Genome-wide association mapping of iron homeostasis in the maize association population

BMC Genetics

Benjamin Stich¹  Claude Urbany¹  Andreas Benke¹ 

[1] Max Planck Institute for Plant Breeding Research, Carl-von-Linné Weg 10, 50829 Köln, Germany

关键词: Marker assisted selection;    Genome-wide association;    Fine-mapping;    Association mapping population;    Fe-efficiency;   

Others  :  1131331
DOI  :  10.1186/s12863-014-0153-0

 received in 2014-06-09, accepted in 2014-09-25,  发布年份 2015

【 摘 要 】

Background

Iron (Fe) deficiency in plants is the result of low Fe soil availability affecting 30% of cultivated soils worldwide. To improve our understanding on Fe-efficiency this study aimed to (i) evaluate the influence of two different Fe regimes on morphological and physiological trait formation, (ii) identify polymorphisms statistically associated with morphological and physiological traits, and (iii) dissect the correlation between morphological and physiological traits using an association mapping population.

Results

The fine-mapping analyses on quantitative trait loci (QTL) confidence intervals of the intermated B73 × Mo17 (IBM) population provided a total of 13 and 2 single nucleotide polymorphisms (SNPs) under limited and adequate Fe regimes, respectively, which were significantly (FDR = 0.05) associated with cytochrome P450 94A1, invertase beta-fructofuranosidase insoluble isoenzyme 6, and a low-temperature-induced 65 kDa protein. The genome-wide association (GWA) analyses under limited and adequate Fe regimes provided in total 18 and 17 significant SNPs, respectively.

Conclusions

Significantly associated SNPs on a genome-wide level under both Fe regimes for the traits leaf necrosis (NEC), root weight (RW), shoot dry weight (SDW), water (H ₂O), and SPAD value of leaf 3 (SP3) were located in genes or recognition sites of transcriptional regulators, which indicates a direct impact on the phenotype. SNPs which were significantly associated on a genome-wide level under both Fe regimes with the traits NEC, RW, SDW, H ₂O, and SP3 might be attractive targets for marker assisted selection as well as interesting objects for future functional analyses.

