{"title":"Click Breeding: Toward programmable crop design.","authors":"Binbin Dai, Yifu Tian, Jingyi Chai, Fenyi Liu, Siheng Chen, Kexuan Tang","doi":"10.1016/j.tplants.2026.06.009","DOIUrl":null,"url":null,"abstract":"<p><p>Breeding for complex traits is constrained by limited predictive accuracy and transferability, particularly when nonadditive genetic effects and genotype-by-environment interactions dominate performance. In this review, we propose 'Click Breeding', a design-driven paradigm that shifts emphasis from ranking individual candidates to generating and stress-testing entire multigenerational breeding programs. In this framework, objectives and constraints are encoded as machine-readable plans; candidate strategies are evaluated via simulations that integrate genetics, physiology, and environment; and selected designs become traceable experimental workflows with governance checkpoints. Click Breeding connects genomic prediction, crop modeling, and laboratory automation into a coherent, auditable design cycle that complements the breeder's judgment. We discuss conceptual foundations, assess technology maturity, and identify biological, computational, and regulatory challenges to making programmable crop design operational.</p>","PeriodicalId":23264,"journal":{"name":"Trends in Plant Science","volume":" ","pages":""},"PeriodicalIF":21.2000,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Plant Science","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.tplants.2026.06.009","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Breeding for complex traits is constrained by limited predictive accuracy and transferability, particularly when nonadditive genetic effects and genotype-by-environment interactions dominate performance. In this review, we propose 'Click Breeding', a design-driven paradigm that shifts emphasis from ranking individual candidates to generating and stress-testing entire multigenerational breeding programs. In this framework, objectives and constraints are encoded as machine-readable plans; candidate strategies are evaluated via simulations that integrate genetics, physiology, and environment; and selected designs become traceable experimental workflows with governance checkpoints. Click Breeding connects genomic prediction, crop modeling, and laboratory automation into a coherent, auditable design cycle that complements the breeder's judgment. We discuss conceptual foundations, assess technology maturity, and identify biological, computational, and regulatory challenges to making programmable crop design operational.
期刊介绍:
Trends in Plant Science is the primary monthly review journal in plant science, encompassing a wide range from molecular biology to ecology. It offers concise and accessible reviews and opinions on fundamental plant science topics, providing quick insights into current thinking and developments in plant biology. Geared towards researchers, students, and teachers, the articles are authoritative, authored by both established leaders in the field and emerging talents.