Last updated: 2020-12-23

Checks: 7 0

Knit directory: TARI_2020GS/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20201215)

The command set.seed(20201215) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

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Cache: none

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File paths: relative

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Repository version: fae176a

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The results in this page were generated with repository version fae176a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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 9c8d010 wolfemd 2020-12-23 Start workflowr project.
Rmd 3d46094 wolfemd 2020-12-15 Start workflowr project.

This repository and website documents all analyses, summary, tables and figures associated with TARI genomic prediction and related procedures (e.g. imputation).

December Imputations

DCas20_5629

GS C1.

Impute with E. Africa Imputation Reference Panel dataset, which can be found on the Cassavabase FTP server here with names e.g. chr*_ImputationReferencePanel_StageVI_91119.vcf.gz with code/documentation here.

Steps:

  1. Convert DCas20_5629 report to VCF for imputation:
  2. Impute DCas20_5629: with East Africa reference panel

Files:

  • RefPanel VCF filename: chr*_ImputationReferencePanel_StageVI_91119.vcf.gz
  • Imputed filename: chr*_DCas20_5629_EA_REFimputed.vcf.gz
  • Post-impute filtered filename: chr*_DCas20_5629_EA_REFimputedAndFiltered.vcf.gz
  • Genome-wide dosage matrix format for use in R:
    • Imputation Reference Panel: DosageMatrix_ImputationReferencePanel_StageVI_91119.rds
    • DCas20_5629 with standard post-impute filter: DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds

HOW TO COMBINE DOSAGE MATRICES: Users will want to combine the genotypes in the imputation reference panel files, with the genotypes in the imputed DArT file. They can have slightly different sets of markers along the columns. Here is a basic example how to combine:

snps_refpanel<-readRDS("DosageMatrix_ImputationReferencePanel_StageVI_91119.rds")
snps_dcas20_5629<-readRDS("DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds")

snps2keep<-colnames(snps_refpanel)[,colnames(snps_refpanel) %in% colnames(snps_dcas20_5629)]
snps<-bind_rows(snps_refpanel[,snps2keep],
                snps_dcas20_5629[,snps2keep])

December Genomic Prediction

Get TARI TP data from Cassavabase. Use it with imputed data to predict GEBV/GETGV for all samples in the new reports (DCas20-5629).

  1. Prepare training dataset: Download data from DB, “Clean” and format DB data.
  2. Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic prediction.
    • Fit mixed-model to multi-trial dataset and extract BLUPs, de-regressed BLUPs and weights. Include two rounds of outlier removal.
  3. Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
  4. Genomic prediction: Predict genomic BLUPs (GEBV and GETGV) for all selection candidates using all available data.
  5. Results: Home for plots and other results.

Files: everything is in the output/ sub-directory.

DOWNLOAD FROM CASSAVABASE FTP SERVER

or

DOWNLOAD FROM GitHub

Data availability and reproducibility

The R package workflowr was used to document this study reproducibly.

Much of the supporting data and output from the analyses documented here are too large for GitHub.

The repository will be mirrored, here: ftp://ftp.cassavabase.org/marnin_datasets/TARI_2020GS/ with all data.

Directory structure of this repository

NOTICE: data/ and output/ are empty on GitHub. Please see ftp://ftp.cassavabase.org/marnin_datasets/TARI_2020GS/ for access.

  1. data/: raw data (e.g. unimputed SNP data)
  2. output/: outputs (e.g. imputed SNP data)
  3. analysis/: most code and workflow documented in .Rmd files
  4. docs/: compiled .html, “knitted” from .Rmd

Supporting functions code/

The analyses in the html / Rmd files referenced above often source R scripts in the code/ sub-folder.


sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      whisker_0.4     knitr_1.30      magrittr_2.0.1 
 [5] R6_2.5.0        rlang_0.4.9     stringr_1.4.0   tools_4.0.2    
 [9] xfun_0.19       git2r_0.27.1    htmltools_0.5.0 ellipsis_0.3.1 
[13] yaml_2.2.1      digest_0.6.27   rprojroot_2.0.2 tibble_3.0.4   
[17] lifecycle_0.2.0 crayon_1.3.4    later_1.1.0.1   vctrs_0.3.5    
[21] promises_1.1.1  fs_1.5.0        glue_1.4.2      evaluate_0.14  
[25] rmarkdown_2.6   stringi_1.5.3   compiler_4.0.2  pillar_1.4.7   
[29] httpuv_1.5.4    pkgconfig_2.0.3