DOI: 10.5061/dryad.fj6q5743r
title: Data from: Leveraging historical trials to predict Fusarium head blight resistance in spring wheat breeding programs

description: Fusarium head blight (FHB) is a fungal disease posing a major threat to
 wheat production. Plant breeding that leverages genotyping is an effective
 method to improve the genetic resistance of cultivars. Started in 1995,
 the uniform regional scab nursery (URSN) consists of germplasm from
 several public breeding programs in the Northern U.S region. Its main
 objective is to showcase new sources of resistance and enable germplasm
 exchange among the cooperators, however, the data from the URSN has not
 been studied. Phenotypic and genotypic data from this nursery was
 gathered, as well as from two current breeding programs in the U.S
 Midwest. Genomic prediction on eight traits related to FHB and agronomic
 traits was applied, and the effects of statistical method, marker density,
 training set size, genetic structure, and genetic architecture of the
 trait were studied. Using the URSN population, RKHS was the best method in
 various prediction settings, with an average accuracy of 0.63, marker
 density could be as low as 500 without decreasing the prediction accuracy,
 and training set optimization was useful for two traits. Furthermore,
 genotypic values were predicted in breeding programs using the URSN
 population as a training set with various prediction scenarios. Predicting
 unrelated populations led to a significant decrease in accuracy but with
 encouraging values for some traits and populations. Ultimately, when
 progressively decreasing the number of lines from breeding populations in
 the training set, the advantage of adding the URSN population was more
 pronounced, with an increase in accuracy up to 0.19.
description: This Read Me file was generated on 2024-11-15 by Charlotte Brault #
 General information ## Title of Dataset Supporting data for *Leveraging
 historical trials to predict Fusarium head blight resistance in wheat
 breeding programs*. ## Author Information ### Principal Investigator
 Contact Information James A. Anderson Department of Agronomy and Plant
 Genetics, University of Minnesota 411 Borlaug Hall, 1991 Buford Circle St.
 Paul, MN 55108 Email: [ander319@umn.edu](mailto:ander319@umn.edu){.email}
 ORCID: 0000-0003-4655-6517 ### Associate or Co-investigator Contact
 Information Jason D. Fiedler USDA-ARS Cereal Crops Research Unit Edward T.
 Schafer Agricultural Research Center, Fargo, ND, USA Email:
 [jason.fiedler@usda.gov](mailto:jason.fiedler@usda.gov){.email} ORCID:
 0000-0001-7736-4484 ### Associate or Co-investigator Contact Information
 Charlotte Brault Department of Agronomy and Plant Genetics, University of
 Minnesota 411 Borlaug Hall, 1991 Buford Circle St. Paul, MN 55108 Email:
 [cbrault@umn.edu](mailto:cbrault@umn.edu){.email} /
 [charlotte.brault@live.com](mailto:charlotte.brault@live.com){.email}
 ORCID: 0000-0001-7892-4236 * Date of data collection (single date, range,
 approximate date): 1995-2023 * Geographic location of data collection
 (where was data collected?): Northern Great Plains-Midwest (MN, ND, SD,
 Manitoba) * Information about funding sources that supported the
 collection of the data: U.S Wheat and Barley Scab Initiative, USDA-ARS. *
 Overview of the data (abstract): Genomic prediction was used as a tool to
 improve the genetic resistance of spring wheat to Fusarium head blight,
 using data from a historical nursery and from two current breeding
 programs. Parameters related to genomic predictive ability were thoroughly
 studied. Predictive ability within the historical nursery was medium to
 high for all the eight traits studied and all methods gave similar
 results. Marker density did not affect predictive ability compared to
 training set size. Training set optimization had mixed results depending
 on the trait. We tested several prediction scenarios useful in a breeding
 context by harnessing the historical dataset in the training set for
 predicting breeding lines. While the lack of genetic relatedness decreased
 the accuracy of genomic prediction, we showed that breeding programs could
 benefit from this historical data by incorporating its information into
 training models, thus reducing the phenotyping effort. # Sharing/Access
 Information Licenses/restrictions placed on the data: CC0 # Data and file
 description ## Data ### URSN and URN populations #### Phenotypic data *
 `URN_cleaned_table_phenot_1992_2023.tsv` Curated phenotypic data for the
 URN population phenotyped for FHB related traits. *
 `URSN_cleaned_table_phenot_1995_2023.tsv` Curated phenotypic data for the
