New math model speeds up crop breeding by reading hidden genetic patterns
Researchers developed a faster way to select crops with multiple desired traits—like higher yield and drought tolerance—by using artificial intelligence to spot complex genetic interactions that traditional breeding methods miss. The technique could help farmers and seed companies bring improved varieties to market years sooner, which matters as climate change pressures global food security.
Originaltitel: Nonlinear genomic selection index accelerates multi-trait crop improvement
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.