Population Genomics of Maize

Resende, Marcela Pedroso Mendes; Filho, Ailton José Crispim; Antunes, Adriana Maria; de Oliveira, Bruna Mendes; de Oliveira, Renato Gonçalves

doi:10.1007/13836_2022_101

Marcela Pedroso Mendes Resende¹,
Ailton José Crispim Filho¹,
Adriana Maria Antunes¹,
Bruna Mendes de Oliveira¹ &
…
Renato Gonçalves de Oliveira¹

Part of the book series: Population Genomics

1 Citations
3 Altmetric

Abstract

One of the most explored crop plants in genomics studies is maize (Zea mays L.). It has served as a model for developing and incorporating biotechnology and genomics approaches in breeding programs of several other crop plants. From this perspective, the genomic information available in the last decades has helped geneticists and breeders better understand maize evolution and diversity, gene flow, admixture, and inbreeding and outbreeding depression, and identify genes and genetic variants related to many traits, increasing genetic gains in breeding programs. In this chapter, we present a review of population genomics aspects related to the genetic history of the origin and domestication of maize, the genes that underlined this process, and the possible introgressions that originated the multiple maize races known. We present the synthesis of information on genetic diversity of maize races and the heterotic groups that have become the genetic base of temperate and tropical commercial germplasms. The genomic diversity of maize, especially after the release of inbred line B73, is presented by focusing on the advances made regarding maize pan-genomes and epigenomics. We also discuss how population genomics helped maize breeding and highlight some results in genome-wide association studies and genomic selection, although these results are not yet fully exploited in maize germplasm conservation. Finally, we briefly present some future perspectives into the application of population genomics in maize conservation, selection, and breeding. Although many advances have already been made in population genomics of maize, the more we elucidate the multiple aspects of maize genetics, the more questions arise, unraveling new key insights into maize population genomics and breeding.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

