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A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments

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Abstract

Key message

Using GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population.

Abstract

Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E−06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize.

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References

  • Ali ML, Sanchez PL, Yu S-b, Lorieux M, Eizenga GC (2010) Chromosome segment substitution lines: a powerful tool for the introgression of valuable genes from Oryza wild species into cultivated rice (O. sativa). Rice 3:218–234

    Google Scholar 

  • Bass HW, Goode JH, Greene TW, Boston RS (1994) Control of ribosome-inactivating protein (RIP) RNA levels during maize seed development. Plant Sci 101:17–30

    CAS  Google Scholar 

  • Bates D, Maechler M, Bolker B, Walker S, Christensen RHB, Singmann H, Dai B, Grothendieck G, Eigen C, Rcpp L (2015) Package ‘lme4’. Convergence 12

  • Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L (2006) Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 113:206–224

    CAS  PubMed  Google Scholar 

  • Bommert P, Nagasawa NS, Jackson D (2013) Quantitative variation in maize kernel row number is controlled by the FASCIATED EAR2 locus. Nat Genet 45:334–337

    CAS  PubMed  Google Scholar 

  • Boss PK, Davies C, Robinson SP (1996) Analysis of the expression of anthocyanin pathway genes in developing Vitis vinifera L. cv Shiraz grape berries and the implications for pathway regulation. Plant Physiol 111:1059–1066

    CAS  PubMed  PubMed Central  Google Scholar 

  • Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC, Goodman MM, Harjes C, Guill K, Kroon DE, Larsson S, Lepak NK, Li H, Mitchell SE, Pressoir G, Peiffer JA, Rosas MO, Rocheford TR, Romay MC, Romero S, Salvo S, Villeda HS, Sofia da Silva H, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen MD (2009) The genetic architecture of maize flowering time. Science 325:714–718

    CAS  Google Scholar 

  • Canales C, Bhatt AM, Scott R, Dickinson H (2002) EXS, a putative LRR receptor kinase, regulates male germline cell number and tapetal identity and promotes seed development in Arabidopsis. Curr Biol 12:1718–1727

    CAS  PubMed  Google Scholar 

  • Chen L, Li YX, Li C, Wu X, Qin W, Li X, Jiao F, Zhang X, Zhang D, Shi Y, Song Y, Li Y, Wang T (2016) Fine-mapping of qGW4.05, a major QTL for kernel weight and size in maize. BMC Plant Biol 16:81

    PubMed  PubMed Central  Google Scholar 

  • Chuck GS, Brown PJ, Meeley R, Hake S (2014) Maize SBP-box transcription factors unbranched2 and unbranched3 affect yield traits by regulating the rate of lateral primordia initiation. Proc Natl Acad Sci USA 111:18775–18780

    CAS  PubMed  Google Scholar 

  • Cui Z, Xia A, Zhang A, Luo J, Yang X, Zhang L, Ruan Y, He Y (2018) Linkage mapping combined with association analysis reveals QTL and candidate genes for three husk traits in maize. Theor Appl Genet 131:2131–2144

    CAS  PubMed  Google Scholar 

  • Gao X, Starmer J, Martin ER (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 32:361–369

    PubMed  Google Scholar 

  • Giraud H, Lehermeier C, Bauer E, Falque M, Segura V, Bauland C, Camisan C, Campo L, Meyer N, Ranc N, Schipprack W, Flament P, Melchinger AE, Menz M, Moreno-González J, Ouzunova M, Charcosset A, Schön C-C, Moreau L (2014) Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize. Genetics 198:1717–1734

    PubMed  PubMed Central  Google Scholar 

  • Guo D, Zhang J, Wang X, Han X, Wei B, Wang J, Li B, Yu H, Huang Q, Gu H, Qu LJ, Qin G (2015) The WRKY transcription factor WRKY71/EXB1 controls shoot branching by transcriptionally regulating RAX Genes in Arabidopsis. Plant Cell 27:3112–3127

