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<p>Linear phenotypic and genomic selection indices assume additivity and linearity, limiting their ability to exploit nonlinear trait relationships. Here, we introduce the Quadratic Genomic Selection Index (QGSI), a genomic extension of the quadratic phenotypic selection index (QPSI) that integrates genomic estimated breeding values (GEBVs) within a unified quadratic framework. QGSI combines additive, squared, and cross-product terms of GEBVs, enabling phenotype-free, rapid-cycle multi-trait selection while capturing genome-wide nonlinear relationships. We evaluate QGSI using two genomic prediction strategies: (i) a maximum-likelihood additive genomic model, and (ii) a nonlinear multi-trait Gaussian kernel model that accommodates epistatic signals. Using 10 simulated maize selection cycles and two real maize and five wheat real datasets, QGSI achieves the highest selection response and the lowest prediction error variance relative to linear and quadratic phenotypic and genomic indices. Thus, combining nonlinear genomic prediction with quadratic selection indices provides a general strategy for accelerating multi-trait crop improvement.</p>

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Nonlinear genomic selection index accelerates multi-trait crop improvement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月20日

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  • J. Jesús Cerón-Rojas,
  • Osval A. Montesinos-López,
  • Abelardo Montesinos-López,
  • Paolo Vitale,
  • Paulino Pérez-Rodríguez,
  • Samuel B. Fernandes,
  • Rodomiro Ortiz,
  • José Crossa

摘要

Linear phenotypic and genomic selection indices assume additivity and linearity, limiting their ability to exploit nonlinear trait relationships. Here, we introduce the Quadratic Genomic Selection Index (QGSI), a genomic extension of the quadratic phenotypic selection index (QPSI) that integrates genomic estimated breeding values (GEBVs) within a unified quadratic framework. QGSI combines additive, squared, and cross-product terms of GEBVs, enabling phenotype-free, rapid-cycle multi-trait selection while capturing genome-wide nonlinear relationships. We evaluate QGSI using two genomic prediction strategies: (i) a maximum-likelihood additive genomic model, and (ii) a nonlinear multi-trait Gaussian kernel model that accommodates epistatic signals. Using 10 simulated maize selection cycles and two real maize and five wheat real datasets, QGSI achieves the highest selection response and the lowest prediction error variance relative to linear and quadratic phenotypic and genomic indices. Thus, combining nonlinear genomic prediction with quadratic selection indices provides a general strategy for accelerating multi-trait crop improvement.