Browsing by Author "Della Coletta, Rafael"

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    Datasets to build marker effect networks
    (2023-01-23) Della Coletta, Rafael; Liese, Sharon E; Fernandes, Samuel B; Mikel, Mark A; Bohn, Martin O; Lipka, Alexander E; Hirsch, Candice N; cnhirsch@umn.edu; Hirsch, Candice N; Hirsch Lab
    This dataset contains the input files to build marker effect networks and identify markers associated with environmental adaptability. These networks are built by adapting commonly used software for building gene co-expression networks with marker effects across growth environments as the input data into the networks. Here, we provide grain yield data from 400 maize hybrids grown across nine environments in the U.S. Midwest, a set of ~10,000 non-redundant markers, and environmental data containing 17 weather parameters in 3-day intervals collected from planting date to the end of the season. For instructions on how to perform this analysis and analysis script, please see https://github.com/HirschLabUMN/meffs_networks. For more details on marker effect networks, please see preprint on https://www.biorxiv.org/content/10.1101/2023.01.19.524532v1.
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    Datasets to test the importance of genetic architecture in marker selection decisions for genomic prediction
    (2023-03-01) Della Coletta, Rafael; Fernandes, Samuel B; Monnahan, Patrick J; Mikel, Mark A; Bohn, Martin O; Lipka, Alexander E; Hirsch, Candice N; cnhirsch@umn.edu; Hirsch, Candice N; Hirsch Lab
    This dataset contains the input files to simulate traits for maize recombinant inbred lines (RILs) and run genomic prediction models with different marker types. Using real genotypic information from 333 maize recombinant inbred lines with single nucleotide polymorphism (SNP) and structural variant (SV) information projected from their seven sequenced parental lines, we simulated traits with different genetic architectures in multiple environments using the R package simplePHENOTYPES. We varied the heritability, the number of quantitative trait loci (QTLs), the type of causative variant (SNPs or SVs), and the variant effect sizes. Weather data from five locations in the U.S. Midwest in 2020 was used to generate a residual correlation matrix among environments. After performing a two-stage analysis with multivariate GBLUP prediction model for each marker type and genetic architecture, we obtained prediction accuracies using two types of cross-validation (CV1 and CV2). For instructions on how to perform this analysis and analysis script, please see https://github.com/HirschLabUMN/genomic_prediction_svs
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    Developing genomic tools to breed for climate-adapted plant varieties
    (2023-03) Della Coletta, Rafael
    Climate change is a major threat to global food security, as current plant varieties used by farmers may not adapt to new growing environments. To mitigate this problem, plant breeders must use all available tools to speed up the development and release of new climate-adapted varieties. In this dissertation, I discuss how the recent advances in crop genomics due to improvements in sequencing technology, genome assembly methods, and computational resources are revolutionizing plant breeding. Particularly, I argue that the analysis of the complete catalog of genetic variation of a crop can provide useful information for plant breeders. I demonstrate that modeling this pan-genome information can increase the accuracy of multi- environment genomic prediction models, a tool widely used by breeders to develop new plant varieties. I also show how utilizing prior information on genetic variants associated with certain phenotypes can help simulate traits that are more realistic and relevant for breeders using digital breeding, a tool where breeders can test many different experiments before deployment in their breeding programs. Finally, I developed a new tool that identifies genetic variants associated with specific environmental factors via network analysis of common datasets available to plant breeders.
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    Genetic and Genomic Analysis of Nonhost Resistance to Wheat Stem Rust in Brachypodium distachyon
    (2016-08) Della Coletta, Rafael
    Wheat stem rust, caused by the fungus Puccinia graminis f.sp. tritici (Pgt), is a devastating disease that has been under control for decades. However, new races of this pathogen have emerged that overcome many important wheat stem rust resistance genes, and their spread toward important areas of wheat production threatens global wheat production. Nonhost resistance in plants, which provides durable and broad-spectrum resistance to non-adapted pathogens, may hold great potential to help in the control of wheat stem rust, but the genetic and molecular basis of nonhost resistance is poorly understood. This research project employed the model plant Brachypodium distachyon (Brachypodium), a nonhost of Pgt, for genetic analysis to map loci associated with nonhost wheat stem rust resistance. Using bulked segregant analysis, next-generation sequencing, and bioinformatics approaches, seven quantitative trait loci were found to contribute to nonhost stem rust resistance in a recombinant inbred population derived from a cross between two Brachypodium genotypes with differing levels of resistance. In a second study, analysis of a Brachypodium recombinant inbred population segregating for an induced mutation that confers susceptibility to wheat stem rust led to the identification of a one base pair deletion in a gene that may be the cause of the mutant’s susceptibility. The gene is a homolog of the Arabidopsis gene TIME FOR COFFEE (TIC), which plays a role both in circadian clock regulation and jasmonate signaling. Collectively, the findings of this research project advance our understanding of the genetic basis of nonhost resistance to wheat stem rust, and will guide future research aiming to identify genes essential to the nonhost resistance response, as well as their mechanisms of action.