Genomic Selection for Climate-Resilient Wheat Breeding: Opportunities and Challenges in Pakistan
DOI:
https://doi.org/10.70749/ijbr.v4i2.2891Keywords:
Genomic Selection, Wheat Breeding, Climate Resilience, Abiotic Stress, Pakistan, Food Security, Marker-Assisted Selection.Abstract
Wheat production in Pakistan faces unprecedented challenges from climate change-induced abiotic stresses, including rising temperatures, erratic rainfall, and widespread soil salinity, which collectively threaten national food security. Genomic selection has emerged as a transformative breeding approach that utilizes genome-wide markers to predict breeding values and accelerate genetic gain for complex polygenic traits. This review synthesizes current advancements in genomic selection methodologies and evaluates their applicability within Pakistan's diverse agro-ecological zones. We examine the physiological responses of wheat to drought, heat, and salinity stress, highlighting the polygenic architecture of tolerance mechanisms that make them ideally suited for genomic prediction approaches. Studies demonstrate that genomic selection can achieve prediction accuracies of 0.5–0.6 for grain yield under stressed environments, with multi-trait models incorporating high-throughput phenotyping data improving accuracy by up to 67% compared to univariate approaches. The integration of environmental covariates and genotype-by-environment interactions further enhances predictive ability across variable climatic conditions. Despite promising results, successful implementation in Pakistan requires addressing critical barriers, including limited phenotyping capacity, high genotyping costs, insufficient training population sizes, and the need for robust statistical models adapted to local germplasm. Strategic investments in infrastructure, capacity building, and collaborative networks between national and international research institutions are essential to harness the full potential of genomic selection for developing climate-resilient wheat varieties tailored to Pakistan's vulnerable production systems.
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1. Prabhu, K. R., Kumar, A., Yumkhaibam, R. S., Janeja, H. S., Krishna, B., & Talekar, N. (2023). A review on conventional and modern breeding approaches for developing climate resilient crop varieties. Journal of Applied and Natural Science, 15(3), 987-997.
https://doi.org/10.31018/jans.v15i3.4653
2. Juliana, P., Singh, R. P., Braun, H., Huerta-Espino, J., Crespo-Herrera, L., Govindan, V., Mondal, S., Poland, J., & Shrestha, S. (2020). Genomic selection for grain yield in the CIMMYT wheat breeding program—Status and perspectives. Frontiers in Plant Science, 11.
https://doi.org/10.3389/fpls.2020.564183
3. Naqvi, R. Z., Siddiqui, H. A., Mahmood, M. A., Najeebullah, S., Ehsan, A., Azhar, M., Farooq, M., Amin, I., Asad, S., Mukhtar, Z., Mansoor, S., & Asif, M. (2022). Smart breeding approaches in post-genomics era for developing climate-resilient food crops. Frontiers in Plant Science, 13.
https://doi.org/10.3389/fpls.2022.972164
4. Budhlakoti, N., Kushwaha, A. K., Rai, A., Chaturvedi, K. K., Kumar, A., Pradhan, A. K., Kumar, U., Kumar, R. R., Juliana, P., Mishra, D. C., & Kumar, S. (2022). Genomic selection: A tool for accelerating the efficiency of molecular breeding for development of climate-resilient crops. Frontiers in Genetics, 13.
https://doi.org/10.3389/fgene.2022.832153
5. Edukondalu, B., Aswini, N., Amaresh, Krishnappa, G., Soundharya, B., Nikhitha, G., Pathy, T. L., Krishna, K., Hari, Y., & Vinayaka. (2026). Accelerating genetic gain through integrated genomic selection in crop plants. Journal of Applied Genetics.
https://doi.org/10.1007/s13353-025-01034-7
6. Juliana, P., Montesinos-López, O. A., Crossa, J., Mondal, S., González Pérez, L., Poland, J., Huerta-Espino, J., Crespo-Herrera, L., Govindan, V., Dreisigacker, S., Shrestha, S., Pérez-Rodríguez, P., Pinto Espinosa, F., & Singh, R. P. (2018). Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat. Theoretical and Applied Genetics, 132(1), 177-194.
https://doi.org/10.1007/s00122-018-3206-3
7. Farhad, M., Kumar, U., Tomar, V., Bhati, P. K., Krishnan J., N., Kishowar-E-Mustarin, Barek, V., Brestic, M., & Hossain, A. (2023). Heat stress in wheat: A global challenge to feed billions in the current era of the changing climate. Frontiers in Sustainable Food Systems, 7.
https://doi.org/10.3389/fsufs.2023.1203721
8. Kumar, S., Jacob, S. R., Mir, R. R., Vikas, V. K., Kulwal, P., Chandra, T., Kaur, S., Kumar, U., Kumar, S., Sharma, S., Singh, R., Prasad, S., Singh, A. M., Singh, A. K., Kumari, J., Saharan, M. S., Bhardwaj, S. C., Prasad, M., Kalia, S., … Singh, K. (2022). Indian wheat genomics initiative for harnessing the potential of wheat Germplasm resources for breeding disease-resistant, nutrient-dense, and climate-resilient cultivars. Frontiers in Genetics, 13.
https://doi.org/10.3389/fgene.2022.834366
9. Tadesse, W. (2019). Genetic gains in wheat breeding and its role in feeding the world. Crop Breeding, Genetics and Genomics.
https://doi.org/10.20900/cbgg20190005
10. Biswas, P. S., Ahmed, M. M., Afrin, W., Rahman, A., Shalahuddin, A. K., Islam, R., Akter, F., Syed, M. A., Sarker, M. R., Ifterkharuddaula, K. M., & Islam, M. R. (2023). Enhancing genetic gain through the application of genomic selection in developing irrigated rice for the favorable ecosystem in Bangladesh. Frontiers in Genetics, 14.