Aci MM, Lupini A, Mauceri A, Morsli A, Khelifi L, Sunseri F. Genetic variation and structure of maize populations from Saoura and Gourara Oasis in Algerian Sahara. BMC Genet. 2018;19(1):1–10. https://doi.org/10.1186/s12863-018-0655-2.
Article Google Scholar
Ali ML, Taylor JH, Jie L, Sun G, William M, Kasha KJ, et al. Molecular mapping of QTLs for resistance to Gibberella Ear Rot, in Corn, caused by Fusarium Graminearum. Genome. 2005;48(3):521–33. https://doi.org/10.1139/G05-014.
Article Google Scholar
Acosta-Pech R, Crossa J, de los Campos G, Teyssèdre S, Claustres B, Pérez-Elizalde S, et al. Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids. Theor Appl Genet. 2017;130(7):1431–40. https://doi.org/10.1007/s00122-017-2898-0.
Article Google Scholar
Adu GB, Badu-Apraku B, Akromah R, Garcia-Oliveira AL, Awuku FJ, Gedil M. Genetic diversity and population structure of early-maturing tropical maize inbred lines using SNP markers. PLoS One. 2019;14(4):1–12. https://doi.org/10.1371/journal.pone.0214810.
Article Google Scholar
Almeida C, Amorim EP, Neto JFB, Filho JAC, Cruz MJ, de Melo Sereno. Genetic variability in populations of sweet corn, common corn and teosinte. Crop Breeding and Applied Biotechnology. 2011;11(1):64–9. https://doi.org/10.1590/s1984-70332011000100009.
Article Google Scholar
Alseekh S, Kostova D, Bulut M, Fernie AR. Genome-wide association studies: assessing trait characteristics in model and crop plants. Cell Mol Life Sci. 2021;78(15):5743–54. https://doi.org/10.1007/s00018-021-03868-w.
Article Google Scholar
Alves FC, Galli G, Matias FI, Vidotti MS, Morosini JS, Fritsche-Neto R. Impact of the complexity of genotype by environment and dominance modeling on the predictive accuracy of maize hybrids in multi-environment prediction models. Euphytica. 2021;217(3):37. https://doi.org/10.1007/s10681-021-02779-y.
Article Google Scholar
Alzate-Marin AL, Cervigni GDL, Moreira MA, Barros EG. Seleção Assistida Por Marcadores Moleculares Visando Ao Desenvolvimento de Plantas Resistentes a Doenças, Com Ênfase Em Feijoeiro e Soja. Fitopatol Bras. 2005;30(4):333–42. https://doi.org/10.1590/s0100-41582005000400001.
Article Google Scholar
Ames NC, Bernardo R. Genomewide predictions as a substitute for a portion of phenotyping in maize. Crop Sci. 2020;60(1):181–9. https://doi.org/10.1002/csc2.20082.
Article Google Scholar
Aschard H, Vilhjálmsson BJ, Greliche N, Morange PE, Trégouët DA, Kraft P. Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. Am J Hum Genet. 2014;94(5):662–76. https://doi.org/10.1016/j.ajhg.2014.03.016.
Article Google Scholar
Atanda SA, Olsen M, Burgueño J, Crossa J, Dzidzienyo D, Beyene Y, et al. Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program. Theor Appl Genet. 2021;134(1):279–94. https://doi.org/10.1007/s00122-020-03696-9.
Article Google Scholar
Atwell S, Huang YS, Vilhjálmsson BJ, Willems G, Horton M, Li Y, et al. Genome-wide association study of 107 phenotypes in Arabidopsis Thaliana inbred lines. Nature. 2010;465(7298):627–31. https://doi.org/10.1038/nature08800.
Article Google Scholar
Avendaño López, Natividad A, de Jesús Sánchez González J, Corral JAR, Larios LDLC, Santacruz-Ruvalcaba F, et al. Seed dormancy in Mexican teosinte. Crop Sci. 2011;51(5):2056–66. https://doi.org/10.2135/cropsci2010.09.0538.
Article Google Scholar
Avery CL, He Q, North KE, Ambite JL, Boerwinkle E. University of North Carolina at Chapel Hill (N01-HC-55015), Baylor Medical College (N01-HC-55016), University of Mississippi Medical Center (N01-HC-55021). PLoS Genet | WwwPlosgeneticsOrg. 2011;7(10):1. https://doi.org/10.1371/journal.pgen.1002322.
Article Google Scholar
Badu-Apraku B, Garcia-Oliveira AL, Petroli CD, Hearne S, Adewale SA, Gedil M. Genetic diversity and population structure of early and extra-early maturing maize germplasm adapted to sub-Saharan Africa. BMC Plant Biol. 2021;21(1):1–15. https://doi.org/10.1186/s12870-021-02829-6.
Article Google Scholar
Bandeirae Sousa, Massaine JC, de Oliveira Couto EG, Pérez-Rodríguez P, Jarquín D, Fritsche-Neto R, et al. Genomic-enabled prediction in maize using Kernel models with genotype × environment interaction. G3 Genes|Genomes|Genet. 2017;7(6):1995–2014. https://doi.org/10.1534/g3.117.042341.
Article Google Scholar
Baseggio M, Murray M, Magallanes-Lundback M, Kaczmar N, Chamness J, Buckler ES, et al. Natural variation for carotenoids in fresh Kernels is controlled by uncommon variants in sweet corn. Plant Genome. 2020;13(1):e20008. https://doi.org/10.1002/tpg2.20008.
Article Google Scholar
Basnet BR, Crossa J, Dreisigacker S, Pérez-Rodríguez P, Manes Y, Singh RP, et al. Hybrid wheat prediction using genomic, pedigree, and environmental covariables interaction models. Plant Genome. 2019;12(1):180051. https://doi.org/10.3835/plantgenome2018.07.0051.
Article Google Scholar
Beadle GW. Teosinte and the origin of maize. J Hered. 1939;30(6):245–7. https://doi.org/10.1093/oxfordjournals.jhered.a104728.
Article Google Scholar
Beadle GW. The ancestry of corn. Sci Am. 1980;242(1):112–9.
Google Scholar
Beckett TJ, Jason Morales A, Koehler KL, Rocheford TR. Genetic relatedness of previously plant-variety-protected commercial maize Inbreds. PLoS One. 2017;12(12):1–23. https://doi.org/10.1371/journal.pone.0189277.
Article Google Scholar
Beissinger TM, Wang L, Crosby K, Durvasula A, Hufford MB, Ross-Ibarra J. Recent demography drives changes in linked selection across the maize genome. Nat Plants. 2016;2(7):16084. https://doi.org/10.1038/nplants.2016.84.
Article Google Scholar
Beló A, Zheng P, Luck S, Shen B, Meyer DJ, Li B, et al. Whole genome scan detects an allelic variant of Fad2 associated with increased oleic acid levels in maize. Mol Gen Genomics. 2008;279(1):1–10. https://doi.org/10.1007/s00438-007-0289-y.
Article Google Scholar
Benavente E, Giménez E. Modern approaches for the genetic improvement of rice, wheat and maize for abiotic constraints-related traits: a comparative overview. Agronomy. 2021;11(2):376. https://doi.org/10.3390/agronomy11020376.
Article Google Scholar
Benke A, Urbany C, Stich B. Genome-wide association mapping of iron homeostasis in the maize association population. BMC Genet. 2015;16(1):1–13. https://doi.org/10.1186/s12863-014-0153-0.
Article Google Scholar
Bernardo R. Prediction of maize single‐cross performance using RFLPs and information from related hybrids. Crop Sci. 1994;34(1):20–5. https://doi.org/10.2135/cropsci1994.0011183X003400010003x.
Article Google Scholar
Bernardo R. Genomewide selection for rapid introgression of exotic germplasm in maize. Crop Sci. 2009;49(2):419–25. https://doi.org/10.2135/cropsci2008.08.0452.
Article Google Scholar
Bernardo R. Essentials of plant breeding. Woodbury: Stemma Press; 2014.
Google Scholar
Bernardo R. Bandwagons I, too, have known. Theor Appl Genet. 2016;129(12):2323–32. https://doi.org/10.1007/s00122-016-2772-5.
Article Google Scholar
Bernardo R, Jianming Y. Prospects for Genomewide selection for quantitative traits in maize. Crop Sci. 2007;47(3):1082–90. https://doi.org/10.2135/cropsci2006.11.0690.
Article Google Scholar
Beyene Y, Gowda M, Olsen M, Robbins KR, Pérez-Rodríguez P, Alvarado G, et al. Empirical comparison of tropical maize hybrids selected through genomic and phenotypic selections. Front Plant Sci. 2019;10(November) https://doi.org/10.3389/fpls.2019.01502.
Bhat JA, Ali S, Salgotra RK, Mir ZA, Dutta S, Jadon V, et al. Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Front Genet. 2016;7(December) https://doi.org/10.3389/fgene.2016.00221.
Bhave MR, Lawrence S, Barton C, Curtis Hannah L. Identification and molecular characterization of Shrunken-2 CDNA Clones of maize. Plant Cell. 1990;2(6):581–8. https://doi.org/10.1105/tpc.2.6.581.
Article Google Scholar
Bonnett OT. The inflorescences of maize. Science. 1954;120(3107):77–87. https://doi.org/10.1126/science.120.3107.77.
Article Google Scholar
Bornowski N, Michel KJ, Hamilton JP, Shujun O, Seetharam AS, Jenkins J, et al. Genomic variation within the maize stiff‐stalk heterotic germplasm Pool. Plant Genome. 2021; https://doi.org/10.1002/tpg2.20114.
Briggs WH, McMullen MD, Gaut BS, Doebley J. Linkage mapping of domestication loci in a large maize–teosinte backcross resource. Genetics. 2007;177(3):1915–28. https://doi.org/10.1534/genetics.107.076497.
Article Google Scholar
Brown WL, Edgar. Anderson. The northern Flint corns. Ann Mo Bot Gard. 1947;34(1):1–28.
Google Scholar
Brown WL, Goodman MM. Races of maize. In: Sprague GF, editor. Corn and corn improvement. Madison: American Society of Agronomy; 1977. p. 49–88.
Google Scholar
Brown PJ, Upadyayula N, Mahone GS, Tian F, Bradbury PJ, Myles S, et al. Distinct genetic architectures for male and female inflorescence traits of maize. PLoS Genet. 2011;7(11):1002383. https://doi.org/10.1371/journal.pgen.1002383.
Article Google Scholar
Buckler ES, Stevens NM. 4. Maize origins, domestication, and selection. In: Darwin’s harvest. Columbia University Press; 2006. p. 67–90. https://doi.org/10.7312/motl13316-005.
Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, et al. The genetic architecture of maize flowering time. Science. 2009;325(5941):714–8. https://doi.org/10.1126/science.1174276.
Article Google Scholar
Buckner B, Miguel PS, Janick-Buckner D, Bennetzen JL. The Y1 gene of maize codes for phytoene synthase. Genetics. 1996;143(1):479–88.
Google Scholar
Butoto EN, Marino TP, Holland JB. Effects of artificial inoculation on trait correlations with resistance to Fusarium ear rot and Fumonisin contamination in maize. Crop Sci. 2021;61(4):2522–33. https://doi.org/10.1002/csc2.20551.
Article Google Scholar
Calderón CI, Yandell BS, Doebley JF. Fine mapping of a QTL associated with Kernel row number on chromosome 1 of maize. PLoS One. 2016;11(3) https://doi.org/10.1371/journal.pone.0150276.
Calfee E, Gates D, Anne L, Taylor Perkins M, Coop G, Ross-Ibarra J. Selective sorting of ancestral introgression in maize and teosinte along an elevational cline. BioRxiv. 2021:2021.03.05.434040. https://doi.org/10.1101/2021.03.05.434040.
Cantelmo NF, Von Pinho RG, Balestre M. Genome-wide prediction for maize single-cross hybrids using the GBLUP model and validation in different crop seasons. Mol Breed. 2017;37(4):51. https://doi.org/10.1007/s11032-017-0651-7.
Article Google Scholar
Carena MJ, Hallauer AR, Miranda Filho JB. Quantitative genetics in maize breeding. Quantitative genetics in maize breeding. 3rd ed. New York: Springer New York; 2010. https://doi.org/10.1007/978-1-4419-0766-0.
Book Google Scholar
Cassani E, Landoni M, Pilu R. Characterization of the Ra1 maize gene involved in inflorescence architecture. Sex Plant Reprod. 2006;19(3):145–50. https://doi.org/10.1007/s00497-006-0031-7.
Article Google Scholar
Cavanagh C, Morell M, Mackay I, Powell W. From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol. 2008;11(2):215–21. https://doi.org/10.1016/j.pbi.2008.01.002.
Article Google Scholar
Chai L, Chen Z, Bian R, Zhai H, Cheng X, Peng H, et al. Dissection of two quantitative trait loci with pleiotropic effects on plant height and spike length linked in coupling phase on the short arm of chromosome 2D of common wheat (Triticum Aestivum L.). Theor Appl Genet. 2018;131(12):2621–37. https://doi.org/10.1007/s00122-018-3177-4.
Article Google Scholar
Chaikam V, Gowda M, Nair SK, Melchinger AE, Boddupalli PM. Genome-wide association study to identify genomic regions influencing spontaneous fertility in maize haploids. Euphytica. 2019;215(8):1–14. https://doi.org/10.1007/s10681-019-2459-5.
Article Google Scholar
Chan SWL, Henderson IR, Jacobsen SE. Gardening the genome: DNA methylation in Arabidopsis Thaliana. Nat Rev Genet. 2005;6(5):351–60. https://doi.org/10.1038/nrg1601.
Article Google Scholar
Chen AH, Lipka AE. The use of targeted marker subsets to account for population structure and relatedness in genome-wide association studies of maize (Zea Mays L.). G3: Genes Genomes Genet. 2016;6(8):2365–74. https://doi.org/10.1534/g3.116.029090.
Article Google Scholar
Chen G, Wang X, Hao J, Yan J, Ding J. Genome-wide association implicates candidate genes conferring resistance to maize rough dwarf disease in maize. PLoS One. 2015;10(11):e0142001. https://doi.org/10.1371/journal.pone.0142001.
Article Google Scholar
Chen J, Shrestha R, Ding J, Zheng H, Chunhua M, Wu J, et al. Genome-wide association study and QTL mapping reveal genomic loci associated with Fusarium ear rot resistance in tropical maize germplasm. G3: Genes Genomes Genet. 2016;6(12):3803–15. https://doi.org/10.1534/g3.116.034561.
Article Google Scholar
Cockram J, Mackay I. Genetic mapping populations for conducting high-resolution trait mapping in plants. In: Varshney RK, Pandey MK, Chitikineni A, editors. Plant genetics and molecular biology. Springer; 2018. p. 109–38.
