Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding.

Advanced Search

Javaid Akhter Bhat, Xianzhong Feng, Zahoor A Mir, Aamir Raina, Kadambot H M Siddique

Author Information

Javaid Akhter Bhat: Zhejiang Lab, Hangzhou, China. ORCID
Xianzhong Feng: Zhejiang Lab, Hangzhou, China.
Zahoor A Mir: ICAR-National Bureau of Plant Genetic Resources, New Delhi, India.
Aamir Raina: Department of Botany, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, India. ORCID
Kadambot H M Siddique: The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, Western Australia, Australia.

PMID: 37401892 DOI: 10.1111/ppl.13969

Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.

Abdulaimma, B., Fergus, P., Chalmers, C. & Montañez, C.C. (2020) Deep learning and genome-wide association studies for the classification of type 2 diabetes. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE. (pp. 1-8).

Ahmar, S., Gill, R.A., Jung, K.H., Faheem, A., Qasim, M.U., Mubeen, M. et al. (2020) Conventional and molecular techniques from simple breeding to speed breeding in crop plants: recent advances and future outlook. International Journal of Molecular Sciences, 7, 2590.

Aksulu, A. & Wade, M.R. (2010) A comprehensive review and synthesis of open-source research. Journal of the Association for Information Systems, 11, 6-656.

Ansarifar, J. & Wang, L. (2019) New algorithms for detecting multi-effect and multi-way epistatic interactions. Bioinformatics, 35(24), 5078-5085.

Arloth, J., Eraslan, G., Andlauer, T.F., Martins, J., Iurato, S., Kühnel, B. et al. (2020) DeepWAS: multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning. PLoS Computational Biology, 16, 1007616.

Arouisse, B., Theeuwen, T.P., Van Eeuwijk, F.A. & Kruijer, W. (2021) Improving genomic prediction using high-dimensional secondary phenotypes. Frontiers in Genetics, 12, 715.

Ashkenazy, N., Feder, M., Shir, O. & Hubner, S. (2022) GWANN: implementing deep learning in genome wide association studies. bioRxiv.

Athar, H.R. & Ashraf, M. (2009) Strategies for crop improvement against salinity and drought stress: an overview. In: Ashraf, M., Ozturk, M. & Athar, H. (Eds.) Salinity and water stress. Tasks for vegetation sciences, Vol. 44. Dordrecht: Springer, pp. 1-16. Available from: https://doi.org/10.1007/978-1-4020-9065-3_1

Atieno, J., Li, Y., Langridge, P., Dowling, K., Brien, C., Berger, B. et al. (2017) Exploring genetic variation for salinity tolerance in chickpea using image-based phenotyping. Scientific Reports, 7(1), 1300.

Azodi, C.B., Bolger, E., McCarren, A., Roantree, M., de Los Campos, G. & Shiu, S.H. (2019) Benchmarking parametric and machine learning models for genomic prediction of complex traits. G3: Genes, Genomes, Genetics, 9(11), 3691-3702.

Bhat, J.A., Ali, S., Salgotra, R.K., Mir, Z.A., Dutta, S., Jadon, V. et al. (2016) Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Frontiers in Genetics, 7, 221.

Bhat, J.A. & Yu, D. (2021) High-throughput NGS-based genotyping and phenotyping: role in genomics-assisted breeding for soybean improvement. Legume Science, 3, 81.

Bohra, A., Saxena, K.B., Varshney, R.K. & Saxena, R.K. (2020) Genomics-assisted breeding for pigeonpea improvement. Theoretical and Applied Genetics, 5, 1721-1737.

Breseghello, F. & Coelho, A.S.G. (2013) Traditional and modern plant breeding methods with examples in rice (Oryza sativa) L. Journal of Agricultural and Food Chemistry, 61, 8277-8286.

Budhlakoti, N., Kushwaha, A.K., Rai, A., Chaturvedi, K.K., Kumar, A., Pradhan, A.K. et al. (2022) Genomic selection: a tool for accelerating the efficiency of molecular breeding for development of climate resilient crops. Frontiers in Genetics, 13, 66.