https://doi.org/10.3389/fgene.2023.1083221
11. Chawla, R., Poonia, A., Samantara, K., Mohapatra, S. R., Naik, S. B., Ashwath, M. N., Djalovic, I. G., & Prasad, P. V. (2023). Green Revolution to genome revolution: Driving better resilient crops against environmental instability. Frontiers in Genetics, 14.
https://doi.org/10.3389/fgene.2023.1204585
12. Zenda, T., Liu, S., Dong, A., Li, J., Wang, Y., Liu, X., Wang, N., & Duan, H. (2021). Omics-facilitated crop improvement for climate resilience and superior nutritive value. Frontiers in Plant Science, 12.
https://doi.org/10.3389/fpls.2021.774994
13. Bhat, J. A., Ali, S., Salgotra, R. K., Mir, Z. A., Dutta, S., Jadon, V., Tyagi, A., Mushtaq, M., Jain, N., Singh, P. K., Singh, G. P., & Prabhu, K. V. (2016). Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Frontiers in Genetics, 7.
https://doi.org/10.3389/fgene.2016.00221
14. Ballén-Taborda, C., Lyerly, J., Smith, J., Howell, K., Brown-Guedira, G., Babar, M. A., Harrison, S. A., Mason, R. E., Mergoum, M., Murphy, J. P., Sutton, R., Griffey, C. A., & Boyles, R. E. (2022). Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat. Frontiers in Genetics, 13.
https://doi.org/10.3389/fgene.2022.964684
15. Saint Pierre, C., Burgueño, J., Crossa, J., Fuentes Dávila, G., Figueroa López, P., Solís Moya, E., Ireta Moreno, J., Hernández Muela, V. M., Zamora Villa, V. M., Vikram, P., Mathews, K., Sansaloni, C., Sehgal, D., Jarquin, D., Wenzl, P., & Singh, S. (2016). Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones. Scientific Reports, 6(1).
https://doi.org/10.1038/srep27312
16. Mazumder, A. K., Yadav, R., Kumar, M., Babu, P., Kumar, N., Singh, S. K., Solanke, A. U., Wani, S. H., Alalawy, A. I., Alasmari, A., & Gaikwad, K. B. (2024). Discovering novel genomic regions explaining adaptation of bread wheat to conservation agriculture through GWAS. Scientific Reports, 14(1).
https://doi.org/10.1038/s41598-024-66903-3
17. Santantonio, N., Atanda, S. A., Beyene, Y., Varshney, R. K., Olsen, M., Jones, E., Roorkiwal, M., Gowda, M., Bharadwaj, C., Gaur, P. M., Zhang, X., Dreher, K., Ayala-Hernández, C., Crossa, J., Pérez-Rodríguez, P., Rathore, A., Gao, S. Y., McCouch, S., & Robbins, K. R. (2020). Strategies for effective use of Genomic information in crop breeding programs serving Africa and South Asia. Frontiers in Plant Science, 11.
https://doi.org/10.3389/fpls.2020.00353
18. KHAN, I., GUL, S., KHAN, N., FAWIBE, O., AKHTAR, N., REHMAN, M., SABAH, N., TAHIR, M., IQBAL, A., NAZ, F., HAQ, I., & RAUF, A. (2023). Stability analysis of wheat through genotype by environment interaction in three regions of Khyber Pakhtunkhwa, Pakistan. SABRAO Journal of Breeding and Genetics, 55(1), 50-60.
https://doi.org/10.54910/sabrao2023.55.1.5
19. Sinha, D., Maurya, A. K., Abdi, G., Majeed, M., Agarwal, R., Mukherjee, R., Ganguly, S., Aziz, R., Bhatia, M., Majgaonkar, A., Seal, S., Das, M., Banerjee, S., Chowdhury, S., Adeyemi, S. B., & Chen, J. (2023). Integrated Genomic selection for accelerating breeding programs of climate-smart cereals. Genes, 14(7), 1484.
https://doi.org/10.3390/genes14071484
20. Nawaz, M., Ali, L., Kashif, M. S., Hassan, I., Chaudhary, M. T., Shahbaz, M., Ali, M. A., Ashraf, S., Huda, A. U., & Mastoi, B. H. (2023). Impacts of temperature on wheat productivity in rice-wheat cropping system of Punjab, Pakistan. Journal of Plant and Environment, 5(1), 01-13.
https://doi.org/10.33687/jpe.005.01.4606
21. Chauhdary, J. N., Li, H., Ragab, R., Rakibuzzaman, M., Khan, A. I., Zhao, J., & Akbar, N. (2024). Climate change impacts on future wheat (Triticum aestivum) yield, growth periods and irrigation requirements: A SALTMED model simulations analysis. Agronomy, 14(7), 1484.
https://doi.org/10.3390/agronomy14071484
22. Kashif, M., Ahmad, I., Zaman, M., Akbar, M., & Chauhdary, J. N. (2024). Response of wheat yield, crop water requirements, and productivity to climate change (Historical simulation and future projection).
https://doi.org/10.21203/rs.3.rs-4526384/v1
23. Fahad, M., Ahmad, I., & Hussain, A. (2020). Implications of 1.5 and 2.0 ºC additional warming for wheat yield using a gridded modeling approach. Atmósfera.
https://doi.org/10.20937/atm.52751
24. Ullah, A., Ahmad, I., Habib-ur-Rehman, Saeed, U., Ahmad, A., Mahmood, A., & Hoogenboom, G. (2019). Climate smart interventions of small-holder farming systems. Climate Change and Agriculture.
https://doi.org/10.5772/intechopen.82872
25. Aslam, M. A., Ahmed, M., Stöckle, C. O., Higgins, S. S., Hassan, F. U., & Hayat, R. (2017). Can growing degree days and photoperiod predict spring wheat phenology? Frontiers in Environmental Science, 5.
https://doi.org/10.3389/fenvs.2017.00057
26. Bhandari, R. K., Paudel, H., & Poudel, M. R. (2024). Performance and stability of elite wheat genotypes under irrigated, heat stress and heat drought environment. CURRENT APPLIED SCIENCE AND TECHNOLOGY, e0259959.
