Abstract
Maize (Zea mays L.) is a staple food and energy crop worldwide. As the closest related wild progenitor of maize, teosinte (Z. mays ssp. Parviglumis) can, therefore, be a rich resource for useful variants lost during domestication. Here, we used the maize inbred line, B73, as the recurrent parent and a teosinte subspecies, K67-11, as the donor, and constructed a introgression line (IL) population. In brief, 10 K single nucleotide polymorphism (SNP) chips were used to genotype 135 maize–teosinte ILs from the BC2F4 population. Then, quantitative trait locus (QTL) mapping was performed for five plant-type and five ear traits across two different environments. In total, 94 putative QTLs were detected in a single environment analysis, whereas 14 QTLs were detected in two environments. In the whole genome, four regions controlling multiple traits were detected by comparing QTL distribution. Thus, these segments were possibly related to the functional regions controlling the aforementioned traits of maize. These results may not only be helpful for fine-mapping of major QTLs related to important agronomic traits in maize but also provide a valuable reference for molecular marker-assisted breeding and related basic research in maize.
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Abbreviations
- ILs:
-
Introgression lines
- RILs:
-
Recombinant inbred lines
- QTL:
-
Quantitative trait loci
- GZ:
-
Guangzhou
- ZH:
-
Zhuhai
- MAS:
-
Marker-assisted selection
- SNP:
-
Single nucleotide polymorphism
- PH:
-
Plant height
- EH:
-
Ear height
- TBN:
-
Tassel branches number
- NE:
-
Number of ears
- LNAE:
-
Leaf number above ear
- EL:
-
Ear length
- ED:
-
Ear diameter
- KNR:
-
Kernel number per row
- KRN:
-
Kernel row number
- SL:
-
Shank length
- DAP:
-
Days after pollination
- RRGB:
-
Recovery rate of genetic background
- PVE:
-
Phenotypic variation explanation
References
Briggs WH, McMullen MD, Gaut BS, Doebley J (2007) Linkage mapping of domestication loci in a large maize teosinte backcross resource. Genetics 177(3):1915–1928. https://doi.org/10.1534/genetics.107.076497
Chen Q, Yang CJ, York AM, Xue W, Daskalska LL, DeValk CA, Krueger KW, Lawton SB, Spiegelberg BG, Schnell JM, Neumeyer MA, Perry JS, Peterson AC, Kim B, Bergstrom L, Yang L, Barber IC, Tian F, Doebley JF (2019) TeoNAM: A nested association mapping population for domestication and agronomic trait analysis in maize. Genetics 213(3):1065–1078. https://doi.org/10.1534/genetics.119.302594
Chen Q, Samayoa LF, Yang CJ, Bradbury PJ, Olukolu BA, Neumeyer MA, Romay MC, Sun Q, Lorant A, Buckler ES, Ross-Ibarra J, Holland JB, Doebley JF (2020) The genetic architecture of the maize progenitor, teosinte, and how it was altered during maize domestication. PLoS Genet 16(5):e1008791. https://doi.org/10.1371/journal.pgen.1008791
Fu YH, Xu GH, Chen HF, Wang XF, Chen QY, Huang C, Li D, Xu DY, Tian JG, Wu WH, Lu SJ, Li C, Tian F (2019) QTL mapping for leaf morphology traits in a large maize-teosinte population. Mol Breeding 39:103. https://doi.org/10.1007/s11032-019-1012-5
Guo T, Yang N, Tong H, Pan Q, Yang X, Tang J, Wang J, Li J, Yan J (2014) Genetic basis of grain yield heterosis in an “immortalized F2” maize population. Theor Appl Genet 127(10):2149–2158. https://doi.org/10.1007/s00122-014-2368-x
Guo ZF, Wang HW, Tao JJ, Ren YH, Xu C, Wu KS, Zou C, Zhang JN, Xu YB (2019) Development of multiple SNP marker panels affordable to breeders through genotyping by target sequencing (GBTS) in maize. Mol Breeding 39:37. https://doi.org/10.1007/s11032-019-0940-4
Han LZ, Qiao YL, Zhang SY, Cao GL, Ye CR, Xu FR, Dai L, Ye JD, Koh HJ (2006) QTL analysis of some agronomic traits in rice under different growing environments. Agri Sci China 5(1):15–22. https://doi.org/10.1016/s1671-2927(06)60014-x
Hospital F, Chevalet C, Mulsa P (1992) Using markers in gene introgression breeding program. Genetics 132(4):1199–1210
Hou X, Liu Y, Xiao Q, Wei B, Zhang X, Gu Y, Wang Y, Chen J, Hu Y, Liu H, Zhang J, Huang Y (2015) Genetic analysis for canopy architecture in an F2:3 population derived from two-type foundation parents across multi-environments. Euphytica 205(2):421–440. https://doi.org/10.1007/s10681-015-1401-8
