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Review Articles

Vol. 13 No. 3 (2026)

Harnessing haplotypes for genetic improvement in cereal crops

DOI
https://doi.org/10.14719/pst.11934
Submitted
23 September 2025
Published
19-06-2026 — Updated on 12-08-2026
Versions

Abstract

Haplotype-based breeding (HBB) is a transformative approach that utilises genome-assisted breeding (GAB) to enhance the precision and efficiency of crop improvement in cereals. Traditional breeding relies on single gene transfer, poor genetic diversity, time-consuming, less complex traits and linkage drag, whereas HBB utilises advanced breeding tools to capture the haplotypes associated with complex genetic variations. Recent advances in high-density genotyping have enabled the identification of superior haplotypes associated with yield, stress resilience and grain nutritional quality in major cereal crops such as rice, wheat, maize, oats and barley. This facilitates the development of superior varieties through gene introgression breeding methods like marker assisted breeding. The integration of HBB with genome-wide association studies (GWAS) and genomic selection (GS) has further improved the resolution and prediction in breeding programs. Advanced technologies like graph genomes, dynamic haplotype analysis, AI-driven haplotype analysis and CRISPR/Cas-mediated genome editing provides new opportunities to design and deploy haplotypes for climate-resilient and nutrient dense varieties. This review emphasises the importance of haplotype breeding in cereal crops, the approaches involved, existing challenges and its future directions to enhance genetic gains for global food and nutritional security. The persistent enhancement in the identification methods, analytical tools and software will pave the way for crop improvement through next-generation breeding programs.

References

  1. 1. Alotaibi BA, Baig MB, Najim MM, Shah AA, Alamri YA. Water scarcity management to ensure food scarcity through sustainable water resources management in Saudi Arabia. Sustainability. 2023;15(13):10648. https://doi.org/10.3390/su151310648
  2. 2. Voss-Fels K, Snowdon RJ. Understanding and utilizing crop genome diversity via high-resolution genotyping. Plant Biotechnol J. 2016;14(4), 1086–94. https://doi.org/10.1111/pbi.12456
  3. 3. Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM. Pitfalls of predicting complex traits from SNPs. Nat Rev Genet. 2013;14(7):507–15. https://doi.org/10.1038/nrg3457
  4. 4. Lu J, Qian Y, Li Z, Yang A, Zhu Y, Li R, et al. Mitochondrial haplotypes may modulate the phenotypic manifestation of the deafness-associated 12S rRNA 1555A>G mutation. Mitochondrion. 2010;10(1):69–81. https://doi.org/10.1016/j.mito.2009.09.007
  5. 5. Bhat JA, Yu D, Bohra A, Ganie SA, Varshney RK. Features and applications of haplotypes in crop breeding. Commun biol. 2021;4(1):1266. https://doi.org/10.1038/s42003-021-02782-y
  6. 6. Sehgal D, Mondal S, Crespo-Herrera L, Velu G, Juliana P, Huerta-Espino J, et al. Haplotype-based genome-wide association study reveals stable genomic regions for grain yield in CIMMYT spring bread wheat. Front Genet. 2020;11:589490. https://doi.org/10.3389/fgene.2020.589490
  7. 7. Chen J, Upadhyaya NM, Ortiz D, Sperschneider J, Li F, Bouton C, et al. Loss of AvrSr50 by somatic exchange in stem rust leads to virulence for Sr50 resistance in wheat. Science. 2017;358(6370):1607–10. https://doi.org/10.1126/science.aao4810
  8. 8. Guo Z, Cao H, Zhao J, Bai S, Peng W, Li J et al. A natural uORF variant confers phosphorus acquisition diversity in soybean. Nat Commun. 2022;13(1):3796. https://doi.org/10.1038/s41467-022-31555-2
  9. 9. Varshney RK, Bohra A, Roorkiwal M, Barmukh R, Cowling WA, Chitikineni A, et al. Fast-forward breeding for a food-secure world. Trends Genet. 2021;37(12):1124–36. https://doi.org/10.1016/j.tig.2021.08.002
  10. 10. Nanda SR, Kumari N, Sharma P, Usha M. Haplotype breeding. In: Nanda SR, Pal K, Tripathi S, Joshi MA, editors. Amalgamation of Recent Efforts in Plant Breeding and Biotechnology. India: Bhumi publishing; 2024. p. 1–12.
