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Translating functional molecular knowledge into crop-breeding success

Nature Reviews Genetics (2026) Cite this article

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Abstract

Historical plant breeding, which optimizes phenotypes through selective crossing guided by phenotypic evaluation and molecular markers, is limited by evolutionary constraints that hinder rapid crop improvement. A new paradigm, precision breeding, circumvents these limitations by targeting genetic variants through functional molecular knowledge. To generate this knowledge at scale, sequence-based deep learning leverages high-quality genome sequence data to predict variant effects at base-pair resolution. When linked to agronomically important traits, these predictions enable breeders to prioritize variants for precision selection or editing. Although it is still in the early stages of development, we foresee three key applications for this approach: introgressing genes from distant breeding pools, purging deleterious mutations and designing new plant ideotypes. Looking ahead, refined computational models will facilitate targeted editing and the systematic redesign of complex physiological processes to address emerging breeding goals under shifting environmental conditions.

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Fig. 1: Precision breeding targets functional variants for selection and editing.
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Fig. 2: Variant discovery in precision breeding.
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Fig. 3: A practical strategy for integrating field trials with precision breeding.
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Authors and Affiliations

  1. Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark

    Guillaume P. Ramstein

  2. Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA

    Jingjing Zhai & Edward S. Buckler

  3. Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA

    Edward S. Buckler & Jean-Luc Jannink

  4. Agricultural Research Service, US Department of Agriculture, Ithaca, NY, USA

    Edward S. Buckler & Jean-Luc Jannink

  5. Génétique Quantitative et Evolution — Le Moulon, Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Gif-sur-Yvette, France

    Maud I. Tenaillon

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  1. Guillaume P. Ramstein
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  2. Jingjing Zhai
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  4. Maud I. Tenaillon
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Supplementary information

Glossary

Biological language models

Self-supervised models trained to predict the likelihood of elements in DNA or protein sequences.

Breeding by design

Creation of new varieties with ideal genetic characteristics defined according to biological (for example, pathways) or computational models (such as crop growth models).

Crop growth models

(CGMs). Computational models consisting of multiple equations that represent physiological processes in plants and dynamically simulate crop growth in response to environmental inputs.

Epistasis

The non-additive interaction between distinct genomic loci — the genotype-by-genotype (G × G) interaction — in which variation at one locus modifies the effect of another locus.

Genetic load

Reduction of fitness owing to the accumulation of deleterious mutations via multiple evolutionary factors (genetic drift, inbreeding, linked selection, recombination, migration).

Genome-wide association study

(GWAS). Study design that applies marker–trait association models at the scale of the entire genome to identify genetic variants associated with specific traits within a population.

Genomic prediction

Statistical modelling approach for estimating breeding values of individuals within a breeding population by utilizing quantitative trait loci or variant effect estimates spread across the entire genome.

Genotype-by-environment

(G × E). Interaction where the effect of a genomic locus depends on environmental conditions.

Introgression

Process by which genomic regions from one species or population are stably integrated into the gene pool of another (through repeated backcrossing or genetic modification).

Knowledge distillation

Machine learning technique in which a smaller ‘student’ model is trained to replicate the predictions of a larger, more complex ’teacher’ model, transferring knowledge while reducing model size and computational cost.

Linkage disequilibrium

Nonrandom association of alleles at different loci, arising because of physical linkage and/or evolutionary factors (mutation, genetic drift, natural selection, population structure).

Pleiotropy

Situation in which variation at a genomic locus influences multiple traits, reflecting multiple effects of a single causal variant (biological pleiotropy) or physical linkage between different causal variants (statistical pleiotropy).

QTL mapping

Statistical association between variation at a genomic quantitative trait locus (QTL) and a molecular, cellular or organismal phenotype, given a particular genomic background and environmental context.

Transcriptome-wide association study

(TWAS). Method used to identify associations between gene expression levels and a specific trait.

Variant effect

Biological effect that a specific genetic variant (alteration in the DNA sequence) has on phenotypes, given a particular genomic background and environmental context.

Zero-shot prediction

Inference on a new task that a model was not explicitly trained on. In the context of biological language models, this generally involves estimating the likelihood of alleles (for example, nucleotides or amino acids) in sequences that were never observed.

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Ramstein, G.P., Zhai, J., Buckler, E.S. et al. Translating functional molecular knowledge into crop-breeding success. Nat Rev Genet (2026). https://doi.org/10.1038/s41576-026-00968-w

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