Dissecting the Genetic Architecture of Carbon Partitioning in Sorghum Using Multiscale Phenotypes

Abstract

Carbon partitioning in plants may be viewed as a dynamic process composed of the many interactions between sources and sinks. The accumulation and distribution of fixed carbon is not dictated simply by the sink strength and number but is dependent upon the source, pathways, and interactions of the system. As such, the study of carbon partitioning through perturbations to the system or through focus on individual traits may fail to produce actionable developments or a comprehensive understanding of the mechanisms underlying this complex process. Using the recently published sorghum carbon-partitioning panel, we collected both macroscale phenotypic characteristics such as plant height, above-ground biomass, and dry weight along with microscale compositional traits to deconvolute the carbon-partitioning pathways in this multipurpose crop. Multivariate analyses of traits resulted in the identification of numerous loci associated with several distinct carbon-partitioning traits, which putatively regulate sugar content, manganese homeostasis, and nitrate transportation. Using a multivariate adaptive shrinkage approach, we identified several loci associated with multiple traits suggesting that pleiotropic and/or interactive effects may positively influence multiple carbon-partitioning traits, or these overlaps may represent molecular switches mediating basal carbon allocating or partitioning networks. Conversely, we also identify a carbon tradeoff where reduced lignin content is associated with increased sugar content. The results presented here support previous studies demonstrating the convoluted nature of carbon partitioning in sorghum and emphasize the importance of taking a holistic approach to the study of carbon partitioning by utilizing multiscale phenotypes.

Registration of the sorghum carbon‐partitioning nested association mapping (CP‐NAM) population

“… a Value in parenthesis represents the old identifier for the recombinant inbred line of the CP_NAM that is associated with genomic and phenotypic resources published by Boatwright et. al., 2022 as well as phenotypic data in Supplemental File 1.…”

“…Several genetic associations were identified for various traits including stand count, days to harvest, plant height, dry and wet weights in GWAS, and linkage mapping of the CP_NAM RILs (Boatwright et al., 2022). Many known plant height QTL ( Dw2 [chr6] , Dw3 [chr7], and Dw1 [chr9]) (reviewed in Boyles et al., 2019) were identified along with putatively novel loci on chr1 and chr8.…”

“…Genomic and phenotypic resources along with the scripts are available on GitHub (https://github.com/jlboat/CP-NAM_2021) and as supplemental material as described by Boatwright et al. (2022).…”

“…The majority of the RIL families showed high mean performance and transgressive segregation for plant height and biomass‐related traits, which portrays the potential of this population for identification of novel allelic combinations controlling the related traits (Tables 2 and 4). High repeatability of many agronomic as well as some of the major biomass component traits suggest that phenotypic selection can effectively be used to improve the trait of interest using selected RIL families of this population (Boatwright et al., 2022). Further experiments involving a wide range of environments can be designed to study the genotype‐by‐environment effects of the complex traits related to carbon partitioning, bioenergy, biomass, and related traits using a whole set of a population.…”

“…The details of the genomic regions associated with the various compositional traits that included ADF, adjusted crude protein, NDF, ash‐free NDF, ash, calcium, chloride, dietary cation–anion difference, dry matter, potassium, lignin, magnesium, moisture, net energy growth, net energy lactation, relative feed value, nonfiber carbohydrates, and WSC are described in Boatwright et al. (2022).…”