  11. 11. Van den Oord EJ, Neale BM. Will haplotype maps be useful for finding genes? Mol Psychiatry. 2004;9(3):227–36. https://doi.org/10.1038/sj.mp.4001449
  12. 12. Hess M, Druet T, Hess A, Garrick D. Fixed-length haplotypes can improve genomic prediction accuracy in an admixed dairy cattle population. Genet Select Evol. 2017;49:1-4. https://doi.org/10.1186/s12711-017-0329-y
  13. 13. Voss-Fels KP, Stahl A, Wittkop B, Lichthardt C, Nagler S, Rose T, et al. Breeding improves wheat productivity under contrasting agrochemical input levels. Nat Plants. 2019;5(7):706-14. https://doi.org/10.1038/s41477-019-0445-5
  14. 14. Coffman SM, Hufford MB andorf CM, Lübberstedt T. Haplotype structure in commercial maize breeding programs in relation to key founder lines. Theor Appl Genet. 2020;133:547–61. https://doi.org/10.1007/s00122-019-03486-y
  15. 15. Liu Y, Wang D, He F, Wang J, Joshi T, Xu D. Phenotype prediction and genome-wide association study using deep convolutional neural network of soybean. Front Genet. 2019;10:1091. https://doi.org/10.3389/fgene.2019.01091
  16. 16. Zaitlen NA, Kang HM, Feolo ML, Sherry ST, Halperin E, Eskin E. Inference and analysis of haplotypes from combined genotyping studies deposited in dbSNP. Genome Res. 2005;15(11):1594–600. https://doi.org/10.1101/gr.4297805
  17. 17. Sinha P, Singh VK, Saxena RK, Khan AW, Abbai R, Chitikineni A et al. Superior haplotypes for haplotype-based breeding for drought tolerance in pigeonpea (Cajanus cajan L.). Plant Biotechnol J. 2020;18(12):2482–90. https://doi.org/10.1111/pbi.13422
  18. 18. N’Diaye A, Haile JK, Cory AT, Clarke FR, Clarke JM, Knox RE et al. Single marker and haplotype-based association analysis of semolina and pasta colour in elite durum wheat breeding lines using a high-density consensus map. PLoS One. 2017;12(1):e0170941. https://doi.org/10.1371/journal.pone.0170941
  19. 19. Matias FI, Galli G, Correia Granato IS, Fritsche-Neto R. Genomic prediction of autogamous and allogamous plants by SNPs and haplotypes. Crop Sci. 2017;57(6):2951–8. https://doi.org/10.2135/cropsci2017.01.0022
  20. 20. Weber SE, Frisch M, Snowdon RJ, Voss-Fels KP. Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets. Front Plant Sci. 2023;14:1217589. https://doi.org/10.3389/fpls.2023.1217589
  21. 21. Meena VK, Thribhuvan R, Dinkar V, Bhatt A, Pandey S, Abhinav et al. Haplotype breeding: fast-track the crop improvements. Planta. 2025;261(3):51. https://doi.org/10.1007/s00425-025-04622-3
  22. 22. Schneider M, Shrestha A, Ballvora A, Léon J. High-throughput estimation of allele frequencies using combined pooled-population sequencing and haplotype-based data processing. Plant methods. 2022;18(1):34. https://doi.org/10.1186/s13007-022-00852-8
  23. 23. Jiang Y, Schmidt RH, Reif JC. Haplotype-based genome-wide prediction models exploit local epistatic interactions among markers. G3. 2018;8:1687–1699. https://doi.org/10.1534/g3.117.300548
  24. 24. de Los Campos G, Sorensen DA, Toro MA. Imperfect linkage disequilibrium generates phantom epistasis (& perils of big data). G3. 2019;9(5):1429–1436. https://doi.org/10.1534/g3.119.400101
  25. 25. Maldonado C, Mora F, Scapim CA, Coan M. Genome-wide haplotype-based association analysis of key traits of plant lodging and architecture of maize identifies major determinants for leaf angle: Hap LA4. PLoS One. 2019;14:e0212925. https://doi.org/10.1371/journal.pone.0212925
