Molecular Breeding

, 39:38 | Cite as

Heterotic grouping based on genetic variation and population structure of maize inbred lines from current breeding program in Sichuan province, Southwest China using genotyping by sequencing (GBS)

  • Yifeng Leng
  • Chenxi Lv
  • Lujiang Li
  • Yong Xiang
  • Chao Xia
  • Rujun Wei
  • Tingzhao Rong
  • Hai LanEmail author


Maize (Zea mays L.), which is an important food crop in the word, displays large genetic diversity. Knowledge of the relationships among maize inbred lines is essential to the maize breeder because it directs the exploitation of germplasm in hybrid production. In this study, the genetic diversity, population structure, and relatedness between pairs of 157 elite maize inbred lines from the current breeding program of Sichuan province in Southwest China were assessed with 4976 polymorphic single-nucleotide polymorphisms (SNPs) developed by genotyping by sequencing (GBS). A total of 91.1% of the inbred lines were considered pure with < 5% heterogeneity, while the remaining 8.9% of the inbred lines had a heterogeneity ranging from 5.5 to 40.0%. Genetic distance between pairs of lines varied from 0.0000 to 2.0702, with 98.79% of the pairs distant. Relative kinship analysis showed that the kinship coefficients for 91.3% of the pairs of lines were above 0.500, which agrees with the pedigree. Cluster and model-based population structure analyses all divided the 157 lines into four groups, which were named Impro-local, Tem-tropic I A, Tem-tropic I B, and Impro-tropic, respectively, based roughly on genetic background of the parents used for breeding. Impro-local group consisted of lines primarily improved from local germplasm; Tem-tropic I A and Tem-tropic I B groups consisted of lines primarily developed from cross or backcross with introduced tropic germplasm, but with different combining ability that had demonstrated by the commercial hybrids; and Impro-tropic group contained lines primarily improved from continuous self-crossing of tropical hybrids and populations. Analysis of molecular variance showed 14.2% of the variation among groups, with the remaining 85.8% attributable to differences within groups. The differentiation between the groups was further validated by the pairwise FST value (0.0904–0.1520), which indicated the moderate genetic differentiation characterizing this panel. The genome-wide average linkage disequilibrium (LD) decay distance was 1.05–1.10 Mb and varied among different chromosomes. The genetic diversity and population structure revealed in this study will help breeders to better understand how to utilize the current maize germplasm in Sichuan province for hybrid breeding.


Distance Single-nucleotide polymorphism Population structure Genetic diversity Southwest China Tem-tropic 



We thank the team members of the Disruptive Materials and Methods Innovation in Maize Breeding in the Sichuan Science and Technology Support Project for providing the maize inbred lines.

Funding information

Financial support was received from the National Key Research and Development Program of China (2016YFD0101206) and the Sichuan Science and Technology Support Project (2016NZ0054).

Supplementary material

11032_2019_946_MOESM1_ESM.xlsx (26 kb)
Table S1 (XLSX 26 kb)
11032_2019_946_MOESM2_ESM.xlsx (498 kb)
Table S2 (XLSX 498 kb)
11032_2019_946_MOESM3_ESM.xlsx (220 kb)
Table S3 (XLSX 220 kb)
11032_2019_946_MOESM4_ESM.xlsx (173 kb)
Table S4 (XLSX 172 kb)