Casanova, M.F., Hensley, M.K., Sokhadze, E.M., El-Baz, A.S., Wang, Y., Li, X. et al. (2014) Effects of weekly low-frequency rTMS on autonomic measures in children with autism spectrum disorder. Frontiers in Human Neuroscience, 8, 851.

Chenu, K., Chapman, S.C., Tardieu, F., McLean, G., Welcker, C. & Hammer, G.L. (2009) Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a “gene-to-phenotype” modeling approach. Genetics, 183, 1507-1523.

Cooper, M., Powell, O., Voss-Fels, K.P., Messina, C.D., Gho, C., Podlich, D.W. et al. (2021) Modelling selection response in plant-breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions. In Silico Plants, 3, diaa016.

Cooper, M., Technow, F., Messina, C., Gho, C. & Totir, L.R. (2016) Use of crop growth models with whole-genome prediction: application to a maize multienvironment trial. Crop Science, 56, 2141-2156.

Crawford, L., Zeng, P., Mukherjee, S. & Zhou, X. (2017) Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS Genetics, 13, 1006869.

Fang, Y., Wang, L., Sapey, E., Fu, S., Wu, T., Zeng, H. et al. (2021) Speed-breeding system in soybean: integrating off-site generation advancement, fresh seeding, and marker-assisted selection. Frontiers in Plant Science, 12, 717077.

Feng, X., Zhan, Y., Wang, Q., Yang, X., Yu, C., Wang, H. et al. (2020) Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping. The Plant Journal, 6, 1448-1461.

Fergus, P., Montanez, C.C., Abdulaimma, B., Lisboa, P., Chalmers, C. & Pineles, B. (2018) Utilizing deep learning and genome wide association studies for epistatic-driven preterm birth classification in African-American women. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2, 668-678.

Ferguson, J.N., Fernandes, S.B., Monier, B., Miller, N.D., Allen, D., Dmitrieva, A. et al. (2021) Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. Plant Physiology, 3, 1481-1500.

Flachowsky, H., Le Roux, P.M., Peil, A., Patocchi, A., Richter, K. & Hanke, M.V. (2011) Application of a high-speed breeding technology to apple (Malus × domestica) based on transgenic early flowering plants and marker-assisted selection. The New Phytologist, 2, 364-377.

Fuentes, A., Yoon, S. & Park, D.S. (2019) Deep learning-based phenotyping system with glocal description of plant anomalies and symptoms. Frontiers in Plant Science, 10, 1321.

Getachew, T. (2019) Pulse crops production opportunities, challenges and its value chain in Ethiopia: a review article. Environment and Earth Science, 9, 20-29.

Gill, T., Gill, S.K., Saini, D.K., Chopra, Y., de Koff, J.P. & Sandhu, K.S. (2022) A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping. Phenomics, 2(3), 156-183.

Goff, S.A., Vaughn, M., McKay, S., Lyons, E., Stapleton, A.E., Gessler, D. et al. (2011) The iPlant collaborative: cyberinfrastructure for plant biology. Frontiers in Plant Science, 2, 34.

Gorjanc, G., Gaynor, R.C. & Hickey, J.M. (2018) Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection. Theoretical and Applied Genetics, 131, 1953-1966.

Goudey, B., Rawlinson, D., Wang, Q., Shi, F., Ferra, H., Campbell, R.M. et al. (2013) GWIS-model-free, fast and exhaustive search for epistatic interactions in case-control GWAS. BMC Genomics, 3, 1-18.

Hammer, G., Cooper, M., Tardieu, F., Welch, S., Walsh, B., van Eeuwijk, F. et al. (2006) Models for navigating biological complexity in breeding improved crop plants. Trends in Plant Science, 11, 587-593.

Hickey, L.T., Germán, S.E., Pereyra, S.A., Diaz, J.E., Ziems, L.A., Fowler, R.A. et al. (2017) Speed breeding for multiple disease resistance in barley. Euphytica, 3, 1-14.

Hill, W.G., Goddard, M.E. & Visscher, P.M. (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genetics, 4, e1000008.

Hina, A., Cao, Y., Song, S., Li, S., Sharmin, R.A., Elattar, M.A. et al. (2020) High-resolution mapping in two RIL populations refines major “QTL Hotspot” regions for seed size and shape in soybean (Glycine max L.). International Journal of Molecular Sciences, 3, 1040.

