[1] |
Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, et al. 2010. Food security: the challenge of feeding 9 billion people. Science 327:812−18 doi: 10.1126/science.1185383 |
[2] |
Adger N, Aggarwal P, Agrawala S, Alcamo J, Allali A, et al. 2007. Technical Summary. In Climate Change 2007 Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York. pp. 25−77. |
[3] |
Zhang MP, Liu YH, Zhang HB. 2021. Molecular breeding for improving yield in maize: recent advances and future perspectives. In Molecular Breeding in Wheat, Maize and Sorghum: Strategies for Improving Abiotic Stress Tolerance and Yield, eds. Hossain MA, Alam M, Seneweera S, Rakshit S, Henry R. Wallingford: CAB International. pp. 380−404. https://doi.org/10.1079/9781789245431.0022 |
[4] |
Collard BCY, Mackill DJ. 2008. Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences 363:557−72 doi: 10.1098/rstb.2007.2170 |
[5] |
Datta A. 2013. Genetic engineering for improving quality and productivity of crops. Agriculture & Food Security 2:15 doi: 10.1186/2048-7010-2-15 |
[6] |
Yogindran S, Rajam MV. 2015. RNAi for crop improvement. In Plant Biology and Biotechnology, eds. Bahadur B, Venkat M, Rajam M, Sahijram L, Krishnamurth K. New Delhi: Springer. pp. 623–37. https://doi.org/10.1007/978-81-322-2283-5_31 |
[7] |
Meuwissen THE, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819−29 doi: 10.1093/genetics/157.4.1819 |
[8] |
Desta ZA, Ortiz R. 2014. Genomic selection: genome-wide prediction in plant improvement. Trends in Plant Science 19:592−601 doi: 10.1016/j.tplants.2014.05.006 |
[9] |
Gao C. 2021. Genome engineering for crop improvement and future agriculture. Cell 184:1621−35 doi: 10.1016/j.cell.2021.01.005 |
[10] |
Liu YH, Zhang MP, Zhang Y, Smith CW, Hague S, et al. 2014. Large-scale cloning and characterization of genes controlling fiber length for deciphering of the molecular basis of fiber quality and development of a gene-based breeding system in cotton. International Plant & Animal Genome Conference XXII, San Diego, California, January 11−15, 2014. Presentation no. 474. Scherago International. |
[11] |
Zhang MP, Zhi H, Chang F, Zhang Y, Liu YH, et al. 2014. Large-scale cloning and characterization of genes controlling grain yield for deciphering of the molecular basis of grain yield and development of a gene-based breeding system in maize. International Plant & Animal Genome Conference XXII, San Diego, California, January 11−15, 2014. Presentation no. 875. Scherago International. |
[12] |
Liu YH, Xu Y, Zhang MP, Cui Y, Sze SH, et al. 2020. Accurate prediction of a quantitative trait using the genes controlling the trait for gene-based breeding in cotton. Frontiers in Plant Science 11:583277 doi: 10.3389/fpls.2020.583277 |
[13] |
Zhang M, Cui Y, Liu YH, Xu W, Sze SH, et al. 2020. Accurate prediction of maize grain yield using its contributing genes for gene-based breeding. Genomics 112:225−36 doi: 10.1016/j.ygeno.2019.02.001 |
[14] |
Zhang M, Liu YH, Wang Y, Sze SH, Scheuring CF, et al. 2022. Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize. Plant Science 324:111424 doi: 10.1016/j.plantsci.2022.111424 |
[15] |
Liu YH, Zhang M, Scheuring CF, Cilkiz M, Sze SH, et al. 2022. 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. Plant Science 316:111153 doi: 10.1016/j.plantsci.2021.111153 |
[16] |
Liu YH, Zhang M, Sze SH, Smith CW, Zhang HB. 2022. 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. Plant Science 321:111318 doi: 10.1016/j.plantsci.2022.111318 |
[17] |
Saint Pierre C, Burgueño J, Crossa J, Fuentes Dávila G, Figueroa López P, et al. 2016. Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones. Scientific Reports 6:27312 doi: 10.1038/srep27312 |
[18] |
Weissbrod O, Geiger D, Rosset S. 2016. Multikernel linear mixed models for complex phenotype prediction. Genome Research 26:969−79 doi: 10.1101/gr.201996.115 |
[19] |
Zenke-Philippi C, Thiemann A, Seifert F, Schrag T, Melchinger AE, et al. 2016. Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles. BMC Genomics 7:262 doi: 10.1186/s12864-016-2580-y |
[20] |
Duhnen A, Gras A, Teyssèdre S, Romestant M, Claustres B, et al. 2017. Genomic selection for yield and seed protein content in soybean: a study of breeding program data and assessment of prediction accuracy. Crop Science 57:1325−37 doi: 10.2135/cropsci2016.06.0496 |
[21] |
Gapare W, Liu S, Conaty W, Zhu QH, Gillespie V. 2018. Historical datasets support genomic selection models for the prediction of cotton fiber quality phenotypes across multiple environments. G3 8:1721−32 doi: 10.1534/g3.118.200140 |
[22] |
Alves FC, Granato ÍSC, Galli G, Lyra DH, Fritsche-Neto R, et al. 2019. Bayesian analysis and prediction of hybrid performance. Plant Methods 15:14 doi: 10.1186/s13007-019-0388-x |
[23] |
Xu S, Xu Y, Gong L, Zhang Q. 2016. Metabolomic prediction of yield in hybrid rice. The Plant Journal 88:219−27 doi: 10.1111/tpj.13242 |
[24] |
Dan Z, Hu J, Zhou W, Yao G, Zhu R, et al. 2016. Metabolic prediction of important agronomic traits in hybrid rice (Oryza sativa L.). Scientific Reports 6:21732 doi: 10.1038/srep21732 |
[25] |
Speed D, Balding DJ. 2014. MultiBLUP: improved SNP-based prediction for complex traits. Genome Research 24:1550−57 doi: 10.1101/gr.169375.113 |
[26] |
Zhang M, Liu YH, Chang CS, Zhi H, Wang S, et al. 2019. Quantification of gene expression while taking into account RNA alternative splicing. Genomics 111:1517−28 doi: 10.1016/j.ygeno.2018.10.009 |
[27] |
Zhang MP, Liu YH, Xu W, Smith CW, Murray SC, et al. 2020. Analysis of the genes controlling three quantitative traits in three diverse plant species reveals the molecular basis of quantitative traits. Scientific Reports 10:10074 doi: 10.1038/s41598-020-66271-8 |