Google Scholar
Coles ND, McMullen MD, Balint-Kurti PJ, Pratt RC, Holland JB. Genetic control of photoperiod sensitivity in maize revealed by joint multiple population analysis. Genetics. 2010;184(3):799–812. https://doi.org/10.1534/genetics.109.110304.
Article Google Scholar
Coletta RD, Qiu Y, Shujun O, Hufford MB, Hirsch CN. How the Pan-genome is changing crop genomics and improvement. Genome Biol. 2021;22(1):1–19. https://doi.org/10.1186/s13059-020-02224-8.
Article Google Scholar
Cook JP, McMullen MD, Holland JB, Tian F, Bradbury P, Ross-Ibarra J, et al. Genetic architecture of maize Kernel composition in the nested association mapping and inbred association panels. Plant Physiol. 2012;158(2):824–34. https://doi.org/10.1104/pp.111.185033.
Article Google Scholar
Costa-Neto G, Fritsche-Neto R, Crossa J. Nonlinear Kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity. 2021a;126(1):92–106. https://doi.org/10.1038/s41437-020-00353-1.
Article Google Scholar
Costa-Neto G, Galli G, Carvalho HF, Crossa J, Fritsche-Neto R. “EnvRtype : a software to interplay enviromics and quantitative genomics in agriculture.” Edited by D-J de Koning. G3 Genes|Genomes|Genetics. 2021b;11(4) https://doi.org/10.1093/g3journal/jkab040.
Cross HZ. ND265: a new parental line of early corn. North Dakota Farm Res. 1989;47(3):19–21.
Google Scholar
Crossa J, De Los G, Campos PP, Gianola D, Burgueño J, Araus JL, et al. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics. 2010;186(2):713–24. https://doi.org/10.1534/genetics.110.118521.
Article Google Scholar
Crossa J, Pérez P, de los Campos G, Mahuku G, Dreisigacker S, Magorokosho C. Genomic selection and prediction in plant breeding. J Crop Improv. 2011;25(3):239–61. https://doi.org/10.1080/15427528.2011.558767.
Article Google Scholar
Crossa J, Pérez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, et al. Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity. 2014;112(1):48–60. https://doi.org/10.1038/hdy.2013.16.
Article Google Scholar
Cui Z, Luo J, Qi C, Ruan Y, Li J, Zhang A, et al. Genome-Wide Association Study (GWAS) reveals the genetic architecture of four husk traits in maize. BMC Genomics. 2016;17(1):1–14. https://doi.org/10.1186/s12864-016-3229-6.
Article Google Scholar
Cui Z, Dong H, Zhang A, Ruan Y, He Y, Zhang Z. Assessment of the potential for genomic selection to improve husk traits in maize. G3 Genes|Genomes|Genetics. 2020;10(10):3741–9. https://doi.org/10.1534/g3.120.401600.
Article Google Scholar
Dalsente Krause M, das Graças Dias KO, dos Santos JPR, de Oliveira AA, Guimarães LJM, Pastina MM, et al. Boosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models. Crop Sci. 2020;60(6):3049–65. https://doi.org/10.1002/csc2.20253.
Article Google Scholar
Dell’Acqua M, Gatti DM, Pea G, Cattonaro F, Coppens F, Magris G, et al. Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea Mays. Genome Biol. 2015;16(1):1–23. https://doi.org/10.1186/s13059-015-0716-z.
Article Google Scholar
Deng C, Lv M, Li X, Zhao X, Li H, Li Z, et al. Identification and fine mapping of QsCR4.01, a novel major Qtl for resistance to Puccinia Polysora in maize. Plant Dis. 2020;104(7):1944–8. https://doi.org/10.1094/PDIS-11-19-2474-RE.
Article Google Scholar
Desta ZA, Ortiz R. Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci. 2014;19(9):592–601. https://doi.org/10.1016/j.tplants.2014.05.006.
Article Google Scholar
Dinesh A, Patil A, Zaidi PH, Kuchanur PH, Vinayan MT, Seetharam K. Genetic diversity, linkage disequilibrium and population structure among CIMMYT maize inbred lines, selected for heat tolerance study. Maydica. 2016;61(29):1–7.
Google Scholar
Ding J, Ali F, Chen G, Li H, Mahuku G, Yang N, et al. Genome-wide association mapping reveals novel sources of resistance to northern corn leaf blight in maize. BMC Plant Biol. 2015;15(1):1–11. https://doi.org/10.1186/s12870-015-0589-z.
Article Google Scholar
Dinges JR, Colleoni C, Myers AM, James MG. Molecular structure of three mutations at the maize sugary1 locus and their allele-specific phenotypic effects. Plant Physiol. 2001;125:1406–18.
Google Scholar
Dion MF, Altschuler SJ, Wu LF, Rando OJ. Genomic characterization reveals a simple histone H4 acetylation code. Proc Natl Acad Sci. 2005;102(15):5501–6. https://doi.org/10.1073/pnas.0500136102.
Article Google Scholar
Doebley J. Molecular evidence and the evolution of maize. Econ Bot. 1990;44(S3):6–27. https://doi.org/10.1007/BF02860472.
Article Google Scholar
Doebley J. The genetics of maize evolution. Annu Rev Genet. 2004;38(1):37–59. https://doi.org/10.1146/annurev.genet.38.072902.092425.
Article Google Scholar
Doebley JF, Iltis HH. Taxonomy of Zea (Gramineae). I. a Subgeneric classification with key to taxa. Am J Bot. 1980;67(6):982–93. https://doi.org/10.1002/j.1537-2197.1980.tb07730.x.
Article Google Scholar
Doebley J, Stec A. Genetic analysis of the morphological differences between maize and teosinte. Genetics. 1991;129(1):285–95.
Google Scholar
Doebley J, Stec A. Inheritance of the morphological differences between maize and teosinte: comparison of results for two F2 populations. Genetics. 1993;134(2):559–70. https://doi.org/10.1093/genetics/134.2.559.
Article Google Scholar
Doebley J, Stec A, Hubbard L. The evolution of apical dominance in maize. Nature. 1997;386(6624):485–8. https://doi.org/10.1038/386485a0.
Article Google Scholar
Doebley JF, Gaut BS, Smith BD. The molecular genetics of crop domestication. Cell. 2006;127(7):1309–21. https://doi.org/10.1016/j.cell.2006.12.006.
Article Google Scholar
Dong Z, Li W, Unger-Wallace E, Yang J, Vollbrecht E, Chuck G. Ideal crop plant architecture is mediated by Tassels Replace Upper Ears1, a BTB/POZ Ankyrin repeat gene directly targeted by TEOSINTE BRANCHED1. Proc Natl Acad Sci. 2017;114(41):E8656–64. https://doi.org/10.1073/pnas.1714960114.
Dorweiler JE, Doebley J. Developmental analysis of Teosinte Glume Architecture1 : a key locus in the evolution of maize (Poaceae). Am J Bot. 1997;84(10):1313–22. https://doi.org/10.2307/2446130.
Article Google Scholar
Duo H, Hossain F, Muthusamy V, Zunjare RU, Goswami R, Chand G, et al. Development of sub-tropically adapted diverse Provitamin-a rich maize Inbreds through marker-assisted pedigree selection, their characterization and utilization in hybrid breeding. PLoS One. 2021;16(2 February):1–22. https://doi.org/10.1371/journal.pone.0245497.
Article Google Scholar
Eichten SR, Swanson-Wagner RA, Schnable JC, Waters AJ, Hermanson PJ, Liu S, et al. Heritable epigenetic variation among maize Inbreds. PLoS Genet. 2011;7(11) https://doi.org/10.1371/journal.pgen.1002372.
Eichten SR, Briskine R, Song J, Li Q, Swanson-Wagner R, Hermanson PJ, et al. Epigenetic and genetic influences on DNA methylation variation in maize populations. Plant Cell. 2013;25(8):2783–97. https://doi.org/10.1105/tpc.113.114793.
Article Google Scholar
Emms DM, Kelly S. STRIDE: species tree root inference from gene duplication events. Mol Biol Evol. 2017;34:3267–78. https://doi.org/10.1093/molbev/msx259.
Article Google Scholar
Emms DM, Kelly S. STAG: species tree inference from all genes. bioRxiv. 2018:267914. https://doi.org/10.1101/267914.
Ertiro BT, Labuschagne M, Olsen M, Das B, Prasanna BM, Gowda M. Genetic dissection of nitrogen use efficiency in tropical maize through genome-wide association and genomic prediction. Front Plant Sci. 2020;11(April):474. https://doi.org/10.3389/fpls.2020.00474.
Article Google Scholar
Evans MMS, Kermicle JL. Teosinte crossing Barrier1, a locus governing hybridization of teosinte with maize. Theor Appl Genet. 2001;103(2–3):259–65. https://doi.org/10.1007/s001220100549.
Article Google Scholar
Eyre-Walker A, Gaut RL, Hilton H, Feldman DL, Gaut BS. Investigation of the bottleneck leading to the domestication of maize. Proc Natl Acad Sci. 1998;95(8):4441–6. https://doi.org/10.1073/pnas.95.8.4441.
Article Google Scholar
Farfan ID, Barrero GN, La Fuente D, Murray SC, Isakeit T, Huang PC, et al. Genome wide association study for drought, Aflatoxin resistance, and important agronomic traits of maize hybrids in the sub-tropics. PLoS One. 2015;10(2):e0117737. https://doi.org/10.1371/journal.pone.0117737.
Article Google Scholar
Flint-Garcia SA, Thornsberry JM, Edward IV SB. Structure of linkage disequilibrium in plants. Annu Rev Plant Biol. 2003;54:357–74. https://doi.org/10.1146/annurev.arplant.54.031902.134907.
Article Google Scholar
Flint-Garcia SA, Thuillet AC, Jianming Y, Pressoir G, Romero SM, Mitchell SE, et al. Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J. 2005;44(6):1054–64. https://doi.org/10.1111/j.1365-313X.2005.02591.x.
Article Google Scholar
Fristche-Neto R, Akdemir D, Jannink J-L. Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs. Theor Appl Genet. 2018;131(5):1153–62. https://doi.org/10.1007/s00122-018-3068-8.
Article Google Scholar
Fritsche-Neto R, Galli G, Borges KLR, Costa-Neto G, Alves FC, Sabadin F, et al. Optimizing genomic-enabled prediction in small-scale maize hybrid breeding programs: a roadmap review. Front Plant Sci. 2021;12(July) https://doi.org/10.3389/fpls.2021.658267.
Fu J, Cheng Y, Linghu J, Yang X, Kang L, Zhang Z, et al. RNA sequencing reveals the complex regulatory network in the maize Kernel. Nat Commun. 2013;4(1):1–12. https://doi.org/10.1038/ncomms3832.
Article Google Scholar
Fukunaga K, Hill J, Vigouroux Y, Matsuoka Y, Jesus Sanchez G, Liu K, et al. Genetic diversity and population structure of teosinte. Genetics. 2005;169(4):2241–54. https://doi.org/10.1534/genetics.104.031393.
Article Google Scholar
Gage JL, Monier B, Giri A, Buckler ES. Ten years of the maize nested association mapping population: impact, limitations, and future directions. Plant Cell. 2020;32(7):2083–93. https://doi.org/10.1105/tpc.19.00951.
Article Google Scholar
Galesloot TE, Van Steen K, Kiemeney LALM, Janss LL, Vermeulen SH. A comparison of multivariate genome-wide association methods. PLoS One. 2014;9(4):95923. https://doi.org/10.1371/journal.pone.0095923.
Article Google Scholar
Galinat WC, Gunnerson JH. Spread of eight-rowed maize from the prehistoric southwest. Harvard University Botanical Museum Leaflets. 1963;20(5):117–60.
Google Scholar
Gallavotti A, Zhao Q, Kyozuka J, Meeley RB, Ritter MK, Doebley JF, et al. The role of Barren Stalk1 in the architecture of maize. Nature. 2004;432(7017):630–5. https://doi.org/10.1038/nature03148.
Article Google Scholar
Gao J, Wang S, Zhou Z, Wang S, Dong C, Cong M, et al. Linkage mapping and genome-wide association reveal candidate genes conferring Thermotolerance of seed-set in maize. J Exp Bot. 2019;70(18):4849–63. https://doi.org/10.1093/jxb/erz171.
Article Google Scholar
Gardiner JM, Coe EH, Melia-Hancock S, Hoisington DA, Chao S. Development of a Core RFLP map in maize using an immortalized F2 population. Genetics. 1993;134:917–30.
Google Scholar
Garin V, Wimmer V, Mezmouk S, Malosetti M, van Eeuwijk F. How do the type of QTL effect and the form of the residual term influence QTL detection in multi-parent populations? A case study in the maize EU-NAM population. Theor Appl Genet. 2017;130(8):1753–64. https://doi.org/10.1007/s00122-017-2923-3.
Article Google Scholar
Gaudin ACM, McClymont SA, Soliman SSM, Raizada MN. The effect of altered dosage of a Mutant Allele of Teosinte branched 1 (Tb1-Ref) on the root system of modern maize. BMC Genet. 2014;15(1):23. https://doi.org/10.1186/1471-2156-15-23.
Article Google Scholar
GCDT. Global Strategy for the Ex Situ Conservation and Utilization of Maize Germplasm. Global Crop Diversity Trust, no. September, 2007. https://www.croptrust.org/wp/wp-content/uploads/2014/12/Maize-Strategy-FINAL-18Sept07.pdf.
Gerdes JT, Behr CF, Coors JG, Tracy WF. Compilation of North American maize breeding germplasm. Madison, WI: Crop Science Society of America; 1993. https://doi.org/10.2135/1993.compilationofnorthamerican.
Gethi JG, Labate JA, Lamkey KR, Smith ME, Kresovich S. SSR variation in important U.S. maize inbred lines. Crop Sci. 2002;42(3):951–7. https://doi.org/10.2135/cropsci2002.9510.
Article Google Scholar
Giordani W, Scapim CA, Ruas PM, de Fátima C, Ruas RC-S, Coan M, et al. Genetic diversity, population structure and AFLP markers associated with maize reaction to southern rust. Bragantia. 2019;78(2):183–96. https://doi.org/10.1590/1678-4499.20180180.
Article Google Scholar
Gireesh C, Sundaram RM, Anantha SM, Pandey MK, Madhav MS, Rathod S, et al. Nested Association Mapping (NAM) populations: present status and future prospects in the genomics era. Crit Rev Plant Sci. 2021;40(1):49–67. https://doi.org/10.1080/07352689.2021.1880019.
Article Google Scholar
