Back

Genetic architecture and functional consequences of lateral root length in maize (Zea mays L.)

Guffanti, F.; Scheuermann, D.; Urbany, C.; Reuscher, S.; Presterl, T.; Ouzunova, M.; Salvi, S.; Callot, C.; Schoen, C.-C.

2025-09-05 genetics
10.1101/2025.09.02.673713 bioRxiv
Show abstract

Understanding the genetic basis of root architecture and its relevance for crop productivity can contribute to the sustainable intensification of agriculture. Leveraging the phenotypic and allelic diversity of an Austrian maize landrace, we dissected the genetic basis of lateral root (LR) length across developmental stages. LR length, a relevant trait for breeding resource-efficient varieties, showed high heritability in our experiments. We discovered eight quantitative trait loci (QTL) for LR length at the reproductive stage R2, overlapping with four QTL at stage R6 but not with QTL detected at vegetative stage V6, suggesting that the genetic regulation of LR length might differ in vegetative and reproductive stages. We fine-mapped qlr1, the most significant QTL for LR length, to a region of 2.3 Mb containing 46 annotated genes. Based on whole-genome sequence and comparative genomics analyses we suggest a candidate gene underlying qlr1. Additionally, we examined the impact of nitrogen, phosphorus, and irrigation treatments on root and shoot development, finding that LR length positively correlates with biomass accumulation under optimal nutrient supply but not under nitrogen stress. Our work provides insights into the genetic regulation of LR length in maize and its relevance for the adaptation to specific growing environments. HighlightMultiple QTL affect lateral root length in maize at different developmental stages. The correlation between lateral root length and biomass accumulation varies under diverse field conditions.

Published in Journal of Experimental Botany (predicted rank #1) · training set

Matching journals

The top 5 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.