Abstract
BackgroundBreeding for new maize varieties with propitious root systems has tremendous potential in improving water and nutrients use efficiency and plant adaptation under suboptimal conditions. To date, most of the previously detected root-related trait genes in maize were new without functional verification. In this study, seven seedling root architectural traits were examined at three developmental stages in a recombinant inbred line population (RIL) of 179 RILs and a genome-wide association study (GWAS) panel of 80 elite inbred maize lines through quantitative trait loci (QTL) mapping and genome-wide association study.ResultsUsing inclusive composite interval mapping, 8 QTLs accounting for 6.44–8.83 % of the phenotypic variation in root traits, were detected on chromosomes 1 (qRDWv3-1-1 and qRDW/SDWv3-1-1), 2 (qRBNv1-2-1), 4 (qSUAv1-4-1, qSUAv2-4-1, and qROVv2-4-1), and 10 (qTRLv1-10-1, qRBNv1-10-1). GWAS analysis involved three models (EMMAX, FarmCPU, and MLM) for a set of 1,490,007 high-quality single nucleotide polymorphisms (SNPs) obtained via whole genome next-generation sequencing (NGS). Overall, 53 significant SNPs with a phenotypic contribution rate ranging from 5.10 to 30.2 % and spread all over the ten maize chromosomes exhibited associations with the seven root traits. 17 SNPs were repeatedly detected from at least two growth stages, with several SNPs associated with multiple traits stably identified at all evaluated stages. Within the average linkage disequilibrium (LD) distance of 5.2 kb for the significant SNPs, 46 candidate genes harboring substantial SNPs were identified. Five potential genes viz. Zm00001d038676, Zm00001d015379, Zm00001d018496, Zm00001d050783, and Zm00001d017751 were verified for expression levels using maize accessions with extreme root branching differences from the GWAS panel and the RIL population. The results showed significantly (P < 0.001) different expression levels between the outer materials in both panels and at all considered growth stages.ConclusionsThis study provides a key reference for uncovering the complex genetic mechanism of root development and genetic enhancement of maize root system architecture, thus supporting the breeding of high-yielding maize varieties with propitious root systems.
Highlights
Breeding for new maize varieties with propitious root systems has tremendous potential in improving water and nutrients use efficiency and plant adaptation under suboptimal conditions
This study provides a key reference for uncovering the complex genetic mechanism of root development and genetic enhancement of maize root system architecture, supporting the breeding of highyielding maize varieties with propitious root systems
Analysis of variance related to the root trait performances of the two parental lines revealed significant to highly significant differences (P < 0.05; P < 0.01; P < 0.001) for all the measured seedling traits and at all the three stages except Root dry weight (RDW)/Root per shoot dry weight (SDW) at V1 stage (Table 1)
Summary
Breeding for new maize varieties with propitious root systems has tremendous potential in improving water and nutrients use efficiency and plant adaptation under suboptimal conditions. As the place of plant and soil interactions, roots play a fundamental role in plant responses to biotic and abiotic stresses [5], and influence significantly many agronomically important traits, including drought and flood tolerance [6,7,8], root-lodging resistance [9], and nutrient use efficiency nitrogen (N), phosphorus(P), and calcium (Ca) under suboptimal growth conditions [10,11,12,13] and resource-challenging environments [2, 14, 15]. It was previously indicated that plant growing systems that nearly mimic the soil media are more stable in mineral elements and environmental factors, and easier to operate for root morphological traits phenotyping in maize [24]. Numerous software frameworks such as ARIA [31], EZ-Rhizo [35], Smart Root [36], WinRhizo [37], Optimas analysis software, Image J [38], Root Nav [32], IJ_Rhizo [39], Root System Analyzer [40], and Root Trace [41] have been broadly used for automated root traits measurements in a high throughput manner
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