Gonzalez VH, Lee EA, Lewis Lukens L, Swanton CJ. The relationship between floret number and plant dry matter accumulation varies with early season stress in maize (Zea Mays L.). Field Crop Res. 2019;238(March):129–38. https://doi.org/10.1016/j.fcr.2019.05.003.
Article Google Scholar
Gorjanc G, Jenko J, Hearne SJ, Hickey JM. Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations. BMC Genomics. 2016;17(1):30. https://doi.org/10.1186/s12864-015-2345-z.
Article Google Scholar
Gowda M, Das B, Makumbi D, Babu R, Semagn K, Mahuku G, et al. Genome-wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm. Theor Appl Genet. 2015;128(10):1957–68. https://doi.org/10.1007/s00122-015-2559-0.
Article Google Scholar
Gowda M, Makumbi D, Das B, Nyaga C, Kosgei T, Crossa J, et al. Genetic dissection of Striga Hermonthica (Del.) Benth. Resistance via genome-wide association and genomic prediction in tropical maize germplasm. Theor Appl Genet. 2021;134(3):941–58. https://doi.org/10.1007/s00122-020-03744-4.
Article Google Scholar
Guimarães CT, da Costae Silva L, Mendes FF, Pastina MM, de Souza IRP, Damasceno CMB. Mapeamento de QTLs e Seleção Assistida Por Marcadores Moleculares. In: de Milho M, Lima R, Borém A, editors. 1st ed; 2018. p. 307–28. Editora UFV.
Google Scholar
Guo Z, Zou C, Liu X, Wang S, Li W-X, Jeffers D, et al. Complex genetic system involved in Fusarium ear rot resistance in maize as revealed by GWAS, bulked sample analysis, and genomic prediction. Plant Dis. 2020;104(6):1725–35. https://doi.org/10.1094/PDIS-07-19-1552-RE.
Article Google Scholar
Guzzon F, Rios LWA, Cepeda GMC, Polo MC, Cabrera AC, Figueroa JM, et al. Conservation and use of Latin American maize diversity: pillar of nutrition security and cultural heritage of humanity. Agronomy. 2021;11(1):172. https://doi.org/10.3390/agronomy11010172.
Article Google Scholar
Haberer G, Young S, Bharti AK, Gundlach H, Raymond C, Fuks G, et al. Structure and architecture of the maize genome. Plant Physiol. 2005;139(4):1612–24. https://doi.org/10.1104/pp.105.068718.
Article Google Scholar
Hake S, Ross-Ibarra J. Genetic, evolutionary and plant breeding insights from the domestication of maize. elife. 2015;4(March) https://doi.org/10.7554/eLife.05861.
Han S, Thomas Miedaner H, Utz F, Schipprack W, Schrag TA, Melchinger AE. Genomic prediction and GWAS of Gibberella ear rot resistance traits in dent and Flint lines of a public maize breeding program. Euphytica. 2018;214(1):6. https://doi.org/10.1007/s10681-017-2090-2.
Article Google Scholar
Hanson MA, Gaut BS, Stec AO, Fuerstenberg SI, Goodman MM, Coe EH, et al. Evolution of anthocyanin biosynthesis in maize Kernels: the role of regulatory and enzymatic loci. Genetics. 1996;143(3):1395–407. https://doi.org/10.1093/genetics/143.3.1395.
Article Google Scholar
Hao Z, Li X, Xie C, Weng J, Li M, Zhang D, et al. Identification of functional genetic variations underlying drought tolerance in maize using Snp markers. J Integr Plant Biol. 2011;53(8):641–52. https://doi.org/10.1111/j.1744-7909.2011.01051.x.
Article Google Scholar
Harjes CE, Rocheford TR, Bai L, Brutnell TP, Kandianis CB, Sowinski SG, et al. Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science. 2008;319(5861):330–3. https://doi.org/10.1126/science.1150255.
Article Google Scholar
Heerwaarden JV, Doebley J, Briggs WH, Glaubitz JC, Goodman MM, De Jesus J, et al. Genetic signals of origin, spread, and introgression in a large sample of maize landraces. Proc Natl Acad Sci. 2011;108(3):1088–92. https://doi.org/10.1073/pnas.1013011108.
Article Google Scholar
Helentjaris T, Slocum M, Wright S, Schaefer A, Nienhuis J. Construction of genetic linkage maps in maize and tomato using restriction fragment length polymorphisms. Theor Appl Genet. 1986;72(6):761–9. https://doi.org/10.1007/BF00266542.
Article Google Scholar
Holland JB, Marino TP, Manching HC, Wisser RJ. Genomic prediction for resistance to Fusarium ear rot and Fumonisin contamination in maize. Crop Sci. 2020;60(4):1863–75. https://doi.org/10.1002/csc2.20163.
Article Google Scholar
Holliday JA, Hallerman EM, Haak DC. Genotyping and sequencing technologies in population genetics and genomics. 2018:83–125. https://doi.org/10.1007/13836_2017_5.
Horn F, Habekuß A, Stich B. Genes involved in barley yellow dwarf virus resistance of maize. Theor Appl Genet. 2014;127(12):2575–84. https://doi.org/10.1007/s00122-014-2400-1.
Article Google Scholar
Hu Y, Colantonio V, Müller BSF, Leach KA, Nanni A, Finegan C, et al. Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn. Nat Commun. 2021;12(1) https://doi.org/10.1038/s41467-021-21380-4.
Huang J, Johnson AD, O’Donnell CJ. PRIMe: a method for characterization and evaluation of pleiotropic regions from multiple genome-wide association studies. Bioinformatics. 2011;27(9):1201–6. https://doi.org/10.1093/bioinformatics/btr116.
Article Google Scholar
Huang J, Zhang J, Li W, Wei H, Duan L, Feng Y, et al. Genome-wide association analysis of ten chilling tolerance indices at the germination and seedling stages in maize. J Integr Plant Biol. 2013;55(8):735–44. https://doi.org/10.1111/jipb.12051.
Article Google Scholar
Huang C, Sun H, Dingyi X, Chen Q, Liang Y, Wang X, et al. ZmCCT9 enhances maize adaptation to higher latitudes. Proc Natl Acad Sci. 2018;115(2):E334–41. https://doi.org/10.1073/pnas.1718058115.
Article Google Scholar
Hubbard L, McSteen P, Doebley J, Hake S. Expression patterns and mutant phenotype of Teosinte Branched1 correlate with growth suppression in maize and teosinte. Genetics. 2002;162(4):1927–35. https://doi.org/10.1093/genetics/162.4.1927.
Article Google Scholar
Hufford MB, Bilinski P, Pyhäjärvi T, Ross-Ibarra J. Teosinte as a model system for population and ecological genomics. Trends Genet. 2012a;28(12):606–15. https://doi.org/10.1016/j.tig.2012.08.004.
Article Google Scholar
Hufford MB, Martínez-Meyer E, Gaut BS, Eguiarte LE, Tenaillon MI. “Inferences from the historical distribution of wild and domesticated maize provide ecological and evolutionary insight.” Edited by John P. Hart. PLoS One. 2012b;7(11):e47659. https://doi.org/10.1371/journal.pone.0047659.
Article Google Scholar
Hufford MB, Xun X, van Heerwaarden J, Pyhäjärvi T, Chia J-M, Cartwright RA, et al. Comparative population genomics of maize domestication and improvement. Nat Genet. 2012c;44(7):808–11. https://doi.org/10.1038/ng.2309.
Article Google Scholar
Hufford MB, Lubinksy P, Pyhäjärvi T, Devengenzo MT, Ellstrand NC, Ross-Ibarra J. “The genomic signature of crop-wild introgression in maize”. Edited by Rodney Mauricio. PLoS Genet. 2013;9(5):e1003477. https://doi.org/10.1371/journal.pgen.1003477.
Article Google Scholar
Hufford MB, Seetharam AS, Woodhouse MR, Chougule KM, Shujun O, Liu J, et al. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science. 2021;373(August):655–62. https://www.biorxiv.org/content/10.1101/2021.01.14.426684v1.abstract
Google Scholar
Hung HY, Shannon LM, Tian F, Bradbury PJ, Chen C, Flint-Garcia SA, et al. ZmCCT and the genetic basis of day-length adaptation underlying the Postdomestication spread of maize. Proc Natl Acad Sci U S A. 2012;109(28):E1913–21. https://doi.org/10.1073/pnas.1203189109.
Article Google Scholar
Iltis HH. From Teosinte to maize: the catastrophic sexual transmutation. Science. 1983;222(4626):886–94. https://doi.org/10.1126/science.222.4626.886.
Article Google Scholar
Iltis HH, Doebley JF. Taxonomy of Zea (Gramineae). II. Subspecific categories in the Zea Mays complex and a generic synopsis. Am J Bot. 1980;67(6):994. https://doi.org/10.2307/2442442.
Article Google Scholar
Jacobson A, Lian L, Zhong S, Bernardo R. General combining ability model for Genomewide selection in a Biparental cross. Crop Sci. 2014;54(3):895–905. https://doi.org/10.2135/cropsci2013.11.0774.
Article Google Scholar
Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, et al. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet. 2014;127(3):595–607. https://doi.org/10.1007/s00122-013-2243-1.
Article Google Scholar
Jarquin D, Howard R, Crossa J, Beyene Y, Gowda M, Martini JWR, et al. Genomic prediction enhanced sparse testing for multi-environment trials. G3: Genes Genomes Genet. 2020;10(8):2725–39. https://doi.org/10.1534/g3.120.401349.
Article Google Scholar
Jarquin D, de Leon N, Romay C, Bohn M, Buckler ES, Ciampitti I, et al. Utility of climatic information via combining ability models to improve genomic prediction for yield within the genomes to fields maize project. Front Genet. 2021;11:592–769. https://doi.org/10.3389/fgene.2020.592769.
Article Google Scholar
Ji L, Neumann DA, Schmitz RJ. Crop epigenomics: identifying, unlocking, and harnessing cryptic variation in crop genomes. Mol Plant. 2015;8(6):860–70. https://doi.org/10.1016/j.molp.2015.01.021.
Article Google Scholar
Jiao Y, Peluso P, Shi J, Liang T, Stitzer MC, Wang B, et al. Improved maize reference genome with single-molecule technologies. Nature. 2017;546(7659):524–7. https://doi.org/10.1038/nature22971.
Article Google Scholar
Jiménez-Galindo JC, Malvar RA, Butrón A, Santiago R, Samayoa LF, Caicedo M, et al. Mapping of resistance to corn borers in a MAGIC population of maize. BMC Plant Biol. 2019;19(1):1–17. https://doi.org/10.1186/s12870-019-2052-z.
Article Google Scholar
Jindal SK, Dhaliwal MS, Meena OP. Molecular advancements in male sterility systems of capsicum: a review. Plant Breed. 2020;139(1):42–64. https://doi.org/10.1111/pbr.12757.
Article Google Scholar
Johnson JS, Krutovsky KV, Rajora OP, Gaddis KD, Cairns DM. Advancing biogeography through population genomics. In: Rajora O, editor. Population genomics. Cham: Population Genomics. Springer; 2018. https://doi.org/10.1007/13836_2018_39.
Chapter Google Scholar
Kadam DC, Potts SM, Bohn MO, Lipka AE, Lorenz AJ. Genomic prediction of single crosses in the early stages of a maize hybrid breeding pipeline. G3 Genes|Genomes|Genetics. 2016;6(11):3443–53. https://doi.org/10.1534/g3.116.031286.
Article Google Scholar
Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, et al. Efficient control of population structure in model organism association mapping. Genetics. 2008;178(3):1709–23. https://doi.org/10.1534/GENETICS.107.080101.
Article Google Scholar
Kaur M, Vikal Y, Kaur H, Pal L, Kaur K, Chawla JS. Mapping quantitative trait loci associated with southern leaf blight resistance in maize (Zea Mays L.). J Phytopathol. 2019;167(10):591–600. https://doi.org/10.1111/jph.12849.
Article Google Scholar
Kato YTA. Cytological studies of maize [Zea Mays L.] and Teosinte [Zea Mexicana Schrader Kuntze] in relation to their origin and evolution. Massachusetts Agricultural Experiment Station, 1976.
Google Scholar
Kato YTA. Chromosome morphology and the origin of maize and its races. Evol Biol. 1984;17:219–53.
Google Scholar
Kato YTA, Lopes RA. Chromosome knobs of the perennial teosintes. Maydica. 1990;35(2):125–41.
Google Scholar
Kearsey MJ, Farquhar AGL. QTL analysis in plants; where are we now? Heredity. 1998;80(2):137–42. https://doi.org/10.1038/sj.hdy.6885001.
Article Google Scholar
Kebede D, Mengesha W, Menkir A, Abe A, Garcia-Oliveira AL, Gedil M. Marker based enrichment of Provitamin a content in two tropical maize synthetics. Sci Rep. 2021;11(1):1–10. https://doi.org/10.1038/s41598-021-94586-7.
Article Google Scholar
Kistler L, Yoshi Maezumi S, Gregorio J, de Souza NAS, Przelomska FM, Costa OS, et al. Multiproxy evidence highlights a complex evolutionary legacy of maize in South America. Science. 2018;362(6420):1309–13. https://doi.org/10.1126/science.aav0207.
Article Google Scholar
Kistler L, Thakar HB, VanDerwarker AM, Domic A, Bergström A, George RJ, et al. Archaeological Central American maize genomes suggest ancient gene flow from South America. Proc Natl Acad Sci. 2020;117(52):33124–9. https://doi.org/10.1073/pnas.2015560117.
Article Google Scholar
Klei L, Luca D, Devlin B, Roeder K. Pleiotropy and principal components of heritability combine to increase power for association analysis. Genet Epidemiol. 2008;32(1):9–19. https://doi.org/10.1002/gepi.20257.
Article Google Scholar
Kuki MC, Scapim CA, Rossi ES, Mangolin CA, Do Amaral AT, Pinto RJB. Genome wide association study for gray leaf spot resistance in tropical maize core. PLoS One. 2018;13(6):e0199539. https://doi.org/10.1371/journal.pone.0199539.
Article Google Scholar
Kuleshov NN. World’s diversity of phenotypes of maize. J Am Soc Agron. 1933;25(10):688–700.
Google Scholar
Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, et al. Genome-wide association study of quantitative resistance to Southern Leaf Blight in the maize nested association mapping population. Nat Genet. Nature Publishing Group. 2011; https://doi.org/10.1038/ng.747.
Ladejobi O, Elderfield J, Gardner KA, Chris Gaynor R, Hickey J, Hibberd JM, et al. Maximizing the potential of multi-parental crop populations. Appl Transl Genom. 2016;11:9–17. https://doi.org/10.1016/j.atg.2016.10.002.
