Dissecting the genetics of cold tolerance in a multiparental maize population

  • Q. Yi
  • R. A. Malvar
  • L. Álvarez-Iglesias
  • B. Ordás
  • Pedro RevillaEmail author
Original Article


Key message

We identify the largest amount of QTLs for cold tolerance in maize; mainly associated with photosynthetic efficiency, which opens new possibilities for genomic selection for cold tolerance in maize.


Breeding for cold tolerance in maize is an important objective in temperate areas. The objective was to carry out a highly efficient study of quantitative trait loci (QTLs) for cold tolerance in maize. We evaluated 406 recombinant inbred lines from a multi-parent advanced generation intercross (MAGIC) population in a growth chamber under cold and control conditions, and in the field at early and normal sowing. We recorded cold tolerance-related traits, including the number of days from sowing to emergence, chlorophyll content and maximum quantum efficiency of photosystem II (Fv/Fm). Association mapping was based on genotyping with near one million single nucleotide polymorphism (SNP) markers. We found 858 SNPs significantly associated with all traits, most of them under cold conditions and early sowing. Most QTLs were associated with chlorophyll and Fv/Fm. Many candidate genes coincided between the current research and previous reports. These results suggest that (1) the MAGIC population is an efficient tool for identifying QTLs for cold tolerance; (2) most QTLs for cold tolerance were associated with Fv/Fm; (3) most of these QTLs were located in specific genomic regions, particularly bin 10.04; (4) the current study allows genetically improving cold tolerance with genome-wide selection.



Best linear unbiased estimators


Genome-wide association analyses


Population of RIL released from the maize inbred lines B73 and Mo17


Multi-parent advanced generations intercross population


Quantitative trait loci


Single nucleotide polymorphism


Soil–plant analyses development is the relative amount of chlorophyll estimated by measuring the absorbance of the leaf in two wavelength regions


Maximum quantum efficiency of photosystem II



This research was supported by the Spanish Plan for Research and Development (AGL2016-77628-R) and funded in part by the European Regional Development Fund (FEDER). The genotypic data were provided by the Biotechnological Institute of the Cornell University USA). Q Yi acknowledges a grant from the China Scholarship Council (CSC).

Author contribution statement

BO and PR have designed the experiments. LAI and PR have conducted the experiments. QY and RAM have made the statistical analyses. QY has written the text. PR has edited and submitted the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