  26. 26. Gupta PK, Rustgi S, Kulwal PL. Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol. 2005;57:461–485. https://doi.org/10.1007/s11103-005-0257-z
  27. 27. Maestri S, Maturo MG, Cosentino E, Marcolungo L, Iadarola B, Fortunati E, et al. A long-read sequencing approach for direct haplotype phasing in clinical settings. Int J Mol Sci. 2020;21(23):9177. https://doi.org/10.3390/ijms21239177
  28. 28. Garg S. Computational methods for chromosome-scale haplotype reconstruction. Genome biol. 2021;22(1):101. https://doi.org/10.1186/s13059-021-02328-9
  29. 29. Ammar R, Paton TA, Torti D, Shlien A, Bader GD. Long read nanopore sequencing for detection of HLA and CYP2D6 variants and haplotypes. F1000Research. 2015;20:4-17. https://doi.org/10.12688/f1000research.6037.2
  30. 30. Zhang S, Liang F, Lei C, Wu J, Fu J, Yang Q, Luo X, Yu G, Wang D, Zhang Y, Lu D. Long-read sequencing and haplotype linkage analysis enabled preimplantation genetic testing for patients carrying pathogenic inversions. J Med Genet. 2019;56(11):741–9. https://doi.org/10.1136/jmedgenet-2018-105976
  31. 31. Liu G, Qiu D, Lu Y, Wu Y, Han X, Jiao Y et al. Identification of Superior Haplotypes and Haplotype Combinations for Grain Size-and Weight-Related Genes for Breeding Applications in Rice (Oryza sativa L.). Genes. 2023;14(12):2201. https://doi.org/10.3390/genes14122201
  32. 32. Yang X, Shaw RK, Li L, Jiang F, Sun J, Fan X. Discovery of candidate genes enhancing kernel protein content in tropical maize introgression lines. BMC Plant Biol. 2024;24(1):1110. https://doi.org/10.1186/s12870-024-05836-5
  33. 33. Zhang F, Wang C, Li M, Cui Y, Shi Y, Wu Z et al. The landscape of gene–CDS–haplotype diversity in rice: Properties, population organization, footprints of domestication and breeding and implications for genetic improvement. Mol Plant. 2021;14(5):787–804.
  34. 34. Li XF, Zhang X, Chen Y, Zhang KL, Liu XJ, Li JP. An analysis of HLA-A,-B and-DRB1 allele and haplotype frequencies of 21,918 residents living in Liaoning, China. PloS One. 2014;9(4):e93082. https://doi.org/10.1371/journal.pone.0093082
  35. 35. Ogawa D, Yamamoto E, Ohtani T, Kanno N, Tsunematsu H, Nonoue Y et al. Haplotype-based allele mining in the Japan-MAGIC rice population. Scientific reports. 2018 Mar 12;8(1):4379. https://doi.org/10.1038/s41598-018-22657-3
  36. 36. Singh N, Singh B, Rai V, Sidhu S, Singh AK, Singh NK. Evolutionary insights based on SNP haplotypes of red pericarp, grain size and starch synthase genes in wild and cultivated rice. Front Plant Sci. 2017;8:972. https://doi.org/10.3389/fpls.2017.00972
  37. 37. Abbai R, Singh VK, Nachimuthu VV, Sinha P, Selvaraj R, Vipparla AK et al. Haplotype analysis of key genes governing grain yield and quality traits across 3K RG panel reveals scope for the development of tailor-made rice with enhanced genetic gains. Plant Biotechnol J. 2019;17(8):1612–22. https://doi.org/10.1111/pbi.13087
  38. 38. Wang Y, Wang X, Zhai L, Zafar S, Shen C, Zhu S et al. A novel effective panicle number per plant 4 haplotype enhances grain yield by coordinating panicle number and grain number in rice. Crop J. 2024;12(1):202–12. https://doi.org/10.1016/j.cj.2023.11.003
  39. 39. Mei S, Zhang G, Jiang J, Lu J, Zhang F. Combining genome-wide association study and gene-based haplotype analysis to identify candidate genes for alkali tolerance at the germination stage in rice. Front Plant Sci. 2022;13:887239. https://doi.org/10.3389/fpls.2022.887239