https://doi.org/10.55003/cast.2024.259959
27. Rehman, N. (2021). ON THE SAME MAP: Gigamapping as a strategic tool to support Pakistan’s policy response to climate change induced food security vulnerabilities (Doctoral dissertation).
https://dx.doi.org/10.7302/8114
28. Hina, T., Adil, S. A., Ashfaq, M., & Ahmad, A. (2019). Economic impact assessment of climatic change sensitivity in rice-wheat cropping system of Pakistan. Indian Journal of Science and Technology, 12(37), 1-17.
https://doi.org/10.17485/ijst/2019/v12i37/147643
29. Saleem, A., Anwar, S., Nawaz, T., Fahad, S., Saud, S., Ur Rahman, T., Khan, M. N., & Nawaz, T. (2024). Securing a sustainable future: The climate change threat to agriculture, food security, and sustainable development goals. Journal of Umm Al-Qura University for Applied Sciences, 11(3), 595-611.
https://doi.org/10.1007/s43994-024-00177-3
30. ALOTAIBI, M. (2023). Climate change, its impact on crop production, challenges, and possible solutions. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 51(1), 13020.
https://doi.org/10.15835/nbha51113020
31. Khan, N. A., Khanal, U., Wilson, C., Shah, A. A., & Tariq, M. A. (2022). The impact of farmers’ adaptation to climate change on rice yields: Implications for sustainable food systems. Sustainability, 14(23), 16035.
https://doi.org/10.3390/su142316035
32. Habib-ur-Rahman, M., Ahmad, A., Raza, A., Hasnain, M. U., Alharby, H. F., Alzahrani, Y. M., Bamagoos, A. A., Hakeem, K. R., Ahmad, S., Nasim, W., Ali, S., Mansour, F., & EL Sabagh, A. (2022). Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia. Frontiers in Plant Science, 13.
https://doi.org/10.3389/fpls.2022.925548
33. Jat, M. L. (2023). Conservation agriculture for regenerating soil health and climate change mitigation in smallholder systems of South Asia. Advances in Agronomy, 183-277.
https://doi.org/10.1016/bs.agron.2023.05.003
34. Arif, G., Riaz, M., Faisal, N., Khattak, M. J., Sathar, Z., Khalil, M., Sadiq, M., Hussain, S., & Khan, K. (2019). Climate, population, and vulnerability in Pakistan: Exploring evidence of linkages for adaptation.
https://doi.org/10.31899/pgy10.1022
35. Khan, N. A., Shah, A. A., Chowdhury, A., Tariq, M. A., & Khanal, U. (2022). Rice farmers’ perceptions about temperature and rainfall variations, respective adaptation measures, and determinants: Implications for sustainable farming systems. Frontiers in Environmental Science, 10.
https://doi.org/10.3389/fenvs.2022.997673
36. Khan, H., Ali, S., & Gul, F. (2023). Impact of climate variability on rice productivity in Pakistan.
https://doi.org/10.21203/rs.3.rs-2606566/v1
37. Aryal, J. P., Sapkota, T. B., Khurana, R., Khatri-Chhetri, A., Rahut, D. B., & Jat, M. L. (2019). Climate change and agriculture in South Asia: Adaptation options in smallholder production systems. Environment, Development and Sustainability, 22(6), 5045-5075.
https://doi.org/10.1007/s10668-019-00414-4
38. Akansu, A. F., & Kizildeniz, T. (2024). Assessing Wheat Yield Responses and Growing Stages Alterations to Diverse Climate Change Scenarios. Eurasian Journal of Agricultural Research, 8(1), 85–95.
https://dergipark.org.tr/en/pub/ejar/issue/85440/1506185
39. Khanzada, A., Yan, K., Hu, W., Malko, M., Khan, K. A., Bao, Y., Elboughdiri, N., & Li, Y. (2025). Heat stress response mechanisms and resilience strategies in wheat. Journal of Agronomy and Crop Science, 211(1).
https://doi.org/10.1111/jac.70023
40. Lamlom, S. F., Abdelghany, A. M., Farouk, A. S., Alwakel, E. S., Makled, K. M., Bukhari, N. A., Hatamleh, A. A., Ren, H., El-Sorady, G. A., & Shehab, A. A. (2025). Biochemical and yield response of spring wheat to drought stress through gibberellic and abscisic acids. BMC Plant Biology, 25(1).
https://doi.org/10.1186/s12870-024-05879-8
41. Yasin, N. A., Khan, T. A., Ali, A., Ahmed, M., & Sardar, R. (2023). Editorial: Environmental extremes threatening food crops. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1172539
42. BHUTTO, L., OSBORNE, C., QUICK, W., SALEH, I., ABDEL-MAKSOUD, M., EL-TAYEB, M., & ABRO, A. (2025). Resilience unveiled: Exploring drought-resilient wheat varieties’adaptations to salinity stress. Applied Ecology and Environmental Research, 23(1), 1141-1153.
https://doi.org/10.15666/aeer/2301_11411153
43. Paudel, H., Bhandari, R., Dhakal, A., Nyaupane, S., Panthi, B., & Ram Poudel, M. (2023). Response of wheat to different abiotic stress conditions: A review. Science Heritage Journal, 7(1), 27-31.
https://doi.org/10.26480/gws.01.2023.27.31
44. EL SABAGH, A. (2019). Wheat (Triticum aestivum L.) production under drought and heat stress – adverse effects, mechanisms and mitigation: A review. Applied Ecology and Environmental Research, 17(4).
https://doi.org/10.15666/aeer/1704_83078332
45. Saha, D., Mishra, K., Pattnayak, C., Dey, P., Singh, M., Yadav, M., ... & Singhal, R. K. (2025). Wheat improvement for nutritional quality and abiotic stress tolerances. Discover Plants, 2(1), 333.
