DOI: 10.5061/dryad.z34tmpgq4
title: Phenome-to-genome insights for evaluating root system architecture in field studies of maize

description: Understanding the genetic basis of root system architecture (RSA) in crops
 requires innovative approaches that enable high-throughput and precise
 phenotyping in field conditions. In this study, we evaluated multiple
 phenotyping and analytical frameworks for quantifying RSA in mature,
 field-grown maize in three field experiments. We used forward and reverse
 genetic approaches to evaluate &gt; 1,700 root crowns sampled from a
 genetically diverse sample of maize including a diversity panel, a
 biparental mapping population, and maize mutant and wild-type alleles at
 two known RSA genes, Deeper Rooting 1 and Rootless 1. Here, we demonstrate
 the utility of increasing the dimensionality of traditional 2D techniques,
 referred to as the ‘2D multi-view’ method, to improve the capture of whole
 root system information for mapping genetic variation influencing RSA.
 Comparison of univariate and multivariate genome-wide association study
 (GWAS) approaches revealed that multivariate traits were effective at
 dissecting complex RSA phenotypes and identifying pleiotropic quantitative
 trait loci (QTL). Overall, 3D root models generated from X-ray computed
 tomography (XRT) and digital phenotyping captured a larger proportion of
 RSA trait variations, as evidenced by genome-wide and single-gene
 analyses. Among the individual root traits, root pulling force (RPF)
 emerged as a highly heritable estimate of RSA that identified the largest
 number of shared QTL with 3D phenotypes. Our study provides further
 evidence that integrating complementary phenotyping technologies and
 statistical frameworks can offer deeper insights into the genetic
 architecture of the global RSA in field-grown maize.
description: # Phenome-To-Genome Insights For Evaluating Root System Architecture In
 Field Studies Of Maize: Open Source Data ## **Description of the data and
 file structure** The submitted dataset contains the raw data
 (**CSU-root-data.xlsx**) in excel format. This README file was generated
 on 2025-09-16 by Kirsten Hein.  [\[Access this dataset on
 Dryad\]](https://doi.org/10.5061/dryad.z34tmpgq4) ### **Date of Data
 Collection** 2019-2023 ### **Contributors** * Kirsten M. Hein, Jack L.
 Mullen, and John K. McKay, Colorado State University, 307 University St.,
 Fort Collins, Colorado, USA * Kirsten M. Hein, Alexander E. Liu, and
 Christopher N Topp, Donald Danforth Plant Science Center, 975 N Warson
 Rd., Saint Louis, Missouri, USA * Mon-Ray Shao, Dümmen Orange,
 Oudecampsweg 35C, De Lier, South Holland, NL ### **Overview** The raw
 phenotypic data from the maize field experiments conducted at the Colorado
 State University Agricultural Research Development and Education Center
 (ARDEC) in Fort Collins, Colorado, USA in the summers of 2019-2023. The
 data is presented in its original format, promoting transparency and
 allowing for varied data processing and analysis methods, thus
 facilitating diverse scientific interpretations and collaborative
 research. Detailed descriptions of materials and methods used in study are
 reported in the accompanying manuscript. ### **File**: CSU-root-data.xlsx
 This excel workbook contains maize root crown measurements collected under
 field conditions from three experimental populations. Each dataset is
 provided as a separate tab within the file, with a shared glossary
 defining data variables. ``` Missing values: Encoded as NA ``` ####
 **Table of Contents** | **Tab** | **Population** | **Data Type** |
 **Description** | | :------- | :------------------ |
 :------------------------------------------- |