Article Google Scholar
Lanes ECM, Viana JMS, Paes GP, Paula MFB, Maia C, Caixeta ET, et al. Population structure and genetic diversity of maize Inbreds derived from tropical hybrids. Genet Mol Res. 2014;13(3):7365–76. https://doi.org/10.4238/2014.September.12.2.
Article Google Scholar
Lauter N, Doebley J. Genetic variation for phenotypically invariant traits detected in teosinte: implications for the evolution of novel forms. Genetics. 2002;160(1):333–42. https://doi.org/10.1093/genetics/160.1.333.
Article Google Scholar
Lee M, Sharopova N, Beavis WD, Grant D, Katt M, Blair D, et al. Expanding the genetic map of maize with the Intermated B73 x Mo17 (IBM) population. Plant Mol Biol. 2002;48(5–6):453–61. https://doi.org/10.1023/A:1014893521186.
Article Google Scholar
Leiboff S, Li X, Heng Cheng H, Todt N, Yang J, Li X, et al. Genetic control of morphometric diversity in the maize shoot apical meristem. Nat Commun. 2015;6(1):1–10. https://doi.org/10.1038/ncomms9974.
Article Google Scholar
Lemmon ZH, Bukowski R, Sun Q, Doebley JF. “The role of Cis regulatory evolution in maize domestication”. Edited by Hunter Fraser. PLoS Genet. 2014;10(11):e1004745. https://doi.org/10.1371/journal.pgen.1004745.
Article Google Scholar
Li Q, Yang X, Shutu X, Cai Y, Zhang D, Han Y, et al. Genome-wide association studies identified three independent polymorphisms associated with α-tocopherol content in maize Kernels. PLoS One. 2012;7(5):e36807. https://doi.org/10.1371/journal.pone.0036807.
Article Google Scholar
Li K, Wang H, Xiaojiao H, Liu Z, Yujin W, Huang C, et al. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet. 2013;45(1):43–50. https://doi.org/10.1038/ng.2484.
Article Google Scholar
Li M, Liu X, Bradbury P, Yu J, Zhang Y-M, Todhunter RJ, et al. Enrichment of statistical power for genome-wide association studies. BMC Biol. 2014a;12(1):1–10. https://doi.org/10.1186/S12915-014-0073-5.
Article Google Scholar
Li Q, Eichten SR, Hermanson PJ, Zaunbrecher VM, Song J, Wendt J, et al. Genetic perturbation of the maize methylome. Plant Cell. 2014b;26(12):4602–16. https://doi.org/10.1105/tpc.114.133140.
Article Google Scholar
Li C, Li YY, Shi Y, Song Y, Zhang D, Buckler ES, et al. Genetic control of the leaf angle and leaf orientation value as revealed by ultra-high density maps in three connected maize populations. PLoS One. 2015;10(3) https://doi.org/10.1371/journal.pone.0121624.
Li X, Zhou Z, Ding J, Yabin W, Zhou B, Wang R, et al. Combined linkage and association mapping reveals QTL and candidate genes for plant and ear height in maize. Front Plant Sci. 2016;7(JUNE2016):833. https://doi.org/10.3389/fpls.2016.00833.
Article Google Scholar
Li X, Jian Y, Xie C, Jun W, Yunbi X, Zou C. Fast diffusion of domesticated maize to temperate zones. Sci Rep. 2017;7(1):1–11. https://doi.org/10.1038/s41598-017-02125-0.
Article Google Scholar
Li E, Liu H, Huang L, Zhang X, Dong X, Song W, et al. Long-range interactions between proximal and distal regulatory regions in maize. Nat Commun. 2019;10(1):2633. https://doi.org/10.1038/s41467-019-10603-4.
Article Google Scholar
Li D, Zhenxiang X, Riliang G, Wang P, Jialiang X, Dengxiang D, et al. Genomic prediction across structured hybrid populations and environments in maize. Plan Theory. 2021;10(6):1174. https://doi.org/10.3390/plants10061174.
Article Google Scholar
Li C, Guan H, Jing X, Li Y, Wang B, Li Y, et al. Genomic insights into historical improvement of heterotic groups during modern hybrid maize breeding. Nat Plants. 2022;8(7):750–63. https://doi.org/10.1038/s41477-022-01190-2.
Article Google Scholar
Li W, Liu H, Cheng ZJ, Ying Hua S, Han HN, Zhang Y, et al. Dna methylation and histone modifications regulate de Novo shoot regeneration in Arabidopsis by modulating Wuschel expression and auxin signaling. PLoS Genet. 2011;7(8) https://doi.org/10.1371/journal.pgen.1002243.
Lian L, Jacobson A, Zhong S, Bernardo R. Genomewide prediction accuracy within 969 maize biparental populations. Crop Sci. 2014;54(4):1514–22. https://doi.org/10.2135/cropsci2013.12.0856.
Article Google Scholar
Lima BC d, Dudek G, Chaves MHM, Martins AG, Missio VC, Missio RF. DIVERSIDADE GENÉTICA EM ACESSOS DE MILHO CRIOULO / Genetic diversity in landrace maize. Brazil J Dev. 2020;6(10):82712–26. https://doi.org/10.34117/bjdv6n10-631.
Article Google Scholar
Lin G, He C, Zheng J, Koo DH, Le H, Zheng H, et al. Chromosome-level genome assembly of a regenerable maize inbred line A188. Genome Biol. 2021;22(1):1–30. https://doi.org/10.1186/s13059-021-02396-x.
Article Google Scholar
Lipka AE, Gore MA, Magallanes-Lundback M, Mesberg A, Lin H, Tiede T, et al. Genome-wide association study and pathway-level analysis of Tocochromanol levels in maize grain. G3: Genes Genomes Genet. 2013;3(8):1287–99. https://doi.org/10.1534/g3.113.006148.
Article Google Scholar
Lipka AE, Kandianis CB, Hudson ME, Jianming Y, Drnevich J, Bradbury PJ, et al. From association to prediction: statistical methods for the dissection and selection of complex traits in plants. Curr Opin Plant Biol. 2015;24(April):110–8. https://doi.org/10.1016/j.pbi.2015.02.010.
Article Google Scholar
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D. FaST linear mixed models for genome-wide association studies. Nat Methods. 2011;8(10):833–5. https://doi.org/10.1038/nmeth.1681.
Article Google Scholar
Liu ZZ, Guo RH, Zhao JR, Cai YL, Wang FG, Cao MJ, et al. Analysis of genetic diversity and population structure of maize landraces from the South Maize Region of China. Agric Sci China. 2010;9(9):1251–62. https://doi.org/10.1016/S1671-2927(09)60214-5.
Article Google Scholar
Liu S, Wang X, Wang H, Xin H, Yang X, Yan J, et al. Genome-wide analysis of ZmDREB genes and their association with natural variation in drought tolerance at seedling stage of Zea Mays L. PLoS Genet. 2013;9(9):e1003790. https://doi.org/10.1371/journal.pgen.1003790.
Article Google Scholar
Liu L, Yanfang D, Huo D, Wang M, Shen X, Yue B, et al. Genetic architecture of maize Kernel row number and whole genome prediction. Theor Appl Genet. 2015a;128(11):2243–54. https://doi.org/10.1007/s00122-015-2581-2.
Article Google Scholar
Liu L, Yanfang D, Shen X, Li M, Sun W, Huang J, et al. KRN4 controls quantitative variation in maize Kernel row number. PLoS Genet. 2015b;11(11):e1005670. https://doi.org/10.1371/journal.pgen.1005670.
Article Google Scholar
Liu N, Xue Y, Guo Z, Li W, Tang J. Genome-wide association study identifies candidate genes for starch content regulation in maize kernels. Front Plant Sci. 2016a;7(JULY2016):1046. https://doi.org/10.3389/fpls.2016.01046.
Article Google Scholar
Liu X, Huang M, Fan B, Buckler ES, Zhang Z. Iterative usage of fixed and random effect models for powerful and efficient genome- wide association studies. PLoS Genet. 2016b;12(2):1–24. https://doi.org/10.1186/1471-2156-13-100.
Article Google Scholar
Liu H, Luo X, Niu L, Yingjie Xiao L, Chen JL, Wang X, et al. Distant EQTLs and non-coding sequences play critical roles in regulating gene expression and quantitative trait variation in maize. Mol Plant. 2017;10(3):414–26. https://doi.org/10.1016/j.molp.2016.06.016.
Article Google Scholar
Liu J, Fernie AR, Yan J. The past, present, and future of maize improvement: domestication, genomics, and functional genomic routes toward crop enhancement. Plant Commun. 2020;1(1):100010. https://doi.org/10.1016/j.xplc.2019.100010.
Article Google Scholar
Liu Y, Guanghui H, Zhang A, Loladze A, Yingxiong H, Wang H, et al. Genome-wide association study and genomic prediction of Fusarium ear rot resistance in tropical maize germplasm. Crop Journal. 2021;9(2):325–41. https://doi.org/10.1016/j.cj.2020.08.008.
Article Google Scholar
Lorenzana RE, Bernardo R. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet. 2009;120(1):151–61. https://doi.org/10.1007/s00122-009-1166-3.
Article Google Scholar
Lu X, Wang J, Wang Y, Wen W, Zhang Y, Jianjun D, et al. Genome-wide association study of maize aboveground dry matter accumulation at seedling stage. Front Genet. 2021;11(January) https://doi.org/10.3389/fgene.2020.571236.
Lu Y, Yan J, Guimarães CT, Taba S, Hao Z, Gao S, et al. Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms. Theor Appl Genet. 2009;120(1):93–115. https://doi.org/10.1007/s00122-009-1162-7.
Article Google Scholar
Luikart G, England PR, Tallmon D, Jordan S, Taberlet P. The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet. 2003;4(12):981–94. https://doi.org/10.1038/nrg1226.
Luikart G, Kardos M, Hand BK, Rajora OP, Aitken SN, Hohenlohe PA. Population genomics: advancing understanding of nature. In: Rajora O, editor. Population genomics. Cham: Population Genomics. Springer; 2018. https://doi.org/10.1007/13836_2018_60.
Chapter Google Scholar
Luo M, Zhao Y, Zhang R, Xing J, Duan M, Li J, et al. Mapping of a major QTL for salt tolerance of mature field-grown maize plants based on SNP markers. BMC Plant Biol. 2017;17(1):1–10. https://doi.org/10.1186/s12870-017-1090-7.
Article Google Scholar
Luo X, Wang B, Gao S, Zhang F, Terzaghi W, Dai M. Genome-wide association study dissects the genetic bases of salt tolerance in maize seedlings. J Integr Plant Biol. 2019;61(6):658–74. https://doi.org/10.1111/jipb.12797.
Article Google Scholar
Lv M, Deng C, Li X, Zhao X, Li H, Li Z, et al. Identification and fine-mapping of RppCML496, a major QTL for resistance to Puccinia Polysora in maize. Plant Genome. 2021;14(1):1–7. https://doi.org/10.1002/tpg2.20062.
Article Google Scholar
Machado AT, Torres C, de Toledo, Lourenço L. Manejo Da Diversidade Genética e Melhoramento Participativo de Milho Em Sistemas Agroecológicos genetic diversity management and maize participatory breeding under agroecological systems. Revista Brasileira de Agroecologia. 2011;6(1):127–36. http://orgprints.org/24142/1/Machado_Manejo.pdf
Google Scholar
Mahato A, Shahi JP, Singh PK, Kumar M. Genetic diversity of sweet corn Inbreds using agro-morphological traits and microsatellite markers. 3 Biotech. 2018;8(8):1–9. https://doi.org/10.1007/s13205-018-1353-5.
Article Google Scholar
Mahuku G, Chen J, Shrestha R, Narro LA, Guerrero KVO, Arcos AL, et al. Combined linkage and association mapping identifies a major QTL (QRtsc8-1), conferring tar spot complex resistance in maize. Theor Appl Genet. 2016;129(6):1217–29. https://doi.org/10.1007/s00122-016-2698-y.
Article Google Scholar
Mammadov J, Sun X, Gao Y, Ochsenfeld C, Bakker E, Ren R, et al. Combining powers of linkage and association mapping for precise dissection of QTL controlling resistance to gray leaf spot disease in maize (Zea Mays L.). BMC Genomics. 2015;16(1):1–16. https://doi.org/10.1186/s12864-015-2171-3.
Article Google Scholar
Mangelsdorf PC. Corn: its origin, evolution, and improvement. Cambridge: Belknap Press of Harvard University Press; 1974.
Google Scholar
Mangelsdorf PC, Fraps GS. A direct quantitative relationship between vitamin A in corn and the number of genes for yellow pigmentation. Science. 1931;73:241–2.
Google Scholar
Mangelsdorf PC, Reeves RG. The origin of maize. Proc Natl Acad Sci. 1938;24(8):303–12. https://doi.org/10.1073/pnas.24.8.303.
Article Google Scholar
Mangelsdorf PC, Reeves RG. Origin of Indian corn and its relatives. Texas: Agricultural and Mechanical College of Texas; 1939.
Google Scholar
Mao H, Wang H, Liu S, Li Z, Yang X, Yan J, et al. A transposable element in a NAC gene is associated with drought tolerance in maize seedlings. Nat Commun. 2015;6(1):1–13. https://doi.org/10.1038/ncomms9326.
Article Google Scholar
Massman JM, Gordillo A, Lorenzana RE, Bernardo R. Genomewide predictions from maize single-cross data. Theor Appl Genet. 2013;126(1):13–22. https://doi.org/10.1007/s00122-012-1955-y.
Article Google Scholar
Matsuoka Y, Vigouroux Y, Goodman MM, Sanchez JG, Buckler E, Doebley J. A single domestication for maize shown by multilocus microsatellite genotyping. Proc Natl Acad Sci. 2002;99(9):6080–4. https://doi.org/10.1073/pnas.052125199.
Article Google Scholar
McLean-Rodríguez FD, Costich DE, Camacho-Villa TC, Pè ME, Dell’Acqua M. Genetic diversity and selection signatures in maize landraces compared across 50 years of in situ and ex situ conservation. Heredity. 2021;126(6):913–28. https://doi.org/10.1038/s41437-021-00423-y.
Article Google Scholar
McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, et al. Genetic properties of the maize nested association mapping population. Science. 2009;325(5941):737–40. https://doi.org/10.1126/science.1174320.
Article Google Scholar
Mendes MP, Lopes C, de Souza. Genomewide prediction of tropical maize single-crosses. Euphytica. 2016;209(3):651–63. https://doi.org/10.1007/s10681-016-1642-1.
Article Google Scholar