    CAS  PubMed  PubMed Central  Google Scholar 

  • Holland JB (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156–161

    CAS  PubMed  Google Scholar 

  • Hussain T, Tausend P, Graham G, Ho J (2007) Registration of IBM2 SYN10 doubled haploid mapping population of maize. J Plant Reg 1(9):966

    Google Scholar 

  • Jansen C, Zhang Y, Liu H, Gonzalez-Portilla PJ, Lauter N, Kumar B, Trucillo-Silva I, Martin JP, Lee M, Simcox K, Schussler J, Dhugga K, Lubberstedt T (2015) Genetic and agronomic assessment of cob traits in corn under low and normal nitrogen management conditions. Theor Appl Genet 128:1231–1242

    PubMed  Google Scholar 

  • Knapp S, Stroup W, Ross W (1985) Exact confidence intervals for heritability on a progeny mean basis 1. Crop Sci 25:192–194

    Google Scholar 

  • Kobayashi S, Ishimaru M, Ding C, Yakushiji H, Goto N (2001) Comparison of UDP-glucose: flavonoid 3-O-glucosyltransferase (UFGT) gene sequences between white grapes (Vitis vinifera) and their sports with red skin. Plant Sci 160:543–550

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  • Li M, Guo X, Zhang M, Wang X, Zhang G, Tian Y, Wang Z (2010) Mapping QTLs for grain yield and yield components under high and low phosphorus treatments in maize (Zea mays L.). Plant Sci 178:454–462

    CAS  Google Scholar 

  • Li CH, Li YX, Sun BC, Peng B, Liu C, Liu ZZ, Yang ZZ, Li QC, Tan WW, Zhang Y, Wang D, Shi YS, Song YC, Wang TY, Li Y (2013a) Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize. Euphytica 193:303–316

    CAS  Google Scholar 

  • Li H, Peng Z, Yang X, Wang W, Fu J, Wang J, Han Y, Chai Y, Guo T, Yang N, Liu J, Warburton ML, Cheng Y, Hao X, Zhang P, Zhao J, Liu Y, Wang G, Li J, Yan J (2013b) Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet 45:43–50

    CAS  PubMed  Google Scholar 

  • Li C, Huang Y, Huang R, Wu Y, Wang W (2018a) The genetic architecture of amylose biosynthesis in maize kernel. Plant Biotechnol J 16:688–695

    CAS  PubMed  Google Scholar 

  • Li X, Tian R, Kamala S, Du H, Li W, Kong Y, Zhang C (2018b) Identification and verification of pleiotropic QTL controlling multiple amino acid contents in soybean seed. Euphytica 214:93

    Google Scholar 

  • Liu PP, Koizuka N, Martin RC, Nonogaki H (2005) The BME3 (Blue Micropylar End 3) GATA zinc finger transcription factor is a positive regulator of Arabidopsis seed germination. Plant J 44:960–971

    CAS  PubMed  Google Scholar 

  • Liu Y, Wang L, Sun C, Zhang Z, Zheng Y, Qiu F (2014) Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments. Theor Appl Genet 127:1019–1037

    CAS  PubMed  Google Scholar 

  • Liu H, Niu Y, Gonzalez-Portilla PJ, Zhou H, Wang L, Zuo T, Qin C, Tai S, Jansen C, Shen Y, Lin H, Lee M, Ware D, Zhang Z, Lubberstedt T, Pan G (2015a) An ultra-high-density map as a community resource for discerning the genetic basis of quantitative traits in maize. BMC Genom 16:1078

    Google Scholar 

  • Liu L, Du Y, Huo D, Wang M, Shen X, Yue B, Qiu F, Zheng Y, Yan J, Zhang Z (2015b) Genetic architecture of maize kernel row number and whole genome prediction. Theor Appl Genet 128:2243–2254

    PubMed  PubMed Central  Google Scholar 

  • Liu C, Zhou Q, Dong L, Wang H, Liu F, Weng J, Li X, Xie C (2016a) Genetic architecture of the maize kernel row number revealed by combining QTL mapping using a high-density genetic map and bulked segregant RNA sequencing. BMC Genom 17:915