 URSN population. #### Genotypic data *
 `URN_URSN_90K_Filt_geno_formatted_imputed_mafFilt_gdose.rds` input
 genotypic file for the URSN population for the 90K array *
 `URN_URSN_3K_geno_formatted_imputed_mafFilt_gdose.rds` for the 3K
 genotypic array. * Table of genomic information for the URN and URSN
 populations: `genotype_info_URN_URSN.tsv`. ### NDSU breeding program *
 `NDSU_URSN_across-pop_list_inputs_v2.rds` Combined phenotypic and genomic
 data from NDSU and URSN populations. ### UMN breeding program *
 `geno_combined_URSN_UMN-BP_imputed_Beagle_v2.rds` Genotypic data from UMN
 breeding program and URSN population. *
 `pheno_combined_URSN_UMN-BP_5traits_v2.rds` Phenotypic data from UMN
 breeding program and URSN population. * `2022 PY Entry list.xlsx` List of
 PY entries for the UMN breeding program for 2022 phenotypic data, with the
 genotype name, locations, and pedigree. * `2023 PY Entry list.xlsx` List
 of PY entries for the UMN breeding program for 2023 phenotypic data, with
 the genotype name, locations, and pedigree. * `UMN_parent_compo.csv` Table
 of the composition of the genotypes used from the UMN breeding program. ##
 Code * `useful_functions.R` Script which wraps custom useful functions for
 pre-processing, analyzing data and producing results * `subsetMrkCV.R`
 Script to subset markers multiple times for testing the effect of marker
 density. * `subsetIndsCV.R` Script to subset individuals multiple times to
 estimate the imputation accuracy. * `imputationQuality.R` Script to
 estimate the imputation accuracy based on predicted genotypes. *
 `beagle.01Mar24.d36.jar` Beagle software for imputation. ## Bash This
 folder contains the bash scripts used to run some of the analyses on the
 cluster. * `launch_array.sh` Script to launch the array job for marker
 selection (with `subset_MrkCV.R`) and genomic prediction within the URSN
 population. * `launch_Rmdscript.sh` Script to launch various Rmd scripts
 for genomic prediction on the cluster. * `loop_imputation_3k_to_90k.sh`
 Script to launch the genomic imputation from the 3K to the 90K genotypic
 array in the URN-URSN population. ## Analysis scripts ### Prepare inputs
 #### Phenotypic data * `extract_blups_URSN_data.Rmd`. In this script, we
 extract the BLUPs from the phenotypic data for the URSN population, from
 `URSN_cleaned_table_phenot_1995_2023.tsv` table. It outputs a table of
 BLUPs: `URSN_geno_blups_lme4-values.tsv`, and a table of fitting
 information: `URSN_lme4_fit-info_all_traits.tsv`. *
 `extract_blups_URN_FHB.Rmd`. In this script, we extract the BLUPs from the
 phenotypic data for the URN population, from
 `URN_cleaned_table_phenot_1992_2023.tsv` table. It outputs a table of
 BLUPs: `URN_geno_blups_lme4-values_FHBtraits.tsv`, and a table of fitting
 information: `URN_lme4_fit-info_all_traits_FHBtraits.tsv`. *
 `analyze_BLUPs_URN_URSN.Rmd`. In this script, we analyze the BLUPs from
 the URSN and URN populations. It outputs plots of phenotypic structure and
 genetic correlations among traits. #### Genomic data URSN *
 `explo_geno_URN_URSN.Rmd`. In this script, we explore the genomic data for
 the URSN population. It outputs plots of the genetic structure, additive
 relatedness and linkage disequilibrium. *
 `URN_URSN_measure_imput_quality.Rmd`. In this script, we load the
 imputation results for the URSN population from the 3K to the 90K array.
 #### URSN and UMN data * `explore_UMN_BP_URSN.Rmd`. In this script, we
 explore the genomic and phenotypic data for the UMN breeding program and
 the URSN population. It outputs plots of the genetic structure (PCA) and
 the genetic correlation and distribution between populations. ### Genomic
 prediction within URSN * `genom_pred_URN_URSN.Rmd`. In this script, we
 perform genomic prediction within the URSN population, using various
 inputs. * `genom_pred_URN_URSN_exclude_orga.Rmd`. In this script, we
 perform genomic prediction by excluding in turn each organization. *
 `genom_pred_URN_URSN_subsetGeno.Rmd`. In this script, we perform genomic
 prediction by subsetting genotypes from the 3K genotypic array to match
 the number of individuals in the 90K genotypic array. *
 `genom_pred_URSN_TSoptSFSI.Rmd`. In this script, we perform genomic
 prediction within the URSN population, using training set optimization
 with sparse selection index. * `analyze_genom_pred_results_URSN.Rmd`. In
 this script, we combine and analyze the results of the genomic prediction
 within the URSN population. All results were combined in a table:
 `URSN_genomic_prediction_all_combined_results.tsv`. ### Genomic prediction
 between URSN and breeding programs * `genom_pred_acrosspop_URN_URSN.Rmd`.
