Skip to main content

Advertisement

SpringerLink
Log in

Meta-analysis combined with syntenic metaQTL mining dissects candidate loci for maize yield

  • Published:
Molecular Breeding Aims and scope Submit manuscript

Abstract

Yield potential and stability improvement with the goal of ensuring global food security is an important priority. Yield has a quantitative nature and is controlled by quantitative trait loci (QTL) and environmental factors. An increasingly large number of maize yield QTL have been identified, and how to integrate and re-analyze them is challenging. To this end, we tried to combine QTL meta-analysis with homology-based cloning techniques to dissect candidate loci/genes for maize yield. We first collected maize yield-related QTL from public resources. Then, 351 collected QTL were iteratively projected and meta-analyzed to obtain metaQTL (MQTL). A total of 54 MQTL were identified and tended to cluster in the maize genome. Seven MQTL containing ten maize orthologs of rice yield genes were dissected and temporarily termed syntenic MQTL. Maize orthologs of three functionally-characterized rice yield genes, GIF1, WFP/IPA1, and DEP1, were specially selected to undergo phylogenetic, proliferation, and selective pattern analysis. The results showed that maize orthologs were closely related to rice yield genes and subjected to mixed selective pressures, including positive selection during selective sweeps. The power of the combined techniques reported here was primarily validated not only by the congruency of MQTL and recently reported maize yield QTL but also by mined syntenic MQTL containing the well-characterized Miniature1 (Mn1) gene for maize kernel size and weight determination. Maize MQTL, especially syntenic MQTL regions, could serve not only for QTL fine-mapping and cloning but also for the marker-assisted selection breeding program. The maize yield candidate loci/genes presented here also deserve further investigation and will provide clues to the molecular bases of grain yield. Additionally, the combined technique described here will find its way into further quantitative trait research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Agrama HA, Eizenga GC, Yan W (2007) Association mapping of yield and its components in rice cultivars. Mol Breed 19:341–356

    Article  Google Scholar 

  • Ajmone-Marsan P, Monfredini G, Ludwig WF, Melchinger AE, Franceschini P, Pagnotto G, Motto M (1995) In an elite cross of maize a major quantitative trait locus controls one-fourth of the genetic variation for grain yield. Theor Appl Genet 90:415–424

    CAS  Google Scholar 

  • Arcade A, Labourdette A, Falque M, Mangin B, Chardon F, Charcosset A, Joets J (2004) BioMercator: integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics 20:2324–2326

    Article  PubMed  CAS  Google Scholar 

  • Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, Angeles ER, Qian Q, Kitano H, Matsuoka M (2005) Cytokinin oxidase regulates rice grain production. Science 309:741–745

    Article  PubMed  CAS  Google Scholar 

  • Austin DF, Lee M (1996) Comparative mapping in F2:3 and F6:7 generations of quantitative trait loci for grain yield and yield components in maize. Theor Appl Genet 92:817–826

    Article  CAS  Google Scholar 

  • Barrero RA, Bellgard M, Zhang X (2011) Diverse approaches to achieving grain yield in wheat. Funct Integr Genomics 11:37–48

    Article  PubMed  CAS  Google Scholar 

  • Blanc G, Wolfe KH (2004) Widespread paleopolyploidy in model plant species inferred from age distributions of duplicate genes. Plant Cell 16:1667–1678

    Article  PubMed  CAS  Google Scholar 

  • Burstin J, Marget P, Huart M, Moessner A, Mangin B, Duchene C, Desprez B, Munier-Jolain N, Duc G (2007) Developmental genes have pleiotropic effects on plant morphology and source capacity, eventually impacting on seed protein content and productivity in pea. Plant Physiol 144:768–781

    Article  PubMed  CAS  Google Scholar 

  • Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, Murigneux A, Charcosset A (2004) Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics 168:2169–2185

    Article  PubMed  CAS  Google Scholar 

  • Cheng WH, Taliercio EW, Chourey PS (1996) The miniature1 seed locus of maize encodes a cell wall invertase required for normal development of endosperm and maternal cells in the pedicel. Plant Cell 8:971–983

