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EasyGeSe - a resource for benchmarking genomic prediction methods.

Carles Quesada-Traver, Daniel Ariza-Suarez¹, Bruno Studer²

¹Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitaetstrasse 2, Zurich, 8092, Switzerland.

|October 24, 2025

Summary

This summary is machine-generated.

EasyGeSe provides diverse datasets for benchmarking genomic prediction models across species. Non-parametric machine learning methods show improved accuracy and computational efficiency compared to traditional approaches.

Keywords:

Benchmarking Database Genomic prediction Genomic selection Machine learning Quantitative genetics

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Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

Genomic prediction uses genotypic data to forecast phenotypes.
Machine learning advances offer new algorithms for genomic prediction.
Systematic benchmarking of genomic prediction methods is limited.

Purpose of the Study:

Introduce EasyGeSe, a resource for testing genomic prediction methods.
Provide curated datasets from diverse species for standardized evaluation.
Facilitate reproducible comparisons of various modeling strategies.

Main Methods:

Compiled and formatted genomic datasets from multiple species (barley, maize, rice, etc.).
Developed R and Python functions for easy data loading.
Benchmarked parametric, semi-parametric, and non-parametric models.

Main Results:

Predictive performance varied significantly across species and traits (mean r = 0.62).
Non-parametric models (Random Forest, LightGBM, XGBoost) showed modest accuracy gains.
Non-parametric methods offered significant computational advantages (faster fitting, lower RAM usage).

Conclusions:

EasyGeSe standardizes data and evaluation for fair, reproducible benchmarking.
The resource promotes broader access to genomic prediction data.
Encourages interdisciplinary researchers to explore novel modeling strategies.