Merrill WL, Hard RJ, Mabry JB, Fritz GJ, Adams KR, Roney JR, et al. The diffusion of maize to the Southwestern United States and its impact. Proc Natl Acad Sci. 2009;106(50):21019–26. https://doi.org/10.1073/pnas.0906075106.
Article Google Scholar
Meuwissen THEE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819–29. https://doi.org/10.1093/genetics/157.4.1819.
Article Google Scholar
Moler ERV, Abakir A, Eleftheriou M, Johnson JS, Krutovsky KV, Lewis LC, et al. Population epigenomics: advancing understanding of phenotypic plasticity, acclimation, adaptation and diseases. 2018:179–260. https://doi.org/10.1007/13836_2018_59.
Mikel MA, Dudley JW. Evolution of North American Dent Corn from public to proprietary germplasm. Crop Sci. 2006;46(3):1193–205. https://doi.org/10.2135/cropsci2005.10-0371.
Article Google Scholar
Môro GV, Fritsche-Neto R. Importância e Usos Do Milho No Brasil. In: Galvão JCC, Borém A, Pimentel MA, editors. Milho: Do Plantio à Colheita. Editora UFV; 2015. p. 9–25.
Google Scholar
Mott R, Talbot CJ, Turri MG, Collins AC, Flint J. A method for fine mapping quantitative trait loci in outbred animal stocks. Proc Natl Acad Sci U S A. 2000;97(23):12649–54. https://doi.org/10.1073/pnas.230304397.
Article Google Scholar
Mutegi E, Snow AA, Rajkumar M, Pasquet R, Ponniah H, Daunay MC, et al. Genetic diversity and population structure of wild/ weedy eggplant (solanum Insanum, Solanaceae) in Southern India: implications for conservation. Am J Bot. 2015;102(1):140–8. https://doi.org/10.3732/ajb.1400403.
Article Google Scholar
N’da HA, Akanvou L, Pokou N’d D, Akanza KP, Kouakou CK, Zoro BIIA. Genetic diversity and population structure of maize landraces from Cte DIvoire. Afr J Biotechnol. 2016;15(44):2507–16. https://doi.org/10.5897/ajb2016.15678.
Article Google Scholar
Nei M. Genetic distance between populations. Am Nat. 1972;1:283–92.
Google Scholar
Nelimor C, Badu-Apraku B, Garcia-Oliveira AL, Tetteh A, Paterne A, N’guetta ASP, et al. Genomic analysis of selected maize landraces from Sahel and Coastal West Africa reveals their variability and potential for genetic enhancement. Genes. 2020;11(9):1–14. https://doi.org/10.3390/genes11091054.
Article Google Scholar
Nelson PT, Coles ND, Holland JB, Bubeck DM, Smith S, Goodman MM. Molecular characterization of maize Inbreds with expired U.S. plant variety protection. Crop Sci. 2008;48(5):1673–85. https://doi.org/10.2135/cropsci2008.02.0092.
Article Google Scholar
O’Reilly PF, Hoggart CJ, Pomyen Y, Calboli FCF, Elliott P, Jarvelin MR, et al. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS One. 2012;7(5):34861. https://doi.org/10.1371/journal.pone.0034861.
Article Google Scholar
Olmos SE, Lia VV, Eyhérabide GH. Genetic diversity and linkage disequilibrium in the argentine public maize inbred line collection. 2017;15(6):515–26. https://doi.org/10.1017/S1479262116000228.
Olukolu BA, Negeri A, Dhawan R, Venkata BP, Sharma P, Garg A, et al. A connected set of genes associated with programmed cell death implicated in controlling the hypersensitive response in maize. Genetics. 2013;193(2):609–20. https://doi.org/10.1534/genetics.112.147595.
Article Google Scholar
Olukolu BA, Wang GF, Vontimitta V, Venkata BP, Marla S, Ji J, et al. A genome-wide association study of the maize hypersensitive defense response identifies genes that cluster in related pathways. PLoS Genet. 2014;10(8):e1004562. https://doi.org/10.1371/journal.pgen.1004562.
Article Google Scholar
Ortiz R, Taba S, Chávez VH, Tovar MM, Yunbi X, Yan J, et al. Conserving and enhancing maize genetic resources as global public goods-a perspective from CIMMYT. Crop Sci. 2010;50(1):13–28. https://doi.org/10.2135/cropsci2009.06.0297.
Article Google Scholar
Owens BF, Gore MA, Magallanes-Lundback M, Tiede T, Diepenbrock CH, Kandianis CB, et al. A Foundation for Provitamin a biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels. Genetics. 2014;198(4):1699–716. https://doi.org/10.1534/genetics.114.169979.
Article Google Scholar
Pace J, Gardner C, Romay C, Ganapathysubramanian B, Lübberstedt T. Genome-wide association analysis of seedling root development in maize (Zea Mays L.). BMC Genomics. 2015;16(1):1–12. https://doi.org/10.1186/s12864-015-1226-9.
Article Google Scholar
Palaisa KA, Morgante M, Williams M, Rafalski A. Contrasting effects of selection on sequence diversity and linkage disequilibrium at two phytoene synthase loci[W]. Plant Cell. 2003;15(8):1795–806. https://doi.org/10.1105/tpc.012526.
Article Google Scholar
Pan Q, Li L, Yang X, Tong H, Shutu X, Li Z, et al. Genome-wide recombination dynamics are associated with phenotypic variation in maize. New Phytol. 2016;210(3):1083–94. https://doi.org/10.1111/nph.13810.
Article Google Scholar
Pan Q, Yuancheng X, Li K, Peng Y, Zhan W, Li W, et al. The genetic basis of plant architecture in 10 maize recombinant inbred line populations. Plant Physiol. 2017;175(2):858–73. https://doi.org/10.1104/pp.17.00709.
Article Google Scholar
Parra JR, de Jesús J, González S, Cordero ÁAJ, Valtierra JAC, López JGM, et al. Maíces Nativos Del Occidente de México 1: Colectas 2004. Scientia-CUCBA. 2006;8(1):1–139.
Google Scholar
Paterniani E, Goodman MM. Races of maize in Brazil and adjacent areas. Edited by CIMMYT, 1977.
Google Scholar
Peiffer JA, Flint-Garcia SA, De Leon N, McMullen MD, Kaeppler SM, Buckler ES. The genetic architecture of maize stalk strength. PLoS One. 2013;8(6):e67066. https://doi.org/10.1371/journal.pone.0067066.
Article Google Scholar
Peiffer JA, Romay MC, Gore MA, Flint-Garcia SA, Zhang Z, Millard MJ, et al. The genetic architecture of maize height. Genetics. 2014;196(4):1337–56. https://doi.org/10.1534/genetics.113.159152.
Article Google Scholar
Perales H, Golicher D. “Mapping the diversity of maize races in Mexico.” edited by Hany A. El-Shemy. PLoS One. 2014;9(12):e114657. https://doi.org/10.1371/journal.pone.0114657.
Article Google Scholar
Platt A, Vilhjálmsson BJ, Nordborg M. Conditions under which genome-wide association studies will be positively misleading. Genetics. 2010;186(3):1045–52. https://doi.org/10.1534/genetics.110.121665.
Article Google Scholar
Poland J, Rutkoski J. Advances and challenges in genomic selection for disease resistance. Annu Rev Phytopathol. 2016;54:79–98. https://doi.org/10.1146/annurev-phyto-080615-100056.
Article Google Scholar
Poland JA, Bradbury PJ, Buckler ES, Nelson RJ. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci U S A. 2011;108(17):6893–8. https://doi.org/10.1073/pnas.1010894108.
Article Google Scholar
Poneleit CG. Breeding white endosperm corn. In: Hallauer AR, editor. Specialty corns. 2nd ed. Boca Raton: CRC Press; 2001. p. 235–73.
Google Scholar
Prasanna BM. Diversity in global maize germplasm: characterization and utilization. J Biosci. 2012;37(5):843–55. https://doi.org/10.1007/s12038-012-9227-1.
Article Google Scholar
Prasanna BM, Pixley K, Warburton ML, Xie CX. Molecular marker-assisted breeding options for maize improvement in Asia. Mol Breed. 2010;26(2):339–56. https://doi.org/10.1007/s11032-009-9387-3.
Article Google Scholar
Preston JC, Wang H, Kursel L, Doebley J, Kellogg EA. The role of Teosinte Glume Architecture ( Tga1 ) in coordinated regulation and evolution of grass glumes and inflorescence axes. New Phytol. 2012;193(1):204–15. https://doi.org/10.1111/j.1469-8137.2011.03908.x.
Article Google Scholar
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9. https://doi.org/10.1038/ng1847.
Article Google Scholar
Price AL, Kryukov GV, de Bakker PIW, Purcell SM, Staples J, Wei LJ, et al. Pooled association tests for rare variants in Exon-resequencing studies. Am J Hum Genet. 2010;86(6):832–8. https://doi.org/10.1016/j.ajhg.2010.04.005.
Article Google Scholar
Pritchard JK, Stephens M, Rosenberg NA, Donnelly P. Association mapping in structured populations. Am J Hum Genet. 2000a;67(1):170–81. https://doi.org/10.1086/302959.
Article Google Scholar
Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000b;155(2):945–59. https://doi.org/10.1093/GENETICS/155.2.945.
Article Google Scholar
Qiu Y, Kaiser C, Schmidt C, Broders K, Robertson AE, Jamann TM. Identification of quantitative trait loci associated with maize resistance to bacterial leaf streak. Crop Sci. 2020;60(1):226–37. https://doi.org/10.1002/csc2.20099.
Article Google Scholar
Quijada P, Shannon LM, Glaubitz JC, Studer AJ, Doebley J. Characterization of a major maize domestication QTL on the short arm of chromosome 1. Maydica. 2009;54(4):401–8.
Google Scholar
Rajora OP, editor. Population genomics. Cham: Springer International Publishing; 2019. https://doi.org/10.1007/978-3-030-04589-0.
Book Google Scholar
Rakshit S, Rakshit A, Patil JV. Multiparent intercross populations in analysis of quantitative traits. J Genet. 2012;91(1):111–7.
Google Scholar
Ranganatha HM, Lohithaswa HC, Pandravada A. Mapping and validation of major quantitative trait loci for resistance to northern corn leaf blight along with the determination of the relationship between resistances to multiple Foliar pathogens of maize (Zea Mays L.). Front Genet. 2021;11(January):1–13. https://doi.org/10.3389/fgene.2020.548407.
Article Google Scholar
Rashid Z, Sofi M, Harlapur SI, Kachapur RM, Dar ZA, Singh PK, et al. Genome-wide association studies in tropical maize germplasm reveal novel and known genomic regions for resistance to northern corn leaf blight. Sci Rep. 2020;10(1):1–16. https://doi.org/10.1038/s41598-020-78928-5.
Article Google Scholar
Revilla P, Rodríguez VM, Ordás A, Rincent R, Charcosset A, Giauffret C, et al. Association mapping for cold tolerance in two large maize inbred panels. BMC Plant Biol. 2016;16(1):1–10. https://doi.org/10.1186/s12870-016-0816-2.
Article Google Scholar
Rice BR, Lipka AE. Diversifying maize genomic selection models. Mol Breed. 2021;41(5):33. https://doi.org/10.1007/s11032-021-01221-4.
Article Google Scholar
Rice BR, Fernandes SB, Lipka AE. Multi-trait genome-wide association studies reveal loci associated with maize inflorescence and leaf architecture. Plant Cell Physiol. Oxford University Press. 2020; https://doi.org/10.1093/pcp/pcaa039.
Riedelsheimer C, Lisec J, Czedik-Eysenberg A, Sulpice R, Flis A, Grieder C, et al. Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc Natl Acad Sci U S A. 2012;109(23):8872–7. https://doi.org/10.1073/pnas.1120813109.
Article Google Scholar
Riedelsheimer C, Brotman Y, Méret M, Melchinger AE, Willmitzer L. The maize leaf Lipidome shows multilevel genetic control and high predictive value for agronomic traits. Sci Rep. 2013;3(1):1–7. https://doi.org/10.1038/srep02479.
Article Google Scholar
Robertsen C, Hjortshøj R, Janss L. Genomic selection in cereal breeding. Agronomy. 2019;9(2):95. https://doi.org/10.3390/agronomy9020095.
Article Google Scholar
Ross-Ibarra J, Tenaillon M, Gaut BS. Historical divergence and gene flow in the Genus Zea. Genetics. 2009;181(4):1399–413. https://doi.org/10.1534/genetics.108.097238.
Article Google Scholar
Sakiyama NS, Ramos HCC, Caixeta ET, Pereira MG. Plant breeding with marker-assisted selection in Brazil. Crop Breed Appl Biotechnol. 2014;14(1):54–60. https://doi.org/10.1590/s1984-70332014000100009.
Article Google Scholar
Samayoa LF, Malvar RA, Olukolu BA, Holland JB, Butrón A. Genome-wide association study reveals a set of genes associated with resistance to the Mediterranean Corn Borer (Sesamia Nonagrioides L.) in a maize diversity panel. BMC Plant Biol. 2015;15(1):1–15. https://doi.org/10.1186/s12870-014-0403-3.
Article Google Scholar
Samayoa LF, Malvar RA, Butrón A. QTL for maize Midparent Heterosis in the heterotic pattern American Dent × European Flint under Corn Borer pressure. Front Plant Sci. 2017;8(April):1–8. https://doi.org/10.3389/fpls.2017.00573.
Article Google Scholar
Sanchez GJJ, Goodman MM, Stuber CW. Isozymatic and morphological diversity in the races of maize of Mexico. Econ Bot. 2000;54(1):43–59.
Google Scholar
Sandhu D, Pudussery MV, Kumar R, Pallete A, Markley P, Bridges WC, et al. Characterization of natural genetic variation identifies multiple genes involved in salt tolerance in maize. Function Integ Genom. 2020;20(2):261–75. https://doi.org/10.1007/s10142-019-00707-x.
Article Google Scholar
Sangoi L, da Costa Bortoluzzi RL. Botânica, Origem, Evolução e Dispersão. In: de Milho M, Lima R, Borém A, editors. . 396. UFV; 2018.
Google Scholar
Santos JP, Dos R, de Castro Vasconcellos RC, Pires LPM, Balestre M, Von Pinho RG. Inclusion of dominance effects in the multivariate GBLUP model. Edited by Qin Zhang. Plos One. 2016;11(4):e0152045. https://doi.org/10.1371/journal.pone.0152045.
Article Google Scholar
Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, et al. The B73 maize genome: complexity, diversity, and dynamics. Science. 2009;326(5956):1112–5. https://doi.org/10.1126/science.1178534.