    PubMed  CAS  Google Scholar 

  • Chia JM, Song C, Bradbury PJ, Costich D, de Leon N, Doebley J, Elshire RJ, Gaut B, Geller L, Glaubitz JC, Gore M, Guill KE, Holland J, Hufford MB, Lai J, Li M, Liu X, Lu Y, McCombie R, Nelson R, Poland J, Prasanna BM, Pyhäjärvi T, Rong T, Sekhon RS, Sun Q, Tenaillon MI, Tian F, Wang J, Xu X, Zhang Z, Kaeppler SM, Ross-Ibarra J, McMullen MD, Buckler ES, Zhang G, Xu Y, Ware D (2012) Maize HapMap2 identifies extant variation from a genome in flux. Nat Genet 44:803–807

    Article  PubMed  CAS  Google Scholar 

  • Chopra VS (2011) Chromosomal organization at the level of gene complexes. Cell Mol Life Sci 68:977–990

    Article  PubMed  CAS  Google Scholar 

  • Chourey PS, Jain M, Li QB, Carlson SJ (2006) Genetic control of cell wall invertases in developing endosperm of maize. Planta 223:159–167

    Article  PubMed  CAS  Google Scholar 

  • Comeron JM (1999) K-Estimator: calculation of the number of nucleotide substitutions per site and the confidence intervals. Bioinformatics 15:763–764

    Article  PubMed  CAS  Google Scholar 

  • Danan S, Veyrieras JB, Lefebvre V (2011) Construction of a potato consensus map and QTL meta-analysis offer new insights into the genetic architecture of late blight resistance and plant maturity traits. BMC Plant Biol 11:16

    Article  PubMed  Google Scholar 

  • de Luna Alves Lima M, de Souza CL, Bento DA Jr, de Souza AP, Carlini-Garcia LA (2006) Mapping QTL for grain yield and plant traits in a tropical maize population. Mol Breed 17:227–239

    Article  Google Scholar 

  • Fan C, Xing Y, Mao H, Lu T, Han B, Xu C, Li X, Zhang Q (2006) GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor Appl Genet 112:1164–1171

    Article  PubMed  CAS  Google Scholar 

  • Gaut BS, Morton BR, McCaig BC, Clegg MT (1996) Substitution rate comparisons between grasses and palms: synonymous rate differences at the nuclear gene Adh parallel rate differences at the plastid gene rbcL. Proc Natl Acad Sci USA 93:10274–10279

    Article  PubMed  CAS  Google Scholar 

  • Guo J, Chen Z, Liu Z, Baobao Wang B, Song W, Wei Li W, Chen J, Dai J, Lai J (2011) Identification of genetic factors affecting plant density response through QTL mapping of yield component traits in maize (Zea mays L.). Euphytica 182:409–422

    Article  Google Scholar 

  • Häberle J, Holzapfel J, Schweizer G, Hartl L (2009) A major QTL for resistance against Fusarium head blight in European winter wheat. Theor Appl Genet 119:325–332

    Article  PubMed  Google Scholar 

  • Hanocq E, Laperche A, Jaminon O, Lainé AL, Le Gouis J (2007) Most significant genome regions involved in the control of earliness traits in bread wheat, as revealed by QTL meta-analysis. Theor Appl Genet 114:569–584

    Article  PubMed  CAS  Google Scholar 

  • Hao W, Lin HX (2010) Toward understanding genetic mechanisms of complex traits in rice. J Genet Genomics 37:653–666

    Article  PubMed  CAS  Google Scholar 

  • Hao Z, Li X, Liu X, Xie C, Li M, Zhang D, Zhang S (2010) Meta-analysis of constitutive and adaptive QTL for drought tolerance in maize. Euphytica 174:165–177

    Article  Google Scholar 

  • He G, Luo X, Tian F, Li K, Zhu Z, Su W, Qian X, Fu Y, Wang X, Sun C, Yang J (2006) Haplotype variation in structure and expression of a gene cluster associated with a quantitative trait locus for improved yield in rice. Genome Res 16:618–626