  40. 40. Li L, Cheng G, Li W, Zhang D, Yu J, Zhou H et al. Utilization of natural alleles and haplotypes of Ctb1 for rice cold adaptability. Gene. 2025;941:149225. https://doi.org/10.1016/j.gene.2025.149225
  41. 41. Kuroha T, Nagai K, Gamuyao R, Wang DR, Furuta T, Nakamori M et al. Ethylene-gibberellin signaling underlies adaptation of rice to periodic flooding. Sci. 2018;361(6398):181–6. 10.1126/science.aat1577
  42. 42. Selvaraj R, Singh AK, Singh VK, Abbai R, Habde SV, Singh UM et al. Superior haplotypes towards development of low glycemic index rice with preferred grain and cooking quality. Scientific Rep. 2021;11(1):10082. https://doi.org/10.1038/s41598-021-87964-8
  43. 43. Rohilla M, Mazumder A, Chowdhury D, Bhardwaj R, Mondal TK. Understanding natural genetic variation for nutritional quality in grain and identification of superior haplotypes in deepwater rice genotypes of Assam, India. Gene. 2024;928:148801. https://doi.org/10.1016/j.gene.2024.148801
  44. 44. Van de Wouw M, van Hintum T, Kik C, van Treuren R, Visser B. Genetic diversity trends in twentieth century crop cultivars: a meta analysis. Theoretical Appl Genet. 2010;120:1241-52. https://doi.org/10.1007/s00122-009-1252-6
  45. 45. Fradgley N, Gardner KA, Cockram J, Elderfield J, Hickey JM, Howell P, et al. A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders. PLoS Biol. 2019;17(2):e3000071. https://doi.org/10.1371/journal.pbio.3000071
  46. 46. Scott MF, Fradgley N, Bentley AR, Brabbs T, Corke F, Gardner KA et al. Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding. Genome Biol. 2021;22(1):137. https://doi.org/10.1186/s13059-021-02354-7
  47. 47. Cha JK, Park H, Kwon Y, Lee SM, Oh KW, Lee JH. Genotyping the high protein content gene NAM-B1 in wheat (Triticum aestivum L.) and the development of a KASP marker to identify a functional haplotype. Agronomy. 2023;13(8):1977. https://doi.org/10.3390/agronomy13081977
  48. 48. Zhang Y, Huang X, Zhang L, Gao W, Ma J, Chen T et al. Genome-wide identification, gene expression and haplotype analysis of the rhomboid-ike gene family in wheat (Triticum aestivum L.). The Plant Genome. 2024;17(2):e20435. https://doi.org/10.1002/tpg2.20435
  49. 49. Roychowdhury R, Ullah N, Ozturk-Gokce ZN, Budak H. Haplotype Mapping Coupled Speed Breeding in Globally Diverse Wheat Germplasm for Genomics-Assisted Breeding. In: The Wheat Genome. Cham: Springer International Publishing; 2023. p. 265–272. https://doi.org/10.1007/978-3-031-38294-9_13
  50. 50. Li Y, Tong L, Deng L, Liu Q, Xing Y, Wang C et al. Evaluation of ZmCCT haplotypes for genetic improvement of maize hybrids. Theoretical Appl Genet. 2017;130:2587-600. https://doi.org/10.1007/s00122-017-2978-1
  51. 51. Bukowski R, Guo X, Lu Y, Zou C, He B, Rong Z et al. Construction of the third-generation Zea mays haplotype map. Gigascience. 2018;7(4):gix134. https://doi.org/10.1093/gigascience/gix134
  52. 52. Das AK, Muthusamy V, Zunjare RU, Baveja A, Chauhan HS, Bhat JS et al. Genetic variability for kernel tocopherols and haplotype analysis of γ-tocopherol methyl transferase (vte4) gene among exotic-and indigenous-maize inbreds. J Food Composition Analysis. 2020;88:103446. https://doi.org/10.1016/j.jfca.2020.103446
  53. 53. Lin M, Qiao P, Matschi S, Vasquez M, Ramstein GP, Bourgault R et al. Integrating GWAS and TWAS to elucidate the genetic architecture of maize leaf cuticular conductance. Plant Physiol. 2022;189(4):2144–58. https://doi.org/10.1093/plphys/kiac198
