TY  - JOUR
A1  - Laitinen, Roosa A. E.
A1  - Nikoloski, Zoran
T1  - Genetic basis of plasticity in plants
JF  - Journal of experimental botany
N2  - The ability of an organism to change its phenotype in response to different environments, termed plasticity, is a particularly important characteristic to enable sessile plants to adapt to rapid changes in their surroundings. Plasticity is a quantitative trait that can provide a fitness advantage and mitigate negative effects due to environmental perturbations. Yet, its genetic basis is not fully understood. Alongside technological limitations, the main challenge in studying plasticity has been the selection of suitable approaches for quantification of phenotypic plasticity. Here, we propose a categorization of the existing quantitative measures of phenotypic plasticity into nominal and relative approaches. Moreover, we highlight the recent advances in the understanding of the genetic architecture underlying phenotypic plasticity in plants. We identify four pillars for future research to uncover the genetic basis of phenotypic plasticity, with emphasis on development of computational approaches and theories. These developments will allow us to perform specific experiments to validate the causal genes for plasticity and to discover their role in plant fitness and evolution.
KW  - Genetic architecture
KW  - GWA
KW  - GxE interaction
KW  - hub genes
KW  - plant adaptation
KW  - plasticity
KW  - variance
Y1  - 2018
U6  - https://doi.org/10.1093/jxb/ery404
SN  - 0022-0957
SN  - 1460-2431
VL  - 70
IS  - 3
SP  - 739
EP  - 745
PB  - Oxford Univ. Press
CY  - Oxford
ER  - 
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  -