    Article  PubMed  CAS  Google Scholar 

  • Ho C, McCouch R, Smith E (2002) Improvement of hybrid yield by advanced backcross QTL analysis in elite maize. Theor Appl Genet 105:440–448

    Article  PubMed  CAS  Google Scholar 

  • Huang X, Qian Q, Liu Z, Sun H, He S, Luo D, Xia G, Chu C, Li J, Fu X (2009) Natural variation at the DEP1 locus enhances grain yield in rice. Nat Genet 41:494–497

    Article  PubMed  CAS  Google Scholar 

  • Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, Li W, Guo Y, Deng L, Zhu C, Fan D, Lu Y, Weng Q, Liu K, Zhou T, Jing Y, Si L, Dong G, Huang T, Lu T, Feng Q, Qian Q, Li J, Han B (2011) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44:32–39

    Article  PubMed  CAS  Google Scholar 

  • Huang YF, Madur D, Combes V, Ky CL, Coubriche D, Jamin P, Jouanne S, Dumas F, Bouty E, Bertin P, Charcosset A, Moreau L (2010) The genetic architecture of grain yield and related traits in Zea maize L. revealed by comparing intermated and conventional populations. Genetics 186:395–404

    Article  PubMed  CAS  Google Scholar 

  • Hufford MB, Xu X, van Heerwaarden J, Pyhäjärvi T, Chia JM, Cartwright RA, Elshire RJ, Glaubitz JC, Guill KE, Kaeppler SM, Lai J, Morrell PL, Shannon LM, Song C, Springer NM, Swanson-Wagner RA, Tiffin P, Wang J, Zhang G, Doebley J, McMullen MD, Ware D, Buckler ES, Yang S, Ross-Ibarra J (2012) Comparative population genomics of maize domestication and improvement. Nat Genet 44:808–811

    Article  PubMed  CAS  Google Scholar 

  • Ishimaru K (2003) Identification of a locus increasing rice yield and physiological analysis of its function. Plant Physiol 133:1083–1090

    Article  PubMed  CAS  Google Scholar 

  • Jiao Y, Wang Y, Xue D, Wang J, Yan M, Liu G, Dong G, Zeng D, Lu Z, Zhu X, Qian Q, Li J (2010) Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat Genet 42:541–544

    Article  PubMed  CAS  Google Scholar 

  • Jiao Y, Zhao H, Ren L, Song W, Zeng B, Guo J, Wang B, Liu Z, Chen J, Li W, Zhang M, Xie S, Lai J (2012) Genome-wide genetic changes during modern breeding of maize. Nat Genet 44:812–815

    Article  PubMed  CAS  Google Scholar 

  • Khowaja FS, Norton GJ, Courtois B, Price AH (2009) Improved resolution in the position of drought-related QTLs in a single mapping population of rice by meta-analysis. BMC Genomics 10:276

    Article  PubMed  Google Scholar 

  • Kraakman AT, Niks RE, Van den Berg PM, Stam P, Van Eeuwijk FA (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168:435–446

    Article  PubMed  CAS  Google Scholar 

  • Lacape JM, Llewellyn D, Jacobs J, Arioli T, Becker D, Calhoun S, Al-Ghazi Y, Liu S, Palaï O, Georges S, Giband M, de Assunção H, Barroso PA, Claverie M, Gawryziak G, Jean J, Vialle M, Viot C (2010) Meta-analysis of cotton fiber quality QTLs across diverse environments in a Gossypium hirsutum × G. barbadense RIL population. BMC Plant Biol 10:132

    Article  PubMed  Google Scholar 

  • Lanaud C, Fouet O, Clément D, Boccara M, Risterucci AM, Surujdeo-Maharaj S, Legavre T, Argout X (2009) A meta–QTL analysis of disease resistance traits of Theobroma cacao L. Mol Breed 24:361–374

    Article  Google Scholar 

  • Laperche A, Brancourt-Hulmel M, Heumez E, Gardet O, Hanocq E, Devienne-Barret F, Le Gouis J (2007) Using genotype × nitrogen interaction variables to evaluate the QTL involved in wheat tolerance to nitrogen constraints. Theor Appl Genet 115:399–415

