Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights
Weiwei Wen
Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights - 2014
Peer-review: Yes - Open Access: Yes | http://ip-science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0028-0836 Peer review Open Access
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.
English
1476-4687 (Revista en electrónico) 0028-0836
https://doi.org/10.1038/ncomms4438
Biological Sciences
Plant sciences
Genetics
Quantitative Trait Loci
Metabolites
Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights - 2014
Peer-review: Yes - Open Access: Yes | http://ip-science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0028-0836 Peer review Open Access
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.
English
1476-4687 (Revista en electrónico) 0028-0836
https://doi.org/10.1038/ncomms4438
Biological Sciences
Plant sciences
Genetics
Quantitative Trait Loci
Metabolites