  1. Andolfatto P, Davison D, Erezyilmaz D, Hu TT, Mast J, Sunayama-Morita T, Stern DL (2011) Multiplexed shotgun genotyping for rapid and efficient genetic mapping. Genome Res 21:610–617CrossRefGoogle Scholar
  2. Bird KA, An H, Gazave E, Gore MA, Pires JC, Robertson LD, Labate JA (2017) Population structure and phylogenetic relationships in a diverse panel of Brassica rapa L. Front Plant Sci 8Google Scholar
  3. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635CrossRefGoogle Scholar
  4. Civardi L, Xia Y, Edwards KJ, Schnable PS, Nikolau BJ (1994) The relationship between genetic and physical distances in the cloned a1-sh2 interval of the Zea mays L. genome. Proc Natl Acad Sci 91:8268–8272CrossRefGoogle Scholar
  5. Cox TS, Kiang YT, Gorman MB, Rodgers DM (1985) Relationship between coefficient of parentage and genetic similarity indices in the Soybean1. Crop Sci 25:529–532CrossRefGoogle Scholar
  6. Crossa J, Beyene Y, Kassa S, Perez P, Hickey JM et al (2013) Genomic prediction in maize breeding populations with genotyping-by-sequencing. G3: genes, genomes. Genetics 3:1903–1926Google Scholar
  7. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:e19379CrossRefGoogle Scholar
  8. Ertiro BT, Semagn K, Das B, Olsen M, Labuschagne M, Worku M, Wegary D, Azmach G, Ogugo V, Keno T, Abebe B, Chibsa T, Menkir A (2017) Genetic variation and population structure of maize inbred lines adapted to the mid-altitude sub-humid maize agro-ecology of Ethiopia using single nucleotide polymorphic (SNP) markers. BMC Genomics 18:777CrossRefGoogle Scholar
  9. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14:2611–2620CrossRefGoogle Scholar
  10. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and windows. Mol Ecol Resour 10:564–567CrossRefGoogle Scholar
  11. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restrictiondata. Genetics 131:629–639Google Scholar
  12. FAO (2018) GIEWS-Global Information and Early Warning System/Country Briefs/China (2018). (accessed 01.12 18)
  13. Heckenberger M, Bohn M, Ziegle JS, Joe LK, Hauser JD, Hutton M, Melchinger AE (2002) Variation of DNA fingerprints among accessions within maize inbred lines and implications for identification of essentially derived varieties. Mol Breed 10:181–191CrossRefGoogle Scholar
  14. Holsinger KE, Weir BS (2009) Genetics in geographically structured populations: defining, estimating and interpreting FST. Nat Rev Genet 10:639–650CrossRefGoogle Scholar
  15. Huang Y (1998a) Achievement and Progress of maize cross breeding in Sichuan Province. Southwest China J Agr Sci 11:31–37Google Scholar
  16. Huang Y (1998b) Progrees in Sichuan maize breeding and exploration of super high yield breeding. Southwest China J Agr Sci 11:47–53Google Scholar
  17. Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, Guan J, Fan D, Weng Q, Huang T, Dong G, Sang T, Han B (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076CrossRefGoogle Scholar
  18. Huang P, Feldman M, Schroder S, Bahri BA, Diao X, Zhi H, Estep M, Baxter I, Devos KM, Kellogg EA (2014) Population genetics of Setaria viridis, a new model system. Mol Ecol 23:4912–4925CrossRefGoogle Scholar
  19. Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806CrossRefGoogle Scholar
  20. Letunic I, Bork P (2016) Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res 44:W242–W245CrossRefGoogle Scholar
  21. Li G, Pan G (2005) The utilization present situation and study advances of the germplasm in southwest maize zone. J Maize Sci 13:3–7Google Scholar
  22. Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K, Wang J (2009a) SNP detection for massively parallel whole-genome resequencing. Genome Res 19:1124–1132CrossRefGoogle Scholar
  23. Li R, Yu C, Li Y, Lam T-W, Yiu S-M, Kristiansen K, Wang J (2009b) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25:1966–1967CrossRefGoogle Scholar
  24. Li Q, Eichten SR, Hermanson PJ, Zaunbrecher VM, Song J, Wendt J, Rosenbaum H, Madzima TF, Sloan AE, Huang J, Burgess DL, Richmond TA, McGinnis KM, Meeley RB, Danilevskaya ON, Vaughn MW, Kaeppler SM, Jeddeloh JA, Springer NM (2014) Genetic perturbation of the maize Methylome. Plant Cell 26:4602–4616CrossRefGoogle Scholar
  25. Liu K, Muse SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129CrossRefGoogle Scholar
  26. Liu Z-Z, Guo R-h, J-r Z, Y-l C, F-g W et al (2010) Analysis of genetic diversity and population structure of maize landraces from the south maize region of China. Agric Sci China 9:1251–1262CrossRefGoogle Scholar
