High-resolution genome-wide association study pinpoints metal transporter and chelator genes involved in the genetic control of element levels in maize grain

Author:

Wu Di1ORCID,Tanaka Ryokei1,Li Xiaowei1,Ramstein Guillaume P2ORCID,Cu Suong3ORCID,Hamilton John P4ORCID,Buell C Robin4ORCID,Stangoulis James3ORCID,Rocheford Torbert5,Gore Michael A1ORCID

Affiliation:

1. Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA

2. Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA

3. College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia

4. Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA

5. Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA

Abstract

AbstractDespite 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.

Funder

National Institute of Food and Agriculture

USDA Hatch

HarvestPlus

National Science Foundation

Cornell University startup funds

Patterson Chair Funds

Publisher

Oxford University Press (OUP)

Subject

Genetics (clinical),Genetics,Molecular Biology

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