With genomic prediction, the plant breeding process can be viewed as genetic space exploration, where models built with existing data can generate predictions to guide the exploration. With this understanding, we investigated several techniques in data mining, the process of knowledge discovery from data, which has been widely used in many areas. By combing knowledges from quantitative genetics and data mining and examining data from maize, rice, and wheat, we demonstrated that effective genomic prediction models can be established with a training set of 2-13 percent of the size of the whole set, enabling an efficient exploration of many genetic combinations. Design thinking is an active process of encouraging people to become more collaborative, mindful, experimental, and metacognitive. Iteratively improving genomics-assisted breeding methods is design thinking in action. Research findings through mining genomic and phenomic data can inform the designing decisions in complex traits dissection and improvement in crops.

PI: Yu, Jianming.
Funding: The National Science Foundation Grant IOS-1238142, the Iowa State University Raymond F. Baker Center for Plant Breeding, and the Iowa State University Plant Sciences Institute.

Related Publications

• Guo, T., X. Yu, X. Li, H. Zhang, C. Zhu, S. Flint-Garcia, M.D. McMullen, J.B. Holland, S.J. Szalman, R.J. Wisser, and J. Yu*. 2019. Optimal designs for genomic selection in hybrid crops. Molecular Plant 12:390-401.