  27. Lu Y, Yan J, Guimarães CT, Taba S, Hao Z, Gao S, Chen S, Li J, Zhang S, Vivek BS, Magorokosho C, Mugo S, Makumbi D, Parentoni SN, Shah T, Rong T, Crouch JH, Xu Y (2009) Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms. Theor Appl Genet 120:93–115CrossRefGoogle Scholar
  28. Mikić S, Kondić-špika A, Brbaklić L, Stanisavljević D, Ćeran M, Trkulja D, Mitrović B (2017) Molecular and phenotypic characterisation of diverse temperate maize inbred lines in Southeast Europe. Zemdirbyste-Agriculture 104:31–40CrossRefGoogle Scholar
  29. Miller MR, Dunham JP, Amores A, Cresko WA, Johnson EA (2007) Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res 17:240–248CrossRefGoogle Scholar
  30. Mir C, Zerjal T, Combes V, Dumas F, Madur D, Bedoya C, Dreisigacker S, Franco J, Grudloyma P, Hao PX, Hearne S, Jampatong C, Laloë D, Muthamia Z, Nguyen T, Prasanna BM, Taba S, Xie CX, Yunus M, Zhang S, Warburton ML, Charcosset A (2013) Out of America: tracing the genetic footprints of the global diffusion of maize. Theor Appl Genet 126:2671–2682CrossRefGoogle Scholar
  31. Moll RH, Salhuana WS, Robinson HF (1962) Heterosis and genetic diversity in variety crosses of maize. Crop Sci 2:197–198CrossRefGoogle Scholar
  32. Moll RH, Lonnquist JH, Velez Fortuno J, Johnson EC (1965) The relationship of Heterosis and genetic divergence in maize. Genetics 52:139–144PubMedPubMedCentralGoogle Scholar
  33. Mumm RH, Dudley JW (1994) A classification of 148 U.S. maize Inbreds: I. Cluster analysis based on RFLPs. Crop Sci 34:842–851CrossRefGoogle Scholar
  34. National Bureau of Statistics of China N (2013) China Statistic Yearbook. 13–2 Output of Agriculture, Animal Husbandry and Fishery
  35. Ndjiondjop M-N, Semagn K, Gouda AC, Kpeki SB, Dro Tia D, Sow M, Goungoulou A, Sie M, Perrier X, Ghesquiere A, Warburton ML (2017) Genetic variation and population structure of Oryza glaberrima and development of a mini-Core collection using DArTseq. Front Plant Sci 8Google Scholar
  36. Nei M (1972) Genetic distance between populations. Am Nat 106:283–292CrossRefGoogle Scholar
  37. Paternaini E, Lonnquist JH (1963) Heterosis in interracial crosses of corn (Zea mays L.). Crop Sci 2:504–507CrossRefGoogle Scholar
  38. Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. The Plant Genome 5:92–102CrossRefGoogle Scholar
  39. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  40. Remington DL, Thornsberry JM, Matsuoka Y, Wilson LM, Whitt SR, Doebley J, Kresovich S, Goodman MM, Buckler ES (2001) Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc Natl Acad Sci 98:11479–11484CrossRefGoogle Scholar
  41. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, Elshire RJ, Acharya CB, Mitchell SE, Flint-Garcia SA, McMullen MD, Holland JB, Buckler ES, Gardner CA (2013) Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol 14:R55CrossRefGoogle Scholar
  42. Sachs MM (2009) Cereal germplasm resources. Plant Physiol 149:148–151CrossRefGoogle Scholar
  43. Schaefer CM, Bernardo R (2013) Population structure and single nucleotide polymorphism diversity of historical Minnesota maize Inbreds. Crop Sci 53:1529–1536CrossRefGoogle Scholar
  44. Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, Minx P, Reily AD, Courtney L, Kruchowski SS, Tomlinson C, Strong C, Delehaunty K, Fronick C, Courtney B, Rock SM, Belter E, du F, Kim K, Abbott RM, Cotton M, Levy A, Marchetto P, Ochoa K, Jackson SM, Gillam B, Chen W, Yan L, Higginbotham J, Cardenas M, Waligorski J, Applebaum E, Phelps L, Falcone J, Kanchi K, Thane T, Scimone A, Thane N, Henke J, Wang T, Ruppert J, Shah N, Rotter K, Hodges J, Ingenthron E, Cordes M, Kohlberg S, Sgro J, Delgado B, Mead K, Chinwalla A, Leonard S, Crouse K, Collura K, Kudrna D, Currie J, He R, Angelova A, Rajasekar S, Mueller T, Lomeli R, Scara G, Ko A, Delaney K, Wissotski M, Lopez G, Campos D, Braidotti M, Ashley E, Golser W, Kim H, Lee S, Lin J, Dujmic Z, Kim W, Talag J, Zuccolo A, Fan C, Sebastian A, Kramer M, Spiegel L, Nascimento L, Zutavern T, Miller B, Ambroise C, Muller S, Spooner W, Narechania A, Ren L, Wei S, Kumari S, Faga B, Levy MJ, McMahan L, van Buren P, Vaughn MW, Ying K, Yeh CT, Emrich SJ, Jia Y, Kalyanaraman A, Hsia AP, Barbazuk WB, Baucom RS, Brutnell TP, Carpita NC, Chaparro C, Chia JM, Deragon JM, Estill JC, Fu Y, Jeddeloh JA, Han Y, Lee H, Li P, Lisch DR, Liu S, Liu Z, Nagel DH, McCann MC, SanMiguel P, Myers AM, Nettleton D, Nguyen J, Penning BW, Ponnala L, Schneider KL, Schwartz DC, Sharma A, Soderlund C, Springer NM, Sun Q, Wang H, Waterman M, Westerman R, Wolfgruber TK, Yang L, Yu Y, Zhang L, Zhou S, Zhu Q, Bennetzen JL, Dawe RK, Jiang J, Jiang N, Presting GG, Wessler SR, Aluru S, Martienssen RA, Clifton SW, McCombie WR, Wing RA, Wilson RK (2009) The B73 maize genome: complexity, diversity, and dynamics. Science (New York, NY) 326:1112–1115CrossRefGoogle Scholar
