Linkage disequilibrium mapping for grain Fe and Zn enhancing QTLs useful for nutrient dense rice breeding

Abstract

Background: High yielding rice varieties are usually low in grain iron (Fe) and zinc (Zn) content. These two micronutrients are involved in many enzymatic activities, lack of which cause many disorders in human body. Bio-fortification is a cheaper and easier way to improve the content of these nutrients in rice grain.

Results: A population panel was prepared representing all the phenotypic classes for grain Fe-Zn content from 485 germplasm lines. The panel was studied for genetic diversity, population structure and association mapping of grain Fe-Zn content in the milled rice. The population showed linkage disequilibrium showing deviation of Hardy-Weinberg's expectation for Fe-Zn content in rice. Population structure at K = 3 categorized the panel population into distinct sub-populations corroborating with their grain Fe-Zn content. STRUCTURE analysis revealed a common primary ancestor for each sub-population. Novel quantitative trait loci (QTLs) namely qFe3.3 and qFe7.3 for grain Fe and qZn2.2, qZn8.3 and qZn12.3 for Zn content were detected using association mapping. Four QTLs, namely qFe3.3, qFe7.3, qFe8.1 and qFe12.2 for grain Fe content were detected to be co-localized with qZn3.1, qZn7, qZn8.3 and qZn12.3 QTLs controlling grain Zn content, respectively. Additionally, some Fe-Zn controlling QTLs were co-localized with the yield component QTLs, qTBGW, OsSPL14 and qPN. The QTLs qFe1.1, qFe3.1, qFe5.1, qFe7.1, qFe8.1, qZn6, qZn7 and gRMm9-1 for grain Fe-Zn content reported in earlier studies were validated in this study.

Conclusion: Novel QTLs, qFe3.3 and qFe7.3 for grain Fe and qZn2.2, qZn8.3 and qZn12.3 for Zn content were detected for these two traits. Four Fe-Zn controlling QTLs and few yield component QTLs were detected to be co-localized. The QTLs, qFe1.1, qFe3.1, qFe5.1, qFe7.1, qFe8.1, qFe3.3, qFe7.3, qZn6, qZn7, qZn2.2, qZn8.3 and qZn12.3 will be useful for biofortification of the micronutrients. Simultaneous enhancement of Fe-Zn content may be possible with yield component traits in rice.

Keywords: Association study; Biofortification; Grain Fe content; Grain Zn content; Linkage disequilibrium.

Fig. 1

Fe and Zn content of 102 genotypes and their frequency distribution in the panel population. a Spider graph showing the Fe and Zn content of the genotypes. b Frequency of high, moderate and low Fe-Zn genotypes in the panel population

Fig. 2

Genotype-by-trait biplot graph showing 102 genotypes in two main principal components for three traits. Fe: grain iron content; Zn: grain zinc content; yld: grain yield (kg/ha); PN: panicle number; DFF: days to 50% flowering. The dot numbers in the figure represent the serial number of the genotypes enlisted in Table 1

Fig. 3

a Graph of delta K value, an ad-hoc statistic related to the rate of change in the log probability of data between successive K values; b Population structure of the 102-panel population placed based on membership probability fractions of individual genotypes at K = 2 and c Population structure of the 102-panel population placed based on membership probability fractions of individual genotypes at K = 3. The genotypes with the probability of ≥80% membership fractions were assigned to corresponding subgroups with others categorized as admixture. The numbers in the figure represent the serial number of the genotypes enlisted in Table 1

Fig. 4

Linkage disequilibrium (LD) decay (r²) curve plotted against the physical distance (base pairs, bp) between pairs of loci on chromosomes in rice. The decay started in million bp estimated by taking 95th percentile of the distribution of r² for all unlinked loci

Fig. 5

Principal coordinate analysis (PCoA) of 102 genotypes in the panel population for grain Fe and Zn content using 100 molecular markers. The dot numbers in the figure represent the serial number of the genotypes enlisted in Table 1. The numbers are coloured on the basis of (a) sub-populations obtained from structure analysis (SP1-green; SP2-blue; SP3-Pink; admix type-red) (b) grain Fe and Zn content (common high grain Fe and Zn content – red; common moderate grain Fe and Zn content – green)

Fig. 6

Unrooted tree using unweighted-neighbour joining method depicting clustering pattern of 102 germplasm lines with respect to 100 molecular markers coloured on the basis of (a) sub-populations obtained from structure analysis (SP1-green; SP2-blue; SP3-Pink; admix type-red), b iron content (High Fe content-red; Moderate Fe content-green; Low Fe content-blue) and c Zinc content (High Zn content-red; Moderate Zn content-green; Low Zn content-blue) in milled rice

Fig. 7

Quantile–Quantile (Q-Q) plot and distribution of marker-trait association from Generalized Linear Model analysis for grain iron-zinc content, panicles/m² and grain yield at (a) p < 0.01 and (b) at p < 0.05

Fig. 8

Fold change of expression of genes OsZIP6 and OsZIP8 under Fe-Zn deficient treatment as compared to that of normal condition in roots and shoots of two contrasting genotypes Kalanamak and Swarna