Hu, X., Xie, W., Wu, C. & Xu, S. (2019) A directed learning strategy integrating multiple omic data improves genomic prediction. Plant Biotechnology Journal, 17, 2011-2020.

Jiang, Z., Tu, H., Bai, B., Yang, C., Zhao, B., Guo, Z. et al. (2021) Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress. The New Phytologist, 1, 440-455.

Jighly, A., Lin, Z., Pembleton, L.W., Cogan, N.O., Spangenberg, G.C., Hayes, B.J. et al. (2019) Boosting genetic gain in allogamous crops via speed breeding and genomic selection. Frontiers in Plant Science, 10, 1364.

Jo, T., Nho, K., Bice, P. & Saykin, A.J. (2022) Deep learning-based identification of genetic variants: application to Alzheimer's disease classification. Briefings in Bioinformatics, 23, 1-11.

Kaler, A.S., Gillman, J.D., Beissinger, T. & Purcell, L.C. (2020) Comparing different statistical models and multiple testing corrections for association mapping in soybean and maize. Frontiers in Plant Science, 10, 1794.

Karikari, B., Chen, S., Xiao, Y., Chang, F., Zhou, Y., Kong, J. et al. (2019) Utilization of interspecific high-density genetic map of RIL population for the QTL detection and candidate gene mining for 100-seed weight in soybean. Frontiers in Plant Science, 10, 1001.

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W. et al. (2017) Lightgbm: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems. p. 30.

Leem, S., Jeong, H.H., Lee, J., Wee, K. & Sohn, K.A. (2014) Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure. Computational Biology and Chemistry, 50, 19-28.

Leminen Madsen, S., Mathiassen, S.K., Dyrmann, M., Laursen, M.S., Paz, L.C. & Jørgensen, R.N. (2020) Open plant phenotype database of common weeds in Denmark. Remote Sensing, 12(8), 1246.

Li, X., Liu, L., Zhou, J. & Wang, C. (2018) Heterogeneity analysis and diagnosis of complex diseases based on deep learning method. Scientific Reports, 1, 1-8.

Lin, K., Gong, L., Huang, Y., Liu, C. & Pan, J. (2019) Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Frontiers in Plant Science, 10, 155.

Lippert, C., Listgarten, J., Davidson, R.I., Baxter, J., Poon, H., Kadie, C.M. et al. (2013) An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data. Scientific Reports, 1, 1-5.

Liu, Y., Wang, D., He, F., Wang, J., Joshi, T. & Xu, D. (2019) Phenotype prediction and genome-wide association study using deep convolutional neural network of soybean. Frontiers in Genetics, 10, 1091.

Ma, W., Qiu, Z., Song, J., Li, J., Cheng, Q., Zhai, J. et al. (2018) A deep convolutional neural network approach for predicting phenotypes from genotypes. Planta, 5, 1307-1318.

Messina, C.D., Technow, F., Tang, T., Totir, R., Gho, C. & Cooper, M. (2018) Leveraging biological insight and environmental variation to improve phenotypic prediction: integrating crop growth models (CGM) with whole genome prediction (WGP). European Journal of Agronomy, 100, 151-162.

Mieth, B., Kloft, M., Rodríguez, J.A., Sonnenburg, S., Vobruba, R., Morcillo-Suárez, C. et al. (2016) Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies. Scientific Reports, 1, 1-14.

Mir, R.R., Reynolds, M., Pinto, F., Khan, M.A. & Bhat, M.A. (2019) High-throughput phenotyping for crop improvement in the genomics era. Plant Science, 282, 60-72.

Misra, T., Arora, A., Marwaha, S., Jha, R.R., Ray, M., Jain, R. et al. (2021) Web-SpikeSegNet: deep learning framework for recognition and counting of spikes from visual images of wheat plants. IEEE Access, 9, 76235-76247.

Montesinos-López, O.A., Martín-Vallejo, J., Crossa, J., Gianola, D., Hernández-Suárez, C.M., Montesinos-López, A. et al. (2019) A benchmarking between deep learning, support vector machine and Bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding. G3: Genes, Genomes, Genetics, 9, 601-618.