122_2019_3482_MOESM1_ESM.xlsx (12 kb)
Supplementary file1 (XLSX 12 kb)
122_2019_3482_MOESM2_ESM.xlsx (13 kb)
Supplementary file2 (XLSX 12 kb)
122_2019_3482_MOESM3_ESM.xlsx (91 kb)
Supplementary file3 (XLSX 90 kb)
122_2019_3482_MOESM4_ESM.xlsx (514 kb)
Supplementary file4 (XLSX 513 kb)
122_2019_3482_MOESM5_ESM.jpg (413 kb)
Supplementary Figure S1. Scheme for the development of the MAGIC population (Jiménez-Galindo et al. 2019) (JPG 413 kb)
122_2019_3482_MOESM6_ESM.jpg (358 kb)
Supplementary Figure S2. Max, Min, and Average Temperature for each day from the 24th April to the 30th June in 2016 at Pontevedra in Spain. The X- and Y- axes indicate the date and temperature (ºC) from the 24th April to the 30th June in 2016, respectively. Note: temperature data are available on the website “” (JPG 358 kb)
122_2019_3482_MOESM7_ESM.jpg (1.5 mb)
Supplementary Figure S3. Distribution of BLUEs for four seedling traits in a MAGIC maize population and its parents in a growth chamber under cold and control conditions, as well as in the field at early and normal sowing dates. Chamber-Cold, Chamber-Control, Field-Early Sowing, and Field-Normal Sowing indicate the chamber under cold condition, the chamber under control condition, the field at early sowing date, and the field at normal sowing date, respectively. The distribution of eight founders is indicated with different colors and arrows (JPG 1583 kb)
122_2019_3482_MOESM8_ESM.jpg (1.1 mb)
Supplementary Figure S4. Manhattan plot from a mixed linear model for early seedling traits in a MAGIC maize population. SNPs above the orange horizontal line surpassed the threshold of p = 2.42×10-5. The different colors indicate the 10 different chromosomes of maize. A and B indicate Manhattan plots for days to emergence in a chamber under cold and control conditions, respectively. C and D indicate Manhattan plots for germination rate in the field at early and normal sowing dates, respectively. E, F, G, and H indicate Manhattan plots for early vigor in a chamber under cold and control conditions, as well as in the field at early and normal sowing dates, respectively. I, J, K, and L indicate Manhattan plots for chlorophyll in a chamber under cold and control conditions, as well as in the field at early and normal sowing dates, respectively. M and N indicate Manhattan plots for maximum quantum efficiency of photosystem II (Fv/Fm) in a chamber under control condition and in the field at early normal sowing date, respectively. O and P indicate Manhattan plots for dry weight in a chamber under cold and control conditions, respectively (JPG 1085 kb)
122_2019_3482_MOESM9_ESM.jpg (1.1 mb)
Supplementary file9 (JPG 1079 kb)
122_2019_3482_MOESM10_ESM.jpg (1.1 mb)
Supplementary file10 (JPG 1083kb)
122_2019_3482_MOESM11_ESM.jpg (571 kb)
Supplementary file11 (JPG 570 kb)
122_2019_3482_MOESM12_ESM.png (50 kb)
Supplementary Figure S5 Graphical results of eight significant gene ontology (GO) terms for 134 candidate genes containing the most significant SNPs within QTLs for five early seedling traits in GO enrichment analysis (PNG 50 kb)
122_2019_3482_MOESM13_ESM.jpg (397 kb)
Supplementary Figure S6. Principal component analysis of the SNPs in the RILs of the MAGIC population (Jiménez-Galindo et al. 2019). The parental lines were shown in the presented Figure (JPG 396 kb)


  1. Agarwal M, Hao Y, Kapoor A, Dong CH, Fujii H, Zheng X, Zhu JK (2006) A R2R3 type MYB transcription factor is involved in the cold regulation of CBF genes and in acquired freezing tolerance. J Biol Chem 281:37636–37645PubMedCrossRefGoogle Scholar
  2. Allam M, Revilla P, Djemel A, Tracy WF, Ordás B (2016) Identification of QTLs involved in cold tolerance in sweet × field corn. Euphytica 208:353–365CrossRefGoogle Scholar
  3. Bandillo N, Raghavan C, Muyco PA, Sevilla MA, Lobina IT, Dilla-Ermita CJ, Tung CW, McCouch S, Thomson M, Mauleon R, Singh RK, Gregorio G, Redoña E, Leung H (2013) Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 6(1):11PubMedPubMedCentralCrossRefGoogle Scholar
  4. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635PubMedCrossRefGoogle Scholar
  5. Chen DH, Ronald PC (1999) A rapid DNA minipreparation method suitable for AFLP and other PCR applications. Plant Mol Biol Rep 17:53–57CrossRefGoogle Scholar
  6. Chinnusamy V, Ohta M, Kanrar S, Lee BH, Hong X, Agarwal M, Zhu JK (2003) ICE1: A regulator of cold-induced transcriptome and freezing tolerance in Arabidopsis. Genes Develop 17:1043–1054PubMedCrossRefGoogle Scholar
  7. Dell’Acqua M, Gatti DM, Pea G, Cattonaro F, Coppens F, Magris G, Hlaing AL, Aung HH, Nelissen H, Baute J, Frascaroli E, Churchill GA, Inzé D, Morgante M, Pè ME (2015) Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays. Genome Biol 16:167PubMedPubMedCentralCrossRefGoogle Scholar
  8. Dong CH, Agarwal M, Zhang Y, Xie Q, Zhu JK (2006) The negative regulator of plant cold responses, HOS1, is a RING E3 ligase that mediates the ubiquitination and degradation of ICE1. Proc Nat Acad Sci USA 103:8281–8286PubMedCrossRefGoogle Scholar
  9. Du H, Zhu J, Su H, Huang M, Wang H, Ding S, Zhang B, Luo A, Wei S, Tian X, Xu Y (2017) Bulked segregant RNA-seq reveals differential expression and SNPs of candidate genes associated with waterlogging tolerance in maize. Front Plant Sci 8:1022PubMedPubMedCentralCrossRefGoogle Scholar
  10. Flint-Garcia SA, Thornsberry JM, Buckler SE IV (2003) Structure of linkage disequilibrium in plants. Ann Rev Plant Biol 54:357–374CrossRefGoogle Scholar
  11. Flint-Garcia SA, Thuillet AC, Yu J, Pressoir G, Romero SM, Mitchell SE, Doebley J, Kresovich S, Goodman MM, Buckler ES (2005) Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J 44:1054–1064PubMedCrossRefGoogle Scholar
  12. Fracheboud Y, Ribaut JM, Messmer R, Stamp P (2002) Identification of quantitative trait loci for cold-tolerance of photosynthesis in maize (Zea mays L.). J Exp Bot 376:1967–1977CrossRefGoogle Scholar
  13. Fracheboud Y, Jompuk C, Ribaut JM, Stamp P, Leipner J (2004) Genetic analysis of cold-tolerance of photosynthesis in maize. Plant Mol Biol 56:241–253PubMedCrossRefGoogle Scholar
  14. Frascaroli E, Revilla P (2018) Genomics of cold tolerance in maize. In: Bennetzen J, Flint-Garcia S, Hirsch C, Tuberosa R (eds) The maize genome. Springer Nature, Switzerland, pp 287–303CrossRefGoogle Scholar
  15. Guan H, Ali F, Pan Q (2017) Dissection of recombination attributes for multiple maize populations using a common SNP assay. Front Plant Sci 8:2063PubMedPubMedCentralCrossRefGoogle Scholar
  16. Guerra-Peraza O, Leipner J, Reimer R, Thuy Nguyen H, Stamp P, Fracheboud Y (2011) Temperature at night affects the genetic control of acclimation to cold in maize seedlings. Maydica 56:366–377Google Scholar
  17. Holland JB, Nyquist WE, Cervantes-Martínez CT (2005) Estimated an interpreting heritability for plant breeding. In: Janick J (ed.) Plant Breeding Reviews. Hoboken, New Jersey, USA: Jonh Wiley & Sons press, Inc pp. 9–112Google Scholar
