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Preparation and Curation of Multiyear, Multilocation, Multitrait Datasets.

Amina Abed¹, Zakaria Kehel²

¹Consortium de recherche sur la pomme de terre du Québec (CRPTQ), Québec, Canada. aminaabed@yahoo.fr.

Methods in Molecular Biology (Clifton, N.J.)

|May 31, 2022

Summary

This summary is machine-generated.

Accurate phenotypic data is crucial for genome-wide association studies (GWAS). This study details essential steps for exploring, curating, and analyzing multienvironment trial (MET) data using linear mixed models (LMMs) to improve genotype-phenotype insights.

Keywords:

Adjusted phenotype per trial Analysis of residuals Combined phenotype across trials Descriptive statistics Design diagnostics Experimental design Genotype × environment Genotype–phenotype association Linear mixed model Multienvironment trials Outliers Raw phenotype per trial

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Area of Science:

Genetics and Bioinformatics
Agricultural Science

Background:

Genome-wide association studies (GWAS) are vital for understanding genotype-phenotype relationships.
The accuracy of phenotypic data significantly impacts the power and reliability of GWAS.
Multienvironment trials (METs) are essential for obtaining robust phenotypic values.

Purpose of the Study:

To outline critical data exploration and analysis steps for MET data.
To demonstrate best practices for curating and adjusting phenotypic data within trials.
To explain the application of linear mixed models (LMMs) for combining MET data.

Main Methods:

Exploration and understanding of raw phenotypic data from METs.
Data curation and adjustment to minimize technical errors and bias.
Application of linear mixed models (LMMs) for robust data integration.

Main Results:

Identification of key steps for reliable phenotypic data analysis in METs.
Demonstration of how LMMs effectively mitigate misestimation of phenotypic values.
Illustrative examples using two distinct datasets for different analytical scenarios.

Conclusions:

Rigorous data handling and appropriate statistical modeling are paramount for successful GWAS.
Proper analysis of MET data enhances the precision of genotype-phenotype association discoveries.
The presented methodology provides a framework for optimizing phenotypic data utilization in genetic studies.