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Agriculture
Volume 11
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10.3390/agriculture11010070
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Article

Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice

by
Shilei Liu
1,2,†,
Wenli Zou
1,2,†,
Xiang Lu
2,
Jianmin Bian
1,
Haohua He
1,*,
Jingguang Chen
2,3,* and
Guoyou Ye
2,4
1
Group of Crop Genetics and Breeding, School of Agriculture Science, Jiangxi Agricultural University, Nanchang 330045, China
2
CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518116, China
3
School of Agriculture, Sun Yat-sen University, Guangzhou 510275, China
4
Rice Breeding Innovation Platform, International Rice Research Institute, DAPO Box 7777, Metro Manila 1301, Philippines
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2021, 11(1), 70; https://doi.org/10.3390/agriculture11010070
Submission received: 2 December 2020 / Revised: 11 January 2021 / Accepted: 14 January 2021 / Published: 16 January 2021
(This article belongs to the Special Issue Rice Breeding and Genetics)
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Abstract

Zinc (Zn) is an essential trace element for the growth and development of both humans and plants. Increasing the accumulation of Zn in rice grains is important for the world’s nutrition and health. In this study, we used a multiparent advanced generation intercross (MAGIC) population constructed using four parental lines and genotyped using a 55 K rice SNP array to identify QTLs related to Zn2+ concentrations in shoots at the seedling stage and grains at the mature stage. Five QTLs were detected as being associated with shoot Zn2+ concentration at the seedling stage, which explained 3.7–5.7% of the phenotypic variation. Six QTLs were detected as associated with grain Zn2+ concentration at the mature stage, which explained 5.5–8.9% of the phenotypic variation. Among the QTLs, qSZn2-1/qGZn2 and qSZn3/qGZn3 were identified as being associated with both the shoot and grain contents. Based on gene annotation and literature information, 16 candidate genes were chosen in the regions of qSZn1, qSZn2-1/qGZn2, qSZn3/qGZn3, qGZn7, and qGZn8. Analysis of candidate genes through qRT-PCR, complementation assay using the yeast Zn-uptake-deficient double-mutant ZHY3, and sequencing of the four parental lines suggested that LOC_Os02g06010 may play an important role in Zn2+ accumulation in indica rice.
Keywords: Zn2+ content; genome-wide association analysis; quantitative trait loci (QTL); MAGIC population; rice Zn2+ content; genome-wide association analysis; quantitative trait loci (QTL); MAGIC population; rice

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MDPI and ACS Style

Liu, S.; Zou, W.; Lu, X.; Bian, J.; He, H.; Chen, J.; Ye, G. Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice. Agriculture 2021, 11, 70. https://doi.org/10.3390/agriculture11010070

AMA Style

Liu S, Zou W, Lu X, Bian J, He H, Chen J, Ye G. Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice. Agriculture. 2021; 11(1):70. https://doi.org/10.3390/agriculture11010070

Chicago/Turabian Style

Liu, Shilei, Wenli Zou, Xiang Lu, Jianmin Bian, Haohua He, Jingguang Chen, and Guoyou Ye. 2021. "Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice" Agriculture 11, no. 1: 70. https://doi.org/10.3390/agriculture11010070

APA Style

Liu, S., Zou, W., Lu, X., Bian, J., He, H., Chen, J., & Ye, G. (2021). Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice. Agriculture, 11(1), 70. https://doi.org/10.3390/agriculture11010070

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