    Google Scholar 

  • Liu H, Shi J, Sun C, Gong H, Fan X, Qiu F, Huang X, Feng Q, Zheng X, Yuan N (2016b) Gene duplication confers enhanced expression of 27-kDa γ-zein for endosperm modification in quality protein maize. Proc Natl Acad Sci USA 113:4964–4969

    CAS  PubMed  Google Scholar 

  • Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016c) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet 12:e1005767

    PubMed  PubMed Central  Google Scholar 

  • Liu H, Zhang L, Wang J, Li C, Zeng X, Xie S, Zhang Y, Liu S, Hu S, Wang J, Lee M, Lubberstedt T, Zhao G (2017a) Quantitative trait locus analysis for deep-sowing germination ability in the maize IBM Syn10 DH population. Front Plant Sci 8:813

    PubMed  PubMed Central  Google Scholar 

  • Liu J, Huang J, Guo H, Lan L, Wang H, Xu Y, Yang X, Li W, Tong H, Xiao Y, Pan Q, Qiao F, Raihan MS, Liu H, Zhang X, Yang N, Wang X, Deng M, Jin M, Zhao L, Luo X, Zhou Y, Li X, Zhan W, Liu N, Wang H, Chen G, Li Q, Yan J (2017b) The conserved and unique genetic architecture of kernel size and weight in maize and rice. Plant Physiol 175:774–785

    PubMed  PubMed Central  Google Scholar 

  • Liu M, Tan X, Yang Y, Liu P, Zhang X, Zhang Y, Wang L, Hu Y, Ma L, Li Z, Zhang Y, Zou C, Lin H, Gao S, Lee M, Lübberstedt T, Pan G, Shen Y (2020) Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping. Plant Biotechnol J 18:207–221

    CAS  PubMed  Google Scholar 

  • Lu Y, Zhang S, Shah T, Xie C, Hao Z, Li X, Farkhari M, Ribaut JM, Cao M, Rong T, Xu Y (2010) Joint linkage-linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize. Proc Natl Acad Sci USA 107:19585–19590

    CAS  PubMed  Google Scholar 

  • Ma XQ, Tang JH, Teng WT, Yan JB, Meng YJ, Li JS (2007) Epistatic interaction is an important genetic basis of grain yield and its components in maize. Mol Breed 20:41–51

    Google Scholar 

  • Ma L, Guan Z, Zhang Z, Zhang X, Zhang Y, Zou C, Peng H, Pan G, Lee M, Shen Y, Lübberstedt T, Tuberosa R (2018a) Identification of quantitative trait loci for leaf-related traits in an IBM Syn10 DH maize population across three environments. Plant Breeding 137:127–138

    CAS  Google Scholar 

  • Ma L, Liu M, Yan Y, Qing C, Zhang X, Zhang Y, Long Y, Wang L, Pan L, Zou C, Li Z, Wang Y, Peng H, Pan G, Jiang Z, Shen Y (2018b) Genetic dissection of maize embryonic callus regenerative capacity using multi-locus genome-wide association studies. Front Plant Sci 9

  • Moore KB, Oishi KK (1993) Characterization of 3-hydroxy-3-methylglutaryl coenzyme A reductase activity during maize seed development, germination, and seedling emergence. Plant Physiol 101:485–491

    CAS  PubMed  PubMed Central  Google Scholar 

  • Pace J, Gardner C, Romay C, Ganapathysubramanian B, Lubberstedt T (2015) Genome-wide association analysis of seedling root development in maize (Zea mays L.). BMC Genom 16:47

    CAS  Google Scholar 

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

    PubMed  Google Scholar 

  • Porebski S, Bailey LG, Baum BR (1997) Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components. Plant Mol Biol Rep 15:8–15

    CAS  Google Scholar 

  • Ribaut JM, Hoisington D (1998) Marker-assisted selection: new tools and strategies. Trend Plant Sci 3:236–239