  54. 54. Zhao Y, Tian H, Li C, Yi H, Zhang Y, Li X et al. HTPdb and HTPtools: Exploiting maize haplotype-tag polymorphisms for germplasm resource analyses and genomics-informed breeding. Plant Commun. 2022;3(4). https://doi.org/10.1016/j.xplc.2022.100331
  55. 55. Lin YC, Mayer M, Valle Torres D, Pook T, Hölker AC, Presterl T et al. Genomic prediction within and across maize landrace derived populations using haplotypes. Front Plant Sci. 2024;15:1351466. https://doi.org/10.3389/fpls.2024.1351466
  56. 56. Sahoo RK, Swain N, Selvaraj S, Nayak G, Sarkar S, Singh NR et al. Haplotypes Differences in Growth Regulating Factor 4 (GRF4) for Yield and Biomass Traits in Rice (Oryza sativa L.). Tropic Plant Biol. 2025;18(1):7. https://doi.org/10.1007/s12042-024-09370-4
  57. 57. Mohanavel W, Ramalingam AP, Ayyenar B, Rajagopalan VR, Mohanavel V, Subburaj S et al. Mining of Candidate Novel Alleles Using GWAS and Haplotype Identification for Rice Blast Resistance. Plant Pathology. 2025;74(3):873–883. https://doi.org/10.1111/ppa.14059
  58. 58. Bian Z, Chen M, Wang L, Ma X, Yu Q, Jia Z et al. Overexpressing OsNF-YB12 elevated the content of jasmonic acid and impaired drought tolerance in rice. Plant Sci. 2025;352:112397. https://doi.org/10.1016/j.plantsci.2025.112397
  59. 59. Zhang X, Qin L, Ge Y, Wang M, Ma M, Zhao C et al. Genome-Wide Association Study Identifies Variants in ZmZEP1 Associated with Zeaxanthin Level in Maize Grains. J Plant Biol. 2025;23:1-6. https://doi.org/10.1007/s12374-024-09453-5
  60. 60. Yang T, Dong J, Xiong X, Zhang L, Wang J, Hu H et al. A Novel Function of GW5 on Controlling the Early Growth Vigor and its Haplotype Effect on Shoot Dry Weight and Grain Size in Rice (Oryza sativa L.). Rice. 2024;17(1):49. https://doi.org/10.1186/s12284-024-00728-6
  61. 61. Duo H, Chhabra R, Muthusamy V, Zunjare RU, Hossain F. Assessing sequence variation, haplotype analysis and molecular characterisation of aspartate kinase2 (ask2) gene regulating methionine biosynthesis in diverse maize inbreds. Mol Genet Genomics. 2024;299(1):7. https://doi.org/10.1007/s00438-024-02096-8
  62. 62. Tong L, Yan M, Zhu M, Yang J, Li Y, Xu M. ZmCCT haplotype H5 improves yield, stalk-rot resistance and drought tolerance in maize. Front Plant Sci. 2022;13:984527. https://doi.org/10.3389/fpls.2022.984527
  63. 63. Zhu Z, Lai X, Zhang Y, Zhang J, Shuang J, Xu S. The selection and utilization of heading date loci in modern wheat breeding. New Crops. 2025;2:100066. https://doi.org/10.1016/j.ncrops.2025.100066
  64. 64. Dong H, Kou C, Hu L, Li Y, Fang Y, Peng C. Haplotype Analysis and Gene Pyramiding for Pre-Harvest Sprouting Resistance in White-Grain Wheat. Int J Mol Sci. 2025;26(2):728. https://doi.org/10.3390/ijms26020728
  65. 65. Brunner SM, Dinglasan E, Baraibar S, Alahmad S, Katsikis C, van der Meer S. Characterizing stay-green in barley across diverse environments: unveiling novel haplotypes. Theoretical Appl Genet. 2024;137(6):120. https://doi.org/10.1007/s00122-024-04612-1
  66. 66. Liu R, Cheng H, Qin D, Xu L, Xu F, Xu Q, et al. Functional characterization and identification of superior haplotypes of barley HvGL7–2H (Hordeum vulgare L.) in grain features. J Integr Agric. 2024. https://doi.org/10.1016/j.jia.2024.03.025
  67. 67. Wonneberger R, Schreiber M, Haaning A, Muehlbauer GJ, Waugh R, Stein N. Major chromosome 5H haplotype switch structures the European two-rowed spring barley germplasm of the past 190 years. Theoretical Appl Genet. 2023;136(8):174. https://doi.org/10.1007/s00122-023-04418-7