Moreira, F.F., Oliveira, H.R., Volenec, J.J., Rainey, K.M. & Brito, L.F. (2020) Integrating high-throughput phenotyping and statistical genomic methods to genetically improve longitudinal traits in crops. Frontiers in Plant Science, 11, 681.

Nabwire, S., Suh, H.K., Kim, M.S., Baek, I. & Cho, B.K. (2021) Application of artificial intelligence in phenomics. Sensors, 13, 4363.

Pérez-Rodríguez, P., Flores-Galarza, S., Vaquera-Huerta, H., del Valle-Paniagua, D.H., Montesinos-López, O.A. & Crossa, J. (2020) Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data. Plant Genome, 2, e20021.

Pook, T., Freudenthal, J., Korte, A. & Simianer, H. (2020) Using local convolutional neural networks for genomic prediction. Frontiers in Genetics, 11, 561497.

Prabhu, S. & Pe'er, I. (2012) Ultrafast genome-wide scan for SNP-SNP interactions in common complex disease. Genome Research, 11, 2230-2240.

Qin, J., Shi, A., Song, Q., Li, S., Wang, F., Cao, Y. et al. (2019) Genome wide association study and genomic selection of amino acid concentrations in soybean seeds. Frontiers in Plant Science, 10, 1445.

Rana, M.M., Takamatsu, T., Baslam, M., Kaneko, K., Itoh, K., Harada, N. et al. (2019) Salt tolerance improvement in rice through efficient SNP marker-assisted selection coupled with speed-breeding. International Journal of Molecular Sciences, 10, 2585.

Ravelombola, W.S., Qin, J., Shi, A., Nice, L., Bao, Y., Lorenz, A. et al. (2020) Genome-wide association study and genomic selection for tolerance of soybean biomass to soybean cyst nematode infestation. PLoS One, 7, e0235089.

Romagnoni, A., Jégou, S., Van Steen, K., Wainrib, G. & Hugot, J.P. (2019) Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data. Scientific Reports, 1, 1-18.

Samineni, S., Sen, M., Sajja, S.B. & Gaur, P.M. (2020) Rapid generation advance (RGA) in chickpea to produce up to seven generations per year and enable speed breeding. Crop Journal, 1, 164-169.

Sandhu, K., Patil, S.S., Pumphrey, M. & Carter, A. (2021) Multitrait machine-and deep-learning models for genomic selection using spectral information in a wheat breeding program. Plant Genome, 14, e20119.

Sandhu, K.S., Aoun, M., Morris, C.F. & Carter, A.H. (2021) Genomic selection for end-use quality and processing traits in soft white winter wheat breeding program with machine and deep learning models. Biology, 10, 689.

Sandhu, K.S., Lozada, D.N., Zhang, Z., Pumphrey, M.O. & Carter, A.H. (2021) Deep learning for predicting complex traits in spring wheat breeding program. Frontiers in Plant Science, 11, 613325.

Sandhu, K.S., Mihalyov, P.D., Lewien, M.J., Pumphrey, M.O. & Carter, A.H. (2021) Combining genomic and phenomic information for predicting grain protein content and grain yield in spring wheat. Frontiers in Plant Science, 12, 613300.

Sandhu, S., Lin, A.L., Brajer, N., Sperling, J., Ratliff, W., Bedoya, A.D. et al. (2020) Integrating a machine learning system into clinical workflows: qualitative study. Journal of Medical Internet Research, 22(11), e22421.

Shakoor, N., Lee, S. & Mockler, T.C. (2017) High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current Opinion in Plant Biology, 38, 184-192.

Shivakumar, M., Nataraj, V., Kumawat, G., Rajesh, V., Chandra, S., Gupta, S. et al. (2018) Speed breeding for Indian Agriculture: a rapid method for development of new crop varieties. Current Science, 7, 1241.

Shook, J.M., Lourenco, D. & Singh, A.K. (2021) PATRIOT: a pipeline for tracing identity-by-descent for chromosome segments to improve genomic prediction in self-pollinating crop species. Frontiers in Plant Science, 12, 2095.

Silva, P.P., Gaudillo, J.D., Vilela, J.A., Roxas-Villanueva, R.M.L., Tiangco, B.J., Domingo, M.R. et al. (2022) A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci. Scientific Reports, 1, 1-10.

