Development and Genetic Control of Plant Architecture and Biomass in the Panicoid Grass, Setaria

Abstract

The architecture of a plant affects its ability to compete for light and to respond to environmental stresses, thus affecting overall fitness and productivity. Two components of architecture, branching and height, were studied in 182 F7 recombinant inbred lines (RILs) at the vegetative, flowering and mature developmental stages in the panicoid C4 model grass system, Setaria. The RIL population was derived from a cross between domesticated S. italica (foxtail millet) and its wild relative S. viridis (green foxtail). In both field and greenhouse trials the wild parent was taller initially, started branching earlier, and flowered earlier, while the domesticated parent was shorter initially, but flowered later, producing a robust tall plant architecture with more nodes and leaves on the main culm and few or no branches. Biomass was highly correlated with height of the plant and number of nodes on the main culm, and generally showed a negative relationship with branch number. However, several of the RILs with the highest biomass in both trials were significantly more branched than the domesticated parent of the cross. Quantitative trait loci (QTL) analyses indicate that both height and branching are controlled by multiple genetic regions, often with QTL for both traits colocalizing in the same genomic regions. Genomic positions of several QTL colocalize with QTL in syntenic regions in other species and contain genes known to control branching and height in sorghum, maize, and switchgrass. Included in these is the ortholog of the rice SD-1 semi-dwarfing gene, which underlies one of the major Setaria height QTL. Understanding the relationships between height and branching patterns in Setaria, and their genetic control, is an important step to gaining a comprehensive knowledge of the development and genetic regulation of panicoid grass architecture.

Time dependent genetic analysis links field and controlled environment phenotypes in the model C4 grass Setaria

“…viridis orthologs of maize Kn1 (15 kB from peak LOD position) and maize D8 (836 kB from peak LOD position) are located within the 1.5 LOD confidence interval of this QTL [23,24]. These candidate genes were also identified in another study using the same interspecific RIL population [16]. None of the S .…”

The miR156‐SPL4 module predominantly regulates aerial axillary bud formation and controls shoot architecture

“…In certain less domesticated species, such as Brachypodium and a number of panicoid grasses (Doust, ), aerial buds continue to develop and contribute to the formation of plant architecture. Very limited information is available on the genetic regulation of aerial bud formation in monocots, although nine QTLs for aerial branching have been identified in millet (Mauro‐Herrera & Doust, ). The study also showed that aerial branching QTLs often overlap with tillering QTLs, although some aerial branching QTL regions are independent (Mauro‐Herrera & Doust, ).…”

Setaria viridis as a Model System to Advance Millet Genetics and Genomics

“…However, the lack of high density marker maps is a major limiting factor for the resolution of these applications. Although many quantitative trait loci (QTLs) have been identified for various agronomic traits such as plant height, flowering time, lodging, and drought tolerance (Mauro-Herrera et al, 2013; Parvathaneni et al, 2013; Sato et al, 2013; Babu et al, 2014; Qie et al, 2014; Mauro-Herrera and Doust, 2016; Rajput et al, 2016), the QTL intervals are often large (>1 Mb) and difficult to fine map. A partial solution is to generate high density linkage maps using technologies like genotyping by sequencing (Moumouni et al, 2015; Fang et al, 2016; Rajput et al, 2016), but the ultimate solution is to build high-quality reference genomes.…”

“…In addition, S. viridis allelic variation can be directly introgressed into foxtail millet through interspecific crosses. Such crosses result in dense molecular markers and additional phenotypic variations, thus greatly facilitating genetic mapping of traits such as flowering time, tillering, and drought tolerance (Mauro-Herrera et al, 2013; Qie et al, 2014; Mauro-Herrera and Doust, 2016). …”