, B37, B73, and to a lesser extent B84, are found in the pedigrees of a majority of commercial seed parent inbred lines. We created high-quality genome assemblies of B84 and four ex-Plant Variety Protection lines LH145 representing B14, NKH8431 of mixed descent, PHB47 representing B37, and PHJ40 which is a Pioneer Hi-Bred early Stiff Stalk type. Sequence was generated using long-read sequencing achieving highly contiguous assemblies of 2.13 to 2.18 Gbp with N50 scaffold lengths greater than 200 Mbp. Inbred-specific gene annotations were generated using a core five-tissue gene expression atlas and transposable element annotation was conducted using de novo and homology-directed methodologies. In comparison to the reference inbred B73, synteny analyses revealed extensive collinearity across the five Stiff Stalk genomes, although unique components of the maize pan-genome were detected. Comparison of this set of Stiff Stalk genome sequences with the original Stiff Stalk population revealed that these inbreds represent only a proportion of variation in the original Stiff Stalk pool and there are highly conserved haplotypes in released public and ex-Plant Variety Protection inbreds. Despite the reduction in variation from the original Stiff Stalk population, substantial genetic and genomic variation was identified supporting the potential for continued breeding success in this pool. The assemblies described here represent Stiff Stalk inbreds that have historical and commercial relevance and provide further insight into the emerging maize pan-genome.</p>
Data from: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices
<p>Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5-17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.</p>
Data from: Genome sequence and virulence variation-related transcriptome profiles of Curvularia lunata, an important maize pathogenic fungus
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Data from: Genomic variation in recently collected maize landraces from Mexico
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Data from: Integrated genome-scale analysis identifies novel genes and networks underlying senescence in maize
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Genomic prediction applied to multiple traits and environments in second season maize hybrids
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Data from: Enhancing genomic prediction with genome-wide association studies in multiparental maize populations
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Data from: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices
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Genomic variation within the maize Stiff Stalk heterotic germplasm pool
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Data from: Archaeological Central American maize genomes suggest ancient gene flow from South America
<p><span><span><span><span><span><span><span><span><span><span><span>Maize (<i>Zea mays</i> ssp. <i>mays</i>) domestication began in southwestern Mexico ~9,000 calendar years before present (cal. BP) and humans dispersed this important grain to South America by at least 7000 cal. BP as a partial domesticate. South America served as a secondary improvement center where the domestication syndrome became fixed and new lineages emerged in parallel with similar processes in Mesoamerica. Later, Indigenous cultivators carried a second major wave of maize southward from Mesoamerica, but it has been unclear until now whether the deeply divergent maize lineages underwent any subsequent gene flow between these regions. Here we report ancient maize genomes (2,300-1,900 cal. BP) from El Gigante rock-shelter, Honduras, that are closely related to ancient and modern maize from South America. Our findings suggest that the second wave of maize brought into South America hybridized with long-established landraces from the first wave, and that some of the resulting newly admixed lineages were then reintroduced to Central America. Direct radiocarbon dates and cob morphological data from the rock-shelter suggest that more productive maize varieties developed between 4,300 and 2,500 cal. BP. We hypothesize that the influx of maize from South America into Central America may have been an important source of genetic diversity as maize was becoming a staple grain in Central and Mesoamerica.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Genome-wide association analysis of stalk biomass and anatomical traits in maize
Background: Maize stover is an important source of crop residues and a promising sustainable energy source in the United States. Stalk is the main component of stover, representing about half of stover dry weight. Characterization of genetic determinants of stalk traits provide a foundation to optimize maize stover as a biofuel feedstock. We investigated maize natural genetic variation in genome-wide association studies (GWAS) to detect candidate genes associated with traits related to stalk biomass (stalk diameter and plant height) and stalk anatomy (rind thickness, vascular bundle density and area). Results: Using a panel of 942 diverse inbred lines, 899,784 RNA-Seq derived single nucleotide polymorphism (SNP) markers were identified. Stalk traits were measured on 800 members of the panel in replicated field trials across years. GWAS revealed 16 candidate genes associated with four stalk traits. Most of the detected candidate genes were involved in fundamental cellular functions, such as regulation of gene expression and cell cycle progression. Two of the regulatory genes (Zmm22 and an ortholog of Fpa) that were associated with plant height were previously shown to be involved in regulating the vegetative to floral transition. The association of Zmm22 with plant height was confirmed using a transgenic approach. Transgenic lines with increased expression of Zmm22 showed a significant decrease in plant height as well as tassel branch number, indicating a pleiotropic effect of Zmm22. Conclusion: Substantial heritable variation was observed in the association panel for stalk traits, indicating a large potential for improving useful stalk traits in breeding programs. Genome-wide association analyses detected several candidate genes associated with multiple traits, suggesting common regulatory elements underlie various stalk traits. Results of this study provide insights into the genetic control of maize stalk anatomy and biomass.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.