【 授权许可】

   
2015 Benke et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

【 参考文献 】

• [1]Mori S: Iron acquisition by plants. Curr Opin Plant Biol 1999, 2:250-253.
• [2]Marschner H: Mineral Nutrition of Higher Plants (Second Edition), UK: Elsevier; 1995.
• [3]Curie C, Briat JF: Iron transport and signaling in plants. Annu Rev Plant Biol 2003, 54:183-206.
• [4]Guerinot M: It’s elementary: Enhancing Fe3+ reduction improves rice yields. Proc Nat Acad Sci USA 2007, 104:7311-7312.
• [5]Vert G, Grotz N, Dédaldéchamp F, Gaymard F, Guerinot M, Briat JF, Curie C: IRT1, an Arabidopsis transporter essential for iron uptake from the soil and for plant growth. Plant Cell 2002, 14:1223-1233.
• [6]Römheld V: Existence of two different strategies for the acquisition of iron in higher plants. In Iron Transport in Microbes, Plants and Animals. Edited by Winkelmann G, van der Helm D, Wiley-VCH. Federal Republic of Germany; 1987:353–374.
• [7]Curie C, Panaviene Z, Loulergue C, Dellaporta S, Briat JF, Walker E: Maize yellow stripe1 encodes a membrane protein directly involved in FeIII uptake. Nature 2001, 409:346-349.
• [8]Kobayashi T, Nishizawa N: Iron uptake, translocation, and regulation in higher plants. Annu Rev Plant Biol 2012, 63:131-152.
• [9]Lee S, Ryoo N, Jeon JS, Guerinot M, An G: Activation of rice Yellow stripe1-like 16 (OsYSL16) enhances iron efficiency. Mol Cells 2012, 33:117-126.
• [10]Urbany C, Benke A, Marsian J, Huettel B, Reinhardt R, Stich B: Ups and downs of a transcriptional landscape shape iron deficiency associated chlorosis of the maize inbreds B73 and Mo17. BMC Plant Biol 2013, 13:213. BioMed Central Full Text
• [11]Lee M, Sharopova N, Beavis W, Grant D, Katt M, Blair D, Hallauer A: Expanding the genetic map of maize with the intermated B73 x Mo17 (IBM) population. Plant Mol Biol 2002, 48:453-461.
• [12]Benke A, Urbany C, Marsian J, Shi R, von Wirén N, Stich B: The genetic basis of natural variation for iron homeostasis in the maize IBM population. BMC Plant Biol 2014, 14:12. BioMed Central Full Text
• [13]Nguyen V, Ribot S, Dolstra O, Niks R, Visser R, van der Linden C: Identification of quantitative trait loci for ion homeostasis and salt tolerance in barley (Hordeum vulgare L.). Mol Breeding 2013, 31:137-152.
• [14]Long T, Tsukagoshi H, Busch W, Lahner B, Salt D, Benfey P: The bHLH transcription factor POPEYE regulates response to iron deficiency in arabidopsis roots. Plant Cell 2010, 22:2219-2236.
• [15]Tijet N, Helvig C, Pinot F, Le Bouquin R, Lesot A, Durst F, Salaün JP, Benveniste I: Functional expression in yeast and characterization of a clofibrate-inducible plant cytochrome P-450 (CYP94A1) involved in cutin monomers synthesis. Biochem J 1998, 332:583-589.
• [16]Colangelo E, Guerinot M: The essential basic helix-loop-helix protein FIT1 is required for the iron deficiency response. Plant Cell 2004, 16:3400-3412.
• [17]Mizutani M, Ward E, Ohta D: Cytochrome p450 superfamily in Arabidopsis thaliana: Isolation of cDNAs, differential expression, and RFLP mapping of multiple cytochromes P450. Plant Mol Biol 1998, 37:39-52.
• [18]Cho JI, Lee SK, Ko S, Kim HK, Jun SH, Lee YH, Seong H, Lee KW, An G, Hahn TR, Jeon JS: Molecular cloning and expression analysis of the cell-wall invertase gene family in rice (Oryza sativa L.). Plant Cell Rep 2005, 24:225-236.
• [19]Nordin K, Vahala T, Palva E: Differential expression of two related, low-temperature-induced genes in Arabidopsis thaliana (L.) Heynh. Plant Mol Biol 1993, 21:641-653.
• [20]Hundertmark M, Hincha D: LEA (Late Embryogenesis Abundant) proteins and their encoding genes in Arabidopsis thaliana. BMC Genomics2008:9–118. 9.
• [21]O’Rourke J, Charlson D, Gonzalez D, Vodkin L, Graham M, Cianzio S, Grusak M, Shoemaker R: Microarray analysis of iron deficiency chlorosis in near-isogenic soybean lines. BMC Genomics2007:8–476. 8.
• [22]Van Inghelandt D, Melchinger A, Martinant JP, Stich B: Genome-wide association mapping of flowering time and northern corn leaf blight (Setosphaeria turcica) resistance in a vast commercial maize germplasm set. BMC Plant Biol2012:12–56. 12.
• [23]Larsson S, Lipka A, Buckler E: Lessons from Dwarf8 on the strengths and weaknesses of structured association mapping. PLoS Genet 2013, 9:e1003246.
• [24]Benke A, Stich B: An analysis of selection on candidate genes for regulation, mobilization, uptake, and transport of iron in maize. Genome 2011, 54:674-683.
• [25]Andersen J, Lübberstedt T: Functional markers in plants. Trends Plant Sci 2003, 8:554-560.
• [26]Flint-Garcia S, Thuillet AC, Yu J, Pressoir G, Romero S, Mitchell S, Doebley J, Kresovich S, Goodman M, Buckler E: Maize association population: A high-resolution platform for quantitative trait locus dissection. Plant J 2005, 44:1054-1064.
• [27]von Wirén N, Marschner H, Römheld V: Roots of iron-efficient maize also absorb phytosiderophore-chelated zinc. Plant Physiol 1996, 111:1119-1125.
• [28]Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol (Clifton, N.J.) 2000, 132:365-386.
• [29]Chakravarti I, Laha R, Roy J: Handbook of Methods of Applied Statistics, Volume I, New York: John Wiley and Sons; 1967.
• [30]Stich B, Möhring J, Piepho HP, Heckenberger M, Buckler E, Melchinger A: Comparison of mixed-model approaches for association mapping. Genetics 2008, 178:1745-1754.
• [31]Kang H, Zaitlen N, Wade C, Kirby A, Heckerman D, Daly M, Eskin E: Efficient control of population structure in model organism association mapping. Genetics 2008, 178:1709-1723.
• [32]Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc. Ser B (Methodological) 1995, 57:289-300.
• [33]Sun G, Zhu C, Kramer M, Yang SS, Song W, Piepho HP, Yu J: Variation explained in mixed-model association mapping. Heredity 2010, 105:333-340.
• [34]Remington D, Thornsberry J, Matsuoka Y, Wilson L, Whitt S, Doebley J, Kresovich S, Goodman M, Buckler E IV: Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc Nat Acad Sci USA 2001, 98:11479-11484.
• [35]R Core Team: R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing; 2012.