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Updated: Feb 22, 2026

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

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Nonlinear genomic selection index accelerates multi-trait crop improvement.

J Jesús Cerón-Rojas¹, Osval A Montesinos-López², Abelardo Montesinos-López³

¹Colegio de Postgraduados, Montecillos, Edo. de México, México.

Nature Communications

|February 20, 2026

Summary

This summary is machine-generated.

Quadratic Genomic Selection Index (QGSI) captures nonlinear relationships for faster crop improvement. This genomic approach improves selection response and prediction accuracy in multi-trait breeding programs.

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Area of Science:

Plant breeding
Quantitative genetics
Genomics

Background:

Linear selection indices limit exploiting complex, nonlinear trait relationships.
Genomic selection has advanced breeding but often relies on additive models.

Purpose of the Study:

Introduce the Quadratic Genomic Selection Index (QGSI) to integrate nonlinear genomic information.
Enable phenotype-free, rapid-cycle, multi-trait selection using genomic estimated breeding values (GEBVs).

Main Methods:

Developed QGSI by extending the quadratic phenotypic selection index (QPSI) to a genomic framework.
Integrated additive, squared, and cross-product terms of GEBVs.
Evaluated QGSI using maximum-likelihood additive and nonlinear Gaussian kernel genomic prediction models.

Main Results:

QGSI demonstrated superior performance across simulated and real datasets (maize and wheat).
Achieved higher selection response and lower prediction error variance compared to linear and quadratic indices.
Effectively captured genome-wide nonlinear relationships and epistatic signals.

Conclusions:

Combining nonlinear genomic prediction with quadratic selection indices accelerates multi-trait crop improvement.
QGSI offers a general strategy for enhancing breeding efficiency by leveraging complex genetic architectures.