Singh, A., Ganapathysubramanian, B., Singh, A.K. & Sarkar, S. (2016) Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21, 110-124.

Song, Y., Duan, X., Wang, P., Li, X., Yuan, X., Wang, Z. et al. (2022) Comprehensive speed breeding: a high-throughput and rapid generation system for long-day crops. Plant Biotechnology Journal, 1, 13-15.

Soualiou, S., Wang, Z., Sun, W., de Reffye, P., Collins, B., Louarn, G. et al. (2021) Functional-structural plant models mission in advancing crop science: opportunities and prospects. Frontiers in Plant Science, 12, 747142.

Spindel, J., Begum, H., Akdemir, D., Virk, P., Collard, B., Redoña, E. et al. (2015) Correction: genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genetics, 6, e1005350.

Sun, H., Depraetere, K., Meesseman, L., Cabanillas Silva, P., Szymanowsky, R., Fliegenschmidt, J. et al. (2022) Machine learning-based prediction models for different clinical risks in different hospitals: evaluation of live performance. Journal of Medical Internet Research, 24(6), e34295.

Sun, T., Wei, Y., Chen, W. & Ding, Y. (2020) Genome-wide association study-based deep learning for survival prediction. Statistics in Medicine, 30, 4605-4620.

Technow, F., Messina, C.D., Totir, L.R. & Cooper, M. (2015) Integrating crop growth models with whole genome prediction through approximate Bayesian computation. PLoS One, 10, e0130855.

Uzal, L.C., Grinblat, G.L., Namías, R., Larese, M.G., Bianchi, J.S., Morandi, E.N. et al. (2018) Seed-per-pod estimation for plant breeding using deep learning. Computers and Electronics in Agriculture, 150, 196-204.

van Bezouw, R.F., Keurentjes, J.J., Harbinson, J. & Aarts, M.G. (2019) Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency. The Plant Journal, 1, 112-133.

van Dijk, A.D.J., Kootstra, G., Kruijer, W. & de Ridder, D. (2021) Machine learning in plant science and plant breeding. Iscience, 24, 101890.

Varshney, R.K., Bohra, A., Yu, J., Graner, A., Zhang, Q. & Sorrells, M.E. (2021) Designing future crops: genomics-assisted breeding comes of age. Trends in Plant Science, 6, 631-649.

Varshney, R.K., Terauchi, R. & McCouch, S.R. (2014) Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLoS Biology, 6, e1001883.

Vieira, C.C., Zhou, J., Usovsky, M., Vuong, T., Howland, A.D., Lee, D. et al. (2022) Exploring machine learning algorithms to unveil genomic regions associated with resistance to southern root-knot nematode in soybeans. Frontiers in Plant Science, 13, 883280.

Vishwakarma, M.K., Yadav, P.S., Rai, V.P., Kumar, U. & Joshi, A.K. (2022) Molecular markers and genomics assisted breeding for improving crop plants. In: Samuel, J., Kumar, A. & Singh, J. (Eds.) Relationship between microbes and the environment for sustainable ecosystem services, Vol. 1. Amsterdam, Netherland: Elsevier Inc, pp. 303-334.

Voss-Fels, K.P., Herzog, E., Dreisigacker, S., Sukumaran, S., Watson, A., Frisch, M. et al. (2019) “SpeedGS” to accelerate genetic gain in spring wheat. In: Miedaner, T. & Korzun, V. (Eds.) Applications of genetic and genomic research in cereals. Amsterdam, Netherland: Elsevier Inc, pp. 303-327.

Walsh, B. & Lynch, M. (2018) Evolution and selection of quantitative traits. Oxford, UK: Oxford Academic. Available from: https://doi.org/10.1093/oso/9780198830870.001.0001

Wang, C., Caragea, D., Kodadinne Narayana, N., Hein, N.T., Bheemanahalli, R., Somayanda, I.M. et al. (2022) Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature. Plant Methods, 1, 1-23.

Wang, H., Yue, T., Yang, J., Wu, W. & Xing, E.P. (2019) Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies. BMC Bioinformatics, 2, 1-11.

