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Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions

Name: Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
Keywords: Genomic Prediction, Genotype-by-environment interactions, Envirotyping, LightGBM, Feature Engineering

Research datakeyboard_double_arrow_right Dataset 09 Jul 2024 English Publisher:Zenodo

Authors: Kuivjogi Fernandes, Igor; Canella Vieira, Caio; Olimpio das Graças Dias, Kaio; Bonfim Fernandes, Samuel;

doi: 10.5281/zenodo.12702650 , 10.5281/zenodo.12702649

Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions

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Abstract

Files generated from the study described in Fernandes et. al (2024) . The file "cvs_h2s.csv" comprises the coefficient of variation and the Cullis heritability for each environment. The file "all_predictions.csv" contains the predictions from all the models evaluated, in different cross-validation (CV) scenarios. The file "coincidence_index.csv" has the Coincidence Index (CI) for each CV and models evaluated in our study. Our study used the multi-environment maize yield trials data from the Genomes to Fields 2022 initiative (Lima et. al 2024).

Related Organizations

University of Arkansas at Fayetteville
United States
Universidade Federal de Viçosa
Brazil

Keywords

Genomic Prediction, Genotype-by-environment interactions, Envirotyping, LightGBM, Feature Engineering

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