    Article  PubMed  CAS  Google Scholar 

  • Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23:2947–2948

    Article  PubMed  CAS  Google Scholar 

  • Li J, Thomson M, McCouch SR (2004) Fine mapping of a grain-weight quantitative trait locus in the pericentromeric region of rice chromosome 3. Genetics 168:2187–2195

    Article  PubMed  CAS  Google Scholar 

  • Li JZ, Zhang ZW, Li YL, Wang QL, Zhou YG (2011a) QTL consistency and meta-analysis for grain yield components in three generations in maize. Theor Appl Genet 122:771–782

    Article  PubMed  CAS  Google Scholar 

  • Li Q, Yang X, Bai G, Warburton ML, Mahuku G, Gore M, Dai J, Li J, Yan J (2010a) Cloning and characterization of a putative GS3 ortholog involved in maize kernel development. Theor Appl Genet 120:753–763

    Article  PubMed  CAS  Google Scholar 

  • Li Q, Li L, Yang X, Warburton ML, Bai G, Dai J, Li J, Yan J (2010b) Relationship, evolutionary fate and function of two maize co-orthologs of rice GW2 associated with kernel size and weight. BMC Plant Biol 10:143

    Article  PubMed  Google Scholar 

  • Li X, Yan W, Agrama H, Jia L, Shen X, Jackson A, Moldenhauer K, Yeater K, McClung A, Wu D (2011b) Mapping QTLs for improving grain yield using the USDA rice mini-core collection. Planta 234:347–361

    Article  PubMed  CAS  Google Scholar 

  • Li XH, Liu XD, Li MS, Zhang SH (2003) Identification of quantitative trait loci for anthesis-silking interval and yield components under drought stress in maize. Acta Bot Sin 45:852–857

    Google Scholar 

  • Li Y, Fan C, Xing Y, Jiang Y, Luo L, Sun L, Shao D, Xu C, Li X, Xiao J, He Y, Zhang Q (2011c) Natural variation in GS5 plays an important role in regulating grain size and yield in rice. Nat Genet 43:1266–1269

    Article  PubMed  CAS  Google Scholar 

  • Li YL, Niu SZ, Dong YB, Cui DQ, Wang YZ, Liu YY, Wei MG (2007) Identification of trait-improving quantitative trait loci for grain yield components from a dent corn inbred line in an advanced backcross BC2F2 population and comparison with its F2:3 population in popcorn. Theor Appl Genet 115:129–140

    Article  PubMed  CAS  Google Scholar 

  • Liu T, Mao D, Zhang S, Xu C, Xing Y (2009) Fine mapping SPP1, a QTL controlling the number of spikelets per panicle, to a BAC clone in rice (Oryza sativa). Theor Appl Genet 118:1509–1517

    Article  PubMed  CAS  Google Scholar 

  • Lu M, Xie CX, Li XH, Hao ZF, Li MS, Weng JF, Zhang DG, Bai L, Zhang SH (2011) Mapping of quantitative trait loci for kernel row number in maize across seven environments. Mol Breed 28:143–152

    Article  CAS  Google Scholar 

  • Mao H, Sun S, Yao J, Wang C, Yu S, Xu C, Li X, Zhang Q (2010a) Linking differential domain functions of the GS3 protein to natural variation of grain size in rice. Proc Natl Acad Sci USA 107:19579–19584

    Article  PubMed  CAS  Google Scholar 

  • Mao SL, Wei YM, Cao W, Lan XJ, Yu M, Chen ZM, Chen GY, Zheng YL (2010b) Confirmation of the relationship between plant height and Fusarium head blight resistance in wheat (Triticum aestivum L.) by QTL meta-analysis. Euphytica 174:343–356

    Article  Google Scholar 

  • Martinez M (2011) Plant protein-coding gene families: emerging bioinformatics approaches. Trends Plant Sci 16:558–567

    Article  PubMed  CAS  Google Scholar 

  • Messmer R, Fracheboud Y, Bänziger M, Vargas M, Stamp P, Ribaut JM (2009) Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits. Theor Appl Genet 119:913–930