  45. Semagn K (2014) Leaf tissue sampling and DNA extraction protocols. In: Besse P (ed) Molecular plant taxonomy: methods and protocols, vol 1115. Human Press, New YorkGoogle Scholar
  46. Semagn K, Beyene Y, Makumbi D, Mugo S, Prasanna BM, Magorokosho C, Atlin G (2012a) Quality control genotyping for assessment of genetic identity and purity in diverse tropical maize inbred lines. Theor Appl Genet 125:1487–1501CrossRefGoogle Scholar
  47. Semagn K, Magorokosho C, Vivek BS, Makumbi D, Beyene Y, Mugo S, Prasanna B, Warburton ML (2012b) Molecular characterization of diverse CIMMYT maize inbred lines from eastern and southern Africa using single nucleotide polymorphic markers. BMC Genomics 13:113CrossRefGoogle Scholar
  48. Teng W, Cao Q, Chen Y, Liu X, Men S, Jing X, Li J (2004) Analysis of maize heterotic groups and patterns during past decade in China. Sci Agric Sin 37:1804–1811Google Scholar
  49. Van Inghelandt D, Reif JC, Dhillon BS, Flament P, Melchinger AE (2011) Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm. Theor Appl Genet 123:11–20CrossRefGoogle Scholar
  50. Van Tassell CP, Smith TPL, Matukumalli LK, Taylor JF, Schnabel RD et al (2008) SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5:247–252CrossRefGoogle Scholar
  51. Wang Q, Huang C, Sun Y(2017) Selection of a research area to construct a virtual case for integrated risk assessment of earthquake and flood. In: RISK ANALYSIS AND MANAGEMENT–TRENDS, CHALLENGES AND EMERGING ISSUES, p 131Google Scholar
  52. Warburton ML, Setimela P, Franco J, Cordova H, Pixley K, Bänziger M, Dreisigacker S, Bedoya C, MacRobert J (2010) Toward a cost-effective fingerprinting methodology to distinguish maize open-pollinated varieties. Crop Sci 50:467–477CrossRefGoogle Scholar
  53. Wen W, Araus JL, Shah T, Cairns J, Mahuku G, Bänziger M, Torres JL, Sánchez C, Yan J (2011) Molecular characterization of a diverse maize inbred line collection and its potential utilization for stress tolerance improvement. Crop Sci 51:2569–2581CrossRefGoogle Scholar
  54. Wen W, Franco J, Chavez-Tovar VH, Yan J, Taba S (2012) Genetic characterization of a Core set of a tropical maize race Tuxpeño for further use in maize improvement. PLoS One 7:e32626CrossRefGoogle Scholar
  55. Wu X, Li Y, Shi Y, Song Y, Wang T, Huang Y, Li Y (2014) Fine genetic characterization of elite maize germplasm using high-throughput SNP genotyping. Theor Appl Genet 127:621–631CrossRefGoogle Scholar
  56. Wu Y, San Vicente F, Huang K, Dhliwayo T, Costich DE, Semagn K, Sudha N, Olsen M, Prasanna BM, Zhang X, Babu R (2016) Molecular characterization of CIMMYT maize inbred lines with genotyping-by-sequencing SNPs. Theor Appl Genet 129:753–765CrossRefGoogle Scholar
  57. Yan J, Shah T, Warburton ML, Buckler ES, McMullen MD, Crouch J (2009) Genetic characterization and linkage disequilibrium estimation of a global maize collection using SNP markers. PLoS One 4:e8451CrossRefGoogle Scholar
  58. Zhang X, Pérez-Rodríguez P, Semagn K, Beyene Y, Babu R et al (2014) Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114:291CrossRefGoogle Scholar
  59. 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 Genomics 17:697CrossRefGoogle Scholar
  60. Zuo M (2001) CROP DIVERSIFICATION IN CHINA. Paper presented at the Crop Diversification in the Asia-Pacific Region, Bangkok, ThailandGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Yifeng Leng
    • 1
    • 2
  • Chenxi Lv
    • 1
    • 2
  • Lujiang Li
    • 1
    • 2
  • Yong Xiang
    • 1
    • 2
  • Chao Xia
    • 1
    • 2
  • Rujun Wei
    • 1
    • 2
  • Tingzhao Rong
    • 1
    • 2
  • Hai Lan
    • 1
    • 2
    Email author
  1. 1.Maize Research InstituteSichuan Agricultural UniversityChengduPeople’s Republic of China
  2. 2.Key Laboratory of Biology and Genetic Improvement of Maize in Southwest RegionMinistry of AgricultureChengduPeople’s Republic of China

Personalised recommendations