Linkage Disequilibrium Mapping for Grain Fe and Zn Enhancing QTLs Useful for Nutrient Dense Rice Breeding

Overview

Journal BMC Plant Biol

Publisher Biomed Central

Specialty Biology

Date 2020 Feb 6

PMID 32019504

Citations 29

Authors

S K Pradhan

E Pandit

S Pawar

R Naveenkumar

S R Barik

S P Mohanty

D K Nayak

S K Ghritlahre

D Sanjiba Rao

J N Reddy

S S C Patnaik

Affiliations

Soon will be listed here.

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.

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References

Pradhan S, Pandit E, Nayak D, Behera L, Mohapatra T . Genes, pathways and transcription factors involved in seedling stage chilling stress tolerance in indica rice through RNA-Seq analysis. BMC Plant Biol. 2019; 19(1):352. PMC: 6694648. DOI: 10.1186/s12870-019-1922-8. View

Garcia-Oliveira A, Tan L, Fu Y, Sun C . Genetic identification of quantitative trait loci for contents of mineral nutrients in rice grain. J Integr Plant Biol. 2009; 51(1):84-92. DOI: 10.1111/j.1744-7909.2008.00730.x. View

Bradbury P, Zhang Z, Kroon D, Casstevens T, Ramdoss Y, Buckler E . TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007; 23(19):2633-5. DOI: 10.1093/bioinformatics/btm308. View

Kumar V, Singh A, Mithra S, Krishnamurthy S, Parida S, Jain S . Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Res. 2015; 22(2):133-45. PMC: 4401324. DOI: 10.1093/dnares/dsu046. View

Johnson A, Kyriacou B, Callahan D, Carruthers L, Stangoulis J, Lombi E . Constitutive overexpression of the OsNAS gene family reveals single-gene strategies for effective iron- and zinc-biofortification of rice endosperm. PLoS One. 2011; 6(9):e24476. PMC: 3167849. DOI: 10.1371/journal.pone.0024476. View

Pradhan S, Pandit E, Pawar S, Bharati B, Chatopadhyay K, Singh S . Association mapping reveals multiple QTLs for grain protein content in rice useful for biofortification. Mol Genet Genomics. 2019; 294(4):963-983. DOI: 10.1007/s00438-019-01556-w. View

Mahender A, Anandan A, Pradhan S, Pandit E . Rice grain nutritional traits and their enhancement using relevant genes and QTLs through advanced approaches. Springerplus. 2016; 5(1):2086. PMC: 5148756. DOI: 10.1186/s40064-016-3744-6. View

Lee G, Yun B, Kim K . Analysis of QTLs Associated with the Rice Quality Related Gene by Double Haploid Populations. Int J Genomics. 2014; 2014:781832. PMC: 4247976. DOI: 10.1155/2014/781832. View

Pritchard J, Stephens M, Donnelly P . Inference of population structure using multilocus genotype data. Genetics. 2000; 155(2):945-59. PMC: 1461096. DOI: 10.1093/genetics/155.2.945. View

10.

Fu Q, Zhang P, Tan L, Zhu Z, Ma D, Fu Y . Analysis of QTLs for yield-related traits in Yuanjiang common wild rice (Oryza rufipogon Griff.). J Genet Genomics. 2010; 37(2):147-57. DOI: 10.1016/S1673-8527(09)60033-3. View

11.

Murray M, Thompson W . Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 1980; 8(19):4321-5. PMC: 324241. DOI: 10.1093/nar/8.19.4321. View

12.

Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q . Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet. 2011; 44(1):32-9. DOI: 10.1038/ng.1018. View

13.

Zhao K, Tung C, Eizenga G, Wright M, Ali M, Price A . Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun. 2011; 2:467. PMC: 3195253. DOI: 10.1038/ncomms1467. View

14.

Peakall R, Smouse P . GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics. 2012; 28(19):2537-9. PMC: 3463245. DOI: 10.1093/bioinformatics/bts460. View

15.

Liu K, Muse S . PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 2005; 21(9):2128-9. DOI: 10.1093/bioinformatics/bti282. View

16.

Anandan A, Anumalla M, Pradhan S, Ali J . Population Structure, Diversity and Trait Association Analysis in Rice (Oryza sativa L.) Germplasm for Early Seedling Vigor (ESV) Using Trait Linked SSR Markers. PLoS One. 2016; 11(3):e0152406. PMC: 4816567. DOI: 10.1371/journal.pone.0152406. View

17.

Zhang M, Pinson S, Tarpley L, Huang X, Lahner B, Yakubova E . Mapping and validation of quantitative trait loci associated with concentrations of 16 elements in unmilled rice grain. Theor Appl Genet. 2013; 127(1):137-65. PMC: 4544570. DOI: 10.1007/s00122-013-2207-5. View

18.

Flint-Garcia S, Thornsberry J, Buckler 4th E . Structure of linkage disequilibrium in plants. Annu Rev Plant Biol. 2003; 54:357-74. DOI: 10.1146/annurev.arplant.54.031902.134907. View

19.

Evanno G, Regnaut S, Goudet J . Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005; 14(8):2611-20. DOI: 10.1111/j.1365-294X.2005.02553.x. View

20.

Kinoshita N, Kato M, Koyasaki K, Kawashima T, Nishimura T, Hirayama Y . Identification of quantitative trait loci for rice grain quality and yield-related traits in two closely related L. subsp. cultivars grown near the northernmost limit for rice paddy cultivation. Breed Sci. 2017; 67(3):191-206. PMC: 5515307. DOI: 10.1270/jsbbs.16155. View