Browsing by Author "Fernandes, Samuel B"

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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