  68. 68. Tao Y, Trusov Y, Zhao X, Wang X, Cruickshank AW, Hunt C et al. Manipulating assimilate availability provides insight into the genes controlling grain size in sorghum. Plant J. 2021;108(1):231–43. https://doi.org/10.1111/tpj.15437
  69. 69. Zhang R, Jia G, Diao X. geneHapR: an R package for gene haplotypic statistics and visualization. BMC Bioinformatics. 2023;24(1):199. https://doi.org/10.1186/s12859-023-05318-9
  70. 70. Tinker NA, Bekele WA, Hattori J. Haplotag: software for haplotype-based genotyping-by-sequencing analysis. G3 (Bethesda). 2016;6(4):857–63. https://doi.org/10.1534/g3.115.024596
  71. 71. Fruzangohar M, Timmins WA, Kravchuk O, Taylor J. HaploMaker: An improved algorithm for rapid haplotype assembly of genomic sequences. GigaScience. 2022;11:giac038. https://doi.org/10.1093/gigascience/giac038
  72. 72. Feng C, Wang X, Wu S, Ning W, Song B, Yan J et al. HAPPE: a tool for population haplotype analysis and visualization in editable excel tables. Frontiers in Plant Sci. 2022;13:927407. https://doi.org/10.3389/fpls.2022.927407
  73. 73. Li X, Shi Z, Gao J, Wang X, Guo K. CandiHap: a haplotype analysis toolkit for natural variation study. Mol Breeding. 2023;43(3):21. https://doi.org/10.1007/s11032-023-01366-4
  74. 74. Moeinzadeh MH, Yang J, Muzychenko E, Gallone G, Heller D, Reinert K et al. Ranbow: a fast and accurate method for polyploid haplotype reconstruction. PLOS Computational Biol. 2020;16(5):e1007843. https://doi.org/10.1371/journal.pcbi.1007843
  75. 75. Sivabharathi RC, Rajagopalan VR, Suresh R, Sudha M, Karthikeyan G, Jayakanthan M et al. Haplotype-based breeding: A new insight in crop improvement. Plant Sci. 2024;346:112129. https://doi.org/10.1016/j.plantsci.2024.112129
  76. 76. Mansueto L, Fuentes RR, Borja FN, Detras J, Abriol-Santos JM, Chebotarov D et al. Rice SNP-seek database update: new SNPs, indels and queries. Nucleic Acids Res. 2017;45(D1):D1075–81. https://doi.org/10.1093/nar/gkw1135
  77. 77. Zhao H, Yao W, Ouyang Y, Yang W, Wang G, Lian X et al. RiceVarMap: a comprehensive database of rice genomic variations. Nucleic Acids Res. 2015;43(D1):D1018–22. https://doi.org/10.1093/nar/gku894
  78. 78. Yonemaru JI, Ebana K, Yano M. HapRice, an SNP haplotype database and a web tool for rice. Plant Cell Physiol. 2014;55(1):e9-e9. https://doi.org/10.1093/pcp/pct188
  79. 79. Bradbury PJ, Casstevens T, Jensen SE, Johnson LC, Miller ZR, Monier B, et al. The Practical Haplotype Graph, a platform for storing and using pangenomes for imputation. Bioinformatics. 2022;38(15):3698–702. https://doi.org/10.1093/bioinformatics/btac410
  80. 80. Yao E, Blake VC, Cooper L, Wight CP, Michel S, Cagirici HB et al. GrainGenes: a data-rich repository for small grains genetics and genomics. Database. 2022;baac034. https://doi.org/10.1093/database/baac034
  81. 81. Scheben A, Verpaalen B, Lawley CT, Chan CK, Bayer PE, Batley J et al. CropSNPdb: a database of SNP array data for Brassica crops and hexaploid bread wheat. The Plant J. 2019;98(1):142–52. https://doi.org/10.1111/tpj.14194
  82. 82. Luo J, Wei C, Liu H, Cheng S, Xiao Y, Wang X et al. MaizeCUBIC: a comprehensive variation database for a maize synthetic population. Database. 2020;baaa044. https://doi.org/10.1093/database/baaa044