  18. Hu S, Lübberstedt T, Zhao G, Lee M (2016) QTL mapping of low-temperature germination ability in the maize IBM Syn4 RIL population. PLoS ONE 11(3):e0152795PubMedPubMedCentralCrossRefGoogle Scholar
  19. Hu G, Li Z, Lu Y, Li C, Gong S, Yan S, Li G, Wang M, Ren H, Guan H, Zhang Z, Qin D, Chai M, Yu J, Li Y, Yang D, Wang T, Zhang Z (2017) Genome-wide association study identifed multiple genetic loci on chilling resistance during germination in maize. Sci Rep 7:10840PubMedPubMedCentralCrossRefGoogle Scholar
  20. Huang BE, George AW, Forrest KL, Kilian A, Hayden MJ, Morell MK, Cavanagh CR (2012) A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotech J 10:826–839CrossRefGoogle Scholar
  21. Huang J, Zhang J, Li W, Hu W, Duan L, Feng Y, Que F, Yue B (2013) Genome wide association analysis of ten chilling tolerance indices at the germination and seedling stages in maize. J Integr Plant Biol 55:735–744PubMedCrossRefGoogle Scholar
  22. Hund A, Fracheboud Y, Soldati A, Frascaroli E, Salvi S, Stamp P (2004) QTL controlling root and shoot traits of maize seedlings under cold stress. Theor Appl Genet 109:618–629PubMedCrossRefGoogle Scholar
  23. Jiménez-Galindo JC, Malvar RA, Butrón A, Santiago R, Samayoa LF and Ordás B (2019) Mapping of resistance to Mediterranean corn borer in a MAGIC population of maize. BMC Plant Biol (accepted)Google Scholar
  24. Jompuk C, Fracheboud Y, Stamp P, Leipner J (2005) Mapping of quantitative trait loci associated with chilling tolerance in maize (Zea mays L.) seedlings grown under field conditions. J Exp Bot 56:1153–1163PubMedCrossRefGoogle Scholar
  25. Kim SH, Kim HS, Bahk S, An J, Yoo Y, Kim JY, Chung WS (2017) Phosphorylation of the transcriptional repressor MYB15 by mitogen activated protein kinase6 is required for freezing tolerance in Arabidopsis. Nuc Acids Res 45:6613–6627CrossRefGoogle Scholar
  26. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, Durrant C, Mott R (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5(7):e1000551PubMedPubMedCentralCrossRefGoogle Scholar
  27. Kucharik CJ (2006) A multidecadal trend of earlier corn planting in the central USA. Agro J 98:1544–1550CrossRefGoogle Scholar
  28. Kurepin LV, Dahal KP, Savitch LV, Singh J, Bode R, Ivanov AG, Hurry V, Hüner NPA (2013) Role of CBFs as integrators of chloroplast redox, phytochrome and plant hormone signaling during cold acclimation. Int J Mol Sci 14:12729–12763PubMedPubMedCentralCrossRefGoogle Scholar
  29. Leipner J, Stamp P (2009) Chilling stress in maize seedlings. In: Bennetzen JL, Hake SC (eds) Handbook of Maize: Its Biology. Springer Press, Inc., New York, pp 291–310CrossRefGoogle Scholar
  30. Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95:221–227PubMedCrossRefGoogle Scholar
  31. Li Y, Li C, Bradbury P, Liu X, Lu F, Romay CM, Glaubitz JC, Wu X, Peng B, Shi Y, Song Y, Zhang D, Buckler ES, Zhang Z, Li Y, Wang T (2016) Identification of genetic variants associated with maize flowering time using an extremely large multigenetic background population. Plant J 86:391–402PubMedCrossRefGoogle Scholar
  32. Li H, Ding Y, Shi Y, Zhang X, Zhang S, Gong Z, Yang S (2017) MPK3- and MPK6-mediated ICE1 phosphorylation negatively regulates ICE1 stability and freezing tolerance in Arabidopsis. Develop Cell 43:630–642CrossRefGoogle Scholar
  33. Li P, Cao W, Fang H, Xu S, Yin S, Zhang Y, Lin D, Wang J, Chen Y, Xu C, Yang Z (2017a) Transcriptomic profiling of the maize (Zea mays L.) leaf response to abiotic stresses at the seedling stage. Front Plant Sci 8: 290Google Scholar
  34. Li X, Wang G, Fu J, Li L, Jia G, Ren L, Lubberstedt T, Wang G, Wang J, Gu R (2018) QTL mapping in three connected populations reveals a set of consensus genomic regions for low temperature germination ability in Zea mays L. Front Plant Sci 9:65PubMedPubMedCentralCrossRefGoogle Scholar
  35. Liu Y, Zhou J (2018) MAPping kinase regulation of ICE1 in freezing tolerance. Trends Plant Sci 23:91–93PubMedCrossRefGoogle Scholar
  36. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C, Brown P, Browne C, Eller M, Guill K, Harjes C, Kroon D, Lepak N, Mitchell SE, Peterson B, Pressoir G, Romero S, Oropeza Rosas M, Salvo S, Yates H, Hanson M, Jones E, Smith S, Glaubitz JC, Goodman M, Ware D, Holland JB, Buckler ES (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740PubMedCrossRefGoogle Scholar
  37. Olukolu B, Wang G, Vontimitta V, Venkata BP, Marla S, Ji J, Gachomo E, Chu K, Negeri A, Benson J, Nelson R, Bradbury P, Nielsen D, Holland JB, Balint-Kurti P, Johal G (2014) A genome-wide association study of the maize hypersensitive defense response identifies genes that cluster in related pathways. PLoS Genet 10:e1004562PubMedPubMedCentralCrossRefGoogle Scholar
  38. Pan Q, Li L, Yang X, Tong H, Xu S, Li Z, Li W, Muehlbauer GJ, Li J, Yan J (2016) Genome-wide recombination dynamics are associated with phenotypic variation in maize. New Phytol 210:1083–1094PubMedCrossRefGoogle Scholar
  39. Peleg Z, Blumwald E (2011) Hormone balance and abiotic stress tolerance in crop plants. Curr Op Plant Biol 14:290–295CrossRefGoogle Scholar
  40. Qin F, Shinozaki K, Yamaguchi-Shinozaki K (2011) Achievements and challenges in understanding plant abiotic stress responses and tolerance. Plant Cell Physiol 52:1569–1582PubMedCrossRefGoogle Scholar
  41. Rebetzke GJ, Verbyla AP, Verbyla KL, Morell MK, Cavanagh CR (2014) Use of a large multiparent wheat mapping population in genomic dissection of coleoptile and seedling growth. Plant Biotech J 12:219–230CrossRefGoogle Scholar
  42. Revilla P, Butrón A, Cartea ME, Malvar RA, Ordás A (2005) Breeding for cold tolerance. In: Ashraf M, Harris PJC (eds) Abiotic Stresses. Plant resistance through breeding and molecular approaches. The Haworth Press Inc, New York, pp 301–398Google Scholar
  43. Revilla P, Rodríguez VM, Ordás A, Rincent R, Charcosset A, Giauffret C, Melchinger AE, Schön CC, Bauer E, Altmann T, Brunel D, Moreno-González J, Campo L, Ouzunova M, Laborde J, Álvarez Á, Ruíz de Galarreta JI, Malvar RA (2014) Cold tolerance in two large maize inbred panels adapted to European climates. Crop Sci 54:1981–1991CrossRefGoogle Scholar
  44. Revilla P, Rodríguez VM, Ordás A, Rincent R, Charcosset A, Giauffret C, Melchinger AE, Schön CC, Bauer E, Altmann T, Brunel D, Moreno-González J, Campo L, Ouzunova M, Álvarez Á, Ruíz de Galarreta JI, Laborde J, Malvar RA (2016) Association mapping for cold tolerance in two large maize inbred panels. BMC Plant Biol 16:127PubMedPubMedCentralCrossRefGoogle Scholar
  45. Rodríguez VM, Butrón A, Malvar RA, Ordás A, Revilla P (2008) QTLs for cold tolerance in the maize IBM population. Int J Plant Sci 169:551–556CrossRefGoogle Scholar
  46. Rodríguez VM, Velasco P, Garrido JL, Revilla P, Ordás A, Butrón A (2013) Genetic regulation of cold-induced albinism in the maize inbred line A661. J Exp Bot 64:3657–3667PubMedPubMedCentralCrossRefGoogle Scholar