https://doi.org/10.1007/s44372-025-00424-9
46. Nakamoto, A. (2024). Abiotic stress tolerance in plants: Molecular and genetic interventions. International Journal of Biology Sciences, 6(1), 139-144.
https://doi.org/10.33545/26649926.2024.v6.i1b.446
47. NAWAZ, M., ALI, Q., AZEEM, M., ASGHAR, F., ISHAQ, H., IQBAL, N., & REHMAN, S. U. (2020). Capparis decidua Edgew (Forssk.) stem extract alleviates the water stress perturbations in wheat (Triticum Aestivum L.) at early growth stage. Journal of Bioresource Management, 7(4), 131-146.
https://doi.org/10.35691/jbm.0202.0159
48. Khalid, M., Kausar, R., Shahzad, A., Ali, G. M., & Begum, S. (2023). Screening and validation of salt-stress responsive cg-SSR markers in wheat (Triticum aestivum L.) germplasm of Pakistan. Molecular Biology Reports, 50(7), 5931-5940.
https://doi.org/10.1007/s11033-023-08519-w
49. Ahmed, H. G., Zeng, Y., Raza, H., Muhammad, D., Iqbal, M., Uzair, M., Khan, M. A., Iqbal, R., & EL Sabagh, A. (2022). Characterization of wheat (Triticum aestivum L.) accessions using morpho-physiological traits under varying levels of salinity stress at seedling stage. Frontiers in Plant Science, 13.
https://doi.org/10.3389/fpls.2022.953670
50. Badr, A., Basuoni, M. M., Ibrahim, M., Salama, Y. E., Abd-Ellatif, S., Abdel Razek, E. S., Amer, K. E., Ibrahim, A. A., & Zayed, E. M. (2024). Ameliorative impacts of gamma-aminobutyric acid (GABA) on seedling growth, physiological biomarkers, and gene expression in eight wheat (Triticum aestivum L.) cultivars under salt stress. BMC Plant Biology, 24(1).
https://doi.org/10.1186/s12870-024-05264-5
51. Bekkering, C., Yu, S., Kuo, C. C., & Tian, L. (2024). Distinct growth patterns in seedling and tillering wheat plants suggests a developmentally restricted role of HYD2 in salt-stress response. Plant Cell Reports, 43(5).
https://doi.org/10.1007/s00299-024-03206-x
52. Arain, S. M., Faheem, M., Laghari, K. A., & Sial, M. A. (2023). Wheat genotypes assessment for salinity tolerance using multivariable selection approaches. JAPS: Journal of Animal & Plant Sciences, 33(2).
https://www.thejaps.org.pk/docs/2023/02/09.pdf
53. ALZAHRANI, O., ABOUSEADAA, H., ABDELMONEIM, T. K., ALSHEHRI, M. A., EL-MOGY, M., EL-BELTAGI, H. S., & ATIA, M. A. (2021). Agronomical, physiological and molecular evaluation reveals superior salt-tolerance in bread wheat through salt-induced priming approach. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 49(2), 12310.
https://doi.org/10.15835/nbha49212310
54. Kumar, S., Purkyastha, S., Roy, C., Ranjan, T., & Deo Ranjan, R. (2021). Genes for different abiotic stresses tolerance in wheat. Plant Stress Physiology.
https://doi.org/10.5772/intechopen.93452
55. Mishra, D. C., Budhlakoti, N., Juliana, P., & Kumar, S. (2023). Editorial: Accelerating genetic gain for key traits using genome-wide association studies and genomic selection: promising breeding tools for sustainable agriculture. Frontiers in Genetics, 14.
https://doi.org/10.3389/fgene.2023.1351870
56. Mahmood, U., Li, X., Fan, Y., Chang, W., Niu, Y., Li, J., Qu, C., & Lu, K. (2022). Multi-omics revolution to promote plant breeding efficiency. Frontiers in Plant Science, 13.
https://doi.org/10.3389/fpls.2022.1062952
57. Baran Singh, R., & Devi, R. (2023). Achieving salinity-tolerance in cereal crops: Major insights into genomics-assisted breeding (GAB). Making Plant Life Easier and Productive Under Salinity - Updates and Prospects [Working Title].
https://doi.org/10.5772/intechopen.112570
58. Raza, A., Zaman, Q. U., Shabala, S., Tester, M., Munns, R., Hu, Z., & Varshney, R. K. (2025). Genomics‐assisted breeding for designing salinity‐smart future crops. Plant Biotechnology Journal, 23(8), 3119-3151.
https://doi.org/10.1111/pbi.70104
59. Javid, S., Bihamta, M. R., Omidi, M., Abbasi, A. R., Alipour, H., & Ingvarsson, P. K. (2022). Genome-wide association study (GWAS) and genome prediction of seedling salt tolerance in bread wheat (Triticum aestivum L.). BMC Plant Biology, 22(1).
https://doi.org/10.1186/s12870-022-03936-8
60. Atak, A. (2023). New perspectives in grapevine (Vitis spp.) breeding. Case Studies of Breeding Strategies in Major Plant Species.
https://doi.org/10.5772/intechopen.105194
61. Kumar, A., Mir, R. R., Sehgal, D., Agarwal, P., & Carter, A. (2021). Editorial: Genetics and genomics to enhance crop production, towards food security. Frontiers in Genetics, 12.
https://doi.org/10.3389/fgene.2021.798308
62. Guo, J., Pradhan, S., Shahi, D., Khan, J., Mcbreen, J., Bai, G., Murphy, J. P., & Babar, M. A. (2020). Increased prediction accuracy using combined Genomic information and physiological traits in a soft wheat panel evaluated in multi-environments. Scientific Reports, 10(1).
https://doi.org/10.1038/s41598-020-63919-3
63. Song, J., Carver, B. F., Powers, C., Yan, L., Klápště, J., El-Kassaby, Y. A., & Chen, C. (2017). Practical application of genomic selection in a doubled-haploid winter wheat breeding program. Molecular Breeding, 37(10).