    Article  PubMed  Google Scholar 

  • Miura K, Ashikari M, Matsuoka M (2011) The role of QTLs in the breeding of high-yielding rice. Trends Plant Sci 16:319–326

    Article  PubMed  CAS  Google Scholar 

  • Miura K, Ikeda M, Matsubara A, Song XJ, Ito M, Asano K, Matsuoka M, Kitano H, Ashikari M (2010) OsSPL14 promotes panicle branching and higher grain productivity in rice. Nat Genet 42:545–549

    Article  PubMed  CAS  Google Scholar 

  • Peng B, Li Y, Wang Y, Liu C, Liu Z, Tan W, Zhang Y, Wang D, Shi Y, Sun B, Song Y, Wang T, Li Y (2011) QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor Appl Genet 122:1305–1320

    Article  PubMed  Google Scholar 

  • Quraishi UM, Murat F, Abrouk M, Pont C, Confolent C, Oury FX, Ward J, Boros D, Gebruers K, Delcour JA, Courtin CM, Bedo Z, Saulnier L, Guillon F, Balzergue S, Shewry PR, Feuillet C, Charmet G, Salse J (2011a) Combined meta-genomics analyses unravel candidate genes for the grain dietary fiber content in bread wheat (Triticum aestivum L.). Funct Integr Genomics 11:71–83

    Article  PubMed  CAS  Google Scholar 

  • Quraishi UM, Abrouk M, Murat F, Pont C, Foucrier S, Desmaizieres G, Confolent C, Rivière N, Charmet G, Paux E, Murigneux A, Guerreiro L, Lafarge S, Le Gouis J, Feuillet C, Salse J (2011b) Cross-genome map based dissection of a nitrogen use efficiency ortho-metaQTL in bread wheat unravels concerted cereal genome evolution. Plant J 65:745–756

    Article  PubMed  CAS  Google Scholar 

  • Riechmann JL, Heard J, Martin G, Reuber L, Jiang C, Keddie J, Adam L, Pineda O, Ratcliffe OJ, Samaha RR, Creelman R, Pilgrim M, Broun P, Zhang JZ, Ghandehari D, Sherman BK, Yu G (2000) Arabidopsis transcription factors: genome-wide comparative analysis among eukaryotes. Science 290:2105–2110

    Article  PubMed  CAS  Google Scholar 

  • Rong J, Feltus FA, Waghmare VN, Pierce GJ, Chee PW, Draye X, Saranga Y, Wright RJ, Wilkins TA, May OL, Smith CW, Gannaway JR, Wendel JF, Paterson AH (2007) Meta-analysis of polyploid cotton QTL shows unequal contributions of subgenomes to a complex network of genes and gene clusters implicated in lint fiber development. Genetics 176:2577–2588

    Article  PubMed  CAS  Google Scholar 

  • Sabadin PK, de Souza CLJ, de Souza AP, Franco AAG (2008) QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas 145:194–203

    Article  Google Scholar 

  • Sakamoto T, Matsuoka M (2008) Identifying and exploiting grain yield genes in rice. Curr Opin Plant Biol 11:209–214

    Article  PubMed  CAS  Google Scholar 

  • Salvi S, Tuberosa R (2005) To clone or not to clone plant QTLs: present and future challenges. Trends Plant Sci 10:297–304

    Article  PubMed  CAS  Google Scholar 

  • Schön CC, Melchinger AE, Boppenmaier J, Brunklaus-Jung E, Herrmann RG, Seitzer JF (1994) RFLP mapping in maize: quantitative trait loci affecting testcross performance of elite european flint lines. Crop Sci 34:378–389

    Article  Google Scholar 

  • Schön CC, Dhillon BS, Utz HF, Melchinger AE (2010) High congruency of QTL positions for heterosis of grain yield in three crosses of maize. Theor Appl Genet 120:321–332

    Article  PubMed  Google Scholar 

  • Shan JX, Zhu MZ, Shi M, Gao JP, Lin HX (2009) Fine mapping and candidate gene analysis of spd6, responsible for small panicle and dwarfness in wild rice (Oryza rufipogon Griff.). Theor Appl Genet 119:827–836