    Google Scholar 

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

    Google Scholar 

  • Su C, Wang W, Gong S, Zuo J, Li S, Xu S (2017) High density linkage map construction and mapping of yield trait QTLs in maize (Zea mays) using the genotyping-by-sequencing (GBS) technology. Front Plant Sci 8:706

    PubMed  PubMed Central  Google Scholar 

  • Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP (2015) Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet 128:353–363

    CAS  PubMed  Google Scholar 

  • Sun C, Zhang F, Yan X, Zhang X, Dong Z, Cui D, Chen F (2017) Genome-wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China. Plant Biotechnol J 15:953–969

    CAS  PubMed  PubMed Central  Google Scholar 

  • Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162

    CAS  Google Scholar 

  • Wang H, Zhang X, Yang H, Liu X, Li H, Yuan L, Li W, Fu Z, Tang J, Kang D (2016) Identification of heterotic loci associated with grain yield and its components using two CSSL test populations in maize. Sci Rep 6:38205

    CAS  PubMed  PubMed Central  Google Scholar 

  • Wang H, Xu S, Fan Y, Liu N, Zhan W, Liu H, Xiao Y, Li K, Pan Q, Li W, Deng M, Liu J, Jin M, Yang X, Li J, Li Q, Yan J (2018) Beyond pathways: genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses. Plant Biotechnol J 16(8):1464–1475

    CAS  PubMed  PubMed Central  Google Scholar 

  • Wen T, Wu M, Shen C, Gao B, Zhu ZX, You C, Lin Z (2018) Linkage and association mapping reveals the genetic basis of brown fibre (Gossypium hirsutum). Plant Biotechnol J 16(9):1654–1666

    CAS  PubMed Central  Google Scholar 

  • Widstrom N, Butron A, Guo B, Wilson D, Snook M, Cleveland T, Lynch R (2003) Control of preharvest aflatoxin contamination in maize by pyramiding QTL involved in resistance to ear-feeding insects and invasion by Aspergillus spp. Eur J Agron 19:563–572

    CAS  Google Scholar 

  • Xiao Y, Tong H, Yang X, Xu S, Pan Q, Qiao F, Raihan MS, Luo Y, Liu H, Zhang X, Yang N, Wang X, Deng M, Jin M, Zhao L, Luo X, Zhou Y, Li X, Liu J, Zhan W, Liu N, Wang H, Chen G, Cai Y, Xu G, Wang W, Zheng D, Yan J (2016) Genome-wide dissection of the maize ear genetic architecture using multiple populations. New Phytol 210:1095–1106

    CAS  PubMed  Google Scholar 

  • Xu C, Zhang H, Sun J, Guo Z, Zou C, Li WX, Xie C, Huang C, Xu R, Liao H, Wang J, Xu X, Wang S, Xu Y (2018) Genome-wide association study dissects yield components associated with low-phosphorus stress tolerance in maize. Theor Appl Genet 131(8):1699–1714

    CAS  PubMed  Google Scholar 

  • Yang N, Lu Y, Yang X, Huang J, Zhou Y, Ali F, Wen W, Liu J, Li J, Yan J (2014) 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 10:e1004573

    PubMed  PubMed Central  Google Scholar 

  • Yang C, Ma Y, He Y, Tian Z, Li J (2018) OsOFP19 modulates plant architecture by integrating the cell division pattern and brassinosteroid signaling. Plant J 93:489–501

    CAS  PubMed  Google Scholar 

  • Yano K, Yamamoto E, Aya K, Takeuchi H, Lo P-c, Hu L, Yamasaki M, Yoshida S, Kitano H, Hirano K, Matsuoka M (2016) Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet 48:927–934

    CAS  PubMed  Google Scholar 

  • Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208

    CAS  PubMed  Google Scholar 

  • Zhai L, Zheng T, Wang X, Wang Y, Chen K, Wang S, Wang Y, Xu J, Li Z (2018) QTL mapping and candidate gene analysis of peduncle vascular bundle related traits in rice by genome-wide association study. Rice (N Y) 11:13

