Literature DB >> 22615396

Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize.

Christian Riedelsheimer1, Jan Lisec, Angelika Czedik-Eysenberg, Ronan Sulpice, Anna Flis, Christoph Grieder, Thomas Altmann, Mark Stitt, Lothar Willmitzer, Albrecht E Melchinger.   

Abstract

The diversity of metabolites found in plants is by far greater than in most other organisms. Metabolic profiling techniques, which measure many of these compounds simultaneously, enabled investigating the regulation of metabolic networks and proved to be useful for predicting important agronomic traits. However, little is known about the genetic basis of metabolites in crops such as maize. Here, a set of 289 diverse maize inbred lines was genotyped with 56,110 SNPs and assayed for 118 biochemical compounds in the leaves of young plants, as well as for agronomic traits of mature plants in field trials. Metabolite concentrations had on average a repeatability of 0.73 and showed a correlation pattern that largely reflected their functional grouping. Genome-wide association mapping with correction for population structure and cryptic relatedness identified for 26 distinct metabolites strong associations with SNPs, explaining up to 32.0% of the observed genetic variance. On nine chromosomes, we detected 15 distinct SNP-metabolite associations, each of which explained more then 15% of the genetic variance. For lignin precursors, including p-coumaric acid and caffeic acid, we found strong associations (P values to ) with a region on chromosome 9 harboring cinnamoyl-CoA reductase, a key enzyme in monolignol synthesis and a target for improving the quality of lignocellulosic biomass by genetic engineering approaches. Moreover, lignin precursors correlated significantly with lignin content, plant height, and dry matter yield, suggesting that metabolites represent promising connecting links for narrowing the genotype-phenotype gap of complex agronomic traits.

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Year:  2012        PMID: 22615396      PMCID: PMC3384205          DOI: 10.1073/pnas.1120813109

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  46 in total

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Authors:  Yurii S Aulchenko; Stephan Ripke; Aaron Isaacs; Cornelia M van Duijn
Journal:  Bioinformatics       Date:  2007-03-23       Impact factor: 6.937

2.  Recombination and linkage disequilibrium in Arabidopsis thaliana.

Authors:  Sung Kim; Vincent Plagnol; Tina T Hu; Christopher Toomajian; Richard M Clark; Stephan Ossowski; Joseph R Ecker; Detlef Weigel; Magnus Nordborg
Journal:  Nat Genet       Date:  2007-08-05       Impact factor: 38.330

3.  System-wide molecular evidence for phenotypic buffering in Arabidopsis.

Authors:  Jingyuan Fu; Joost J B Keurentjes; Harro Bouwmeester; Twan America; Francel W A Verstappen; Jane L Ward; Michael H Beale; Ric C H de Vos; Martijn Dijkstra; Richard A Scheltema; Frank Johannes; Maarten Koornneef; Dick Vreugdenhil; Rainer Breitling; Ritsert C Jansen
Journal:  Nat Genet       Date:  2009-01-25       Impact factor: 38.330

4.  Variation explained in mixed-model association mapping.

Authors:  G Sun; C Zhu; M H Kramer; S-S Yang; W Song; H-P Piepho; J Yu
Journal:  Heredity (Edinb)       Date:  2010-02-10       Impact factor: 3.821

5.  A genome-wide association study of metabolic traits in human urine.

Authors:  Karsten Suhre; Henri Wallaschofski; Johannes Raffler; Nele Friedrich; Robin Haring; Kathrin Michael; Christina Wasner; Alexander Krebs; Florian Kronenberg; David Chang; Christa Meisinger; H-Erich Wichmann; Wolfgang Hoffmann; Henry Völzke; Uwe Völker; Alexander Teumer; Reiner Biffar; Thomas Kocher; Stephan B Felix; Thomas Illig; Heyo K Kroemer; Christian Gieger; Werner Römisch-Margl; Matthias Nauck
Journal:  Nat Genet       Date:  2011-05-15       Impact factor: 38.330

Review 6.  Advances in modifying lignin for enhanced biofuel production.

Authors:  Blake A Simmons; Dominique Loqué; John Ralph
Journal:  Curr Opin Plant Biol       Date:  2010-03-30       Impact factor: 7.834

7.  Understanding the evolution of defense metabolites in Arabidopsis thaliana using genome-wide association mapping.

Authors:  Eva K F Chan; Heather C Rowe; Daniel J Kliebenstein
Journal:  Genetics       Date:  2009-09-07       Impact factor: 4.562

8.  QTL and candidate genes phytoene synthase and zeta-carotene desaturase associated with the accumulation of carotenoids in maize.

Authors:  J C Wong; R J Lambert; E T Wurtzel; T R Rocheford
Journal:  Theor Appl Genet       Date:  2003-10-02       Impact factor: 5.699

9.  Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines.