Article Google Scholar
Scossa F, Alseekh S, Fernie AR. Integrating multi-omics data for crop improvement. J Plant Physiol. 2021;257(February):153352. https://doi.org/10.1016/j.jplph.2020.153352.
Article Google Scholar
Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, et al. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet. 2012;44(7):825–30. https://doi.org/10.1038/ng.2314.
Article Google Scholar
Semagn K, Bjørnstad A, Ndjiondjop MN. Principles, requirements and prospects of genetic mapping in plants. Afr J Biotechnol. 2006;5(25):2569–87. https://doi.org/10.4314/ajb.v5i25.56082.
Article Google Scholar
Sherwin JC, Reacher MH, Dean WH, Ngondi J. Epidemiology of vitamin A deficiency and Xerophthalmia in at-risk populations. Trans R Soc Trop Med Hyg. 2012;106(4):205–14. https://doi.org/10.1016/j.trstmh.2012.01.004.
Article Google Scholar
Shikha K, Shahi JP, Vinayan MT, Zaidi PH, Singh AK, Sinha B. Genome-wide association mapping in maize: status and prospects. 3. Biotech. 2021;11(5):244. https://doi.org/10.1007/s13205-021-02799-4.
Article Google Scholar
Shu G, Cao G, Li N, Wang A, Wei F, Li T, et al. Genetic variation and population structure in China summer maize germplasm. Sci Rep. 2021;11(1):1–13. https://doi.org/10.1038/s41598-021-84732-6.
Article Google Scholar
Singh BD, Singh AK. Marker-assisted plant breeding: principles and practices. New Delhi: Springer India; 2015. https://doi.org/10.1007/978-81-322-2316-0.
Book Google Scholar
Singh J, Sharma S, Kaur A, Vikal Y, Cheema AK, Bains BK, et al. Marker-assisted pyramiding of lycopene-ε-cyclase, β-carotene Hydroxylase1 and Opaque2 genes for development of biofortified maize hybrids. Sci Rep. 2021;11(1):1–15. https://doi.org/10.1038/s41598-021-92010-8.
Article Google Scholar
Sitonik C, Suresh LM, Beyene Y, Olsen MS, Makumbi D, Oliver K, et al. Genetic architecture of maize chlorotic mottle virus and maize lethal necrosis through GWAS, linkage analysis and genomic prediction in tropical maize germplasm. Theor Appl Genet. 2019;132(8):2381–99. https://doi.org/10.1007/s00122-019-03360-x.
Article Google Scholar
Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet. 2013; https://doi.org/10.1038/nrg3461.
De Souza IRP, Guilhen JHS, De Andrade CDLT, De Pinto M, O, De Lana UG, P, Pastina MM. Major effect Qtl on chromosome 3 conferring maize resistance to sugarcane Mosaic virus. Revista Brasileira de Milho e Sorgo. 2019;18(3):322–39. https://doi.org/10.18512/1980-6477/rbms.v18n3p322-339.
Stagnati L, Rahjoo V, Samayoa LF, Holland JB, Borrelli VMG, Busconi M, et al. A genome-wide association study to understand the effect of Fusarium Verticillioides infection on seedlings of a maize diversity panel. G3: Genes, Genomes, Genetics. 2020;10(4):1685–96. https://doi.org/10.1534/g3.119.400987.
Article Google Scholar
Stanley A, Menkir A, Paterne A, Ifie B, Tongoona P, Unachukwu N, et al. Genetic diversity and population structure of maize inbred lines with varying levels of resistance to. Plan Theory. 2020;9:1223.
Google Scholar
Stearns FW. One hundred years of pleiotropy: a retrospective. Genetics. 2010; https://doi.org/10.1534/genetics.110.122549.
Stitzer MC, Ross‐Ibarra J. Maize domestication and gene interaction. New Phytol. 2018;220(2):395–408. https://doi.org/10.1111/nph.15350.
Article Google Scholar
Strigens A, Freitag NM, Gilbert X, Grieder C, Riedelsheimer C, Schrag TA, et al. Association mapping for chilling tolerance in Elite Flint and Dent maize inbred lines evaluated in growth chamber and field experiments. Plant Cell Environ. 2013;36(10):1871–87. https://doi.org/10.1111/pce.12096.
Article Google Scholar
Studer A, Zhao Q, Ross-Ibarra J, Doebley J. Identification of a functional transposon insertion in the maize domestication gene Tb1. Nat Genet. 2011;43(11):1160–3. https://doi.org/10.1038/ng.942.
Article Google Scholar
Studer AJ, Wang H, Doebley JF. Selection during maize domestication targeted a gene network controlling plant and inflorescence architecture. Genetics. 2017;207(2):755–65. https://doi.org/10.1534/genetics.117.300071.
Article Google Scholar
Sturtevant EL. Varieties of corn. Washington: USDA Off. Exp. Stn. Bull; 1899.
Google Scholar
Sun H, Zhai L, Teng F, Li Z, Zhang Z. QRgls1.06, a major QTL conferring resistance to Gray leaf spot disease in maize. Crop J. 2021;9(2):342–50. https://doi.org/10.1016/j.cj.2020.08.001.
Article Google Scholar
Suwarno WB, Pixley KV, Palacios-Rojas N, Kaeppler SM, Babu R. Genome-wide association analysis reveals new targets for carotenoid biofortification in maize. Theor Appl Genet. 2015;128(5):851–64. https://doi.org/10.1007/s00122-015-2475-3.
Article Google Scholar
Swanson-Wagner RA, Eichten SR, Kumari S, Tiffin P, Stein JC, Ware D, et al. Pervasive gene content variation and copy number variation in maize and its undomesticated progenitor. Genome Res. 2010;20(12):1689–99. https://doi.org/10.1101/gr.109165.110.
Article Google Scholar
Swanson-Wagner R, Briskine R, Schaefer R, Hufford MB, Ross-Ibarra J, Myers CL, et al. Reshaping of the maize transcriptome by domestication. Proc Natl Acad Sci U S A. 2012;109(29):11878–83. https://doi.org/10.1073/pnas.1201961109.
Article Google Scholar
Tang JD, Andy Perkins W, Williams P, Warburton ML. Using genome-wide associations to identify metabolic pathways involved in maize aflatoxin accumulation resistance. BMC Genomics. 2015;16(1):1–12. https://doi.org/10.1186/s12864-015-1874-9.
Article Google Scholar
Tao Y, Liu Q, Wang H, Zhang Y, Huang X, Wang B, et al. Identification and fine-mapping of a QTL, QMrdd1, that confers recessive resistance to maize rough Dwarf disease. BMC Plant Biol. 2013;13(1):1–13. https://doi.org/10.1186/1471-2229-13-145.
Article Google Scholar
Technow F, Riedelsheimer C, Schrag TA, Melchinger AE. Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects. Theor Appl Genet. 2012;125(6):1181–94. https://doi.org/10.1007/s00122-012-1905-8.
Article Google Scholar
Teixeira FF, Costa FM. Catacterização de Recursos Genéticos de Milho. Embrapa Milho e Sorgo. 2010;1:10.
Google Scholar
Teng F, Zhai L, Liu R, Bai W, Wang L, Huo D, et al. ZmGA3ox2, a candidate gene for a major QTL, QPH3.1, for plant height in maize. Plant J. 2013;73(3):405–16. https://doi.org/10.1111/tpj.12038.
Article Google Scholar
Thirunavukkarasu N, Hossain F, Arora K, Sharma R, Shiriga K, Mittal S, et al. Functional mechanisms of drought tolerance in subtropical maize (Zea Mays L.) identified using genome-wide association mapping. BMC Genomics. 2014;15(1):1–12. https://doi.org/10.1186/1471-2164-15-1182.
Article Google Scholar
Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, et al. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nature Genet. 2011;43. Nature Publishing Group https://doi.org/10.1038/ng.746.
USDA. World Agricultural Production. 2021. https://apps.fas.usda.gov/psdonline/circulars/production.pdf.
van der Sluis S, Posthuma D, Dolan CV. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genet. 2013;9(1):1003235. https://doi.org/10.1371/journal.pgen.1003235.
Article Google Scholar
Van Inghelandt D, Melchinger AE, Lebreton C, Stich B. Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet. 2010;120(7):1289–99. https://doi.org/10.1007/s00122-009-1256-2.
Article Google Scholar
Van Inghelandt D, Melchinger AE, 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 Biol. 2012;12(1):1–15. https://doi.org/10.1186/1471-2229-12-56.
Article Google Scholar
Vann L, Kono T, Pyhäjärvi T, Hufford MB, Ross-Ibarra J. Natural variation in Teosinte at the domestication locus Teosinte Branched1 ( Tb1 ). PeerJ. 2015;3(April):e900. https://doi.org/10.7717/peerj.900.
Article Google Scholar
Vega-Alvarez I, Santacruz-Varela A, Rocandio-Rodríguez M, Córdova-Téllez L, López-Sánchez H, Muñoz-Orozco A, et al. Genetic diversity and structure of native maize races from Northwestern Mexico. Pesquisa Agropecuaria Brasileira. 2017;52(11):1023–32. https://doi.org/10.1590/S0100-204X2017001100008.
Article Google Scholar
Vigouroux Y, Glaubitz JC, Matsuoka Y, Goodman MM, Sanchez JG, Doebley J. Population structure and genetic diversity of new world maize races assessed by DNA microsatellites. Am J Bot. 2008;95(10):1240–53. https://doi.org/10.3732/ajb.0800097.
Article Google Scholar
Villa TC, Camacho NM, Scholten M, Ford-Lloyd B. Defining and identifying crop landraces. Plant Genet Resour. 2005;3(3):373–84. https://doi.org/10.1079/pgr200591.
Article Google Scholar
Vinayan MT, Seetharam K, Raman Babu PH, Zaidi MB, Nair SK. Genome wide association study and genomic prediction for Stover quality traits in tropical maize (Zea Mays L.). Sci Rep. 2021;11(1):1–14. https://doi.org/10.1038/s41598-020-80118-2.
Article Google Scholar
Visscher PM, Yang J. A Plethora of pleiotropy across complex traits. Nat Genet. 2016; https://doi.org/10.1038/ng.3604.
Vollbrecht E, Springer PS, Goh L, Buckler IV ES, Martienssen R. Architecture of floral branch systems in maize and related grasses. Nature. 2005;436(7054):1119–26. https://doi.org/10.1038/nature03892.
Article Google Scholar
Wallace JG, Bradbury PJ, Zhang N, Gibon Y, Stitt M, Buckler ES. “Association mapping across numerous traits reveals patterns of functional variation in maize” Edited by Justin O Borevitz. PLoS Genet. 2014;10(12):e1004845. https://doi.org/10.1371/journal.pgen.1004845.
Article Google Scholar
Wang Q, Dooner HK. Remarkable variation in maize genome structure inferred from haplotype diversity at the Bz locus. Proc Natl Acad Sci. 2006;103(47):17644–9. https://doi.org/10.1073/pnas.0603080103.
Article Google Scholar
Wang H, Nussbaum-Wagler T, Li B, Zhao Q, Vigouroux Y, Faller M, et al. The origin of the naked grains of maize. Nature. 2005;436(7051):714–9. https://doi.org/10.1038/nature03863.
Article Google Scholar
Wang H, Studer AJ, Zhao Q, Meeley R, Doebley JF. Evidence that the origin of naked Kernels during maize domestication was caused by a single amino acid substitution in Tga1. Genetics. 2015a;200(3):965–74. https://doi.org/10.1534/genetics.115.175752.
Article Google Scholar
Wang X, Yang Z, Chenwu X. A comparison of genomic selection methods for breeding value prediction. Sci Bullet. 2015b;60(10):925–35. https://doi.org/10.1007/s11434-015-0791-2.
Article Google Scholar
Wang H, Liu R, Yang J, Liu H, Sun Y. Theoretical model for elliptical tube laterally impacted by two parallel rigid plates. Appl Math Mech (English Edition). 2016a;37(2):227–36. https://doi.org/10.1007/s10483-016-2027-8.
Article Google Scholar
Wang X, Wang H, Liu S, Ferjani A, Li J, Yan J, et al. Genetic variation in ZmVPP1 contributes to drought tolerance in maize seedlings. Nat Genet. 2016b;48(10):1233–41. https://doi.org/10.1038/ng.3636.
Article Google Scholar
Wang C, Yang Q, Wang W, Li Y, Guo Y, Zhang D, et al. A transposon-directed epigenetic change in ZmCCT underlies quantitative resistance to Gibberella stalk rot in maize. New Phytol. 2017a;215(4):1503–15. https://doi.org/10.1111/nph.14688.
Article Google Scholar
Wang L, Beissinger TM, Lorant A, Ross-Ibarra C, Ross-Ibarra J, Hufford MB. The interplay of demography and selection during maize domestication and expansion. Genome Biol. 2017b;18(1):215. https://doi.org/10.1186/s13059-017-1346-4.
Article Google Scholar
Wang X, Yang X, Zhongli H, Chenwu X. Genomic selection methods for crop improvement: current status and prospects. Crop J. Crop Science Society of China/ Institute of Crop Sciences. 2018a; https://doi.org/10.1016/j.cj.2018.03.001.
Wang X, Chen Q, Yaoyao W, Lemmon ZH, Guanghui X, Huang C, et al. Genome-wide analysis of transcriptional variability in a large maize-teosinte population. Mol Plant. 2018b;11(3):443–59. https://doi.org/10.1016/j.molp.2017.12.011.
Article Google Scholar
Wang Q‐J, Yuan Y, Liao Z, Jiang Y, Wang Q, Zhang L, et al. Genome‐wide association study of 13 traits in maize seedlings under low phosphorus stress. Plant Genome. 2019;12(3):190039. https://doi.org/10.3835/plantgenome2019.06.0039.
Article Google Scholar
Wang N, Wang H, Zhang A, Liu Y, Diansi Y, Hao Z, et al. Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing. Theor Appl Genet. 2020;133(10):2869–79. https://doi.org/10.1007/s00122-020-03638-5.
Article Google Scholar
Warburton ML, Garrison Wilkes S, Taba AC, Mir C, Dumas F, Madur D, et al. Gene flow among different Teosinte Taxa and into the domesticated maize gene pool. Genet Resour Crop Evol. 2011;58(8):1243–61. https://doi.org/10.1007/s10722-010-9658-1.
Article Google Scholar
Warburton ML, Tang JD, Windham GL, Hawkins LK, Murray SC, Wenwei X, et al. Genome-wide association mapping of aspergillus Flavus and Aflatoxin accumulation resistance in maize. Crop Sci. 2015;55(5):1857–67. https://doi.org/10.2135/cropsci2014.06.0424.
Article Google Scholar
Ward JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58(301):236–44.