    Google Scholar 

  • Zhang X, Zhang H, Li L, Lan H, Ren Z, Liu D, Wu L, Liu H, Jaqueth J, Li B, Pan G, Gao S (2016) Characterizing the population structure and genetic diversity of maize breeding germplasm in Southwest China using genome-wide SNP markers. BMC Genom 17:697

    Google Scholar 

  • Zhang C, Zhou Z, Yong H, Zhang X, Hao Z, Zhang F, Li M, Zhang D, Li X, Wang Z, Weng J (2017) Analysis of the genetic architecture of maize ear and grain morphological traits by combined linkage and association mapping. Theor Appl Genet 130:1011–1029

    CAS  PubMed  Google Scholar 

  • Zhang J, Gizaw SA, Bossolini E, Hegarty J, Howell T, Carter AH, Akhunov E, Dubcovsky J (2018a) Identification and validation of QTL for grain yield and plant water status under contrasting water treatments in fall-sown spring wheats. Theor Appl Genet 131(8):1741–1759

    PubMed  PubMed Central  Google Scholar 

  • Zhang Y, Liu P, Zhang X, Zheng Q, Chen M, Ge F, Li Z, Sun W, Guan Z, Liang T, Zheng Y, Tan X, Zou C, Peng H, Pan G, Shen Y (2018b) Multi-locus genome-wide association study reveals the genetic architecture of stalk lodging resistance-related traits in maize. Front Plant Sci 9:611

    PubMed  PubMed Central  Google Scholar 

  • Zhang Z, Zhang X, Lin Z, Wang J, Xu M, Lai J, Yu J, Lin Z (2018c) The genetic architecture of nodal root number in maize. Plant J 93:1032–1044

    CAS  PubMed  Google Scholar 

  • Zhao Z, Zhang H, Fu Z, Chen H, Lin Y, Yan P, Li W, Xie H, Guo Z, Zhang X, Tang J (2017) Genetic-based dissection of arsenic accumulation in maize using a genome-wide association analysis method. Plant Biotechnol J 16(5):1085–1093

    PubMed  PubMed Central  Google Scholar 

  • Zhu X-M, Shao X-Y, Pei Y-H, Guo X-M, Li J, Song X-Y, Zhao M-A (2018) Genetic diversity and genome-wide association study of major ear quantitative traits using high-density SNPs in maize. Front Plant Sci 9:966

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by the Major Project of China on New varieties of GMO Cultivation (2016ZX08003003).

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  1. Xiaoxiang Zhang and Zhongrong Guan have contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China

    Xiaoxiang Zhang, Zhaoling Li, Peng Liu, Langlang Ma, Yinchao Zhang, Lang Pan, Shijiang He, Yanling Zhang, Peng Li, Fei Ge, Chaoying Zou, Yongcong He, Shibin Gao, Guangtang Pan & Yaou Shen

  2. State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Chengdu, 611130, China

    Shibin Gao & Yaou Shen

  3. Chongqing Yudongnan Academy of Agricultural Sciences, Chongqing, 408000, China

    Zhongrong Guan

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Contributions

This study was conceived by YS and ZG. XZ, ZG, ZL, LM, YZ, LP, SH, YZ, PL, PL, CZ, FG, and YH participated in the management of the experiment and yield-related trait measurements in this work. Collections of all plant materials were performed by SG and GP. XZ and PL conducted the data analysis. XZ wrote the initial manuscript, and YS reviewed the manuscript and made significant revision contributions. All authors read and approved the final manuscript.

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Correspondence to Yaou Shen.

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This study does not include human or animal subjects.

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Communicated by Alain Charcosset.

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Fig. S1:

Genetic map for the maize IBM Syn10 DH population. (PDF 135 kb)

Fig. S2:

Distributions of investigated agronomic traits for the association population and IBM Syn10 DH population in different environments. (PDF 688 kb)

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Zhang, X., Guan, Z., Li, Z. et al. A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theor Appl Genet 133, 2881–2895 (2020). https://doi.org/10.1007/s00122-020-03639-4

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