Phenotyping, genome-wide dissection, and prediction of maize root architecture for temperate adaptability

Abstract

Root System Architecture (RSA) plays an essential role in influencing maize yield by enhancing anchorage and nutrient uptake. Analyzing maize RSA dynamics holds potential for ideotype-based breeding and prediction, given the limited understanding of the genetic basis of RSA in maize. Here, we obtained 16 root morphology-related traits (R-traits), 7 weight-related traits (W-traits), and 108 slice-related microphenotypic traits (S-traits) from the meristem, elongation, and mature zones by cross-sectioning primary, crown, and lateral roots from 316 maize lines. Significant differences were observed in some root traits between tropical/subtropical and temperate lines, such as primary and total root diameters, root lengths, and root area. Additionally, root anatomy data were integrated with genome-wide association study (GWAS) to elucidate the genetic architecture of complex root traits. GWAS identified 809 genes associated with R-traits, 261 genes linked to W-traits, and 2577 key genes related to 108 slice-related traits. We confirm the function of a candidate gene, fucosyltransferase5 (FUT5), in regulating root development and heat tolerance in maize. The different FUT5 haplotypes found in tropical/subtropical and temperate lines are associated with primary root features and hold promising applications in molecular breeding. Furthermore, we performed machine learning prediction models of RSA using root slice traits, achieving high prediction accuracy. Collectively, our study offers a valuable tool for dissecting the genetic architecture of RSA, along with resources and predictive models beneficial for molecular design breeding and genetic enhancement.

Keywords

GWAS / machine learning / maize / prediction / Root System Architecture

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