Machine learning approaches for crop improvement
- Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability ofHighly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement.…
| Author details: | Hao TongORCiDGND, Zoran NikoloskiORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1016/j.jplph.2020.153354 |
| ISSN: | 0176-1617 |
| ISSN: | 1618-1328 |
| Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/33385619 |
| Title of parent work (English): | Journal of plant physiology : biochemistry, physiology, molecular biology and biotechnology of plants |
| Subtitle (English): | leveraging phenotypic and genotypic big data |
| Publisher: | Elsevier |
| Place of publishing: | München |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2020/12/29 |
| Publication year: | 2020 |
| Release date: | 2023/03/24 |
| Tag: | GxE interaction; genomic prediction; genomic selection; machine learning; multi-omics; multiple; traits |
| Volume: | 257 |
| Article number: | 153354 |
| Number of pages: | 13 |
| Funding institution: | European UnionEuropean Commission [739582] |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
| Peer review: | Referiert |
| Publishing method: | Open Access / Hybrid Open-Access |
| License (German): |  CC-BY - Namensnennung 4.0 International |
