[1]

Zhang C, Jiang S, Tian Y, Dong X, Xiao J, et al. 2023. Smart breeding driven by advances in sequencing technology. Modern Agriculture 1(1):43−56

doi: 10.1002/moda.8
[2]

Wallace JG, Rodgers-Melnick E, Buckler ES. 2018. On the Road to Breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annual Review of Genetics 52:421−444

doi: 10.1146/annurev-genet-120116-024846
[3]

Heffner EL, Sorrells ME, Jannink JL. 2009. Genomic Selection for Crop Improvement. Crop Science 49:1−12

doi: 10.2135/cropsci2008.08.0512
[4]

Jannink JL, Lorenz AJ, Iwata H. 2010. Genomic selection in plant breeding: from theory to practice. Briefings in Functional Genomics 9(2):166−177

doi: 10.1093/bfgp/elq001
[5]

Desta ZA, Ortiz R. 2014. Genomic selection: genome-wide prediction in plant improvement. Trends in Plant Science 19(9):592−601

doi: 10.1016/j.tplants.2014.05.006
[6]

Hickey LT, N Hafeez A, Robinson H, Jackson SA, Leal-Bertioli SCM, et al. 2019. Breeding crops to feed 10 billion. Nature biotechnology 37(7):744−754

doi: 10.1038/s41587-019-0152-9
[7]

Lou RN, Jacobs A, Wilder AP, Therkildsen NO. 2021. A beginner's guide to low-coverage whole genome sequencing for population genomics. Molecular Ecology 30(23):5966−5993

doi: 10.1111/mec.16077
[8]

Das S, Abecasis GR, Browning BL. 2018. Genotype Imputation from Large Reference Panels. Annual Review of Genomics and Human Genetics 19:73−96

doi: 10.1146/annurev-genom-083117-021602
[9]

Davies RW, Kucka M, Su D, Shi S, Flanagan M, et al. 2021. Rapid genotype imputation from sequence with reference panels. Nature Genetics 53(7):1104−1111

doi: 10.1038/s41588-021-00877-0
[10]

Zou M, Xia Z. 2022. Hyper-seq: A novel, effective and flexible marker-assisted selection and genotyping approach. The Innovation 3(4):100254

doi: 10.1016/j.xinn.2022.100254
[11]

Xiao N, Pan C, Li Y, Wu, Y, Cai, Y, et al. 2021. Genomic insight into balancing high yield, good quality, and blast resistance of japonica rice. Genome Biology 22(1):283

doi: 10.1186/s13059-021-02488-8
[12]

Cui Y, Li R, Li G, Zhang F, Zhu T, et al. 2020. Hybrid breeding of rice via genomic selection. Plant Biotechnology Journal 18(1):57−67

doi: 10.1111/pbi.13170
[13]

Xu S, Xu Y, Gong L, Zhang Q. 2016. Metabolomic prediction of yield in hybrid rice. The Plant Journal 88(2):219−227

doi: 10.1111/tpj.13242
[14]

Chen W, Gao Y, Xie W, Gong L, Lu K, et al. 2014. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nature Genetics 46(7):714−721

doi: 10.1038/ng.3007
[15]

Wang W, Yu Z, Meng J, Zhou P, Luo T, et al. 2020. Rice phenolamindes reduce the survival of female adults of the white-backed planthopper Sogatella furcifera. Scientific Reports 10(1):5778

doi: 10.1038/s41598-020-62752-y
[16]

Xia J, Yamaji N, Ma JF. 2014. An appropriate concentration of arginine is required for normal root growth in rice. Plant Signaling & Behavior 9(4):e28717

doi: 10.4161/psb.28717
[17]

Liu F, Fang S, Wang Q, Wang H, Niu J, et al. 2024. Effects of different concentrations of exogenous amino acids on the growth and related physiological indexes of rice seedlings. Crop Magazine 2024(2):71−79

doi: 10.16035/j.issn.1001-7283.2024.02.009
[18]

Meuwissen TH, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819−1829

doi: 10.1093/genetics/157.4.1819
[19]

Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, et al. 2014. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theoretical and Applied Genetics 127:595−607

doi: 10.1007/s00122-013-2243-1
[20]

Costa-Neto G, Fritsche-Neto R, Crossa J. 2021. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity 126:92−106

doi: 10.1038/s41437-020-00353-1
[21]