 :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Glossary | - | - | Definitions of data variables used across all tables. | | Table1.1 | SAM maize panel | Root pulling force | Root pulling force (kg) measured in 312 genotypes from the Shoot Apical Meristem (SAM) maize diversity panel grown under field conditions with two irrigation treatments (wet and dry). | | Table1.2 | SAM maize panel | Root biomass | Root biomass (g) measured in 312 genotypes from the SAM maize diversity panel grown under field conditions with two irrigation treatments (wet and dry). | | Table1.3 | SAM maize panel | 2D single-view image analysis (DIRT feature) | Root crown traits extracted from single-view 2D imaging of 312 genotypes from the SAM maize diversity panel using the Digital Imaging of Root Traits (DIRT) software (Das et al., 2015). | | Table1.4 | SAM maize panel | 3D image analysis (RCAP feature) | Root crown traits extracted from 3D X-ray computed tomography of 312 genotypes from the SAM maize diversity panel grown under field conditions with two irrigation treatments (wet and dry), using the Root Crown Analysis Pipeline (RCAP; Shao et al., 2021). | | Table2.1 | CML69 x B73 NAM RIL | 2D single-view image analysis (DIRT feature) | Root crown traits extracted from single-view 2D imaging of the CML69 x B73 nested association mapping population (NAM) of recombinant inbred maize lines (RIL) using the DIRT software. | | Table2.2 | CML69 x B73 NAM RIL | 2D multi-view image analysis (DIRT feature) | Root crown traits extracted from multi-view 2D imaging of the CML69 x B73 NAM RIL maize population, based on aggregate genotype averages from three camera angles using the DIRT software. | | Table3.1 | RSA mutants | Root biomass | Root biomass (g) measured in maize plants carrying mutant and wild-type alleles at two characterized root system architecture genes: DEEPER ROOTING 1 (DRO1) and Rootless1 (Rt1). | | Table3.2 | RSA mutants | 2D single-view image analysis (DIRT feature) | Root crown traits extracted from single-view 2D imaging of maize plants carrying mutant and wild-type alleles at DRO1 and Rt1, using the DIRT software (Das et al., 2015). | | Table3.3 | RSA mutants | 2D multi-view image analysis (DIRT feature) | Root crown traits extracted from multi-view 2D imaging of maize plants carrying mutant and wild-type alleles at DRO1 and Rt1, based on aggregate genotype averages from three camera angles using the DIRT software. | | Table3.4 | RSA mutants | 3D image analysis (RCAP feature) | Root crown traits extracted from 3D X-ray computed tomography of maize plants carrying mutant and wild-type alleles at DRO1 and Rt1 using RCAP. | #### **Column Header Metadata:** * **Year**: Year of data collection * **Site**: Field experiment location * **Population**: Experimental maize population  1. Shoot Apical Meristem (SAM) maize diversity population 2. Nested association mapping population of recombinant inbred lines (CML69 x B73) 3. Root system architecture (RSA) mutant and wild-type allele lines - **Treatment**: Irrigation treatment applied (wet, dry). *Applies* *to the 2019 SAM maize population*  * **Field.Replicate**: Field replicate identifier  * **Plot**: Unique field plot identifier * **Plot.Replicate**: Plot-level replicate identifier * **Genotype**: Genotype identifier * **Gene**: Target genes known to affect root system architecture: *DEEPER ROOTING 1* (*DRO1*); *Rootless1* (*Rt1*) * **Allele**: Target alleles at *DRO1* and *Rt1* * **GDD**: Number of growing degree days at harvest * **Perspective**: Camera identifier for output 2D multi-view imaging datasets (octagon, sideview1, sideview2) ### **Image analysis software** * **DIRT** (Digital Imaging of Root Traits): [Das et al. (2015)](https://pubmed.ncbi.nlm.nih.gov/26535051/) * **RCAP** (Root Crown Analysis Pipeline):  [Shao et al. (2021)](https://doi.org/10.34133/2021/9859254) * [https://github.com/Topp-Roots-Lab/3d-root-crown-analysis-pipeline/](https://github.com/Topp-Roots-Lab/3d-root-crown-analysis-pipeline/) ### **Genomic Data Analysis:** Genome-wide association study was performed on 312 genotypes from the Shoot Apical Meristem (SAM) maize diversity panel using the GAPIT implementation of FarmCPU ([Lipka et al., 2012](https://doi.org/10.1093/bioinformatics/bts444); [Liu et al., 2016](https://doi.org/10.1371/journal.pgen.1005767); [Woods et al., 2022](https://doi.org/10.3389/fpls.2022.883209 )). The initial HapMap genotype matrix for the SAM panel contained 1.2 million single nucleotide polymorphisms ([Leiboff et al., 2015](https://doi.org/10.1038/ncomms9974)), which was refined to 860,000 after imposing a minor allele frequency filter of 5%. The first three principal components and a kinship matrix calculated using GAPIT were used as covariates to control for population structure.