  83. 83. Gui S, Yang L, Li J, Luo J, Xu X, Yuan J, et al. ZEAMAP, a comprehensive database adapted to the maize multi-omics era. iScience. 2020;23(6):101241. https://doi.org/10.1016/j.isci.2020.101241
  84. 84. Liu H, Wang F, Xiao Y, Tian Z, Wen W, Zhang X et al. MODEM: multi-omics data envelopment and mining in maize. Database. 2016;baw117. https://doi.org/10.1093/database/baw117
  85. 85. Tan C, Chapman B, Wang P, Zhang Q, Zhou G, Zhang XQ, et al. BarleyVarDB: a database of barley genomic variation. Database (Oxford). 2020;baaa091. https://doi.org/10.1093/database/baaa091
  86. 86. Li T, Bian J, Tang M, Shangguan H, Zeng Y, Luo R et al. BGFD: an integrated multi-omics database of barley gene families. BMC Plant Biol. 2022;22(1):454. https://doi.org/10.1186/s12870-022-03846-9
  87. 87. Blake VC, Birkett C, Matthews DE, Hane DL, Bradbury P, Jannink JL. The Triticeae Toolbox: combining phenotype and genotype data to advance small-grains breeding. Plant Genome. 2016;9(2):plantgenome2014–12. https://doi.org/10.3835/plantgenome2014.12.0099
  88. 88. Wang X, Xie L, Fang J, Pang Y, Xu J, Li Z. Identification of candidate genes that affect the contents of 17 amino acids in the rice grain using a genome-wide haplotype association study. Rice. 2023;16(1):40. https://doi.org/10.1186/s12284-023-00658-9
  89. 89. Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de Los Campos G et al. Genomic selection in plant breeding: methods, models and perspectives. Trends Plant Sci. 2017;22(11):961–975. https://doi.org/10.1016/j.tplants.2017.08.011
  90. 90. Meher PK, Rustgi S, Kumar A. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results. Heredity. 2022;128(6):519–530. https://doi.org/10.1038/s41437-022-00539-9
  91. 91. Alemu A, Batista L, Singh PK, Ceplitis A, Chawade A. Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat. Theor Appl Genet. 2023;136(4):92. https://doi.org/10.1007/s00122–023-04352–8
  92. 92. Cheng H, Concepcion GT, Feng X, Zhang H, Li H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods. 2021;18:170–175. https://doi.org/10.1038/s41592-020-01056-5
  93. 93. Yuan Y, Scheben A, Edwards D, Chan TF. Toward haplotype studies in polyploid plants to assist breeding. Mol Plant. 2021;14(12):1969–1972. https://doi.org/10.1016/j.molp.2021.11.004
  94. 94. Ganapati RK, Chen K, Zhao X, Zheng T, Zhang F, Zhai L et al. Genome-Wide Association Study and Haplotype Analysis Jointly Identify New Candidate Genes for Alkaline Tolerance at Seedling Stage in Rice. Rice Sci. 2025;32(4):537–548. https://doi.org/10.1016/j.rsci.2025.04.006
  95. 95. Wang H, Cimen E, Singh N, Buckler E. Deep learning for plant genomics and crop improvement. Curr Opin Plant Biol. 2020;54:34-41. https://doi.org/10.1016/j.pbi.2019.12.010
  96. 96. Tong H, Nikoloski Z. Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. J Plant Physiol. 2021;257:153354. https://doi.org/10.1016/j.jplph.2020.153354
  97. 97. Kawakita S, Yamasaki M, Teratani R, Yabe S, Kajiya-Kanegae H, Yoshida H et al. Dual ensemble approach to predict rice heading date by integrating multiple rice phenology models and machine learning-based genetic parameter regression models. Agric For Meteorol. 2024;344:109821. https://doi.org/10.1016/j.agrformet.2023.109821
  98. 98. Hou Y, Gan J, Fan Z, Sun L, Garg V, Wang Y, et al. Haplotype-based pangenomes reveal genetic variations and climate adaptations in moso bamboo populations. Nat Commun. 2024;15(1):8085. https://doi.org/10.1038/s41467-024-52376-5

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