    Article  PubMed  CAS  Google Scholar 

  • Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, Yano M (2008) Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet 40:1023–1028

    Article  PubMed  CAS  Google Scholar 

  • Sibov ST, de Souza CL, Garcia AA Jr, Silva AR, Garcia AF, Mangolin CA, Benchimol LL, de Souza AP (2003) Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, plant height, ear height and grain moisture. Hereditas 139:107–115

    Article  PubMed  Google Scholar 

  • Song XJ, Huang W, Shi M, Zhu MZ, Lin HX (2007) A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet 39:623–630

    Article  PubMed  CAS  Google Scholar 

  • Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol 24:1596–1599

    Article  PubMed  CAS  Google Scholar 

  • Truntzler M, Barrière Y, Sawkins MC, Lespinasse D, Betran J, Charcosset A, Moreau L (2010) Meta-analysis of QTL involved in silage quality of maize and comparison with the position of candidate genes. Theor Appl Genet 121:1465–1482

    Article  PubMed  CAS  Google Scholar 

  • Tuberosa R, Sanguineti MC, Landi P, Giuliani MM, Salvi S, Conti S (2002) Identification of QTLs for root characteristics in maize grown in hydroponics and analysis of their overlap with QTLs for grain yield in the field at two water regimes. Plant Mol Biol 48:697–712

    Article  PubMed  CAS  Google Scholar 

  • Veldboom LR, Lee M (1994) Molecular-marker-facilitated studies of morphological traits in maize. II: determination of QTLs for grain yield and yield components. Theor Appl Genet 89:451–458

    Article  CAS  Google Scholar 

  • Wang E, Wang J, Zhu X, Hao W, Wang L, Li Q, Zhang L, He W, Lu B, Lin H, Ma H, Zhang G, He Z (2008) Control of rice grain-filling and yield by a gene with a potential signature of domestication. Nat Genet 40:1370–1374

    Article  PubMed  CAS  Google Scholar 

  • Wang P, Zhou G, Cui K, Li Z, Yu S (2012a) Clustered QTL for source leaf size and yield traits in rice (Oryza sativa L.). Mol Breed 29:99–113

    Article  CAS  Google Scholar 

  • Wang S, Wu K, Yuan Q, Liu X, Liu Z, Lin X, Zeng R, Zhu H, Dong G, Qian Q, Zhang G, Fu X (2012b) Control of grain size, shape and quality by OsSPL16 in rice. Nat Genet 44:950–954

    Article  PubMed  CAS  Google Scholar 

  • Wang Y, Deng D, Zhang R, Wang S, Bian Y, Yin Z (2012c) Systematic analysis of plant-specific B3 domain-containing proteins based on the genome resources of 11 sequenced species. Mol Biol Rep 39:6267–6282

    Article  PubMed  CAS  Google Scholar 

  • Wang Y, Deng D, Shi Y, Miao N, Bian Y, Yin Z (2012d) Diversification, phylogeny and evolution of auxin response factor (ARF) family: insights gained from analyzing maize ARF genes. Mol Biol Rep 39:2401–2415

    Article  PubMed  CAS  Google Scholar 

  • Wang Y, Deng D, Bian Y, Xie Q, Lv Y (2010) Cloning and characterization of DNA topoisomerase I gene Top1 from maize (Zea mays L.). Plant Sci 178:47–54

    Article  CAS  Google Scholar 

  • Welcker C, Sadok W, Dignat G, Renault M, Salvi S, Charcosset A, Tardieu F (2011) A common genetic determinism for sensitivities to soil water deficit and evaporative demand: meta-analysis of quantitative trait Loci and introgression lines of maize. Plant Physiol 157:718–729

    Article  PubMed  CAS  Google Scholar 

  • Weng J, Gu S, Wan X, Gao H, Guo T, Su N, Lei C, Zhang X, Cheng Z, Guo X, Wang J, Jiang L, Zhai H, Wan J (2008) Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight. Cell Res 18:1199–1209

    Article  PubMed  CAS  Google Scholar 

  • Xiang K, Reid LM, Zhang ZM, Zhu XY, Pan GT (2012) Characterization of correlation between grain moisture and ear rot resistance in maize by QTL meta-analysis. Euphytica 183:185–195