Pérez-Martín JE, Bonte D, Osorio S, Posé D. 2026. Metabolic plasticity and adaptive evolution in Fragaria vesca: bridging wild diversity to crop improvement. Frontiers in Plant Science 16:1729002

doi: 10.3389/fpls.2025.1729002
[22]

Yun J, Burnett AC, Rogers A, Des Marais DL. 2025. Genotype by environment interactions in gene regulation underlie the response to soil drying in the model grass Brachypodium distachyon. Molecular Biology and Evolution 42(10):msaf218

doi: 10.1093/molbev/msaf218
[23]

Xu Y, Yang W, Qiu J, Zhou K, Yu G, et al. 2024. Metabolic marker-assisted genomic prediction improves hybrid breeding. Plant Communications 6(1):101199

doi: 10.1016/j.xplc.101199
[24]

Xu S, Zhu D, Zhang Q. 2014. Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proceedings of the National Academy of Sciences of the United States of America 111(34):12456−12461

doi: 10.1073/pnas.1413750111
[25]

Zhang X, Pérez-Rodríguez P, Burgueño J, Olsen M, Buckler E, et al. 2017. Rapid Cycling Genomic Selection in a Multiparental Tropical Maize Population. G3 7(7):2315−2326

doi: 10.1534/g3.117.043141
[26]

Rasheed A, Hao Y, Xia X, Khan A, Xu Y, et al. 2017. Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Molecular Plant 10(8):1047−1064

doi: 10.1016/j.molp.2017.06.008
[27]

Guo Z, Yang Q, Huang F, Zheng H, Sang Z, et al. 2021. Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip. Plant Communications 2(6):100230

doi: 10.1016/j.xplc.2021.100230
[28]

DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, et al. 2011. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics 43(5):491−498

doi: 10.1038/ng.806
[29]

Alex Buerkle C, Gompert Z. 2013. Population genomics based on low coverage sequencing: how low should we go? Molecular Ecology 22(11):3028−3035

doi: 10.1111/mec.12105
[30]

Wang Q, He M, Zhou Y, Xu R, Liang T, et al. 2025. Hyper-seq technology and genome-wide selection breeding of soybeans. Agronomy 15(2):264

doi: 10.3390/agronomy15020264
[31]

Lu Y, Xia C, Wang Z, Liu Q, Zhu M, et al. 2025. Assessment of the prediction accuracy of genomic selection for rice amylose content and gel consistency. Agronomy 15(2):336

doi: 10.3390/agronomy15020336
[32]

Wang Z, Xia C, Lu Y, Liu Q, Zou M, et al. 2024. Optimizing genomic selection methods to improve prediction accuracy of sugarcane single-stalk weight. Agronomy 14(12):2842

doi: 10.3390/agronomy14122842
[33]

Kim K, Nawade B, Nam J, Chu S, Ha J, et al. 2022. Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice. Frontiers in Plant Science 13:1036177

doi: 10.3389/fpls.2022.1036177
[34]

Tanaka R, Lui-King J, Mandaharisoa ST, Rakotondramanana M, Ranaivo HN, et al. 2024. Correction: From gene banks to farmer's fields: using genomic selection to identify donors for a breeding program in rice to close the yield gap on smallholder farms. Theoretical and Applied Genetics 137(6):124

doi: 10.1007/s00122-024-04622-z
[35]

Mahantesh, Ganesamurthy K, Das S, Saraswathi R, Gopalakrishnan C, et al. 2022. Analysis of the efficiency of genomic selection models for predicting sheath blight resistance in rice (Oryza sativa L.). International Journal of Bio-resource and Stress Management 13(3):268−275

doi: 10.23910/1.2022.2763
[36]

Huang M, Balimponya EG, Mgonja EM, McHale LK, Luzi-Kihupi A, et al. 2019. Use of genomic selection in breeding rice (Oryza sativa L.) for resistance to rice blast (Magnaporthe oryzae). Molecular Breeding 39:114

doi: 10.1007/s11032-019-1023-2
[37]

Yang C, Shen S, Zhou S, Li Y, Mao Y, et al. 2022. Rice metabolic regulatory network spanning the entire life cycle. Molecular Plant 15(2):258−275

doi: 10.1016/j.molp.2021.10.005