  47. Rodríguez VM, Butrón A, Rady MOA, Soengas P, Revilla P (2014) Identification of QTLs involved in the response to cold stress in maize (Zea mays L.). Mol Breed 33:363–371CrossRefGoogle Scholar
  48. Sen TZ, Harper LC, Schaeffer ML, Andorf CM, Seigfried TE, Campbell DA, Lawrence CJ (2010) Choosing a genome browser for a model organism database: surveying the maize community. Database 2010: baq007 http: //database. Scholar
  49. Shi Y, Li G, Tian Z, Wang Z, Wang X, Zhu Y, Chen Y, Guo S, Qi J, Zhang X, Ku L (2016) Genetic dissection of seed vigour traits in maize (Zea mays L.) under low-temperature conditions. J Genet 95:1017–1022PubMedCrossRefGoogle Scholar
  50. Strigens A, Grieder C, Haussmann BIG, Melchinger AE (2012) Genetic variation among inbred lines and testcrosses of maize for early growth parameters and their relationship to final dry matter yield. Crop Sci 52:1084–1092CrossRefGoogle Scholar
  51. Strigens A, Freitag NM, Gilbert X, Grieder C, Riedelsheimer C, Schrag TA, Messmer R, Melchinger AE (2013) Association mapping for chilling tolerance in elite flint and dent maize inbred lines evaluated in growth chamber and field experiments. Plant Cell Environ 36:1871–1887PubMedCrossRefGoogle Scholar
  52. Tacke E, Korfhage C, Michel D, Maddaloni M, Motto M, Lanzini S, Salami S, Döring HP (1995) Transposon tagging of the maize Glossy2 locus with the transposable element En/Spm. Plant J 8:907–917PubMedCrossRefGoogle Scholar
  53. Xiao Y, Tong H, Yang X, Xu S, Pan Q, Qiao F, Raihan MS, Luo Y, Liu H, Zhang X, Yang N, Wang X, Deng M, Jin M, Zhao L, Luo X, Zhou Y, Li X, Liu J, Zhan W, Liu N, Wang H, Chen G, Cai Y, Xu G, Wang W, Zheng D, Yan J (2016) Genome-wide dissection of the maize ear genetic architecture using multiple populations. New Phytol 210:1095–1106PubMedCrossRefGoogle Scholar
  54. Xiao Y, Liu H, Wu L, Warburton M, Yan J (2017) Genome-wide association studies in maize: praise and stargaze. Mol Plant 10:359–374PubMedCrossRefGoogle Scholar
  55. Xue D, Zhang X, Lu X, Chen G, Chen ZH (2017) Molecular and evolutionary mechanisms of cuticular wax for plant drought tolerance. Front Plant Sci 8:621PubMedPubMedCentralCrossRefGoogle Scholar
  56. Yadav SK (2010) Cold stress tolerance mechanisms in plants. A review. Agron Sustain Develop 30:515–527CrossRefGoogle Scholar
  57. Yan J, Wu Y, Li W, Qin X, Wang Y, Yue B (2017) Genetic mapping with testcrossing associations and F2:3 populations reveals the importance of heterosis in chilling tolerance at maize seedling stage. Sci Rep 7:3232PubMedPubMedCentralCrossRefGoogle Scholar
  58. Yang T, Shad Ali G, Yang L, Du L, Reddy AS, Poovaiah BW (2010) Calcium/calmodulin-regulated receptor-like kinase CRLK1 interacts with MEKK1 in plants. Plant Signal Behav 5:991–994PubMedPubMedCentralCrossRefGoogle Scholar
  59. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genet 38:203–208PubMedCrossRefGoogle Scholar
  60. Yu X, Jiang L, Wu R, Meng X, Zhang A, Li N, Xia Q, Qi X, Pang J, Xu ZY, Liu B (2016) The core subunit of a chromatin-remodeling complex, ZmCHB101, plays essential roles in maize growth and development. Sci Rep 6:38504PubMedPubMedCentralCrossRefGoogle Scholar
  61. Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nature Genet 44:355–360CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Q. Yi
    • 1
    • 2
  • R. A. Malvar
    • 1
  • L. Álvarez-Iglesias
    • 1
  • B. Ordás
    • 1
  • Pedro Revilla
    • 1
    Email author
  1. 1.Misión Biológica de Galicia (CSIC)PontevedraSpain
  2. 2.College of AgronomySichuan Agricultural UniversityChengduChina

Personalised recommendations