Authors:  Susanna Atwell; Yu S Huang; Bjarni J Vilhjálmsson; Glenda Willems; Matthew Horton; Yan Li; Dazhe Meng; Alexander Platt; Aaron M Tarone; Tina T Hu; Rong Jiang; N Wayan Muliyati; Xu Zhang; Muhammad Ali Amer; Ivan Baxter; Benjamin Brachi; Joanne Chory; Caroline Dean; Marilyne Debieu; Juliette de Meaux; Joseph R Ecker; Nathalie Faure; Joel M Kniskern; Jonathan D G Jones; Todd Michael; Adnane Nemri; Fabrice Roux; David E Salt; Chunlao Tang; Marco Todesco; M Brian Traw; Detlef Weigel; Paul Marjoram; Justin O Borevitz; Joy Bergelson; Magnus Nordborg
Journal:  Nature       Date:  2010-03-24       Impact factor: 49.962

10.  A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species.

Authors:  Robert J Elshire; Jeffrey C Glaubitz; Qi Sun; Jesse A Poland; Ken Kawamoto; Edward S Buckler; Sharon E Mitchell
Journal:  PLoS One       Date:  2011-05-04       Impact factor: 3.240

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  139 in total

1.  Genome-wide association study (GWAS) of carbon isotope ratio (δ13C) in diverse soybean [Glycine max (L.) Merr.] genotypes.

Authors:  Arun Prabhu Dhanapal; Jeffery D Ray; Shardendu K Singh; Valerio Hoyos-Villegas; James R Smith; Larry C Purcell; C Andy King; Perry B Cregan; Qijian Song; Felix B Fritschi
Journal:  Theor Appl Genet       Date:  2014-11-04       Impact factor: 5.699

2.  Natural Variation of Plant Metabolism: Genetic Mechanisms, Interpretive Caveats, and Evolutionary and Mechanistic Insights.

Authors:  Nicole E Soltis; Daniel J Kliebenstein
Journal:  Plant Physiol       Date:  2015-08-13       Impact factor: 8.340

3.  Evolutionary Metabolomics Identifies Substantial Metabolic Divergence between Maize and Its Wild Ancestor, Teosinte.

Authors:  Guanghui Xu; Jingjing Cao; Xufeng Wang; Qiuyue Chen; Weiwei Jin; Zhen Li; Feng Tian
Journal:  Plant Cell       Date:  2019-06-21       Impact factor: 11.277

4.  High-density genotyping: an overkill for QTL mapping? Lessons learned from a case study in maize and simulations.

Authors:  Michael Stange; H Friedrich Utz; Tobias A Schrag; Albrecht E Melchinger; Tobias Würschum
Journal:  Theor Appl Genet       Date:  2013-07-17       Impact factor: 5.699

5.  Genome-wide association analysis for nine agronomic traits in maize under well-watered and water-stressed conditions.

Authors:  Yadong Xue; Marilyn L Warburton; Mark Sawkins; Xuehai Zhang; Tim Setter; Yunbi Xu; Pichet Grudloyma; James Gethi; Jean-Marcel Ribaut; Wanchen Li; Xiaobo Zhang; Yonglian Zheng; Jianbing Yan
Journal:  Theor Appl Genet       Date:  2013-07-25       Impact factor: 5.699

Review 6.  Genomic and epigenetic insights into the molecular bases of heterosis.

Authors:  Z Jeffrey Chen
Journal:  Nat Rev Genet       Date:  2013-06-11       Impact factor: 53.242

Review 7.  Cell metabolomics.

Authors:  Aihua Zhang; Hui Sun; Hongying Xu; Shi Qiu; Xijun Wang
Journal:  OMICS       Date:  2013-08-29

8.  Genetic analysis of the metabolome exemplified using a rice population.

Authors:  Liang Gong; Wei Chen; Yanqiang Gao; Xianqing Liu; Hongyan Zhang; Caiguo Xu; Sibin Yu; Qifa Zhang; Jie Luo
Journal:  Proc Natl Acad Sci U S A       Date:  2013-11-20       Impact factor: 11.205

9.  High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth.

Authors:  Xuehai Zhang; Chenglong Huang; Di Wu; Feng Qiao; Wenqiang Li; Lingfeng Duan; Ke Wang; Yingjie Xiao; Guoxing Chen; Qian Liu; Lizhong Xiong; Wanneng Yang; Jianbing Yan
Journal:  Plant Physiol       Date:  2017-01-30       Impact factor: 8.340

10.  Genome-wide association mapping combined with reverse genetics identifies new effectors of low water potential-induced proline accumulation in Arabidopsis.

Authors:  Paul E Verslues; Jesse R Lasky; Thomas E Juenger; Tzu-Wen Liu; M Nagaraj Kumar
Journal:  Plant Physiol       Date:  2013-11-11       Impact factor: 8.340

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