https://doi.org/10.1007/s11032-017-0715-8
64. Mróz, T., Shafiee, S., Crossa, J., Montesinos-Lopez, O. A., & Lillemo, M. (2024). Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat. Molecular Breeding, 44(1).
https://doi.org/10.1007/s11032-024-01449-w
65. Meena, V. K., Singh Shekhawat, H. V., Chand, S., Choudhary, K., Sharma, J. K., & Lekha, L. (2023). Advances in molecular marker technology and their significance in plant improvement strategies. Recent Trends in Plant Breeding and Genetic Improvement.
https://doi.org/10.5772/intechopen.1002773
66. Gidamo, G. H. (2023). Genomic selection: A faster strategy for plant breeding. Case Studies of Breeding Strategies in Major Plant Species.
https://doi.org/10.5772/intechopen.105398
67. Jazayeri, S. M., & Torres, R. V. (2017). Genomic and transcriptomic approaches toward plant selection. Journal of Science and Research: Revista Ciencia e Investigación, 2(8), 54.
https://doi.org/10.26910/issn.2528-8083vol2iss8.2017pp54-64
68. Li, M., Qi, X., Ni, M., & Lam, H. (2016). Silicon era of carbon-based life: Application of genomics and bioinformatics in crop stress research. Crop Breeding, 137-165.
https://doi.org/10.1201/9781315365084-6
69. Thistlethwaite, F. R., Ratcliffe, B., Klápště, J., Porth, I., Chen, C., Stoehr, M. U., & El-Kassaby, Y. A. (2017). Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform. BMC Genomics, 18(1).
https://doi.org/10.1186/s12864-017-4258-5
70. Blinkov, A. O., Kroupin, P. Y., Dmitrieva, A. R., Kocheshkova, A. A., Karlov, G. I., & Divashuk, M. G. (2025). Speed breeding: Protocols, application and achievements. Frontiers in Plant Science, 16.
https://doi.org/10.3389/fpls.2025.1680955
71. Ćeran, M., Miladinović, D., Đorđević, V., Trkulja, D., Radanović, A., Glogovac, S., & Kondić-Špika, A. (2024). Genomics-assisted speed breeding for crop improvement: Present and future. Frontiers in Sustainable Food Systems, 8.
https://doi.org/10.3389/fsufs.2024.1383302
72. Kang, Y., Choi, C., Kim, J. Y., Min, K. D., & Kim, C. (2023). Optimizing genomic selection of agricultural traits using K-wheat core collection. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1112297
73. Guo, X., Sarup, P., Jahoor, A., Jensen, J., & Christensen, O. F. (2023). Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in Barley. Genetics Selection Evolution, 55(1).
https://doi.org/10.1186/s12711-023-00835-w
74. Tu, H., & Liao, C. (2024). Correction: A modified Bayesian optimization approach for determining a training set to identify the best genotypes from a candidate population in Genomic selection. Journal of Agricultural, Biological and Environmental Statistics.
https://doi.org/10.1007/s13253-024-00641-x
75. Iwata, H., Minamikawa, M. F., Kajiya-Kanegae, H., Ishimori, M., & Hayashi, T. (2016). Genomics-assisted breeding in fruit trees. Breeding Science, 66(1), 100-115.
https://doi.org/10.1270/jsbbs.66.100
76. Shamshad, M., & Sharma, A. (2018). The usage of Genomic selection strategy in plant breeding. Next Generation Plant Breeding.
https://doi.org/10.5772/intechopen.76247
77. Civan, P., Rincent, R., Danguy-Des-Deserts, A., Elsen, J., & Bouchet, S. (2021). Population genomics along with quantitative genetics provides a more efficient valorization of crop plant genetic diversity in breeding and pre-breeding programs. Population Genomics, 225-288.
https://doi.org/10.1007/13836_2021_97
78. Assefa, T., Assibi Mahama, A., Brown, A. V., Cannon, E. K., Rubyogo, J. C., Rao, I. M., Blair, M. W., & Cannon, S. B. (2019). A review of breeding objectives, genomic resources, and marker-assisted methods in common bean (Phaseolus vulgaris L.). Molecular Breeding, 39(2).
https://doi.org/10.1007/s11032-018-0920-0
79. Sverrisdóttir, E., Byrne, S., Sundmark, E. H., Johnsen, H. Ø., Kirk, H. G., Asp, T., Janss, L., & Nielsen, K. L. (2017). Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing. Theoretical and Applied Genetics, 130(10), 2091-2108.
https://doi.org/10.1007/s00122-017-2944-y
80. Vieira, R. A., Nogueira, A. P., & Fritsche-Neto, R. (2025). Optimizing the selection of quantitative traits in plant breeding using simulation. Frontiers in Plant Science, 16.
https://doi.org/10.3389/fpls.2025.1495662
81. Werner, C. R., Gaynor, R. C., Gorjanc, G., Hickey, J. M., Kox, T., Abbadi, A., Leckband, G., Snowdon, R. J., & Stahl, A. (2020). How population structure impacts Genomic selection accuracy in cross-validation: Implications for practical breeding. Frontiers in Plant Science, 11.
https://doi.org/10.3389/fpls.2020.592977
82. Lohithaswa, H. C., Showkath Babu, B. M., Sowmya, M. S., Banakar, S. K., Mallikarjuna, N., Jadesha, G., Mallikarjuna, M. G., Balasundara, D. C., & Anand, P. (2025). Assessment of the potential for genomic selection to improve resistance to fusarium stalk rot in maize. Frontiers in Plant Science, 16.
https://doi.org/10.3389/fpls.2025.1631408
83. Gebremedhin, A., Li, Y., Shunmugam, A. S., Sudheesh, S., Valipour-Kahrood, H., Hayden, M. J., Rosewarne, G. M., & Kaur, S. (2024). Genomic selection for target traits in the Australian lentil breeding program. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1284781
84. Michel, S., Ametz, C., Gungor, H., Epure, D., Grausgruber, H., Löschenberger, F., & Buerstmayr, H. (2016). Genomic selection across multiple breeding cycles in applied bread wheat breeding. Theoretical and Applied Genetics, 129(6), 1179-1189.
