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Bem-vindo! Este repositório reúne código, dados e relatórios reprodutíveis associados às análises do Projeto NextGen Cassava (Ano 6 – 2022/2023), conduzidas no programa de melhoramento de mandioca da Embrapa.
O objetivo é avaliar o progresso genético e a integração de características de rendimento e qualidade em populações GS (C0, C1, C2), utilizando modelos mistos, BLUPs, índices de seleção e seleção genômica.
flowchart TD
A[Input Data<br/>Phenotypes, metadata, fieldbooks] --> B[Data Cleaning<br/>Population structure (C0, C1, C2)]
B --> C[BLUPs & Heritability<br/>Mixed models with metan]
C --> D[Selection Indices<br/>Yield + Quality traits]
D --> E[Integration<br/>Drought tolerance, NIRS, GWAS]
E --> F[Results & Visualizations<br/>Genetic gain, trait distributions, clone ranking]Clone o repositório:
git clone https://github.com/WevertonGomesCosta/analisys-next-gen-2022.gitInstale as dependências em R:
install.packages(c("tidyverse", "metan", "ggplot2", "ggpubr", "data.table", "genomicMateSelectR"))Rode os arquivos .Rmd em analysis/ para
reproduzir as análises.
Contribuições são bem-vindas via:
- Issues para discussão de melhorias
- Pull requests para correções
- Sugestões de extensões metodológicas
Este trabalho está licenciado sob CC BY-NC-SA
4.0.
Para uso comercial ou modificações significativas, contate os
autores.
Welcome! This repository contains code, data, and reproducible reports associated with the analyses of the NextGen Cassava Project (Year 6 – 2022/2023), conducted within the cassava breeding program at Embrapa.
The goal is to evaluate genetic progress and integration of yield and quality traits in GS populations (C0, C1, C2), using mixed models, BLUPs, selection indices, and genomic selection.
flowchart TD
A[Input Data<br/>Phenotypes, metadata, fieldbooks] --> B[Data Cleaning<br/>Population structure (C0, C1, C2)]
B --> C[BLUPs & Heritability<br/>Mixed models with metan]
C --> D[Selection Indices<br/>Yield + Quality traits]
D --> E[Integration<br/>Drought tolerance, NIRS, GWAS]
E --> F[Results & Visualizations<br/>Genetic gain, trait distributions, clone ranking]Clone the repository:
git clone https://github.com/WevertonGomesCosta/analisys-next-gen-2022.gitInstall dependencies in R:
install.packages(c("tidyverse", "metan", "ggplot2", "ggpubr", "data.table", "genomicMateSelectR"))Run the .Rmd files in analysis/ to
reproduce the analyses.
Contributions are welcome via:
- Opening issues for improvement discussions
- Submitting pull requests for fixes
- Suggesting methodological extensions
This work is licensed under CC BY-NC-SA
4.0.
For commercial use or significant modifications, please contact the
authors.
sessionInfo()R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
LAPACK version 3.12.1
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_3.6.5 knitr_1.50 rlang_1.1.6
[5] xfun_0.53 stringi_1.8.7 promises_1.3.3 jsonlite_2.0.0
[9] workflowr_1.7.2 glue_1.8.0 rprojroot_2.1.1 git2r_0.36.2
[13] htmltools_0.5.8.1 httpuv_1.6.16 sass_0.4.10 rmarkdown_2.29
[17] evaluate_1.0.5 jquerylib_0.1.4 tibble_3.3.0 fastmap_1.2.0
[21] yaml_2.3.10 lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.2
[25] compiler_4.5.1 fs_1.6.6 Rcpp_1.1.0 pkgconfig_2.0.3
[29] rstudioapi_0.17.1 later_1.4.4 digest_0.6.37 R6_2.6.1
[33] pillar_1.11.1 magrittr_2.0.4 bslib_0.9.0 tools_4.5.1
[37] cachem_1.1.0 Weverton Gomes da Costa, Pós-Doutorando, Embrapa Mandioca e Fruticultura, wevertonufv@gmail.com↩︎