TY  - JOUR
A1  - Tong, Hao
A1  - Nikoloski, Zoran
T1  - Machine learning approaches for crop improvement
BT  - leveraging phenotypic and genotypic big data
JF  - Journal of plant physiology : biochemistry, physiology, molecular biology and biotechnology of plants
N2  - 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 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.
KW  - genomic selection
KW  - genomic prediction
KW  - machine learning
KW  - multiple
KW  - traits
KW  - multi-omics
KW  - GxE interaction
Y1  - 2020
U6  - https://doi.org/10.1016/j.jplph.2020.153354
SN  - 0176-1617
SN  - 1618-1328
VL  - 257
PB  - Elsevier
CY  - München
ER  -