https://doi.org/10.1007/s00122-016-2694-2
85. Yamamoto, E., Matsunaga, H., Onogi, A., Ohyama, A., Miyatake, K., Yamaguchi, H., Nunome, T., Iwata, H., & Fukuoka, H. (2016). Efficiency of genomic selection for breeding population design and phenotype prediction in tomato. Heredity, 118(2), 202-209.
https://doi.org/10.1038/hdy.2016.84
86. Chakradhar, T., Hindu, V., & Reddy, P. S. (2017). Genomic-based-breeding tools for tropical maize improvement. Genetica, 145(6), 525-539.
https://doi.org/10.1007/s10709-017-9981-y
87. Chivatá-Peña, L. V., Perilla-Henao, L. M., & Soto Sedano, J. C. (2023). Unraveling the genetic architecture of complex traits in plants. Acta Biológica Colombiana, 28(3).
https://doi.org/10.15446/abc.v28n3.98891
88. Çiftçi, Y. O. (2018). Next generation plant breeding.
https://doi.org/10.5772/intechopen.71234
89. Jeon, D., Kang, Y., Lee, S., Choi, S., Sung, Y., Lee, T., & Kim, C. (2023). Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1092584
90. Kumar Singh, V., Pundir, S., Chaturvedi, D., Kaur, A., Pandey, A., Mandal, S., Kumar, R., Kumar Singh, R., Pal Bhati, H., Singh Dhanda, P., Yadav, A., Kole, C., & Kaushik, P. (2024). Enhancing maize (Zea maysL.) crop through advanced techniques: A comprehensive approach. Agricultural Sciences.
https://doi.org/10.5772/intechopen.114029
91. Gemenet, D. C., Lindqvist-Kreuze, H., De Boeck, B., Da Silva Pereira, G., Mollinari, M., Zeng, Z., Craig Yencho, G., & Campos, H. (2020). Sequencing depth and genotype quality: Accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops. Theoretical and Applied Genetics, 133(12), 3345-3363.
https://doi.org/10.1007/s00122-020-03673-2
92. Montesinos-López, O. A., Crespo-Herrera, L., Pierre, C. S., Cano-Paez, B., Huerta-Prado, G. I., Mosqueda-González, B. A., Ramos-Pulido, S., Gerard, G., Alnowibet, K., Fritsche-Neto, R., Montesinos-López, A., & Crossa, J. (2024). Feature engineering of environmental covariates improves plant genomic-enabled prediction. Frontiers in Plant Science, 15.
https://doi.org/10.3389/fpls.2024.1349569
93. Allier, A. (2020). Contributions to Genetic Diversity Management in Maize Breeding Programs using Genomic Selection (Doctoral dissertation, Université Paris-Saclay).
https://pastel.archives-ouvertes.fr/tel-03499495
94. Badu-Apraku, B., Talabi, A. O., Fakorede, M. A., Fasanmade, Y., Gedil, M., Magorokosho, C., & Asiedu, R. (2019). Yield gains and associated changes in an early yellow Bi-parental maize population following genomic selection for striga resistance and drought tolerance. BMC Plant Biology, 19(1).
https://doi.org/10.1186/s12870-019-1740-z
95. Hussain, B., Akpınar, B. A., Alaux, M., Algharib, A. M., Sehgal, D., Ali, Z., Aradottir, G. I., Batley, J., Arnaud Bellec, Bentley, A. R., Cagirici, H. B., Cattivelli, L., Choulet, F., Cockram, J., Desiderio, F., Devaux, P., Munevver Dogramaci, Dorado, G., Dreisigacker, S., & Edwards, D. (2022). Capturing Wheat Phenotypes at the Genome Level. Frontiers in Plant Science, 13.
https://doi.org/10.3389/fpls.2022.851079
96. Maulana, F., D. Anderson, J., J. Butler, T., & Ma, X. (2020). Improving dual-purpose winter wheat in the southern Great Plains of the United States. Recent Advances in Grain Crops Research.
https://doi.org/10.5772/intechopen.86417
97. Bartholomé, J., Frouin, J., Brottier, L., CAO, T., Boisnard, A., Ahmadi, N., & Courtois, B. (2022). Genomic selection for salinity tolerance in japonica rice.
https://doi.org/10.21203/rs.3.rs-2007613/v1
98. Itam, M. O., Mega, R., Gorafi, Y. S., Yamasaki, Y., Tahir, I. S., Akashi, K., & Tsujimoto, H. (2021). Genomic analysis for heat and combined heat–drought resilience in bread wheat under field conditions. Theoretical and Applied Genetics, 135(1), 337-350.
https://doi.org/10.1007/s00122-021-03969-x
99. Aloisi, I., Yacoubi, I., Gadaleta, A., Schwember, A. R., & Marcotuli, I. (2023). Editorial: Exploiting wheat biodiversity and agricultural practices for tackling the effects of climate change. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1257502
100. Said, A. A., Moursi, Y. S., & Sallam, A. (2022). Association mapping and candidate genes for physiological non-destructive traits: Chlorophyll content, canopy temperature, and specific leaf area under normal and saline conditions in wheat. Frontiers in Genetics, 13.
https://doi.org/10.3389/fgene.2022.980319
101. Iqbal, M. S., Taylor, C. M., Kotula, L., Malik, A. I., & Erskine, W. (2025). Dissecting the genetic and phenotypic basis of salinity tolerance in mungbean: Insights from multi-stage phenotyping, GWAS and genomic prediction. Theoretical and Applied Genetics, 138(9).
https://doi.org/10.1007/s00122-025-04983-z
102. Bartholomé, J., Frouin, J., Brottier, L., Cao, T., Boisnard, A., Ahmadi, N., & Courtois, B. (2023). Genomic selection for salinity tolerance in japonica rice. PLOS ONE, 18(9), e0291833.
