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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 52:421−44

Jonas E, de Koning DJ. 2013. Does genomic selection have a future in plant breeding? Trends in Biotechnology 31:497−504

Lande R, Thompson R. 1990. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743−56

Ribaut JM, Hoisington D. 1998. Marker-assisted selection: new tools and strategies. Trends in Plant Science 3:236−39

Meuwissen THE, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819−29

Ertiro BT, Zeleke H, Friesen D, Blummel M, Twumasi-Afriyie S. 2013. Relationship between the performance of parental inbred lines and hybrids for food-feed traits in maize (Zea mays L.) in Ethiopia. Field Crops Research 153:86−93

Meuwissen THE, Goddard ME. 1996. The use of marker haplotypes in animal breeding schemes. Genetics Selection Evolution 28:161

Whittaker JC, Thompson R, Denham MC. 2000. Marker-assisted selection using ridge regression. Genetics Research 75:249−52

Heffner EL, Sorrells ME, Jannink JL. 2009. Genomic selection for crop improvement. Crop Science 49:1−12

Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK. 2007. Molecular markers in a commercial breeding program. Crop Science 47:S-154−S-163

Li J, Cheng D, Guo S, Chen C, Wang Y, et al. 2023. Genome-wide association and genomic prediction for resistance to southern corn rust in DH and testcross populations. Frontiers in Plant Science 14:1109116

Hayes BJ, Daetwyler HD, Bowman P, Moser G, Tier B, et al. 2009. Accuracy of genomic selection: comparing theory and results. Association for the Advancement of Animal Breeding and Genetics 18:34−37

VanRaden PM. 2007. Genomic measures of relationship and inbreeding. INTERBULL Bulletin 37:33−36

VanRaden PM. 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91:4414−23

Astle W, Balding DJ. 2009. Population structure and cryptic relatedness in genetic association studies. Statistical Science 24:451−71

Aguilar I, Misztal I, Legarra A, Tsuruta S. 2011. Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. Journal of Animal breeding and Genetics 128:422−28

Legarra A, Christensen OF, Aguilar I, Misztal I. 2014. Single Step, a general approach for genomic selection. Livestock Science 166:54−65

Zhang Z, Liu J, Ding X, Bijma P, de Koning DJ, Zhang Q. 2010. Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS One 5:e12648

Wang J, Zhou Z, Zhang Z, Li H, Liu D, et al. 2018. Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity 121:648−62

Wang Q, Tian F, Pan Y, Buckler ES, Zhang Z. 2014. A SUPER powerful method for genome wide association study. PLoS One 9:e107684

Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, et al. 2010. Mixed linear model approach adapted for genome-wide association studies. Nature Genetics 42:355−60

Endelman JB. 2011. Ridge regression and other kernels for genomic selection with R package rrBLUP. The Plant Genome 4:250−55

Lorenzana RE, Bernardo R. 2009. Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theoretical and Applied Genetics 120:151−61

De Los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MP. 2013. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327−45

Pérez P, de los Campos G. 2014. Genome-wide regression and prediction with the BGLR statistical package. Genetics 198:483−95

Park T, Casella G. 2008. The Bayesian Lasso. Journal of the American Statistical Association 103:681−86

De Los Campos G, Naya H, Gianola D, Crossa J, Legarra A, et al. 2009. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375−85

Mutshinda CM, Sillanpää MJ. 2010. Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction. Genetics 186:1067−75

Legarra A, Robert-Granié C, Croiseau P, Guillaume F, Fritz S. 2011. Improved Lasso for genomic selection. Genetics Research 93:77−87

Habier D, Fernando RL, Kizilkaya K, Garrick DJ. 2011. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12:186

Pong-Wong R, Woolliams JA. Bayes U: a genomic prediction method based on the horseshoe prior. Proc. 10 ^th World Congress of Genetics Applied to Livestock Production, Vancouver, BC. Canada, 2014. 3 pp

Shi S, Li X, Fang L, Liu A, Su G, et al. 2021. Genomic prediction using Bayesian regression models with global–local prior. Frontiers in Genetics 12:628205

Wang T, Chen YPP, Bowman PJ, Goddard ME, Hayes BJ. 2016. A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping. BMC Genomics 17:744

Cheng H, Qu L, Garrick DJ, Fernando RL. 2015. A fast and efficient Gibbs sampler for BayesB in whole-genome analyses. Genetics Selection Evolution 47:80

Azevedo CF, de Resende MDV, Fonseca e Silva F, Viana JMS, Valente MSF, et al. 2015. Ridge, Lasso and Bayesian additive-dominance genomic models. BMC Genetics 16:105

Vieira IC, Dos Santos JPR, Pires LPM, Lima BM, Gonçalves FMA, et al. 2017. Assessing non-additive effects in GBLUP model. Genetics and Molecular Research 16:gmr16029632

Piepho HP, Möhring J, Melchinger AE, Büchse A. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209−28

