Back

Structural variation at lhcb6 underlies genetic variation in photosystem II maximum quantum efficiency in maize

Urzinger, S.; Wuerstl, L.; Avramova, V.; Urbany, C.; Scheuermann, D.; Presterl, T.; Reuscher, S.; Mayer, M.; Brajkovic, S.; Ouzunova, M.; Ordas, B.; Westhoff, P.; Schoen, C. C.

2025-09-18 genetics
10.1101/2025.09.16.676272 bioRxiv
Show abstract

Targeted utilization of native genetic diversity can expand the genetic basis of traits that exhibit limited genetic variation in elite breeding material and can enhance our understanding of the genotype-phenotype relationships associated with complex traits in crops. In a genome-wide association study in a European maize landrace we identified quantitative trait loci (QTL) that affected the maximum quantum efficiency of photosystem II (Fv/Fm) in field experiments. In a forward genetic approach, we focused on a QTL on chromosome 10, explaining a large proportion of the genetic variance for Fv/Fm in growth stages V4 (35%) and V6 (47%), for genetic dissection and candidate gene discovery. Integrating molecular and physiological information we show that allelic variation at the gene encoding LIGHT HARVESTING CHLOROPHYLL A/B BINDING PROTEIN6 (LHCB6), a component of the photosystem II (PSII) light-harvesting complex (LHCII) antenna, underlies the variation in Fv/Fm. We demonstrate that the allelic variation results from a hAT transposon insertion at lhcb6 and is associated with differential accumulation of the LHCII antenna components LHCB6 and LHCB3, leading to differences in non-photochemical quenching (NPQ) and plant biomass accumulation. Based on proteomic analyses we propose candidate genes that compensate the unfavorable effects caused by impaired LHCII antenna assembly. Our work provides novel insights into the function of lhcb6 in the context of LHCII antenna assembly and demonstrates the value of natural variation in landraces for the understanding and genetic improvement of complex photosynthetic processes.

Matching journals

The top 4 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.