Last updated: 2021-05-05

Checks: 7 0

Knit directory: NRCRI_2021GS/

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

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Environment: empty

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Seed: set.seed(20210427)

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Session information: recorded

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Cache: none

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File paths: relative

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Repository version: 94ec811

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    Untracked:  output/cvresults_ADE_1_2021May03.rds
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    Untracked:  output/genomicPredictions_NRCRI_2021May03.csv
    Untracked:  output/maxNOHAV_byStudy.csv

Unstaged changes:
    Modified:   README.md

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 94ec811 wolfemd 2021-05-05 Completed prediction. Build site and push to GitHub / Cassavabase FTP server. Share to NRCRI.
html e8b3eeb wolfemd 2021-05-03 Build site.
Rmd c887639 wolfemd 2021-05-03 Publish site up through cleanTPdata step to generate cleaned TP data before continuing pipeline.
Rmd 4627607 wolfemd 2021-04-27 Start workflowr project.

Welcome to my research website.

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

April 2021 (DCas21_5841 - NRCRI C3b)

I think this report corresponds to the GS C3b. Last NRCRI imputation and prediction was October 2020 corresponding to a C3a, I assume.

Imputation of genotyping project

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 DCas21-5841 report to VCF for imputation:
  2. Impute DCas21-5841: with West Africa reference panel

Files:

  • RefPanel VCF filename: chr*_ImputationReferencePanel_StageIIpartI_72219.vcf.gz
  • Imputed filename: chr*_DCas21_5841_WA_REFimputed.vcf.gz
  • Post-impute filtered filename: chr*_DCas21_5841_WA_REFimputedAndFiltered.vcf.gz
  • Genome-wide dosage matrix format for use in R: DosageMatrix_DCas21_5841_WA_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_RefPanelAndGSprogeny_ReadyForGP_73019.rds")
snps_dcas21_5841<-readRDS("DosageMatrix_DCas21_5841_WA_REFimputedAndFiltered.rds")

snps2keep<-colnames(snps_refpanel)[,colnames(snps_refpanel) %in% colnames(snps_dcas20_5629)]
snps<-bind_rows(snps_refpanel[,snps2keep],
                snps_dcas21_5841[,snps2keep])

Genomic Prediction

Get NRCRI TP data from Cassavabase. Use it with imputed data to predict GEBV/GETGV for all samples in the new reports (DCas21-5841).

  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.
    • Compare prediction accuracy with vs. without IITA’s training data to augment.
  4. Genomic prediction: Predict genomic BLUPs (GEBV and GETGV) for all selection candidates using all available data.
  5. Results: Plots, results and recommendations.

OUTPUT / FILES: everything is in the output/ sub-directory.

  • GEBVs for parent selection: GEBV_NRCRI_ModelA_2021May03.csv
  • GETGVs for variety advancement: GETGV_NRCRI_ModelADE_2021May03.csv
  • Tidy, long-form CSV of predictions, including PEVs: genomicPredictions_NRCRI_2021May03.csv

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/NRCRI_2021GS/ with all data.

Directory structure of this repository

NOTICE: data/ and output/ are empty on GitHub. Please see ftp://ftp.cassavabase.org/marnin_datasets/NRCRI_2021GS/ 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 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

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_1.0.6        whisker_0.4       knitr_1.32        magrittr_2.0.1   
 [5] R6_2.5.0          rlang_0.4.10      fansi_0.4.2       stringr_1.4.0    
 [9] tools_4.0.3       xfun_0.22         utf8_1.2.1        git2r_0.28.0     
[13] jquerylib_0.1.3   htmltools_0.5.1.1 ellipsis_0.3.1    rprojroot_2.0.2  
[17] yaml_2.2.1        digest_0.6.27     tibble_3.1.1      lifecycle_1.0.0  
[21] crayon_1.4.1      later_1.1.0.1     sass_0.3.1        vctrs_0.3.7      
[25] promises_1.2.0.1  fs_1.5.0          glue_1.4.2        evaluate_0.14    
[29] rmarkdown_2.7     stringi_1.5.3     bslib_0.2.4       compiler_4.0.3   
[33] pillar_1.6.0      jsonlite_1.7.2    httpuv_1.5.5      pkgconfig_2.0.3