Google Scholar
Weatherwax P. The history of corn. Sci Mon. 1950;71
Google Scholar
Wei LJ, Johnson WE. Combining dependent tests with incomplete repeated measurements. Biometrika. 1985;72(2):359–64. https://doi.org/10.1093/biomet/72.2.359.
Article Google Scholar
Wei M, Fu J, Li X, Wang Y, Li Y. Influence of Dent Corn genetic backgrounds on QTL detection for plant-height traits and their relationships in high-oil maize. J Appl Genet. 2009;50(3):225–34. https://doi.org/10.1007/BF03195676.
Article Google Scholar
Wellhausen EJ, Roberts LM, Hernandez EX, Mangelsdorf PC. Races of maize in Mexico. Cambridge: Bussey Inst. Harvard University Press; 1952.
Google Scholar
Wen W, Franco J, Chavez-Tovar VH, Yan J, Taba S. Genetic characterization of a core set of a tropical maize race Tuxpeño for further use in maize improvement. PLoS One. 2012;7(3):1–10. https://doi.org/10.1371/journal.pone.0032626.
Article Google Scholar
Wen W, Li D, Li X, Gao Y, Li W, Li H, et al. Metabolome-based genome-wide association study of maize Kernel leads to novel biochemical insights. Nat Commun. 2014;5(1):1–10. https://doi.org/10.1038/ncomms4438.
Article Google Scholar
Wen J, Shen Y, Xing Y, Wang Z, Han S, Li S, et al. QTL mapping of Fusarium ear rot resistance in maize. Plant Dis. 2021;105(3):558–65. https://doi.org/10.1094/PDIS-02-20-0411-RE.
Article Google Scholar
Wen W, Taba S, Shah T, Chavez Tovar VH, Yan J. Detection of genetic integrity of conserved maize (Zea Mays L.) germplasm in Genebanks using SNP markers. Genet Resour Crop Evol. 2011;58(2):189–207. https://doi.org/10.1007/s10722-010-9562-8.
Article Google Scholar
Weng J, Xie C, Hao Z, Wang J, Liu C, Li M, et al. Genome-wide association study identifies candidate genes that affect plant height in Chinese elite maize (Zea Mays l.) inbred lines. PLoS One. 2011;6(12):e29229. https://doi.org/10.1371/journal.pone.0029229.
Article Google Scholar
Whipple CJ, Kebrom TH, Weber AL, Yang F, Hall D, Meeley R, et al. Grassy Tillers1 promotes apical dominance in maize and responds to shade signals in the grasses. Proc Natl Acad Sci. 2011;108(33):E506–12. https://doi.org/10.1073/pnas.1102819108.
Article Google Scholar
Whitt SR, Wilson LM, Tenaillon MI, Gaut BS, Buckler ES. Genetic diversity and selection in the maize starch pathway. Proc Natl Acad Sci. 2002;99(20):12959–62. https://doi.org/10.1073/pnas.202476999.
Article Google Scholar
Wills DM, Whipple CJ, Takuno S, Kursel LE, Shannon LM, Ross-Ibarra J, et al. “From many, one: genetic control of prolificacy during maize domestication.” edited by Hopi E. Hoekstra. PLoS Genet. 2013;9(6):e1003604. https://doi.org/10.1371/journal.pgen.1003604.
Article Google Scholar
Wright SI. The effects of artificial selection on the maize genome. Science. 2005;308(5726):1310–4. https://doi.org/10.1126/science.1107891.
Article Google Scholar
Wu X, Li YY, Shi Y, Song Y, Zhang D, Li C, et al. Joint-linkage mapping and GWAS reveal extensive genetic loci that regulate male inflorescence size in maize. Plant Biotechnol J. 2016a;14(7):1551–62. https://doi.org/10.1111/pbi.12519.
Article Google Scholar
Wu Y, Vicente FS, Huang K, Dhliwayo T, Costich DE, Semagn K, et al. Molecular characterization of CIMMYT maize inbred lines with genotyping-by-sequencing SNPs. Theor Appl Genet. 2016b;129(4):753–65. https://doi.org/10.1007/s00122-016-2664-8.
Article Google Scholar
Xiao Y, Tong H, Yang X, Shizhong X, Pan Q, Qiao F, et al. Genome-wide dissection of the maize ear genetic architecture using multiple populations. New Phytol. 2016;210(3):1095–106. https://doi.org/10.1111/nph.13814.
Article Google Scholar
Xiao Y, Liu H, Liuji W, Warburton M, Yan J. Genome-wide association studies in maize: praise and stargaze. Mol Plant. Cell Press. 2017; https://doi.org/10.1016/j.molp.2016.12.008.
Xu Y. Envirotyping for deciphering environmental impacts on crop plants. Theor Appl Genet. 2016; https://doi.org/10.1007/s00122-016-2691-5.
Xu G, Wang X, Huang C, Dingyi X, Li D, Tian J, et al. Complex genetic architecture underlies maize tassel domestication. New Phytol. 2017;214(2):852–64. https://doi.org/10.1111/nph.14400.
Article Google Scholar
Xu G, Lyu J, Li Q, Liu H, Wang D, Zhang M, et al. Evolutionary and functional genomics of DNA methylation in maize domestication and improvement. Nat Commun. 2020;11(1):5539. https://doi.org/10.1038/s41467-020-19333-4.
Article Google Scholar
Xue Y, Warburton ML, Sawkins M, Zhang X, Setter T, Yunbi X, et al. Genome‑wide association analysis for nine agronomic traits in maize under well‑watered and water‑stressed conditions. Theor Appl Genet. 2013;126(10):2587–96. https://doi.org/10.1007/s00122-013-2158-x.
Article Google Scholar
Xue S, Bradbury PJ, Casstevens T, Holland JB. Genetic architecture of domestication-related traits in maize. Genetics. 2016;204(1):99–113. https://doi.org/10.1534/genetics.116.191106.
Article Google Scholar
Yang Q, Li Z, Li W, Lixia K, Wang C, Ye J, et al. CACTA-like transposable element in ZmCCT attenuated photoperiod sensitivity and accelerated the postdomestication spread of maize. Proc Natl Acad Sci U S A. 2013;110(42):16969–74. https://doi.org/10.1073/pnas.1310949110.
Article Google Scholar
Yang N, Yanli L, Yang X, Huang J, Zhou Y, Ali F, et al. Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet. 2014;10(9):e1004573. https://doi.org/10.1371/journal.pgen.1004573.
Article Google Scholar
Yang J, Jiang H, Yeh CT, Jianming Y, Jeddeloh JA, Nettleton D, et al. Extreme-phenotype genome-wide association study (XP-GWAS): a method for identifying trait-associated variants by sequencing pools of individuals selected from a diversity panel. Plant J. 2015;84(3):587–96. https://doi.org/10.1111/tpj.13029.
Article Google Scholar
Yang J, Liu Z, Chen Q, Yanzhi Q, Tang J, Lübberstedt T, et al. Mapping of QTL for grain yield components based on a DH population in maize. Sci Rep. 2020;10(1):1–11. https://doi.org/10.1038/s41598-020-63960-2.
Article Google Scholar
Yi Q, Malvar RA, Álvarez-Iglesias L, Ordás B, Revilla P. Dissecting the genetics of cold tolerance in a multiparental maize population. Theor Appl Genet. 2020;133(2):503–16. https://doi.org/10.1007/s00122-019-03482-2.
Article Google Scholar
Yi Q, Álvarez-Iglesias L, Malvar RA, Romay MC, Revilla P. A worldwide maize panel revealed new genetic variation for cold tolerance. Theor Appl Genet. 2021;134(4):1083–94. https://doi.org/10.1007/s00122-020-03753-3.
Article Google Scholar
Yu J, Buckler ES. Genetic association mapping and genome Organization of Maize. Curr Opin Biotechnol. 2006; https://doi.org/10.1016/j.copbio.2006.02.003.
Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38(2):203–8. https://doi.org/10.1038/ng1702.
Article Google Scholar
Yu J, Holland JB, McMullen MD, Buckler ES. Genetic design and statistical power of nested association mapping in maize. Genetics. 2008;178(1):539–51. https://doi.org/10.1534/genetics.107.074245.
Article Google Scholar
Yu J, Xu F, Wei Z, Zhang X, Chen T, Pu L. Epigenomic landscape and epigenetic regulation in maize. Theor Appl Genet. 2020;133(5):1467–89. https://doi.org/10.1007/s00122-020-03549-5.
Article Google Scholar
Yu D, Wang H, Wei G, Qin T, Sun P, Youlin L, et al. Genetic diversity and population structure of popcorn germplasm resources using genome-wide SNPs through genotyping-by-sequencing. Genet Resour Crop Evol. 2021;68(6):2379–89. https://doi.org/10.1007/s10722-021-01137-0.
Article Google Scholar
Yuan G, Chen B, Peng H, Zheng Q, Li Y, Xiang K, et al. QTL mapping for resistance to ear Rot caused by Fusarium Graminearum using an IBM Syn10 DH population in maize. Mol Breed. 2020;40(9). https://doi.org/10.1007/s11032-020-01158-0.
Zenda T, Liu S, Dong A, Duan H. Advances in cereal crop genomics for resilience under climate change. Life. 2021;11(6):1–34. https://doi.org/10.3390/life11060502.
Article Google Scholar
Zhang Z, Ersoz E, Lai C-QQ, Todhunter RJ, Tiwari HK, Gore MA, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 2010;42(4):355–60. https://doi.org/10.1038/ng.546.
Article Google Scholar
Zhang N, Gibon Y, Wallace JG, Lepak N, Li P, Dedow L, et al. Genome-wide association of carbon and nitrogen metabolism in the maize nested association mapping population. Plant Physiol. 2015;168(2):575–83. https://doi.org/10.1104/pp.15.00025.
Article Google Scholar
Zhang X, Warburton ML, Setter T, Liu H, Xue Y, Yang N, et al. Genome-wide association studies of drought-related metabolic changes in maize using an enlarged SNP panel. Theor Appl Genet. 2016;129(8):1449–63. https://doi.org/10.1007/s00122-016-2716-0.
Article Google Scholar
Zhang W, Xue G, Shi X, Zhu B, Wang Z, Gao H, et al. PCA-based multiple-trait GWAS analysis: a powerful model for exploring pleiotropy. Animals. 2018;8(12) https://doi.org/10.3390/ani8120239.
Zhang H, Yin L, Wang M, Yuan X, Liu X. Factors affecting the accuracy of genomic selection for agricultural economic traits in maize, cattle, and pig populations. Front Genet. 2019a;10(MAR) https://doi.org/10.3389/fgene.2019.00189.
Zhang Y, Wan J, He L, Lan H, Li L. Genome-wide association analysis of plant height using the maize F1 population. Plan Theory. 2019b;8(10):432. https://doi.org/10.3390/plants8100432.
Article Google Scholar
Zhang Y, Yu H, Guan Z, Liu P, He Y, Zou C, et al. Combined linkage mapping and association analysis reveals genetic control of maize kernel moisture content. Physiol Plant. 2020;170(4):508–18. https://doi.org/10.1111/ppl.13180.
Article Google Scholar
Zhao Y, Mette MF, Reif JC. “Genomic selection in hybrid breeding” edited by F. Ordon. Plant Breed. 2015;134(1):1–10. https://doi.org/10.1111/pbr.12231.
Article Google Scholar
Zhao J, Li H, Yuhui X, Yin Y, Huang T, Zhang B, et al. A consensus and saturated genetic map provides insight into genome anchoring, Synteny of Solanaceae and leaf- and fruit-related QTLs in Wolfberry (Lycium Linn.). BMC Plant Biol. 2021;21(1):1–13. https://doi.org/10.1186/s12870-021-03115-1.
Article Google Scholar
Zheng Y, Yuan F, Huang Y, Zhao Y, Jia X, Zhu L, et al. Genome-wide association studies of grain quality traits in maize. Sci Rep. 2021;11(1):9797. https://doi.org/10.1038/s41598-021-89276-3.
Article Google Scholar
Zhao X, Luo L, Cao Y, Liu Y, Li Y, Wenmei W, et al. Genome-wide association analysis and QTL mapping reveal the genetic control of cadmium accumulation in maize leaf. BMC Genomics. 2018;19(1):1–13. https://doi.org/10.1186/s12864-017-4395-x.
Article Google Scholar
Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44(7):821–4. https://doi.org/10.1038/ng.2310.
Article Google Scholar
Zhou X, Stephens M. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods. 2014;11(4):407–9. https://doi.org/10.1038/nmeth.2848.
Article Google Scholar
Zhu C, Gore M, Buckler ES, Yu J. Status and prospects of association mapping in plants. Plant Genome. 2008;1(1):5–20. https://doi.org/10.3835/plantgenome2008.02.0089.
Article Google Scholar
Zila CT, Fernando Samayoa L, Santiago R, Butrón A, Holland JB. A genome-wide association study reveals genes associated with Fusarium ear rot resistance in a maize core diversity panel. G3: Genes Genomes Genet. 2013;3(11):2095–104. https://doi.org/10.1534/g3.113.007328.
Article Google Scholar
Zila CT, Ogut F, Romay MC, Gardner CA, Buckler ES, Holland JB. Genome-wide association study of Fusarium ear rot disease in the U.S.A. maize inbred line collection. BMC Plant Biol. 2014;14(1):1–15. https://doi.org/10.1186/s12870-014-0372-6.
Article Google Scholar

Download references

Author information

Authors and Affiliations

Escola de Agronomia, Universidade Federal de Goiás, Goiânia, GO, Brazil
Marcela Pedroso Mendes Resende, Ailton José Crispim Filho, Adriana Maria Antunes, Bruna Mendes de Oliveira & Renato Gonçalves de Oliveira

Authors

Marcela Pedroso Mendes Resende
View author publications
You can also search for this author in PubMed Google Scholar
Ailton José Crispim Filho
View author publications
You can also search for this author in PubMed Google Scholar
Adriana Maria Antunes
View author publications
You can also search for this author in PubMed Google Scholar
Bruna Mendes de Oliveira
View author publications
You can also search for this author in PubMed Google Scholar
Renato Gonçalves de Oliveira
View author publications
You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcela Pedroso Mendes Resende .

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Cite this chapter

Resende, M.P.M., Filho, A.J.C., Antunes, A.M., de Oliveira, B.M., de Oliveira, R.G. (2022). Population Genomics of Maize. In: Population Genomics. Springer, Cham. https://doi.org/10.1007/13836_2022_101

Download citation

DOI: https://doi.org/10.1007/13836_2022_101
Published: 19 October 2022
Publisher Name: Springer, Cham

Publish with us

Policies and ethics