Back

Haplotype-rich cis-regulation underlies transcriptomic diversity across the breeding history of maize (Zea mays)

Grzybowski, M. W.; Schnable, J. C.

2026-02-20 plant biology
10.64898/2026.02.19.706772 bioRxiv
Show abstract

O_LIUnderstanding how regulatory variation evolves under selection is central to linking genetic diversity with phenotypic evolution,yet the determinants of transcriptomic diversity in crops remain unclear. Maize, with its strong population structure shaped by modern breeding, provides a powerful system to investigate regulatory variation. C_LIO_LIWe analyzed RNA-seq data from a large maize diversity panel and integrated population-level expression analyses with high-resolution cis-eQTN fine-mapping. Although nucleotide diversity was reduced by nearly half in some heterotic groups, transcriptomic diversity declined by only 10-20%. C_LIO_LIFine-mapping revealed that gene expression variation is predominantly controlled by multiple small-effect cis-regulatory variants with the majority of genes where cis-eQTN were identified exhibiting three or more functionally distinct haplotypes. Expression divergence between heterotic groups scaled with allele frequency differentiation at standing regulatory variants and intensified during recent breeding, consistent with reweighting of pre-existing regulatory variation. C_LIO_LIGenes under stronger evolutionary constraint harboured regulatory variants with smaller effects, suggesting purifying selection acting on the magnitude of regulatory perturbations. Together, these results show that transcriptomic diversity in maize is less sensitive to population bottlenecks than nucleotide diversity, yet remains shaped by polygenic regulatory architectures that are constrained by selection. C_LI

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.