A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation

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.

Mapping Floral Genetic Architecture in Prunus mume, an Ornamental Woody Plant

“…Three mapping populations used in this study were described in the previous study ( Jiang et al, 2019 ), where an allometric model was developed to map growth QTLs in mei. Here, we focus on QTL mapping for floral traits.…”

“…Mei possesses remarkable diversity and variation in flower size, morphology, architecture, and color, making it a favorable choice of ornamental plants in the floriculture industry. Since mei was sequenced ( Zhang et al, 2012 ), considerable research has been carried out for the genetic dissection of botanical traits for this species, including stem growth, branch display, and heterophylly ( Sun et al, 2013 , 2014 , 2018 ; Zhang et al, 2015 ; Jiang et al, 2019 ). In a recent genome-wide association study (GWAS) conducted by a set of 333 cultivars, Zhang et al (2018) identified important QTLs for petal color, stigma color, calyx color, and bud color, some of which are located with the regions of candidate genes, such as v-myb avian myeloblastosis viral oncogene homolog 108 ( MYB108 ).…”

Inferring multilayer interactome networks shaping phenotypic plasticity and evolution

“…Let denote the vector of marginal (net) genetic effects of SNP s on phenotypic plasticity estimated by coFunMap. Based on evolutionary game theory, the net genetic effect of a SNP can be decomposed into its independent effect and dependent effect component 31 . This decomposition is mathematically specified by a system of nLV-based ordinary differential equations (ODEs), expressed as where is the derivative of the net genetic effect of SNP s on phenotypic plasticity at time t , is a time-varying function that characterizes the independent genetic effect of SNP s that occurs when it is assumed to be in isolation, is a time-varying function that characterizes the dependent genetic effect of SNP s that arises from the influence of another SNP on it, and and are a set of parameters that fit the independent and dependent functions, respectively.…”

“…Existing approaches are founded on reductionist thinking, which can only identify individual key QTLs at a time. However, it is becoming increasingly clear that a deeper genetic understanding of phenotypic plasticity as a complex trait requires not only a detailed characterization of its underlying individual genes, but also of their interactions as a cohesive whole 26 – 31 . A recently emerging theory, known as omnigenic theory, states that complex traits are controlled by all genes carried by an organism 32 .…”