    Article  CAS  Google Scholar 

  • Xiao W, Zhao J, Fan S, Li L, Dai J, Xu M (2007) Mapping of genome-wide resistance gene analogs (RGAs) in maize (Zea mays L.). Theor Appl Genet 115:501–508

    Article  PubMed  CAS  Google Scholar 

  • Xing Y, Zhang Q (2010) Genetic and molecular bases of rice yield. Annu Rev Plant Biol 61:421–442

    Article  PubMed  CAS  Google Scholar 

  • Xue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q (2008) Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat Genet 40:761–767

    Article  PubMed  CAS  Google Scholar 

  • Yan JB, Tang H, Huang YQ, Zheng YL, Li JS (2006) Quantitative trait loci mapping and epistatic analysis for grain yield and yield components using molecular markers with an elite maize hybrid. Euphytica 149:121–131

    Article  CAS  Google Scholar 

  • Yang M, Ding G, Shi L, Feng J, Xu F, Meng J (2010) Quantitative trait loci for root morphology in response to low phosphorus stress in Brassica napus. Theor Appl Genet 121:181–193

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported by grants from the National Natural Science Foundation of China (31201213), National Basic Research Program (2009CB118400), Priority Academic Program Development of Jiangsu Higher Education Institutions, Scientific and Technological Support Program of Jiangsu Province (BE2011303), Innovative Foundation of Yangzhou University (2010CXJ035), and Practicality and Innovation Training Project for College Students in Jiangsu Province.

Author information

Authors and Affiliations

  1. Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Plant Functional Genomics of Ministry of Education, College of Agriculture, Yangzhou University, Yangzhou, 225009, China

    Yijun Wang, Zhengjin Huang, Dexiang Deng, Rong Zhang, Suxin Wang, Yunlong Bian, Zhitong Yin & Xiangming Xu

  2. Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, China

    Haidong Ding

Authors
  1. Yijun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengjin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dexiang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haidong Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Suxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yunlong Bian
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhitong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiangming Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yijun Wang.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Figure 1

Map projection. Collected maize yield QTL were iteratively projected on the target map IBM2 2008 Neighbors to build an integrated map (TIFF 1652 kb)

Supplementary Figure 2

Meta analysis. Projected maize yield QTL were meta-analyzed. Finally, a total of 54 MQTL were identified and distributed unevenly in maize 10 chromosomes (PPT 3990 kb)

Supplementary Figure 3

Phylogeny of GIF1-like proteins. GIF1-like proteins were primarily classified into algae, dicot, and mixed groups, although dicot and mixed groups could be further separated into several subgroups. It was GRMZM2G095725 but MN1 that was positioned in the subtree containing rice GIF1. Number in parentheses represents gene number. Scale bar 0.1 denotes 0.1 amino acid substitution per site (TIFF 84 kb)

Supplementary Table 1

Detailed information on 351 maize yield QTL (XLSX 26 kb)

Supplementary Table 2

QTL number change by meta-analysis (DOC 37 kb)

Supplementary Table 3

Rice yield-related genes reported by Huang et al. (2011) and their maize orthologs (DOC 46 kb)

Supplementary Table 4

Number of SBP, DEP1- and GIF1-like proteins in seven species (DOC 32 kb)

Supplementary Table 5

Duplication time estimation of the segmental block (DOC 30 kb)

Supplementary Table 6

SSR markers flanking MQTL regions (DOC 141 kb)

Supplementary Table 7

Selective signals survey of ten genes in MQTL regions (DOCX 16 kb)

Supplementary text file 1

Sequence information on SBP, DEP1- and GIF1-like proteins (TXT 144 kb)

Supplementary text file 2

List of abbreviations of terms referred in the text (DOCX 13 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Y., Huang, Z., Deng, D. et al. Meta-analysis combined with syntenic metaQTL mining dissects candidate loci for maize yield. Mol Breeding 31, 601–614 (2013). https://doi.org/10.1007/s11032-012-9818-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11032-012-9818-4

Keywords

Search

Navigation