Zuk O, Hechter E, Sunyaev SR, Lander ES. 2012. The mystery of missing heritability: genetic interactions create phantom heritability. Proceedings of the National Academy of Sciences of the United States of America 109:1193−98

Ma C, Xin M, Feldmann KA, Wang X. 2014. Machine learning–based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis. The Plant Cell 26:520−37

Abdollahi-Arpanahi R, Gianola D, Peñagaricano F. 2020. Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes. Genetics Selection Evolution 52:12

Gianola D, Fernando RL, Stella A. 2006. Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173:1761−76

Gianola D, Van Kaam JBCHM. 2008. Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289−303

De Los Campos G, Gianola D, Rosa GJM. 2009. Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation. Journal of Animal Science 87:1883−87

De los Campos G, Gianola D, Rosa GJM, Weigel KA, Crossa J. 2010. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genetics Research 92:295−308

Long N, Gianola D, Rosa GJM, Weigel KA, Kranis A, González-Recio O. 2010. Radial basis function regression methods for predicting quantitative traits using SNP markers. Genetics Research 92:209−25

Cortes C, Vapnik V. 1995. Support-vector networks. Machine Learning 20:273−97

Chang CC, Lin CJ. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2:27

Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. 1998. Support vector machines. IEE Intelligent Systems and their Applications 13:18−28

Zhao W, Lai X, Liu D, Zhang Z, Ma P, et al. 2020. Applications of support vector machine in genomic prediction in pig and maize populations. Frontiers in Genetics 11:598318

Targhi MVA, Jafarabadi GA, Aminafshar M, Kashan NEJ. 2019. Comparison of non-parametric methods in genomic evaluation of discrete traits. Gene Reports 15:100379

Breiman L. 2001. Random forests. Machine Learning 45:5−32

Breiman L, Friedman JH, Olshen RA, Stone CJ. 1984. Classification and regression trees. New York: Chapman and Hall/CRC. 368 pp. doi: 10.1201/9781315139470

Naderi S, Yin T, König S. 2016. Random forest estimation of genomic breeding values for disease susceptibility over different disease incidences and genomic architectures in simulated cow calibration groups. Journal of Dairy Science 99:7261−73

Sarkar RK, Rao AR, Meher PK, Nepolean T, Mohapatra T. 2015. Evaluation of random forest regression for prediction of breeding value from genomewide SNPs. Journal of Genetics 94:187−92

Waldmann P. 2016. Genome-wide prediction using Bayesian additive regression trees. Genetics Selection Evolution 48:42

Friedman JH. 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics 29:1189−232

Ke G, Meng Q, Finley T, Wang T, Chen W, et al. 2017. LightGBM: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30:3149−57

Chen T, Guestrin C. 2016. XGBoost: a scalable tree boosting system. In Proceedings of the 22 ^nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, 2016, San Francisco, California, USA: Association for Computing Machinery. pp. 785–94. doi: 10.1145/2939672.293978

Dorogush AV, Ershov V, Gulin A. 2018. CatBoost: gradient boosting with categorical features support. arXiv 00:1810.11363

Yan J, Xu Y, Cheng Q, Jiang S, Wang Q, et al. 2021. LightGBM: accelerated genomically designed crop breeding through ensemble learning. Genome Biology 22:271

Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, et al. 2019. A primer on deep learning in genomics. Nature Genetics 51:12−18

Bellot P, De Los Campos G, Pérez-Enciso M. 2018. Can deep learning improve genomic prediction of complex human traits? Genetics 210:809−19

Montesinos-López A, Montesinos-López OA, Gianola D, Crossa J, Hernández-Suárez CM. 2018. Multi-environment genomic prediction of plant traits using deep learners with dense architecture. G3 8:3813−28

Montesinos-López OA, Martín-Vallejo J, Crossa J, Gianola D, Hernández-Suárez CM, 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 9:601−18

Pérez-Enciso M, Zingaretti LM. 2019. A guide on deep learning for complex trait genomic prediction. Genes 10:553

Fukushima K. 1980. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36:193−202

Lecun Y, Bottou L, Bengio Y, Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86:2278−324

Ma W, Qiu Z, Song J, Li J, Cheng Q, et al. 2018. A deep convolutional neural network approach for predicting phenotypes from genotypes. Planta 248:1307−18

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

Wang K, Abid MA, Rasheed A, Crossa J, Hearne S, et al. 2023. DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants. Molecular Plant 16:279−93

Wang N, Wang H, Zhang A, Liu Y, Yu D, et al. 2020. Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing. Theoretical and Applied Genetics 133:2869−79

Juliana P, Singh RP, Braun HJ, Huerta-Espino J, Crespo-Herrera L, et al. 2020. Genomic selection for grain yield in the CIMMYT wheat breeding program—status and perspectives. Frontiers in Plant Science 11:564183

Tessema BB, Liu H, Sørensen AC, Andersen JR, Jensen J. 2020. Strategies using genomic selection to increase genetic gain in breeding programs for wheat. Frontiers in Genetics 11:578123

