High‐throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare)

Abstract

SummaryTo optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images. Plant shoot modelling over time was used to measure the initiation and elongation of leaves in a bi‐parental barley mapping population under low and high soil salinity. We detected quantitative trait loci (QTL) related to shoot growth per se, using both simple 2D total shoot measurements and our approach of measuring individual leaves. In addition, we detected QTL specific to leaf elongation and not to total shoot size. Of particular importance was the detection of a QTL on chromosome 3H specific to the early responses of leaf elongation to salt stress, a locus that could not be detected without the computer vision tools developed in this study.

Dissecting new genetic components of salinity tolerance in two-row spring barley at the vegetative and reproductive stages

“…[ 73 ] were unable to find overlapping QTL when studying barley during the germination and seedling stages, arguing that different tolerance mechanisms could be involved in the different stages. QTL that contribute to a certain trait can have a transient nature and dynamically change with time and developmental stages [ 54 , 74 , 75 ].…”

Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data

“…Once the intervals are chosen, the sPSA values for the end points of these intervals and the mean sPSA AGR and sPSA RGR for each interval are extracted. Such traits have been successfully used in QTL and GWAS analyses, as published results [2,6,7,9,25] demonstrate.…”

“…Additional imaging variables, like maximum height and top view convex hull, could be used to form extracted traits. The transpiration use efficiency (TUE) [2], the water use index (WUI) [8] and total and individual leaf length [9] have also been employed as traits. Further, traits could be based on chemical measurements or results from hyperspectral imaging data.…”

“…responses per se or as a precursor to genetic analysis [1][2][3][4][5][6][7][8][9][10][11][12][13]. Because it involves non-invasive phenotyping of the same plants at different time points, it is now possible to measure many plants at many time points so that the precision of growth analyses [14] is much improved.…”

Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci

“…Growing evidence demonstrates quantitative trait loci (QTL) can be deterministic QTL (dQTL) represented as the differential allelic variation which affects the whole growth process, unaffected by environmental stimuli or opportunistic (oQTL) responding to biotic/abiotic stimuli (Wu, Wang, Zhao, & Cheverud, 2004). Function mapping has identified QTL associated with dynamic traits (a) within narrow time periods, (b) throughout the lifecycle, and (c) at specific physiological growth stages (Bac‐Molenaar, Vreugdenhil, Granier, & Keurentjes, 2015; Campbell et al., 2017; Feldman et al., 2017; Ward et al., 2019). Patterns of temporal QTL associations using field‐based HTP systems have been demonstrated for soybean canopy cover (Xavier, Hall, Hearst, Cherkauer, & Rainey, 2017), cotton stress‐response traits (Pauli, Andrade‐Sanchez, et al., 2016), spring barley biomass accumulation (Neumann et al., 2017), wheat plant height (Lyra et al., 2020), rice yield components (Tanger et al., 2017), and triticale plant height (Würschum et al., 2014).…”