GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment
    Oxford University Press (OUP) ; 2021
    In:  G3 Genes|Genomes|Genetics Vol. 11, No. 2 ( 2021-04-12)
    Details
    In: G3 Genes|Genomes|Genetics, Oxford University Press (OUP), Vol. 11, No. 2 ( 2021-04-12)
    Abstract: High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.
    Type of Medium: Online Resource
    ISSN: 2160-1836
    URL: Article
    DOI: 10.1093/g3journal/jkaa050
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2629978-1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Genetic Dissection of Intermated Recombinant Inbred Lines Using a New Genetic Map of Maize
    Oxford University Press (OUP) ; 2006
    In:  Genetics Vol. 174, No. 3 ( 2006-11-01), p. 1671-1683
    Details
    In: Genetics, Oxford University Press (OUP), Vol. 174, No. 3 ( 2006-11-01), p. 1671-1683
    Abstract: A new genetic map of maize, ISU–IBM Map4, that integrates 2029 existing markers with 1329 new indel polymorphism (IDP) markers has been developed using intermated recombinant inbred lines (IRILs) from the intermated B73 × Mo17 (IBM) population. The website http://magi.plantgenomics.iastate.edu provides access to IDP primer sequences, sequences from which IDP primers were designed, optimized marker-specific PCR conditions, and polymorphism data for all IDP markers. This new gene-based genetic map will facilitate a wide variety of genetic and genomic research projects, including map-based genome sequencing and gene cloning. The mosaic structures of the genomes of 91 IRILs, an important resource for identifying and mapping QTL and eQTL, were defined. Analyses of segregation data associated with markers genotyped in three B73/Mo17-derived mapping populations (F2, Syn5, and IBM) demonstrate that allele frequencies were significantly altered during the development of the IBM IRILs. The observations that two segregation distortion regions overlap with maize flowering-time QTL suggest that the altered allele frequencies were a consequence of inadvertent selection. Detection of two-locus gamete disequilibrium provides another means to extract functional genomic data from well-characterized plant RILs.
    Type of Medium: Online Resource
    ISSN: 1943-2631
    URL: Article
    DOI: 10.1534/genetics.106.060376
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2006
    detail.hit.zdb_id: 1477228-0
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Functional Analysis of Maize RAD51 in Meiosis and Double-Strand Break Repair
    Oxford University Press (OUP) ; 2007
    In:  Genetics Vol. 176, No. 3 ( 2007-07-01), p. 1469-1482
    Details
    In: Genetics, Oxford University Press (OUP), Vol. 176, No. 3 ( 2007-07-01), p. 1469-1482
    Abstract: In Saccharomyces cerevisiae, Rad51p plays a central role in homologous recombination and the repair of double-strand breaks (DSBs). Double mutants of the two Zea mays L. (maize) rad51 homologs are viable and develop well under normal conditions, but are male sterile and have substantially reduced seed set. Light microscopic analyses of male meiosis in these plants reveal reduced homologous pairing, synapsis of nonhomologous chromosomes, reduced bivalents at diakinesis, numerous chromosome breaks at anaphase I, and that & gt;33% of quartets carry cells that either lack an organized nucleolus or have two nucleoli. This indicates that RAD51 is required for efficient chromosome pairing and its absence results in nonhomologous pairing and synapsis. These phenotypes differ from those of an Arabidopsis rad51 mutant that exhibits completely disrupted chromosome pairing and synapsis during meiosis. Unexpectedly, surviving female gametes produced by maize rad51 double mutants are euploid and exhibit near-normal rates of meiotic crossovers. The finding that maize rad51 double mutant embryos are extremely susceptible to radiation-induced DSBs demonstrates a conserved role for RAD51 in the repair of mitotic DSBs in plants, vertebrates, and yeast.
    Type of Medium: Online Resource
    ISSN: 1943-2631
    URL: Article
    DOI: 10.1534/genetics.106.062604
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2007
    detail.hit.zdb_id: 1477228-0
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes
    Oxford University Press (OUP) ; 2006
    In:  Bioinformatics Vol. 22, No. 15 ( 2006-08-01), p. 1863-1870
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 15 ( 2006-08-01), p. 1863-1870
    Abstract: Motivation: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to & lt;70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT–PCR (qRT–PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. Availability: The data presented can be viewed at under GEO accession no. GSE3017. Contact: schnable@iastate.edu Supplementary information: Data from these experiments can be viewed at
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btl270
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2006
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Yield prediction through integration of genetic, environment, and management data through deep learning
    Oxford University Press (OUP) ; 2023
    In:  G3: Genes, Genomes, Genetics Vol. 13, No. 4 ( 2023-04-11)
    Details
    In: G3: Genes, Genomes, Genetics, Oxford University Press (OUP), Vol. 13, No. 4 ( 2023-04-11)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2160-1836
    URL: Article
    DOI: 10.1093/g3journal/jkad006
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2629978-1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    Single molecule analysis indicates stimulation of MUTYH by UV-DDB through enzyme turnover
    Oxford University Press (OUP) ; 2021
    In:  Nucleic Acids Research Vol. 49, No. 14 ( 2021-08-20), p. 8177-8188
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. 14 ( 2021-08-20), p. 8177-8188
    Abstract: The oxidative base damage, 8-oxo-7,8-dihydroguanine (8-oxoG) is a highly mutagenic lesion because replicative DNA polymerases insert adenine (A) opposite 8-oxoG. In mammalian cells, the removal of A incorporated across from 8-oxoG is mediated by the glycosylase MUTYH during base excision repair (BER). After A excision, MUTYH binds avidly to the abasic site and is thus product inhibited. We have previously reported that UV-DDB plays a non-canonical role in BER during the removal of 8-oxoG by 8-oxoG glycosylase, OGG1 and presented preliminary data that UV-DDB can also increase MUTYH activity. In this present study we examine the mechanism of how UV-DDB stimulates MUTYH. Bulk kinetic assays show that UV-DDB can stimulate the turnover rate of MUTYH excision of A across from 8-oxoG by 4–5-fold. Electrophoretic mobility shift assays and atomic force microscopy suggest transient complex formation between MUTYH and UV-DDB, which displaces MUTYH from abasic sites. Using single molecule fluorescence analysis of MUTYH bound to abasic sites, we show that UV-DDB interacts directly with MUTYH and increases the mobility and dissociation rate of MUTYH. UV-DDB decreases MUTYH half-life on abasic sites in DNA from 8800 to 590 seconds. Together these data suggest that UV-DDB facilitates productive turnover of MUTYH at abasic sites during 8-oxoG:A repair.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab591
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1472175-2
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 7
    Online Resource
    Online Resource
    qTeller: a tool for comparative multi-genomic gene expression analysis
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 38, No. 1 ( 2021-12-22), p. 236-242
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 38, No. 1 ( 2021-12-22), p. 236-242
    Abstract: Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses. Results To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms’ databases. Availability and implementation The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btab604
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...