Huang XH, Feng Q, Qian Q, Zhao Q, Wang L, Wang AH, Guan JP, Fan DL, Weng QJ, Huang T, Dong GJ, Sang T, Han B (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19(6):1068–1076. https://doi.org/10.1101/gr.089516.108
Huang C, Chen Q, Xu G, Xu D, Tian J, Tian F (2016) Identification and fine mapping of quantitative trait loci for the number of vascular bundle in maize stem. J Integr Plant Biol 58(1):81–90. https://doi.org/10.1111/jipb.12358
Irfan M, Ting ZT, Yang W, Zhang CY, Qing M, Zhang LJ, Feng L (2013) Modification of CTAB protocol for maize genomic DNA extraction. Res J Biotechnol 8(1):41–45
Jian QB, Biao ZX, Ying WY, Lin HD (2008) Mapping of QTL for three panicle exsertion-related traits in rice under different growing environments. Acta Agron Sin 34(3):389–396
Ku L, Zhang L, Tian Z, Guo S, Su H, Ren Z, Wang Z, Li G, Wang X, Zhu Y, Zhou J, Chen Y (2015) Dissection of the genetic architecture underlying the plant density response by mapping plant height-related traits in maize (Zea mays L). Mol Genet Genom 290(4):1223–1233. https://doi.org/10.1007/s00438-014-0987-1
Li B, Tian L, Zhang JY, Huang L, Han FX, Yan SR, Wang LZ, Zheng HK, Sun JM (2014) Construction of a high-density genetic map based on large-scale markers developed by specific length amplified fragment sequencing (SLAF-seq) and its application to QTL analysis for isoflavone content in Glycine max. BMC Genomics 15:1086. https://doi.org/10.1186/1471-2164-15-1086
Li D, Wang X, Zhang X, Chen Q, Xu G, Xu D, Wang C, Liang Y, Wu L, Huang C, Tian J, Wu Y, Tian F (2016) The genetic architecture of leaf number and its genetic relationship to flowering time in maize. New Phytol 210(1):256–268. https://doi.org/10.1111/nph.13765
Ma L, Guan Z, Zhang Z, Zhang X, Zhang Y, Zou C, Peng H, Pan G, Lee M, Shen Y, Lübberstedt T, Tuberosa R (2018) Identification of quantitative trait loci for leaf-related traits in an IBM Syn10 DH maize population across three environments. Plant Breeding 137(2):127–138. https://doi.org/10.1111/pbr.12566
Matsuoka Y, Vigouroux Y, Goodman MM, Sanchez GJ, Buckler E, Doebley J (2002) A single domestication for maize shown by multilocus microsatellite genotyping. Proc Natl Acad Sci USA 99(9):6080–6084. https://doi.org/10.1073/pnas.052125199
Mccouch S, Cho Y, Yano M, Paul E, Blinstrub M, Morishima H, Kinosita T, Mccouch SR, Cho YG, Yano M, Paul E, Mccouch S, Yano M, Yano P, Morishima H, Kinoshita T, Mccouch S, Cho Y, Paule E, Blinstrue M, Morishima HM, Mccouch S, Kinoshita T (1997) Report on QTL nomenclature. Rice Genet Newsl 14(1):11–13
Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3(3):269–283. https://doi.org/10.1016/j.cj.2015.01.001
Omori F, Mano Y (2007) QTL mapping of root angle in F2 populations from maize ’B73’× teosinte “Zea luxurians.” Plant Root 1:57–65. https://doi.org/10.3117/plantroot.1.57
Poland JA, Brown PJ, Sorrells ME, Jannink JL (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE. https://doi.org/10.1371/journal.pone.0032253
Qiu L, Guo Y, Li Y, Wang XB, Zhou GA, Liu ZX, Zhou SR, Li XH, Ma YZ, Wang JK, Wan JM (2011) Novel gene discovery of crops in China: status, challenging, and perspective. Acta Agron Sin. https://doi.org/10.3724/SP.J.1006.2011.00001
Quijada P, Shannon LM, Glaubitz JC, Studer AJ, Doebley J (2009) Characterization of a major maize domestication QTL on the short srm of Chromosome 1. Maydica 54(4):401–408
Semagn K, Bjornstad A, Ndjiondjop MN (2006) Principles, requirements and prospects of genetic mapping in plants. Afr J Biotechnol 5(25):2569–2587
Shannon L (2012) The genetic architecture of maize domestication and range expansion. The University of Wisconsin-Madison, Madison, WI
Sivasakthi K, Thudi M, Tharanya M, Kale SM, Kholova J, Halime MH, Jaganathan D, Baddam R, Thirunalasundari T, Gaur PM, Varshney RK, Vadez V (2018) Plant vigour QTLs co-map with an earlier reported QTL hotspot for drought tolerance while water saving QTLs map in other regions of the chickpea genome. BMC Plant Biol 18(1):29. https://doi.org/10.1186/s12870-018-1245-1
Stolting KN, Nipper R, Lindtke D, Caseys C, Waeber S, Castiglione S, Lexer C (2013) Genomic scan for single nucleotide polymorphisms reveals patterns of divergence and gene flow between ecologically divergent species. Mol Ecol 22(3):842–855. https://doi.org/10.1111/mec.12011