https://doi.org/10.1371/journal.pone.0291833
103. Avia, K., Coelho, S. M., Montecinos, G. J., Cormier, A., Lerck, F., Mauger, S., Faugeron, S., Valero, M., Cock, J. M., & Boudry, P. (2017). High-density genetic map and identification of QTLs for responses to temperature and salinity stresses in the model Brown alga Ectocarpus. Scientific Reports, 7(1).
https://doi.org/10.1038/srep43241
104. Brault, C., Conley, E. J., Read, A. C., Green, A. J., Glover, K. D., Cook, J. P., Gill, H. S., Fiedler, J. D., & Anderson, J. A. (2025). Improving genomic prediction for plant disease using environmental covariates. Plant Methods, 21(1).
https://doi.org/10.1186/s13007-025-01418-0
105. Rashid, M. A., Sajjad, M., Gul, A., & Zhao, Y. (2023). Editorial: Genomic selection and characterization in cereals. Frontiers in Genetics, 13.
https://doi.org/10.3389/fgene.2022.1092095
106. Martini, J. W., Hearne, S. J., Gardunia, B., Wimmer, V., & Toledo, F. H. (2022). Editorial: Genomic selection: Lessons learned and perspectives. Frontiers in Plant Science, 13.
https://doi.org/10.3389/fpls.2022.890434
107. Bohra, A., Gahlaut, V., Perovic, D., & Varshney, R. K. (2023). Editorial: Genetics and epigenetics: Plausible role in development of climate resilient crops. Frontiers in Genetics, 14.
https://doi.org/10.3389/fgene.2023.1165843
108. Tadesse, W., Gataa, Z. E., Rachdad, F. E., Baouchi, A. E., Kehel, Z., & Alemu, A. (2023). Single- and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment. Molecular Genetics and Genomics, 298(6), 1515-1526.
https://doi.org/10.1007/s00438-023-02074-6
109. Raza, A., Tabassum, J., Fakhar, A. Z., Sharif, R., Chen, H., Zhang, C., Ju, L., Fotopoulos, V., Siddique, K. H., Singh, R. K., Zhuang, W., & Varshney, R. K. (2022). Smart reprograming of plants against salinity stress using modern biotechnological tools. Critical Reviews in Biotechnology, 43(7), 1035-1062.
https://doi.org/10.1080/07388551.2022.2093695
110. Pandey, M. K., Mir, R. R., & Sreenivasulu, N. (2023). The plant Genome special section: Grain quality and nutritional genomics for breeding next‐generation crops. The Plant Genome, 16(4).
https://doi.org/10.1002/tpg2.20396
111. Mora-Poblete, F., Maldonado, C., Henrique, L., Uhdre, R., Scapim, C. A., & Mangolim, C. A. (2023). Multi-trait and multi-environment genomic prediction for flowering traits in maize: A deep learning approach. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1153040
112. Kaushal, S., Gill, H. S., Billah, M. M., Khan, S. N., Halder, J., Bernardo, A., Amand, P. S., Bai, G., Glover, K., Maimaitijiang, M., & Sehgal, S. K. (2024). Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning. Frontiers in Plant Science, 15.
https://doi.org/10.3389/fpls.2024.1410249
113. Dharmateja, P., Yadav, R., Kumar, M., Babu, P., Jain, N., Mandal, P. K., Pandey, R., Shrivastava, M., Gaikwad, K. B., Bainsla, N. K., Tomar, V., Sugumar, S., Saifi, N., & Ranjan, R. (2022). Genome-wide association studies reveal putative QTLs for physiological traits under contrasting phosphorous conditions in wheat (Triticum aestivum L.). Frontiers in Genetics, 13.
https://doi.org/10.3389/fgene.2022.984720
114. Zeffa, D. M., Júnior, L. P., De Assis, R., Delfini, J., Marcos, A. W., Koltun, A., Baba, V. Y., Constantino, L. V., Uhdre, R. S., Nogueira, A. F., Moda-Cirino, V., Scapim, C. A., & Gonçalves, L. S. (2024). Multi-locus genome-wide association study for phosphorus use efficiency in a tropical maize germplasm. Frontiers in Plant Science, 15.
https://doi.org/10.3389/fpls.2024.1366173
115. Juliana, P., Singh, R. P., Braun, H., Huerta-Espino, J., Crespo-Herrera, L., Payne, T., Poland, J., Shrestha, S., Kumar, U., Joshi, A. K., Imtiaz, M., Rahman, M. M., & Toledo, F. H. (2020). Retrospective quantitative genetic analysis and Genomic prediction of global wheat yields. Frontiers in Plant Science, 11.
https://doi.org/10.3389/fpls.2020.580136
116. Zhang, Y., Jia, Z., & Dunwell, J. M. (2023). Editorial: The applications of new multi-locus GWAS methodologies in the genetic dissection of complex traits, volume II. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1340767
117. Sinha, P., Singh, V. K., Bohra, A., Kumar, A., Reif, J. C., & Varshney, R. K. (2021). Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits. Theoretical and Applied Genetics, 134(6), 1829-1843.
https://doi.org/10.1007/s00122-021-03847-6
118. Naqvi, R. Z., Mahmood, M. A., Mansoor, S., Amin, I., & Asif, M. (2024). Omics-driven exploration and mining of key functional genes for the improvement of food and fiber crops. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1273859
119. Shahzad, K., Mubeen, I., Zhang, M., Zhang, X., Wu, J., & Xing, C. (2022). Progress and perspective on cotton breeding in Pakistan. Journal of Cotton Research, 5(1).
https://doi.org/10.1186/s42397-022-00137-4
120. Ur Rehman, S., Ali Sher, M., Saddique, M. A., Ali, Z., Khan, M. A., Mao, X., Irshad, A., Sajjad, M., Ikram, R. M., Naeem, M., & Jing, R. (2021). Development and exploitation of KASP assays for genes underpinning drought tolerance among wheat cultivars from Pakistan. Frontiers in Genetics, 12.
