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Dryad

Phenome-to-genome insights for evaluating root system architecture in field studies of maize

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Sep 17, 2025 version files 3.48 MB
Sep 18, 2025 version files 3.48 MB

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Abstract

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 > 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.