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bioRxiv · 10.1101/2025.11.08.687332

Highly polygenic control of photosynthetic responses to nighttime temperature in Arabidopsis studied by genomic prediction

dos Santos Sa, A. C.·Bazarova, A.·Fischbach, A.·Kesselheim, S.·Stich, B.·Matsubara, S.
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Abstract

O_LIRising nighttime temperature (Tnight) can reduce crop yields, while low Tnight may restrict plant growth and development. Despite these quantifiable effects of Tnight, the genetic basis underlying plant responses to Tnight remains unclear. We investigated natural variation in long-term response of effective photosynthetic efficiency (Fq/Fm) to Tnight among Arabidopsis accessions. C_LIO_LIGenome-wide association study (GWAS) was conducted for Fq/Fm of the accessions grown under 15{degrees}C or 20{degrees}C Tnight. The associated single nucleotide polymorphisms (SNPs) were identified and incorporated in genomic prediction (GP) models to assess the improvement of prediction accuracy. The predictions were experimentally validated in an independent, genetically diverse population. C_LIO_LIGWAS revealed highly polygenic architecture of Fq/Fm, with associated SNPs varying across Tnight conditions and measurement days. Notably, 15{degrees}C Tnight stabilized the contributions of a subset of associated SNPs, whereas 20{degrees}C Tnight enhanced day-to-day variations in SNP-trait associations. The GWAS-derived SNPs significantly improved the prediction accuracy of GP models, indicating their collective influence. The validation experiment confirmed the identification of low-Fq/Fm accessions in 15{degrees}C Tnight. C_LIO_LIThe results uncover the genetic underpinnings of long-term Fq/Fm response to cool vs warm nights and establish a framework for leveraging GWAS and GP to explore complex traits, such as photosynthesis, toward breeding climate-resilient crops. C_LI

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dos Santos Sa, A. C., Bazarova, A., Fischbach, A., Kesselheim, S., Stich, B., Matsubara, S.. 2025-11-10. Highly polygenic control of photosynthetic responses to nighttime temperature in Arabidopsis studied by genomic prediction. https://doi.org/10.1101/2025.11.08.687332

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