|FOX, CAROLYN - University Of Illinois|
|SANZ-SAEZ, ALVARO - Auburn University|
|SERBIN, SHAWN - Brookhaven National Laboratory|
|KUMAGAI, ETSUSHI - Iwate University|
|KRAUSE, MATHEUS - Iowa State University|
|XAVIER, ALENCAR - Corteva Agriscience|
|SPECHT, JAMES - University Of Nebraska|
|BEAVIS, WILLIAM - Iowa State University|
|DIERS, BRIAN - University Of Illinois|
|Ainsworth, Elizabeth - Lisa|
Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/3/2022
Publication Date: 5/8/2022
Citation: Montes, C.M., Fox, C., Sanz-Saez, A., Serbin, S.P., Kumagai, E., Krause, M.D., Xavier, A., Specht, J.E., Beavis, W.D., Bernacchi, C.J., Diers, B.W., Ainsworth, E.A. 2022. High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population. Genetics. 221(2). Article iyac065. https://doi.org/10.1093/genetics/iyac065.
Interpretive Summary: Photosynthesis is a key target for improving crop production, but measuring photosynthetic capacity is time-consuming. In this study, we developed models to predict photosynthetic capacity from leaf reflectance in soybean. We then applied the models to a large multiparental population of soybean and genetically mapped markers associated with photosynthesis. We found that photosynthetic capacity mapped to a region of chromosome 19 containing multiple copies of the genes encoding the Rubisco small subunit. We also found that photosynthetic capacity was not correlated with seed yield in the population. However, leaf carbon and nitrogen content and leaf specific leaf area were strongly correlated with yield, and could be used by breeders in selection. This study is among the first to use leaf reflectance spectrometry to map the genetic architecture of photosynthesis.
Technical Abstract: High-throughput methods for rapid phenotyping of core functional traits are necessary to improve crop performance to meet the growing demands placed on agriculture. Photosynthesis is a key target to improve crop production in many species including soybean (Glycine max). Phenotyping leaf structural, biochemical, and photosynthetic traits has historically been slow and destructive. In this work, full-range (500 – 2400 nm) reflectance spectroscopy measurements were made at the leaf-level and used to rapidly estimate leaf traits, including two of the most widely reported rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate and maximum electron transport. Eleven models were produced from a diverse population of soybean over multiple field seasons which accurately predicted photosynthetic parameters, chlorophyll content, leaf carbon and nitrogen content, and specific leaf area (R2 range from 0.56 to 0.96). The Soybean Nested Association Mapping population showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco and maximum electron transport mapped to a region of chromosome 10 containing a fructose-1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1, 5 bisphosphate and the sucrose biosynthetic pathway. The rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in soybean at this time. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis. Because of the diversity of soybean material and the large number of samples used in the development of the high-throughput phenotyping models, they should be applicable to other soybean populations and provide a useful non-destructive tool for rapid surveys of important physiological traits.