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  • Title: QTL mapping and phenotypic variation of root anatomical traits in maize (Zea mays L.).
    Author: Burton AL, Johnson J, Foerster J, Hanlon MT, Kaeppler SM, Lynch JP, Brown KM.
    Journal: Theor Appl Genet; 2015 Jan; 128(1):93-106. PubMed ID: 25326723.
    Abstract:
    Root anatomical trait variation is described for three maize RIL populations. Six quantitative trait loci (QTL) are presented for anatomical traits: root cross-sectional area, % living cortical area, aerenchyma area, and stele area. Root anatomy is directly related to plant performance, influencing resource acquisition and transport, the metabolic cost of growth, and the mechanical strength of the root system. Ten root anatomical traits were measured in greenhouse-grown plants from three recombinant inbred populations of maize [intermated B73 × Mo17 (IBM), Oh43 × W64a (OhW), and Ny821 × H99 (NyH)]. Traits included areas of cross section, stele, cortex, aerenchyma, and cortical cells, percentages of the cortex occupied by aerenchyma, and cortical cell file number. Significant phenotypic variation was observed for each of the traits, with maximum values typically seven to ten times greater than minimum values. Means and ranges were similar for the OhW and NyH populations for all traits, while the IBM population had lower mean values for the majority of traits, but a 50% greater range of variation for aerenchyma area. A principal component analysis showed a similar trait structure for the three families, with clustering of area and count traits. Strong correlations were observed among area traits in the cortex, stele, and cross-section. The aerenchyma and percent living cortical area traits were independent of other traits. Six QTL were identified for four of the traits. The phenotypic variation explained by the QTL ranged from 4.7% (root cross-sectional area, OhW population) to 12.0% (percent living cortical area, IBM population). Genetic variation for root anatomical traits can be harnessed to increase abiotic stress tolerance and provide insights into mechanisms controlling phenotypic variation for root anatomy.
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