Li Feng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Li Feng has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Ang Dong
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Caifeng Li
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Dengcheng Yang
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Jinshuai Zhao
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Peng Jiang
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Rongling Wu
Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems Beijing Institute of Mathematical Sciences and Applications Beijing China
1 shared publication
Latest Publications
- Open Access
- 1 January 2021
- Published by Maximum Academic Press in Forestry Research
- Vol. 1 (1) , 1-10
Genetic dissection of growth trajectories in forest trees: From FunMap to FunGraph
Growth is the developmental process involving important genetic components. Functional mapping (FunMap) has been used as an approach to map quantitative trait loci (QTLs) governing growth trajectories by incorporating growth equations. FunMap is based on reductionism thinking, with a power to identify a small set of significant QTLs from the whole pool of genome-wide markers. Yet, increasing evidence shows that a complex trait is controlled by all genes the organism may possibly carry. Here, we describe and demonstrate a different mapping approach that encapsulates all markers into genetic interaction networks. This approach, symbolized as FunGraph, combines functional mapping, evolutionary game theory, and prey-predator theory into mathematical graphs, allowing the observed genetic effect of a locus to be decomposed into its independent component (resulting from this locus’ intrinsic capacity) and dependent component (due to extrinsic regulation by other loci). Using FunGraph, we can visualize and trace the roadmap of how each locus interact with every other locus to impact growth. In a population-based association study of Euphrates poplar, we use FunGraph to identify the previously neglected genetic interaction effects that contribute to the genetic architecture of juvenile stem growth. FunGraph could open up a novel gateway to comprehend the global genetic control mechanisms of complex traits.
- 1 January 2021
- Published by Maximum Academic Press in Forestry Research
- Vol. 1 (1) , 1-10
Open AccessGenetic dissection of growth trajectories in forest trees: From FunMap to FunGraph
Growth is the developmental process involving important genetic components. Functional mapping (FunMap) has been used as an approach to map quantitative trait loci (QTLs) governing growth trajectories by incorporating growth equations. FunMap is based on reductionism thinking, with a power to identify a small set of significant QTLs from the whole pool of genome-wide markers. Yet, increasing evidence shows that a complex trait is controlled by all genes the organism may possibly carry. Here, we describe and demonstrate a different mapping approach that encapsulates all markers into genetic interaction networks. This approach, symbolized as FunGraph, combines functional mapping, evolutionary game theory, and prey-predator theory into mathematical graphs, allowing the observed genetic effect of a locus to be decomposed into its independent component (resulting from this locus’ intrinsic capacity) and dependent component (due to extrinsic regulation by other loci). Using FunGraph, we can visualize and trace the roadmap of how each locus interact with every other locus to impact growth. In a population-based association study of Euphrates poplar, we use FunGraph to identify the previously neglected genetic interaction effects that contribute to the genetic architecture of juvenile stem growth. FunGraph could open up a novel gateway to comprehend the global genetic control mechanisms of complex traits.