{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T01:51:51Z","timestamp":1791597111794,"version":"4.3.4"},"reference-count":34,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>AI-powered gene-based breeding (AI-GBB) has the potential to be a next-generation breeding technology. However, it is unclear whether agronomic genes identified with one population can be reliably used for AI-GBB across diverse populations, environments, and eco-agriculture systems. We evaluated 474 cotton fiber length (GFL) genes using two diverse populations grown across 11 environments in China and the United States. Four hundred and seventy-five functional SNPs significantly impacting fiber length were identified from 143 of the genes and tested for AI-GBB. Either the 475 functional SNPs or their 143 SNPs with one SNP per gene predicted fiber lengths across environments using either the total number of their favorable alleles or themselves as DNA markers at accuracies of 0.75\u20130.89, outperforming genomic selection by several times. The prediction model trained in one environment accurately and consistently predicted fiber length in all other environments. We found that the GFL genes enriched in proteolysis, protein binding, and UDP-sugar pyro-phosphorylases play important roles in their predictability. Our results demonstrate that agronomic genes identified from one population can be robust predictors of quantitative traits across diverse populations and environments for AI-GBB. The functional SNPs provide the most desirable tools for enhanced cotton breeding with all molecular breeding methods developed to date, and the utility knowledge of the genes is necessary for the development and deployment of AI-GBB in all crops.<\/jats:p>","DOI":"10.3390\/ijms27198908","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T01:40:17Z","timestamp":1791596417000},"page":"8908","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Genes Accurately Predicting Fiber Length for AI-Powered Gene-Based Breeding Across Populations and Environments in Cotton"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4003-4340","authenticated-orcid":false,"given":"Yuan","family":"Ye","sequence":"first","affiliation":[{"name":"Department of Soil and Crop Sciences, Texas A&M AgriLife Research and Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun-Hua","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Soil and Crop Sciences, Texas A&M AgriLife Research and Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. Wayne","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Soil and Crop Sciences, Texas A&M AgriLife Research and Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sing-Hoi","family":"Sze","sequence":"additional","affiliation":[{"name":"Departments of Computer Science and Engineering and Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3322-6263","authenticated-orcid":false,"given":"Hong-Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Soil and Crop Sciences, Texas A&M AgriLife Research and Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hossain, M.A., Alam, M., Seneweera, S., Rakshit, S., and Henry, R. (2021). Molecular Breeding for Improving Yield in Maize: Recent Advances and Future Perspectives. Molecular Breeding in Wheat, Maize and Sorghum: Strategies for Improving Abiotic Stress Tolerance and Yield, CABI.","DOI":"10.1079\/9781789245431.0000"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e005","DOI":"10.48130\/tp-0024-0005","article-title":"Gene-Based Breeding (GBB), a Novel Discipline of Biological Science and Technology for Plant and Animal Breeding","volume":"3","author":"Zhang","year":"2024","journal-title":"Trop. Plants"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.ygeno.2019.02.001","article-title":"Accurate Prediction of Maize Grain Yield Using Its Contributing Genes for Gene-Based Breeding","volume":"112","author":"Zhang","year":"2020","journal-title":"Genomics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111424","DOI":"10.1016\/j.plantsci.2022.111424","article-title":"Genome-Wide Identification of Genes Enabling Accurate Prediction of Hybrid Performance from Parents across Environments and Populations for Gene-Based Breeding in Maize","volume":"324","author":"Zhang","year":"2022","journal-title":"Plant Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Y.-H., Xu, Y., Zhang, M., Cui, Y., Sze, S.-H., Smith, C.W., Xu, S., and Zhang, H.-B. (2020). Accurate Prediction of a Quantitative Trait Using the Genes Controlling the Trait for Gene-Based Breeding in Cotton. Front. Plant Sci., 11.","DOI":"10.3389\/fpls.2020.583277"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111153","DOI":"10.1016\/j.plantsci.2021.111153","article-title":"Accurate Prediction of Complex Traits for Individuals and Offspring from Parents Using a Simple, Rapid, and Efficient Method for Gene-Based Breeding in Cotton and Maize","volume":"316","author":"Liu","year":"2022","journal-title":"Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111318","DOI":"10.1016\/j.plantsci.2022.111318","article-title":"Analysis of the Genes Controlling Cotton Fiber Length Reveals the Molecular Basis of Plant Breeding and the Genetic Potential of Current Cultivars for Continued Improvement","volume":"321","author":"Liu","year":"2022","journal-title":"Plant Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1534\/g3.118.200140","article-title":"Historical Datasets Support Genomic Selection Models for the Prediction of Cotton Fiber Quality Phenotypes Across Multiple Environments","volume":"8","author":"Gapare","year":"2018","journal-title":"G3 Genes Genomes Genet."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s00438-019-01599-z","article-title":"Evaluation of Genomic Selection Methods for Predicting Fiber Quality Traits in Upland Cotton","volume":"295","author":"Islam","year":"2020","journal-title":"Mol. Genet. Genom."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Billings, G.T., Jones, M.A., Rustgi, S., Bridges, W.C., Holland, J.B., Hulse-Kemp, A.M., and Campbell, B.T. (2022). Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs. Plants, 11.","DOI":"10.3390\/plants11111446"},{"key":"ref_11","unstructured":"Food and Agriculture Organization of the United Nations (2022). FAO FAOSTAT, Food and Agriculture Organization of the United Nations."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111576","DOI":"10.1016\/j.plantsci.2022.111576","article-title":"Tissue Specific Expression of Bacterial Cellulose Synthase (Bcs) Genes Improves Cotton Fiber Length and Strength","volume":"328","author":"Akhtar","year":"2023","journal-title":"Plant Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiao, J., Chang, S., Wang, F., Yang, J., Ismayil, A., Wu, P., Wang, L., and Li, H. (2025). Genes Affecting Cotton Fiber Length: A Systematic Review and Meta-Analysis. Plants, 14.","DOI":"10.3390\/plants14081203"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1038\/ng.3887","article-title":"Genomic Analyses in Cotton Identify Signatures of Selection and Loci Associated with Fiber Quality and Yield Traits","volume":"49","author":"Fang","year":"2017","journal-title":"Nat. Genet."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"D672","DOI":"10.1093\/nar\/gkae909","article-title":"KEGG: Biological Systems Database as a Model of the Real World","volume":"53","author":"Kanehisa","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/s41437-022-00537-x","article-title":"Genomic Prediction of Cotton Fibre Quality and Yield Traits Using Bayesian Regression Methods","volume":"129","author":"Li","year":"2022","journal-title":"Heredity"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4734","DOI":"10.1038\/s41467-025-59983-w","article-title":"Reveal Genomic Insights into Cotton Domestication and Improvement Using Gene Level Functional Haplotype-Based GWAS","volume":"16","author":"Qi","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10074","DOI":"10.1038\/s41598-020-66271-8","article-title":"Analysis of the Genes Controlling Three Quantitative Traits in Three Diverse Plant Species Reveals the Molecular Basis of Quantitative Traits","volume":"10","author":"Zhang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1093\/bioinformatics\/btp352","article-title":"1000 Genome Project Data Processing Subgroup the Sequence Alignment\/Map Format and SAMtools","volume":"25","author":"Li","year":"2009","journal-title":"Bioinformatics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2987","DOI":"10.1093\/bioinformatics\/btr509","article-title":"A Statistical Framework for SNP Calling, Mutation Discovery, Association Mapping and Population Genetical Parameter Estimation from Sequencing Data","volume":"27","author":"Li","year":"2011","journal-title":"Bioinformatics"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1016\/j.ygeno.2018.10.009","article-title":"Quantification of Gene Expression While Taking into Account RNA Alternative Splicing","volume":"111","author":"Zhang","year":"2019","journal-title":"Genomics"},{"key":"ref_22","unstructured":"Graur, D., and Li, W.-H. (2000). Fundamentals of Molecular Evolution, Sinauer Associates. [2nd ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1297-9686-43-7","article-title":"Genome-Wide Prediction of Discrete Traits Using Bayesian Regressions and Machine Learning","volume":"43","author":"Forni","year":"2011","journal-title":"Genet. Sel. Evol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13639","DOI":"10.1038\/s41598-021-93120-z","article-title":"Bayesian Ridge Regression Shows the Best Fit for SSR Markers in Psidium guajava among Bayesian Models","volume":"11","author":"Viana","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s00122-007-0627-9","article-title":"Support Vector Machine Regression for the Prediction of Maize Hybrid Performance","volume":"115","author":"Maenhout","year":"2007","journal-title":"Theor. Appl. Genet."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ogutu, J.O., Schulz-Streeck, T., and Piepho, H.-P. (2012). Genomic Selection Using Regularized Linear Regression Models: Ridge Regression, Lasso, Elastic Net and Their Extensions. BMC Proc., 6.","DOI":"10.1186\/1753-6561-6-S2-S10"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial Least-Squares Regression: A Tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection Via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"250","DOI":"10.3835\/plantgenome2011.08.0024","article-title":"Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP","volume":"4","author":"Endelman","year":"2011","journal-title":"Plant Genome"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1038\/nbt.1883","article-title":"Full-Length Transcriptome Assembly from RNA-Seq Data without a Reference Genome","volume":"29","author":"Grabherr","year":"2011","journal-title":"Nat. Biotechnol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1038\/nprot.2013.084","article-title":"De Novo Transcript Sequence Reconstruction from RNA-Seq Using the Trinity Platform for Reference Generation and Analysis","volume":"8","author":"Haas","year":"2013","journal-title":"Nat. Protoc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, B., and Dewey, C.N. (2011). RSEM: Accurate Transcript Quantification from RNA-Seq Data with or without a Reference Genome. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-323"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"iyaf243","DOI":"10.1093\/genetics\/iyaf243","article-title":"CottonGen 2025: A Knowledgebase for Cotton Genomics, Genetics, and Breeding Research","volume":"232","author":"Yu","year":"2026","journal-title":"Genetics"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8908\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T01:45:08Z","timestamp":1791596708000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8908"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,7]]},"references-count":34,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["ijms27198908"],"URL":"https:\/\/doi.org\/10.3390\/ijms27198908","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,7]]}}}