@article{TongNikoloski2020,
  author    = {Tong, Hao and Nikoloski, Zoran},
  title     = {Machine learning approaches for crop improvement},
  series = {Journal of plant physiology : biochemistry, physiology, molecular biology and biotechnology of plants},
  volume    = {257},
  journal   = {Journal of plant physiology : biochemistry, physiology, molecular biology and biotechnology of plants},
  publisher = {Elsevier},
  address   = {M{\"u}nchen},
  issn      = {0176-1617},
  doi       = {10.1016/j.jplph.2020.153354},
  pages     = {13},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{WittBuchmannBlomeyeretal.2011,
  author    = {Witt, Stephanie H. and Buchmann, Arlette F. and Blomeyer, Dorothea and Nieratschker, Vanessa and Treutlein, Jens and Esser, G{\"u}nter and Schmidt, Martin H. and Bidlingmaier, Martin and Wiedemann, Klaus and Rietschel, Marcella and Laucht, Manfred and Wuest, Stefan and Zimmermann, Ulrich S.},
  title     = {An interaction between a neuropeptide Y gene polymorphism and early adversity modulates endocrine stress responses},
  series = {Psychoneuroendocrinology},
  volume    = {36},
  journal   = {Psychoneuroendocrinology},
  number    = {7},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0306-4530},
  doi       = {10.1016/j.psyneuen.2010.12.015},
  pages     = {1010 -- 1020},
  year      = {2011},
  abstract  = {Interindividual variability in the regulation of the human stress system accounts for a part of the individual's liability to stress-related diseases. These differences are influenced by environmental and genetic factors. Early childhood adversity is a well-studied environmental factor affecting an individual's stress response which has been shown to be modulated by gene environment interaction (GxE). Neuropeptide Y (NPY) plays a role in stress regulation and genetic variation in NPY may influence stress responses. In this study, we analyzed the association of a common variant in the NPY gene promoter, rs16147, with cortisol and ACTH responses to acute psychosocial stress in young adults from the Mannheim Study of Children at Risk (MARS), an ongoing epidemiological cohort study following the outcome of early adversity from birth into adulthood. We found evidence of a GxE interaction between rs16147 and early adversity significantly affecting HPA axis responses to acute psychosocial stress. These findings suggest that the neurobiological mechanisms linking early adverse experience and later neuroendocrine stress regulation are modulated by a gene variant whose functional relevance is documented by increasing convergent evidence from in vitro, animal and human studies.},
  language  = {en}
}