Last updated: 2025-01-16
Checks: 6 1
Knit directory:
Genomic-Selection-for-Drought-Tolerance-Using-Genome-Wide-SNPs-in-Casava/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of
the R Markdown file created these results, you’ll want to first commit
it to the Git repo. If you’re still working on the analysis, you can
ignore this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the
HTML.
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.
The command set.seed(20221020) 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.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 7db14ab. 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:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data/allchrAR08.txt
Ignored: output/BLUPS_density_med_row_col.png
Untracked files:
Untracked: ETA_1_ScaleBayesA.dat
Untracked: analysis/GWS_BayesA.Rmd
Untracked: analysis/GWS_BayesB.Rmd
Untracked: analysis/GWS_G-BLUP.Rmd
Untracked: analysis/GWS_RF.Rmd
Untracked: analysis/GWS_RKHS.Rmd
Untracked: analysis/GWS_RR-BLUP.Rmd
Untracked: analysis/clone_selection.Rmd
Untracked: analysis/figure/GWS.Rmd/
Untracked: data/Artigo/
Untracked: data/geno.rds
Untracked: data/pheno.rds
Untracked: mu.dat
Untracked: output/GEBVS_DOM.RDS
Untracked: output/GEBVS_G_BLUP.RDS
Untracked: output/GEBVS_RR_BLUP.RDS
Untracked: output/G_matrix.rds
Untracked: output/results_cv_GEBVS_DOM.RDS
Untracked: output/results_cv_G_BLUP.RDS
Untracked: output/results_cv_RR_BLUP.RDS
Untracked: varE.dat
Unstaged changes:
Modified: Genomic-Selection-for-Drought-Tolerance-Using-Genome-Wide-SNPs-in-Casava.Rproj
Modified: analysis/GWS.Rmd
Modified: analysis/_site.yml
Modified: analysis/index.Rmd
Modified: analysis/mixed_models.Rmd
Modified: data/SNPs.rds
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 | 78cd01a | WevertonGomesCosta | 2025-01-13 | Update index.Rmd and index.html |
| html | 78cd01a | WevertonGomesCosta | 2025-01-13 | Update index.Rmd and index.html |
| html | 21def57 | WevertonGomesCosta | 2025-01-13 | update index and phenotype.html |
| Rmd | 77e21a6 | Weverton Gomes | 2025-01-13 | Update Index and phenotype |
| Rmd | 54c6e28 | Weverton Gomes | 2025-01-07 | update site, index.rmd, license.rmd, phenotype.rmd |
| html | 02143bc | Weverton Gomes | 2023-10-27 | add about, index and license html |
| Rmd | 286b492 | Weverton Gomes | 2023-10-27 | Update Scripts and README |
| html | 90dc112 | WevertonGomesCosta | 2022-11-17 | Update |
| html | d930880 | WevertonGomesCosta | 2022-11-11 | Update |
| Rmd | 5988c27 | WevertonGomesCosta | 2022-11-11 | Update |
| html | 5988c27 | WevertonGomesCosta | 2022-11-11 | Update |
| Rmd | bf7b1d3 | WevertonGomesCosta | 2022-11-11 | Update |
| html | bf7b1d3 | WevertonGomesCosta | 2022-11-11 | Update |
| Rmd | b78c842 | WevertonGomesCosta | 2022-10-20 | Start workflowr project. |
Welcome to the Genomic Selection for Drought Tolerance project website! This initiative by EMBRAPA Mandioca focuses on utilizing Genome Wide GBS and/or DART in Cassava to achieve drought tolerance through genomic selection.
This project aims to analyze the phenotypic data of Brazilian drought trials to evaluate the performance of various genotypes under drought conditions. The goal is to identify the genotypes that exhibit superior performance and resilience. The analysis follows a structured workflow, including data import and manipulation, exploratory data analysis, and genotype-environment analysis using mixed-effect models. This project details the estimation of Best Linear Unbiased Predictions (BLUPs) using mixed models, along with extensive Exploratory Data Analysis (EDA) and necessary manipulations to accurately estimate the BLUPs.
We used packages such as DataExplorer, metan, and data.table to perform extensive exploratory data analysis on our dataset. The goal was to understand the data structure, identify missing values, and detect patterns that might influence the subsequent analyses.
The GxE analysis aims to evaluate the performance of genotypes across different environments to identify those with stable performance and high resilience. We used mixed-effect models to estimate the variance components and calculate BLUPs for each genotype.
For GWS, we employed the original marker matrix, followed by necessary data manipulations to organize and prepare the matrix for model input. We used various models, including:
(Note: Some models might be computationally intensive.)
The clone selection analysis aims to identify the best genotypes for drought tolerance based on the GWS results. We used the BLUPs and genomic predictions to rank the genotypes and select the best-performing ones.
We will now obtain the joint results of all methods and compare them. Additionally, we will adopt a selection model to optimize the selection of cassava clones for EMBRAPA’s cassava breeding program.
Our aim is to select clones with high GEBVs and high GETGVs, identifying clones with strong intrinsic potential as well as those that can be crossbred to produce high-potential hybrid clones.
R version 4.3.3 (2024-02-29 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
[5] LC_TIME=Portuguese_Brazil.utf8
time zone: America/Sao_Paulo
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 cli_3.6.3 knitr_1.49 rlang_1.1.4
[5] xfun_0.50 stringi_1.8.4 promises_1.3.2 jsonlite_1.8.9
[9] workflowr_1.7.1 glue_1.8.0 rprojroot_2.0.4 git2r_0.35.0
[13] htmltools_0.5.8.1 httpuv_1.6.15 sass_0.4.9 rmarkdown_2.29
[17] tibble_3.2.1 evaluate_1.0.3 jquerylib_0.1.4 fastmap_1.2.0
[21] yaml_2.3.10 lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.1
[25] compiler_4.3.3 fs_1.6.5 pkgconfig_2.0.3 Rcpp_1.0.14
[29] rstudioapi_0.17.1 later_1.4.1 digest_0.6.37 R6_2.5.1
[33] pillar_1.10.1 magrittr_2.0.3 bslib_0.8.0 tools_4.3.3
[37] cachem_1.1.0 Pós-Doutorando, Embrapa Mandioca e Fruticultura, weverton.costa@ufv.br↩︎