Cryptic variation alters gene dosage sensitivity to shape inflorescence architecture in tomato
Phenotypic diversity arises in large part from genetic variants at multiple interacting loci, many of which alter gene dosage rather than abolish gene function. Dosage-sensitive variants, which often produce nonlinear phenotypic outcomes, can be exploited to fine-tune quantitative traits for crop improvement using genome editing. However, the phenotypic outcomes of individual variants can differ substantially across genetic backgrounds, as segregating alleles may modulate allelic effects in unexpected ways. Yet, how genetic background shapes gene dosage effects remains underexplored. Here, we show that MADS-box gene dosage effects, which can be used to tune tomato inflorescence architecture for optimal fruit yield, differ profoundly between distinct genetic backgrounds. We mapped the genetic basis of this background dependency and identified the cryptic modifier locus suppressor of branching 2 (sb2), which contains the conserved floral identity gene ANANTHA. We show that natural variation at sb2 modulates how inflorescence architecture responds to MADS-box dosage effects from natural and engineered loss-of-function mutations. Our findings illustrate how cryptic genetic variants can reshape gene dosage relationships and underscore the importance of characterizing such hidden variation for predictive engineering of quantitative traits using genome editing. Significance StatementAdvances in crop genome editing enable precise modifications of gene dosage to fine-tune quantitative traits in crop improvement, but the predictability of such strategies remains limited. We show that hidden genetic differences, known as cryptic variation, can alter how gene dosage changes influence plant growth and development. Using tomato inflorescence architecture as a model, we characterize a natural cryptic modifier locus, suppressor of branching 2 (sb2), that modifies the effects of natural and engineered mutations in dosage-sensitive MADS-box genes. Our findings demonstrate that gene dosage effects depend on genetic background and highlight an often-unrecognized constraint on precision breeding by genome editing. Accounting for similar cases of cryptic variation will be essential for predictable engineering of quantitative traits in crops.