High-resolution genome-wide association study pinpoints metal transporter and chelator genes involved in the genetic control of element levels in maize grain. Issue 4 (2nd March 2021)

• Record Type: Journal Article
• Authors: Wu, Di; Tanaka, Ryokei; Li, Xiaowei; Ramstein, Guillaume P; Cu, Suong; Hamilton, John P; Buell, C Robin; Stangoulis, James; Rocheford, Torbert; Gore, Michael A
• Editors: Morrell, P L
• Is Part Of: G3. Volume 11:Issue 4(2021)
• Publication Date: 2021-03-02
• Subjects: genome-wide association study -- whole-genome prediction -- elements -- grain -- maize; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkab059
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Despite its importance to plant function and human health, the genetics underpinning element levels in maize grain remain largely unknown. Through a genome-wide association study in the maize Ames panel of nearly 2, 000 inbred lines that was imputed with ∼7.7 million SNP markers, we investigated the genetic basis of natural variation for the concentration of 11 elements in grain. Novel associations were detected for the metal transporter genes rte2 ( rotten ear2 ) and irt1 ( iron-regulated transporter1 ) with boron and nickel, respectively. We also further resolved loci that were previously found to be associated with one or more of five elements (copper, iron, manganese, molybdenum, and/or zinc), with two metal chelator and five metal transporter candidate causal genes identified. The nas5 ( nicotianamine synthase5 ) gene involved in the synthesis of nicotianamine, a metal chelator, was found associated with both zinc and iron and suggests a common genetic basis controlling the accumulation of these two metals in the grain. Furthermore, moderate predictive abilities were obtained for the 11 elemental grain phenotypes with two whole-genome prediction models: Bayesian Ridge Regression (0.33–0.51) and BayesB (0.33–0.53). Of the two models, BayesB, with its greater emphasis on large-effect loci, showed ∼4–10% higher predictive abilities for nickel, molybdenum, and copper. Altogether, our findings contribute to an improved genotype-phenotype map for grain element accumulation in maize.