 In this script, we perform genomic prediction between the URSN population
 and the URN population. * `genom_pred_across-pop_UMN-BP_URSN.Rmd`. In this
 script, we perform genomic prediction between the URSN population and the
 UMN breeding program. * `genom_pred_across-pop_UMN-BP_subset_UMN.Rmd`. In
 this script, we perform genomic prediction between the URSN population and
 a subset of the UMN lines. * `genom_pred_across-pop_NDSU_URSN.Rmd`. In
 this script, we perform genomic prediction between the URSN population and
 the NDSU breeding program. *
 `genom_pred_across-pop_NDSU_URSN_subset_NDSU.Rmd`. In this script, we
 perform genomic prediction between the URSN population and a subset of the
 NDSU lines. * `analyze_across_pop_GP.Rmd`. In this script, we gather and
 analyze the results of the genomic prediction between the URSN population
 and the URN/UMN/NDSU population. All results were combined in a table:
 `URN_UMN_NDSU_genomic_prediction_all_combined_results.tsv`. ### Genomic
 prediction with training set optimization *
 `genom_pred_acrosspop_TSopt_URN_URSN.Rmd`. In this script, we perform
 genomic prediction between the URSN population and the URN population,
 using training set optimization. *
 `genom_pred_across-pop_TSopt_UMN-BP_URSN.Rmd`. In this script, we perform
 genomic prediction between the URSN population and the UMN breeding
 program, using training set optimization. *
 `genom_pred_across-pop_TSopt_NDSU_URSN.Rmd`. In this script, we perform
 genomic prediction between the URSN population and the NDSU breeding
 program, using training set optimization. ### Other * `plot_figures.Rmd`.
 In this script, we generate the figures for the manuscript, specifically,
 we load the PCA genetic structure plots for the NDSU and UMN breeding
 programs and the URSN population and plot it together. * `index.Rmd`. This
 script is the index of the project, it contains the description of the
 project and the links to the different scripts. * `about.Rmd`. This script
 contains the information about the project. * `license.Rmd`. This script
 contains the license information. ## Output * `genotype_info_URN_URSN.tsv`
 Table of genomic information for the URN and URSN populations. *
 `URSN_geno_blups_lme4-values_95-23.tsv` Table of BLUPs for the URSN
 population, `URSN_df_deregressed_blups_8traits_95-23.tsv` the table of
 deregressed BLUPs, and `URSN_geno_blups_var_lme4-values_95-23.Rdata` Rdata
 file of the variance of BLUPs. * `URSN_lme4_fit-info_all_traits_95-23.tsv`
 Table of fitting information for the URSN population. *
 `URN_geno_blups_lme4-values_FHBtraits.tsv` Table of BLUPs for the URN
 population and `URN_geno_blups_var_lme4-values_FHBtraits.Rdata` Rdata file
 of the variance of BLUPs. * `URN_lme4_fit-info_all_traits_FHBtraits.tsv`
 Table of fitting information for the URN population. *
 `res_GP_TSopt_within_across-pop_URN_URSN_7traits.tsv` Table of results for
 the training set optimization genomic prediction within the URN population
 and between the URSN and URN populations. *
 `URN_UMN_NDSU_genomic_prediction_all_combined_results.tsv` Table of
 results for the genomic prediction between the URSN population and the
 URN/UMN/NDSU population, generated in `analyze_across_pop_GP.Rmd`. *
 `URSN_genomic_prediction_all_combined_results.tsv` Table of results for
 the genomic prediction within the URSN population. *
 `fig_repro_URSN_UMN_BP_PCA_inds_geno_orga.rds`. This file contains the PCA
 outputs of the genetic structure of the UMN breeding program and the URSN
 population, it has been generated in `explore_UMN_BP_URSN.Rmd` and is used
 in `plot_figures.Rmd`. * **output/figures** folder. This folder contains
 the figures generated in all scripts. * **output/genom_pred** folder. This
 folder contains the outputs of genomic predictions. * **output/lme4**
 folder. This folder contains the outputs BLUPs prediction and fitting
 information. * **output/imputation** folder. This folder contains the
 outputs of the imputation quality. The **docs** folder contains the
 rendered Rmd scripts in html format. # Software and versions * R version
 v4.4.0 and RStudio v2024.04.01+748 * vcftools 0.1.17 * Beagle v5.4 #
 Version changes ### November 2024 Update following manuscript revision: *
 Update genotype names with released cultivar names * Add genotype random
 subset in URSN genomic selection * Add genomic imputation accuracy
 assessment: * Bash script and R-scripts to subset individuals and markers
 * Imputation for each chromosome, save imputed genotypes * Measure of
 imputation accuracy * Gather all imputation results