Graph statistics theory of individualized quantitative genetics under haplotype-resolved genome assembly
Abstract
Quantitative genetics is essential for genetic dissection of complex traits, yet the existing theory fails to illustrate a comprehensive landscape of genetic control mechanisms driving phenotypic variation and evolution. Here, we develop a statistical approach to assemble all genome loci into omnigenic interactome networks from diplotyped sequencing data. Such networks can not only capture dominance, epistasis, and pleiotropy and leverage these genetic concepts as bidirectional, signed, and weighted interactions among alleles and nonalleles, but also establish a framework for dissecting the genetic architecture of any single individual. While traditional approaches can only estimate coarse-grained genetic parameters at the population level, our approach can portray a fine-grained picture involving how each allele acts and interacts with every other allele for a single individual, thus facilitating its genome editing and genome engineering. By analyzing transcriptomic data of two diplotyped cultivars of a woody plant, our approach can interpret the genetic mechanisms underlying this species’ cold resistance and interorgan communication. Our network-centric approach, generalized as a graph statistics theory, builds the foundation of individualized quantitative genetics, a theory that can make genetics even more transformational to precision breeding or precision medicine.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (15)
Lidan Sun
State Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University
Yangyang Bian
Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications
Dengcheng Yang
Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications
Runtian Miao
State Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University
Yihan Meng
Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications
Jincan Che
Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications
Ziwei Li
Zimo Li
State Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University
Haoning Wang
State Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University
Shuang Wu
Juan Meng
State Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University
Yu Wang
Christopher Griffin
Applied Research Laboratory, The Pennsylvania State University
Shing-Tung Yau
Yau Mathematical Sciences Center, Jingzhai, Tsinghua University
Rongling Wu
Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications