A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation

Libo Jiang, Hexin Shi, Mengmeng Sang, Chenfei Zheng, Yige Cao, Xuli Zhu, Xiaokang Zhuo, Tangren Cheng, Qixiang Zhang, Rongling Wu, Lidan Sun

Research output: Contribution to journalArticlepeer-review

Abstract

How one trait developmentally varies as a function of others shapes a spectrum of biological phenomena. Despite its importance to trait dissection, the understanding of whether and how genes mediate such developmental covariation is poorly understood. We integrate developmental allometry equations into the functional mapping framework to map specific QTLs that govern the correlated development of different traits. Based on evolutionary game theory, we assemble and contextualize these QTLs into an intricate but organized network coded by bidirectional, signed, and weighted QTL-QTL interactions. We use this approach to map shoot height-diameter allometry QTLs in an ornamental woody species, mei (Prunus mume). We detect “pioneering” QTLs (piQTLs) and “maintaining” QTLs (miQTLs) that determine how shoot height varies with diameter and how shoot diameter varies with height, respectively. The QTL networks inferred can visualize how each piQTL regulates others to promote height growth at a cost of diameter growth, how miQTL regulates others to benefit radial growth at a cost of height growth, and how piQTLs and miQTLs regulate each other to form a pleiotropic web of primary and secondary growth in trees. Our approach provides a unique gateway to explore the genetic architecture of developmental covariation, a widespread phenomenon in nature.

Original languageEnglish (US)
Article number1557
JournalFrontiers in Plant Science
Volume10
DOIs
StatePublished - Dec 18 2019

All Science Journal Classification (ASJC) codes

  • Plant Science

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