Chung PY, Liao CT. 2020. Identification of superior parental lines for biparental crossing via genomic prediction. PLoS One 15:e0243159

Chung PY, Liao CT. 2022. Selection of parental lines for plant breeding via genomic prediction. Frontiers in Plant Science 13:934767

Sun X, Qu L, Garrick DJ, Dekkers JCM, Fernando RL. 2012. A fast EM algorithm for BayesA-like prediction of genomic breeding values. PLoS One 7:e49157

Jiang S, Cheng Q, Yan J, Fu R, Wang X. 2020. Genome optimization for improvement of maize breeding. Theoretical and Applied Genetics 133:1491−502

Hayes BJ, Visscher PM, Goddard ME. 2009. Increased accuracy of artificial selection by using the realized relationship matrix. Genetics Research 91:47−60

Joshi R, Skaarud A, Alvarez AT, Moen T, Ødegård J. 2021. Bayesian genomic models boost prediction accuracy for survival to Streptococcus agalactiae infection in Nile tilapia (Oreochromus nilioticus). Genetics Selection Evolution 53:37

Meher PK, Rustgi S, Kumar A. 2022. Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results. Heredity 128:519−30

Jarquín D, Kocak K, Posadas L, Hyma K, Jedlicka J, et al. 2014. Genotyping by sequencing for genomic prediction in a soybean breeding population. BMC Genomics 15:740

Jia Z. 2017. Controlling the overfitting of heritability in genomic selection through cross validation. Scientific Reports 7:13678

Jubair S, Tucker JR, Henderson N, Hiebert CW, Badea A, et al. 2021. GPTransformer: a transformer-based deep learning method for predicting Fusarium related traits in barley. Frontiers in Plant Science 12:761402

Zhang H, Wang X, Pan Q, Li P, Liu Y, et al. 2019. QTG-Seq accelerates QTL fine mapping through QTL partitioning and whole-genome sequencing of bulked segregant samples. Molecular Plant 12:426−37

Crossa J, Fritsche-Neto R, Montesinos-Lopez OA, Costa-Neto G, Dreisigacker S, et al. 2021. The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data. Frontiers in Genetics 12:651480

Weyen J. 2021. Applications of doubled haploids in plant breeding and applied research. In Doubled Haploid Technology, ed. Segui-Simarro JM. New York, NY: Humana. Volume 2287. pp. 23–39. doi: 10.1007/978-1-0716-1315-3_2

Wang N, Yuan Y, Wang H, Yu D, Liu Y, et al. 2020. Applications of genotyping-by-sequencing (GBS) in maize genetics and breeding. Scientific Reports 10:16308

Rich-Griffin C, Stechemesser A, Finch J, Lucas E, Ott S, et al. 2020. Single-cell transcriptomics: a high-resolution avenue for plant functional genomics. Trends in Plant Science 25:186−97

Liu Y, Lu S, Liu K, Wang S, Huang L, et al. 2019. Proteomics: a powerful tool to study plant responses to biotic stress. Plant Methods 15:135

Jamil IN, Remali J, Azizan KA, Nor Muhammad NA, Arita M, et al. 2020. Systematic Multi-Omics Integration (MOI) approach in plant systems biology. Frontiers in Plant Science 11:944

Khaki S, Khalilzadeh Z, Wang L. 2020. Predicting yield performance of parents in plant breeding: a neural collaborative filtering approach. PLoS One 15:e0233382

Harfouche AL, Jacobson DA, Kainer D, Romero JC, Harfouche AH, et al. 2019. Accelerating climate resilient plant breeding by applying next-generation artificial intelligence. Trends in Biotechnology 37:1217−35

Liang M, An B, Chang T, Deng T, Du L, et al. 2022. Incorporating kernelized multi-omics data improves the accuracy of genomic prediction. Journal of Animal Science and Biotechnology 13:103

Devlin J, Chang MW, Lee K, Toutanova K. 2018. Bert: pre-training of deep bidirectional transformers for language understanding. arXiv 00:1810.04805

Ma X, Wang H, Wu S, Han B, Cui D, et al. 2024. DeepCCR: large-scale genomics-based deep learning method for improving rice breeding. Plant Biotechnology Journal 22:2691−93

Gao P, Zhao H, Luo Z, Lin Y, Feng W, et al. 2023. SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding. Briefings in Bioinformatics 24:bbad349

Wu C, Zhang Y, Ying Z, Li L, Wang J, et al. 2023. A transformer-based genomic prediction method fused with knowledge-guided module. Briefings in Bioinformatics 25:bbad438

Ren Y, Wu C, Zhou H, Hu X, Miao Z. 2024. Dual-extraction modeling: a multi-modal deep-learning architecture for phenotypic prediction and functional gene mining of complex traits. Plant Communications 5:101002

Yan J, Wang X. 2023. Machine learning bridges omics sciences and plant breeding. Trends in Plant Science 28:199−210

Khaki S, Wang L. 2019. Crop yield prediction using deep neural networks. Frontiers in Plant Science 10:621