Wanga, M.A., Shimelis, H., Mashilo, J. & Laing, M.D. (2021) Opportunities and challenges of speed breeding: a review. Plant Breeding, 2, 185-194.

Ward, B.P., Brown-Guedira, G., Tyagi, P., Kolb, F.L., Van Sanford, D.A., Sneller, C.H. et al. (2019) Multienvironment and multitrait genomic selection models in unbalanced early-generation wheat yield trials. Crop Science, 59, 491-507.

Watson, A., Ghosh, S., Williams, M.J., Cuddy, W.S., Simmonds, J., Rey, M.D. et al. (2018) Speed breeding is a powerful tool to accelerate crop research and breeding. Nature Plants, 1, 23-29.

Watson, A., Hickey, L.T., Christopher, J., Rutkoski, J., Poland, J. & Hayes, B.J. (2019) Multivariate genomic selection and potential of rapid indirect selection with speed breeding in spring wheat. Crop Science, 5, 1945-1959.

Weih, M., Adam, E., Vico, G. & Rubiales, D. (2022) Application of crop growth models to assist breeding for intercropping: opportunities and challenges. Frontiers in Plant Science, 13, 720486.

Westhues, C.C., Mahone, G.S., da Silva, S., Thorwarth, P., Schmidt, M., Richter, J.C. et al. (2021) Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks. Frontiers in Plant Science, 12, 699589.

Wolter, F., Schindele, P. & Puchta, H. (2019) Plant breeding at the speed of light: the power of CRISPR/Cas to generate directed genetic diversity at multiple sites. BMC Plant Biology, 1, 1-8.

Wu, J., Devlin, B., Ringquist, S., Trucco, M. & Roeder, K. (2010) Screen and clean: a tool for identifying interactions in genome-wide association studies. Genetic Epidemiology, 3, 275-285.

Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M.S. et al. (2022) Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Molecular Plant, 15(11), 1664-1695.

Yan, J., Xu, Y., Cheng, Q., Jiang, S., Wang, Q., Xiao, Y. et al. (2021) LightGBM: accelerated genomically designed crop breeding through ensemble learning. Genome Biology, 1, 1-24.

Yao, C., Spurlock, D.M., Armentano, L.E., Page, C.D., Jr., VandeHaar, M.J., Bickhart, D.M. et al. (2013) Random forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle. Journal of Dairy Science, 10, 6716-6729.

Yoosefzadeh-Najafabadi, M., Eskandari, M., Torabi, S., Torkamaneh, D., Tulpan, D. & Rajcan, I. (2022) Machine-learning-based genome-wide association studies for uncovering QTL underlying soybean yield and its components. International Journal of Molecular Sciences, 10, 5538.

Yu, X., Wu, Z., Zheng, H., Li, M. & Tan, T. (2020) How urban agglomeration improve the emission efficiency? A spatial econometric analysis of the Yangtze River Delta urban agglomeration in China. Journal of Environmental Management, 260, 110061.

Zargar, S.M., Raatz, B., Sonah, H., Bhat, J.A., Dar, Z.A., Agrawal, G.K. et al. (2015) Recent advances in molecular marker techniques: insight into QTL mapping, GWAS and genomic selection in plants. Journal of Crop Science and Biotechnology, 5, 293-308.

Zhang, N., Zhou, X., Kang, M., Hu, B.G., Heuvelink, E. & Marcelis, L.F. (2023) Machine learning versus crop growth models: an ally, not a rival. AoB Plants, 15, plac061.

Zhou, W., Bellis, E.S., Stubblefield, J., Causey, J., Qualls, J., Walker, K. et al. (2019) Minor QTLs mining through the combination of GWAS and machine learning feature selection. bioRxiv, p. 712190.

Zingaretti, L.M., Gezan, S.A., Ferrão, L.F.V., Osorio, L.F., Monfort, A., Muñoz, P.R. et al. (2020) Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species. Frontiers in Plant Science, 11, 25.

2021PE0AC04/Zhejiang Lab
/The University of Western Australia

Crops, Agricultural

Genome-Wide Association Study

Artificial Intelligence

Plant Breeding

Genomics

Journal Article Review

OpenLB
Open Library of Bioscience