Genetic architecture of photosynthesis energy partitioning as revealed by a genome-wide association approach.

The photosynthesis process is determined by the intensity level and spectral quality of the light; therefore, leaves need to adapt to a changing environment. The incident energy absorbed can exceed the sink capability of the photosystems, and, in this context, photoinhibition may occur in both photosystem II (PSII) and photosystem I (PSI). Quantum yield parameters analyses reveal how the energy is managed. These parameters are genotype-dependent, and this genotypic variability is a good opportunity to apply mapping association strategies to identify genomic regions associated with photosynthesis energy partitioning. An experimental and mathematical approach is proposed for the determination of an index which estimates the energy per photon flux for each spectral bandwidth (Δλ) of the light incident (QI index). Based on the QI, the spectral quality of the plant growth, environmental lighting, and the actinic light of PAM were quantitatively very similar which allowed an accurate phenotyping strategy of a rice population. A total of 143 genomic single regions associated with at least one trait of chlorophyll fluorescence were identified. Moreover, chromosome 5 gathers most of these regions indicating the importance of this chromosome in the genetic regulation of the photochemistry process. Through a GWAS strategy, 32 genes of rice genome associated with the main parameters of the photochemistry process of photosynthesis in rice were identified. Association between light-harvesting complexes and the potential quantum yield of PSII, as well as the relationship between coding regions for PSI-linked proteins in energy distribution during the photochemical process of photosynthesis is analyzed.

Light-use efficiency and energy partitioning in rice is cultivar dependent.

One of the main limitations of rice yield in regions of high productive performance is the light-use efficiency (LUE). LUE can be determined at the whole-plant level or at the photosynthetic apparatus level (quantum yield). Both vary according to the intensity and spectral quality of light. The aim of this study was to analyze the cultivar dependence regarding LUE at the plant level and quantum yield using four rice cultivars and four light environments. To achieve this, two in-house Light Systems were developed: Light System I which generates white light environments (spectral quality of 400-700 nm band) and Light System II which generates a blue-red light environment (spectral quality of 400-500 nm and 600-700 nm bands). Light environment conditioned the LUE and quantum yield in PSII of all evaluated cultivars. In white environments, LUE decreased when light intensity duplicated, while in blue-red environments no differences on LUE were observed. Energy partition in PSII was determined by the quantum yield of three de-excitation processes using chlorophyll fluorescence parameters. For this purpose, a quenching analysis followed by a relaxation analysis was performed. The damage of PSII was only increased by low levels of energy in white environments, leading to a decrease in photochemical processes due to the closure of the reaction centers. In conclusion, all rice cultivars evaluated in this study were sensible to low levels of radiation, but the response was cultivar dependent. There was not a clear genotypic relation between LUE and quantum yield.

Genome-Wide Association Study Using Historical Breeding Populations Discovers Genomic Regions Involved in High-Quality Rice.

Rice ( L.) is one of the most important staple food crops in the world; however, there has recently been a shift in consumer demand for higher grain quality. Therefore, understanding the genetic architecture of grain quality has become a key objective of rice breeding programs. Genome-wide association studies (GWAS) using large diversity panels have successfully identified genomic regions associated with complex traits in diverse crop species. Our main objective was to identify genomic regions associated with grain quality and to identify and characterize favorable haplotypes for selection. We used two locally adapted rice breeding populations and historical phenotypic data for three rice quality traits: yield after milling, percentage of head rice recovery, and percentage of chalky grain. We detected 22 putative quantitative trait loci (QTL) in the same genomic regions as starch synthesis, starch metabolism, and cell wall synthesis-related genes are found. Additionally, we found a genomic region on chromosome 6 in the population that was associated with all quality traits and we identified favorable haplotypes. Furthermore, this region is linked to the gene that codes for a starch branching enzyme I, which is implicated in starch granule formation. In , we also found two putative QTL linked to , , and . Our study provides an insight into the genetic basis of rice grain chalkiness, yield after milling, and head rice, identifying favorable haplotypes and molecular markers for selection in breeding programs.