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Knit directory: Genomic-Selection-for-Drought-Tolerance-Using-Genome-Wide-SNPs-in-Casava/

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Rmd b78c842 WevertonGomesCosta 2022-10-20 Start workflowr project.

Genomic Selection for Drought Tolerance

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.

About the Project

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.

Project Structure

1. Exploratory Data Analysis (EDA)

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.

2. Genotype x Environment Interaction Analysis (GxE)

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.

3. Genomic Wide Selection (GWS))

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.)

Study Highlights


sessionInfo()
R version 4.3.1 (2023-06-16 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):
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[37] bslib_0.5.1       tools_4.3.1       cachem_1.0.8

  1. Pós-Doutorando, Embrapa Mandioca e Fruticultura, ↩︎