Studer AJ, Doebley JF (2012) Evidence for a natural allelic series at the maize domestication locus teosinte branched1. Genetics 191(3):951-U533. https://doi.org/10.1534/genetics.112.138479
Tang S, Teng Z, Zhai T, Fang X, Liu F, Liu D, Zhang J, Liu D, Wang S, Zhang K, Shao Q, Tan Z, Paterson AH, Zhang Z (2014) Construction of genetic map and QTL analysis of fiber quality traits for upland cotton (Gossypium hirsutum L). Euphytica 201(2):195–213. https://doi.org/10.1007/s10681-014-1189-y
Tanksley SD, Grandillo S, Fulton TM, Zamir D, Eshed Y, Petiard V, Lopez J, Beck-Bunn T (1996) Advanced backcross QTL analysis in a cross between an elite processing line of tomato and its wild relative L pimpinellifolium. Theor Appl Genet 92(2):213–224. https://doi.org/10.1007/BF00223378
Tian JG, Wang CL, Xia JL, Wu LS, Xu GH, Wu WH, Li D, Qin WC, Han X, Chen QY, Jin WW, Tian F (2019) Teosinte ligule allele narrows plant architecture and enhances high-density maize yields. Science 365(6454):658–664. https://doi.org/10.1126/science.aax5482
Tsonev S, Todorovska E, Avramova V, Kolev S, Abu-Mhadi N, Christov NK (2009) Genomics assisted improvement of drought tolerance in Maize: QTL approaches. Biotechnol Biotec Eq 23(4):1410–1413. https://doi.org/10.2478/V10133-009-0004-8
Wang JR, Luo MC, Chen ZX, You FM, Wei YM, Zheng YL, Dvorak J (2013) Aegilops tauschii single nucleotide polymorphisms shed light on the origins of wheat D-genome genetic diversity and pinpoint the geographic origin of hexaploid wheat. New Phytol 198(3):925–937. https://doi.org/10.1111/nph.12164
Weber AL, Zhao Q, McMullen MD, Doebley JF (2009) Using association mapping in teosinte to investigate the function of maize selection-candidate genes. PLoS ONE 4(12):e8227. https://doi.org/10.1371/journal.pone.0008227
Xu G, Wang X, Huang C, Xu D, Li D, Tian J, Chen Q, Wang C, Liang Y, Wu Y, Yang X, Tian F (2017) Complex genetic architecture underlies maize tassel domestication. New Phytol 214(2):852–864. https://doi.org/10.1111/nph.14400
Xue WY, Xing YZ, Weng XY, Zhao Y, Tang WJ, Wang L, Zhou HJ, Yu SB, Xu CG, Li XH, Zhang QF (2008) Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat Genet 40(6):761–767. https://doi.org/10.1038/ng.143
Yang C, Tang DG, Zhang L, Liu J, Rong TZ (2015) Identification of QTL for ear row number and two-ranked versus many-ranked ear in maize across four environments. Euphytica 206(1):33–47. https://doi.org/10.1007/s10681-015-1466-4
Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136(4):1457–1468
Zhang XH, Huang CL, Wu D, Qiao F, Li WQ, Duan LF, Wang K, Xiao YJ, Chen GX, Liu Q, Xiong LZ, Yang WN, Yan JB (2017) High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol 173(3):1554–1564. https://doi.org/10.1104/pp.16.01516
Zhang X, Lu M, Xia A, Xu T, Cui Z, Zhang R, Liu W, He Y (2021) Genetic analysis of three maize husk traits by QTL mapping in a maize-teosinte population. BMC Genomics 22(1):386. https://doi.org/10.1186/s12864-021-07723-x
Zhou ZQ, Zhang CS, Zhou Y, Hao ZF, Wang ZH, Zeng X, Di H, Li MS, Zhang DG, Yong HJ, Zhang SH, Weng JF, Li XH (2016) Genetic dissection of maize plant architecture with an ultra-high density bin map based on recombinant inbred lines. BMC Genomics 17(1):1–15. https://doi.org/10.1186/s12864-016-2555-z
Acknowledgements
This work was supported by the National Natural Science Foundation of China (32101706), the Natural Science Foundation of Guangdong Province of China (2021A1515010552), the Key Area Research and Development Program of Guangdong Province, China (2018B020202013), Science and Technology Innovation Team of Hubei University of Arts and Sciences(2021kptd01), and Xiangyang Youth Science and Technology Talent Development Plan.
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Wang, X., Liao, C., Wang, X. et al. Construction of maize–teosinte introgression line population and identification of major quantitative trait loci. Euphytica 217, 179 (2021). https://doi.org/10.1007/s10681-021-02912-x
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DOI: https://doi.org/10.1007/s10681-021-02912-x