3D Sorghum Reconstructions from Depth Images Identify QTL Regulating Shoot Architecture.

Ryan F Mccormick,Sandra K Truong,John E Mullet

doi:10.1104/pp.16.00948

Abstract

Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.

Highlights

The rate limiting step for crop improvement and for dissecting the genetic bases of agriculturally important traits has shifted from genotyping to phenotyping, creating what is referred to as the phenotyping bottleneck (Houle et al, 2010; Furbank and Tester, 2011)
We report quantitative trait loci (QTL) for shoot architecture traits such as shoot height, leaf angle, and leaf length, and we demonstrate that the relative contributions to phenotypic variability of the QTL change with respect to time
A time-of-flight depth camera was used to image sorghum plants from a recombinant inbred line (RIL) population, and we developed an image processing pipeline to reconstruct 3D sorghum plants and make automated measurements from the reconstructions

Summary

Introduction

The rate limiting step for crop improvement and for dissecting the genetic bases of agriculturally important traits has shifted from genotyping to phenotyping, creating what is referred to as the phenotyping bottleneck (Houle et al, 2010; Furbank and Tester, 2011). Approaches to alleviate the plant phenotyping bottleneck fall into two broad categories: approaches that increase the number of individuals that can be grown and evaluated (Fahlgren et al, 2015), and approaches that predict performance in silico to prioritize individuals to grow and evaluate (Hammer et al, 2010; Technow et al, 2015) Both of these approaches will be instrumental for increasing the rate of crop improvement, and both approaches are facilitated by advances in image-based phenotyping; multiple plant measurements can be rapidly acquired from images, and data from imagebased phenotyping approaches can inform performance prediction (Spalding and Miller, 2013; Pound et al, 2014). Commercial platforms, including the Scanalyzer series from Lemnatec

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