Last updated: 2024-08-19

Checks: 2 0

Knit directory: URSN_GS/

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Unstaged changes:
    Modified:   output/URN_UMN_NDSU_genomic_prediction_all_combined_results.tsv

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd c8d8849 cbrault 2024-06-20 minor modifs
Rmd 5e1f352 cbrault 2024-06-07 small changes
Rmd 12cc4ed cbrault 2024-06-05 first commit
Rmd 845b808 cbrault 2024-05-30 Start workflowr project.

Prepare inputs

URSN and URN populations

  • Extract BLUPs URSN. In this script, we extract BLUPs from phenotypic data for the URSN population from 1995 to 2023 for lines that have been genotyped.

  • Extract BLUPs URN. In this script, we extract BLUPs from phenotypic data for the URN population from 1992 to 2023 for lines that have not been genotyped and phenotyped for FHB related traits.

  • BLUP analysis URN & URSN. In this script, we analyze the BLUPs for the URSN and URN populations, and plot the genetic structure and correlations.

  • Explore genomic data. In this script, we explore the genomic data for the URSN and URN populations, and plot additive relatedness and genetic structure.

Include bash scripts to generate genomic matrix with marker subset.

Breeding populations

NDSU

  • Explore NDSU data. In this script, we explore the NDSU breeding population data, and plot the genetic structure and correlations.

  • Combine URSN and NDSU data. In this script, we combine the URSN and NDSU data for genomic prediction.

UMN

  • Explore UMN data. In this script, we explore the UMN breeding population data, and plot the genetic structure and correlations.

Genomic prediction (GP)

Initial

  • GP URN & URSN. In this script, we load either URN or URSN data and perform genomic prediction within each population for all traits and using various models.

  • GP URSN exclude organization. In this script, we load URSN data and perform genomic prediction excluding one organization at a time from training set for all traits and using various models.

  • GP between URSN and URN. In this script, we load URSN and URN data and perform genomic prediction across populations with different scenarios for all common traits.

  • GP between URSN and NDSU. In this script, we load URSN and NDSU breeding population data and perform genomic prediction across populations with different scenarios for all common traits.

  • GP between URSN and UMN with various NDSU training set sizes. In this script, we load URSN and UMN breeding population data and perform genomic prediction across populations with 2 scenarios for all common traits, for an increasing number of UMN genotypes.

  • GP between URSN and UMN. In this script, we load URSN and UMN breeding population data and perform genomic prediction across populations with different scenarios for all common traits.

  • GP between URSN and UMN with various UMN training set sizes. In this script, we load URSN and UMN breeding population data and perform genomic prediction across populations with 2 scenarios for all common traits, for an increasing number of UMN genotypes.

With training set optimization

Analyze results

  • Analyze GP results. In this script, we analyze the genomic prediction results for the various scenarios and plot the prediction accuracy.

  • Analyze GP results across populations. In this script, we analyze the genomic prediction results across populations for the various scenarios and plot the prediction accuracy.

  • Plot figures. In this script, we plot the figures for the genetic structure for NDSU and UMN population.