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, 127 (1), 137-65

Mapping and Validation of Quantitative Trait Loci Associated With Concentrations of 16 Elements in Unmilled Rice Grain

Mapping and Validation of Quantitative Trait Loci Associated With Concentrations of 16 Elements in Unmilled Rice Grain

Min Zhang et al. Theor Appl Genet.

Abstract

QTLs controlling the concentrations elements in rice grain were identified in two mapping populations. The QTLs were clustered such that most genomic regions were associated with more than one element. In this study, quantitative trait loci (QTLs) affecting the concentrations of 16 elements in whole, unmilled rice (Oryza sativa L.) grain were identified. Two rice mapping populations, the ‘Lemont’ × ‘TeQing’ recombinant inbred lines (LT-RILs), and the TeQing-into-Lemont backcross introgression lines (TILs) were used. To increase opportunity to detect and characterize QTLs, the TILs were grown under two contrasting field conditions, flooded and irrigated-but-unflooded. Correlations between the individual elements and between each element with grain shape, plant height, and time of heading were also studied. Transgressive segregation was observed among the LT-RILs for all elements. The 134 QTLs identified as associated with the grain concentrations of individual elements were found clustered into 39 genomic regions, 34 of which were found associated with grain element concentration in more than one population and/or flooding treatment. More QTLs were found significant among flooded TILs (92) than among unflooded TILs (47) or among flooded LT-RILs (40). Twenty-seven of the 40 QTLs identified among the LT-RILs were associated with the same element among the TILs. At least one QTL per element was validated in two or more population/environments. Nearly all of the grain element loci were linked to QTLs affecting additional elements, supporting the concept of element networks within plants. Several of the grain element QTLs co-located with QTLs for grain shape, plant height, and days to heading; but did not always differ for grain elemental concentration as predicted by those traits alone. A number of interesting patterns were found, including a strong Mg–P–K complex.

Figures

Fig. 1
Fig. 1
Histograms showing the range of the LS means observed among the population of 280 LT-RILs for the 16 elemental traits, all LT-RIL fields were flooded till all plots were mature and harvested. Elements are listed starting with the five macroelements (those reaching >100 ppm in rice grains), in order of their mean grain concentration, followed by the 11 remaining elements in alphabetical order. Also indicated are the LS Means (±1 standard deviation) of the multiple repeat check plots of the parental lines, Lemont and TeQing, grown in those same flooded fields
Fig. 2
Fig. 2
A total of 127 QTLs for grain element content were identified among the LT-RIls and the TILs. QTLs found significant among the LT-RILs are indicated to the left of the chromosome lines; QTLs identified among TILs are to the right. QTLs for grain element concentration often clustered in chromosomal regions as diagrammed here, and as indicated in Table 3. QTLs identified for grain shape dimensions are also indicated, as are QTLs for plant height and days to heading, which were determined in prior studies (Pinson et al. 2005, 2012)

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References

    1. Abdi H (2007) The Bonferroni and Šidák corrections for multiple comparisons. In: Salkind NJ (ed) Encyclopedia of measurement and statistics. Sage Publications, Thousand Oaks, pp. 103–107. http://www.utdallas.edu/~herve/Abdi-Bonferroni2007-pretty.pdf
    1. Arao T, Kawasaki A, Baba K, More S, Matsumoto S. Effects of water management on cadmium and arsenic accumulation and dimethylarsinic acid concentrations in Japanese rice. Environ Sci Technol. 2009;43:9361–9367. doi: 10.1021/es9022738. - DOI - PubMed
    1. Baxter IR. Ionomics: studying the social network of mineral nutrients. Curr Opin Plant Biol. 2009;12:381–386. doi: 10.1016/j.pbi.2009.05.002. - DOI - PMC - PubMed
    1. Baxter IR, Vitek O, Lahner B, Muthukumar B, Borghi M, Morrissey J, Guerinot ML, Salt DE. The leaf ionome as multivariable system to detect a plant’s physiological status. Proc Natl Acad Sci USA. 2008;105:12081–12086. doi: 10.1073/pnas.0804175105. - DOI - PMC - PubMed
    1. Baxter IR, Gustin JL, Settles AM, Hoekenga OA. Ionomic characterization of maize kernels in the intermated B73 × Mo17 population. Crop Sci. 2012;53:208–220.

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