https://doi.org/10.3389/fgene.2021.684702
121. Broccanello, C., Bellin, D., DalCorso, G., Furini, A., & Taranto, F. (2023). Genetic approaches to exploit landraces for improvement of triticum turgidum SSP. durum in the age of climate change. Frontiers in Plant Science, 14.
https://doi.org/10.3389/fpls.2023.1101271
122. Manickavelu, A., Hattori, T., Yamaoka, S., Yoshimura, K., Kondou, Y., Onogi, A., Matsui, M., Iwata, H., & Ban, T. (2017). Genetic nature of elemental contents in wheat grains and its Genomic prediction: Toward the effective use of wheat landraces from Afghanistan. PLOS ONE, 12(1), e0169416.
https://doi.org/10.1371/journal.pone.0169416
123. Islam, M., Abdullah, Zubaida, B., Amin, N., Khan, R. I., Shafqat, N., Masood, R., Waseem, S., Tahir, J., Ahmed, I., Naeem, M., & Ahmad, H. (2021). Agro-morphological, yield, and genotyping-by-Sequencing data of selected wheat (Triticum aestivum) Germplasm from Pakistan. Frontiers in Genetics, 12.
https://doi.org/10.3389/fgene.2021.617772
124. Babalola, K. O., Monacelli, N., Gozzi, M., Ceccarelli, S., Folloni, S., & Galaverna, G. (2025). Genetic diversity and climate change adaptation in wheat: A systematic review of landraces, composite cross populations, and evolutionary populations. Frontiers in Sustainable Food Systems, 9.
https://doi.org/10.3389/fsufs.2025.1504922
125. Dwivedi, S. L., Scheben, A., Edwards, D., Spillane, C., & Ortiz, R. (2017). Assessing and exploiting functional diversity in Germplasm pools to enhance abiotic stress adaptation and yield in cereals and food legumes. Frontiers in Plant Science, 8.
https://doi.org/10.3389/fpls.2017.01461
126. Vikram, P., Sehgal, D., Prasad, M., Singh, S., & Raman, H. (2024). Editorial: Harnessing crop biodiversity and genomics assisted pre-breeding approaches for next generation climate-smart varieties, volume II. Frontiers in Plant Science, 15.
https://doi.org/10.3389/fpls.2024.1387016
127. Mir, R. R., Kudapa, H., Pramod, S., & Lewis, R. S. (2022). Editorial: Biotechnological and genomic approaches for enhancing agronomic performance of crops. Frontiers in Genetics, 13.
https://doi.org/10.3389/fgene.2022.991630
128. Cseh, A., Poczai, P., Kiss, T., Balla, K., Berki, Z., Horváth, Á., Kuti, C., & Karsai, I. (2021). Exploring the legacy of central European historical winter wheat landraces. Scientific Reports, 11(1).
https://doi.org/10.1038/s41598-021-03261-4
129. Mondal, S., Rutkoski, J. E., Velu, G., Singh, P. K., Crespo-Herrera, L. A., Guzmán, C., Bhavani, S., Lan, C., He, X., & Singh, R. P. (2016). Harnessing diversity in wheat to enhance grain yield, climate resilience, disease and insect pest resistance and nutrition through conventional and modern breeding approaches. Frontiers in Plant Science, 7.
https://doi.org/10.3389/fpls.2016.00991
130. Borgohain, T., Suma, R., Muttappagol, M., Saikia, B., Keithellakpam, A., Laskar, A., Hiremath, S. S., Basu, U., Velmurugan, N., Palakolanu, S. R., & Chikkaputtaiah, C. (2026). Precision breeding in a changing climate: Unlocking resilience through omics and gene editing. Functional & Integrative Genomics, 26(1).
https://doi.org/10.1007/s10142-025-01796-7
131. Razzaq, A., Kaur, P., Akhter, N., Wani, S. H., & Saleem, F. (2021). Next-generation breeding strategies for climate-ready crops. Frontiers in Plant Science, 12.
https://doi.org/10.3389/fpls.2021.620420
132. Montesinos-López, O. A., Montesinos-López, A., Mosqueda-González, B. A., Delgado-Enciso, I., Chavira-Flores, M., Crossa, J., Dreisigacker, S., Sun, J., & Ortiz, R. (2025). Genomic prediction powered by multi-omics data. Frontiers in Genetics, 16.
https://doi.org/10.3389/fgene.2025.1636438
133. Hickey, J. M., Chiurugwi, T., Mackay, I., & Powell, W. (2017). Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nature Genetics, 49(9), 1297-1303.
https://doi.org/10.1038/ng.3920
134. Ducrocq, V., Laloe, D., Swaminathan, M., Rognon, X., Tixier-Boichard, M., & Zerjal, T. (2018). Genomics for ruminants in developing countries: From principles to practice. Frontiers in Genetics, 9.
https://doi.org/10.3389/fgene.2018.00251
135. Farzana, L., Islam, S. S., Rahman, M., Ferdus, H., & Hossain, M. M. (2025). Addressing Bipolaris sorokiniana in wheat: Challenges, management strategies, and future directions for sustainable food security. Discover Agriculture, 3(1).
https://doi.org/10.1007/s44279-025-00389-z
136. Subedi, M., Ghimire, B., Bagwell, J. W., Buck, J. W., & Mergoum, M. (2023). Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities. Frontiers in Genetics, 13.
https://doi.org/10.3389/fgene.2022.1032601
137. Kazi, A. M., Ali, N., Ibrahim, A., Napar, A. A., Jamil, M., Hussain, S., Mahmood, Z., Delgado, R., Rosas, V., Cortes, A., & Rajaram, S. (2017). Tissue culture mediated Allelic diversification and Genomic enrichment of wheat to combat production constraints and address food security. Plant Tissue Culture and Biotechnology, 27(1), 89-140.
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