ArticlePlant biotechnology journal2020
Genomic prediction of maize microphenotypes provides insights for optimizing selection and mining diversity.
Article in Plant biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis.The plant genome · 2024Article
- DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences.Current genomics · 2024Article
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- Maximizing efficiency in sunflower breeding through historical data optimization.Plant methods · 2024Article
- What plant breeding may (and may not) look like in 2050?The plant genome · 2024Review
- Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Scientific reports · 2024Article
- Genomic prediction of tocochromanols in exotic-derived maize.The plant genome · 2023Article
- A comparison of methods for training population optimization in genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean.The plant genome · 2023Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- Plant synthetic epigenomic engineering for crop improvement.Science China. Life sciences · 2022Review
- Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize.The plant genome · 2021Article
- 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.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Developmental genetics of maize vegetative shoot architecture.Molecular breeding : new strategies in plant improvement · 2021Article
- TrainSel: An R Package for Selection of Training Populations.Frontiers in genetics · 2021Article
- Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction.Frontiers in genetics · 2021Article
- Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview.Frontiers in plant science · 2021Article
- Genomic prediction of maize microphenotypes provides insights for optimizing selection and mining diversity.Plant biotechnology journal · 2020Article
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11 authors.
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Abstract
Effective evaluation of millions of crop genetic stocks is an essential component of exploiting genetic diversity to achieve global food security. By leveraging genomics and data analytics, genomic prediction is a promising strategy to efficiently explore the potential of these gene banks by starting with phenotyping a small designed subset. Reliable genomic predictions have enhanced selection of many macroscopic phenotypes in plants and animals. However, the use of genomicprediction strategies for analysis of microscopic phenotypes is limited. Here, we exploited the power of genomic prediction for eight maize traits related to the shoot apical meristem (SAM), the microscopic stem cell niche that generates all the above-ground organs of the plant. With 435 713 genomewide single-nucleotide polymorphisms (SNPs), we predicted SAM morphology traits for 2687 diverse maize inbreds based on a model trained from 369 inbreds. An empirical validation experiment with 488 inbreds obtained a prediction accuracy of 0.37-0.57 across eight traits. In addition, we show that a significantly higher prediction accuracy was achieved by leveraging the U value (upper bound for reliability) that quantifies the genomic relationships of the validation set with the training set. Our findings suggest that double selection considering both prediction and reliability can be implemented in choosing selection candidates for phenotyping when exploring new diversity is desired. In this case, individuals with less extreme predicted values and moderate reliability values can be considered. Our study expands the turbocharging gene banks via genomic prediction from the macrophenotypes into the microphenotypic space.
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