Association Mapping Reveals Multiple QTLs for Grain Protein Content in Rice Useful for Biofortification

Overview

Journal Mol Genet Genomics

Specialty Genetics

Date 2019 Apr 10

PMID 30963249

Citations 18

Authors

S K Pradhan

E Pandit

S Pawar

Barsha Bharati

K Chatopadhyay

S Singh

P Dash

J N Reddy

Affiliations

Soon will be listed here.

Abstract

Rice is the staple food for majority of the global population. But, rice grain has low protein content (PC). Mapping of QTLs controlling grain PC is essential for enhancement of the trait through breeding programs. A shortlisted panel population for grain protein content was studied for genetic diversity, population structure and association mapping for grain PC. Phenotyping results showed a wide variation for grain PC. The panel population showed a moderate level of genetic diversity estimated through 98 molecular markers. AMOVA and structure analysis indicated linkage disequilibrium for grain PC and deviation of Hardy-Weinberg's expectation. The analysis showed 15% of the variation among populations and 73% among individuals in the panel population. STRUCTURE analysis categorized the panel population into three subpopulations. The analysis also revealed a common primary ancestor for each subpopulation with few admix individuals. Marker-trait association using 98 molecular markers detected 7 strongly associated QTLs for grain PC by both MLM and GLM analysis. Three novel QTLs qPC3.1, qPC5.1 and qPC9.1 were detected for controlling the grain PC. Four reported QTLs viz., qPC3, QPC8, qPC6.1 and qPC12.1 were validated for use in breeding programs. Reported QTLs, qPC6, qPC6.1 and qPC6.2 may be same QTL controlling PC in rice. A very close marker RM407 near to protein controlling QTL, qProt8 and qPC8, was detected. The study provided clue for simultaneous improvement of PC with high grain yield in rice. The strongly associated markers with grain PC, namely qPC3, qPC3.1, qPC5.1, qPC6.1, qPC8, qPC9.1 and qPC12.1, will be useful for their pyramiding for developing protein rich high yielding rice.

Citing Articles

A Simple Narrative Review of Progress on the Processing and Utilization of Functional Rice.

Yi Z, Chen D, Zhou X, Guo J, Chen K, Ye C Foods. 2024; 13(23).

PMID: 39682983 PMC: 11641044. DOI: 10.3390/foods13233911.

Profiling and Improvement of Grain Quality Traits for Consumer Preferable Basmati Rice in the United States.

Prodhan Z, Samonte S, Sanchez D, Talukder S Plants (Basel). 2024; 13(16).

PMID: 39204762 PMC: 11359321. DOI: 10.3390/plants13162326.

Transfer of Stress Resilient QTLs and Panicle Traits into the Rice Variety, Reeta through Classical and Marker-Assisted Breeding Approaches.

Barik S, Moharana A, Pandit E, Behera A, Mishra A, Mohanty S Int J Mol Sci. 2023; 24(13).

PMID: 37445885 PMC: 10341874. DOI: 10.3390/ijms241310708.

Phylogenomic Analysis of micro-RNA Involved in Juvenile to Flowering-Stage Transition in Photophilic Rice and Its Sister Species.

Dash P, Gupta P, Sreevathsa R, Pradhan S, Sanjay T, Mohanty M Cells. 2023; 12(10).

PMID: 37408207 PMC: 10216681. DOI: 10.3390/cells12101370.

Mapping the Genomic Regions Controlling Germination Rate and Early Seedling Growth Parameters in Rice.

Mohanty S, Nayak D, Sanghamitra P, Barik S, Pandit E, Behera A Genes (Basel). 2023; 14(4).

PMID: 37107660 PMC: 10138111. DOI: 10.3390/genes14040902.

References

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

Zimmermann M, Hurrell R . Improving iron, zinc and vitamin A nutrition through plant biotechnology. Curr Opin Biotechnol. 2002; 13(2):142-5. DOI: 10.1016/s0958-1669(02)00304-x. View

Garris A, McCouch S, Kresovich S . Population structure and its effect on haplotype diversity and linkage disequilibrium surrounding the xa5 locus of rice (Oryza sativa L.). Genetics. 2003; 165(2):759-69. PMC: 1462795. DOI: 10.1093/genetics/165.2.759. View

Aluko G, Martinez C, Tohme J, Castano C, Bergman C, Oard J . QTL mapping of grain quality traits from the interspecific cross Oryza sativa x O. glaberrima. Theor Appl Genet. 2004; 109(3):630-9. DOI: 10.1007/s00122-004-1668-y. View

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

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

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

Weng J, Gu S, Wan X, Gao H, Guo T, Su N . Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight. Cell Res. 2008; 18(12):1199-209. DOI: 10.1038/cr.2008.307. View

Chen H, He H, Zou Y, Chen W, Yu R, Liu X . Development and application of a set of breeder-friendly SNP markers for genetic analyses and molecular breeding of rice (Oryza sativa L.). Theor Appl Genet. 2011; 123(6):869-79. DOI: 10.1007/s00122-011-1633-5. View

10.

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

11.

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

12.

Zhang P, Li J, Li X, Liu X, Zhao X, Lu Y . Population structure and genetic diversity in a rice core collection (Oryza sativa L.) investigated with SSR markers. PLoS One. 2011; 6(12):e27565. PMC: 3229487. DOI: 10.1371/journal.pone.0027565. View

13.

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

14.

Singh N, Roy Choudhury D, Singh A, Kumar S, Srinivasan K, Tyagi R . Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties. PLoS One. 2013; 8(12):e84136. PMC: 3868579. DOI: 10.1371/journal.pone.0084136. View

15.

Terao T, Hirose T . Control of grain protein contents through SEMIDWARF1 mutant alleles: sd1 increases the grain protein content in Dee-geo-woo-gen but not in Reimei. Mol Genet Genomics. 2014; 290(3):939-54. DOI: 10.1007/s00438-014-0965-7. View

16.

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

17.

Pan Y, Zhang H, Zhang D, Li J, Xiong H, Yu J . Genetic analysis of cold tolerance at the germination and booting stages in rice by association mapping. PLoS One. 2015; 10(3):e0120590. PMC: 4366098. DOI: 10.1371/journal.pone.0120590. View

18.

Salgotra R, Gupta B, Bhat J, Sharma S . Genetic Diversity and Population Structure of Basmati Rice (Oryza sativa L.) Germplasm Collected from North Western Himalayas Using Trait Linked SSR Markers. PLoS One. 2015; 10(7):e0131858. PMC: 4517777. DOI: 10.1371/journal.pone.0131858. View

19.

Bagchi T, Sharma S, Chattopadhyay K . Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran. Food Chem. 2015; 191:21-7. DOI: 10.1016/j.foodchem.2015.05.038. View

20.

Huang Y, Sun C, Min J, Chen Y, Tong C, Bao J . Association Mapping of Quantitative Trait Loci for Mineral Element Contents in Whole Grain Rice (Oryza sativa L.). J Agric Food Chem. 2015; 63(50):10885-92. DOI: 10.1021/acs.jafc.5b04932. View