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Acta Agronomica Sinica ›› 2024, Vol. 50 ›› Issue (2): 373-382.doi: 10.3724/SP.J.1006.2024.33021

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Genomic prediction of maize agronomic and quality traits using multi-omics data

YANG Jing-Lei1,**(), WU Bing-Jie1,**(), WANG An-Zhou1, XIAO Ying-Jie1,2,*()   

  1. 1National Key Laboratory of Crop Genetic Improvement / Huazhong Agricultural University, Wuhan 430070, Hubei, China
    2Hubei Hongshan Laboratory, Wuhan 430070, Hubei, China
  • Received:2023-04-02 Accepted:2023-09-13 Online:2024-02-12 Published:2023-10-09
  • Contact: *E-mail: yxiao25@mail.hzau.edu.cn
  • About author:**Contributed equally to this work
  • Supported by:
    National Natural Science Foundation of China for Youth-Talent Fund(32122066)

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Abstract:

Genomic selection predicts unknown phenotypes by using high-density genetic markers covering the genome. In the plant, this method allows early selection for traits, retaining dominant individuals and saving costs for field management and phenotype identification, which greatly accelerating the breeding process. In this study, genomic, transcriptomic, and metabolomic data were used for genomic prediction of agronomic and quality traits of maize by using two statistical models, rrBLUP, and LASSO. We found that the order of predictive power was genomic data, transcriptomic data, and metabolomic data. For different traits, genomic prediction was more powerful than agronomic traits for quality traits. For both rrBLUP and LASSO models, rrBLUP was the best model for all traits when using genomic data, 53 traits were the best predicted by rrBLUP and 2 traits were the best predicted by LASSO when using transcriptomic data, 43 traits were the best predicted by rrBLUP and 12 traits were the best predicted by LASSO, and 12 traits were the best predicted by LASSO based on metabolomic data. In addition, when performing genomic prediction using different lineages, the accuracy of predicting the temperate maize from the tropic maize was slightly better than that of predicting the tropic maize from the temperate. For quality traits, we found the cross-lineage prediction was higher than the within-lineage prediction. This study systematically evaluated the differences in the predictive ability of maize agronomic and quality traits based on various multi-omics data and statistical models, which providing a theoretical basis for future genomic breeding of important agricultural traits in maize.

Key words: maize, agronomic and quality trait, genomic prediction, multi-omics data

Fig. 1

Prediction differences in agronomic traits and quality traits based on genomic data analysis A: density distribution map for the prediction accuracy of two phenotypic traits by using genomic data. B: prediction accuracy for predicting 20 agronomic traits and 35 quality traits by using genomic data."

Fig. 2

Differences in trait prediction between omics data A: predictive differences in predicting different types of traits using different omics data; B: classification of prediction results of 55 trait based on the different omics data; C: prediction differences between two types of traits using different chromosomal markers; D: prediction differences between two types of traits using three tissue data; E: prediction differences between two types of traits using metabolite data from different environments. *: P < 0.05; **: P < 0.01; ***: P < 0.01."

Fig. 3

Integrated evaluation of models and omic-data combinations on trait prediction A: the best prediction model for predicting 55 traits using different omic data; B: prediction accuracy changes between two prediction models for 55 traits with different omics data; C: the best combination of omics data and models for predicting 55 traits."

Fig. 4

Influence of material genealogy on prediction by omics data A, B: the effects of different material genealogy based on different omics data on predicted phenotype; C-E: the effects of different genealogical materials on the prediction of two types of traits based on different omics data."

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