Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights

Weiwei Wen, Dong Li, Xiang Li, Yanqiang Gao, Wenqiang Li, Huihui Li, Jie Liu, Haijun Liu, Wei Chen, Jie Luo () and Jianbing Yan ()
Additional contact information
Weiwei Wen: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Dong Li: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Xiang Li: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Yanqiang Gao: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Wenqiang Li: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Huihui Li: Institute of Crop Science, CIMMYT China Office, Chinese Academy of Agricultural Sciences
Jie Liu: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Haijun Liu: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Wei Chen: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Jie Luo: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University
Jianbing Yan: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University

Nature Communications, 2014, vol. 5, issue 1, 1-10

Abstract: Abstract Plants produce a variety of metabolites that have a critical role in growth and development. Here we present a comprehensive study of maize metabolism, combining genetic, metabolite and expression profiling methodologies to dissect the genetic basis of metabolic diversity in maize kernels. We quantify 983 metabolite features in 702 maize genotypes planted at multiple locations. We identify 1,459 significant locus–trait associations (P≤1.8 × 10−6) across three environments through metabolite-based genome-wide association mapping. Most (58.5%) of the identified loci are supported by expression QTLs, and some (14.7%) are validated through linkage mapping. Re-sequencing and candidate gene association analysis identifies potential causal variants for five candidate genes involved in metabolic traits. Two of these genes were further validated by mutant and transgenic analysis. Metabolite features associated with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/ncomms4438 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4438

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms4438

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().