np²QTL: networking phenotypic plasticity quantitative trait loci across heterogeneous environments

Abstract

Summary Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment‐induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part‐whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta−Volterra ordinary differential equations (LVODE) into an R‐based computing platform, np2QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np2QTL constructs a bidirectional, signed and weighted network of QTL−QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype−environment interactions over any range of environmental change. The utility of np2QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np2QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.

Characterization, costs, cues and future perspectives of phenotypic plasticity

“…2A ). New approaches, like np 2 QTL, not only enable the mapping of genetic loci associated with plasticity but also enable the estimation and testing of the slope and curvature of reaction norms to uncover cryptic genetic variation of plastic responses ( Ye et al , 2019 ). The selection of the range and severity of environmental factors is an important consideration to understand plastic responses.…”

A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation

“…Through mathematical modeling, this discipline has found its new applications to studying the developmental pattern of trait covariation (Zhu et al, 2016;Wang et al, 2017;Fu et al, 2018). The combination of functional mapping and evolutionary game theory has led to the identification of QTL-QTL networks for phenotypic plasticity (Ye et al, 2019). In this article, we extend this combination approach to reconstructing genetic networks that code how different QTLs interact with each other to affect developmental covariation.…”

Modeling genome-wide by environment interactions through omnigenic interactome networks

“…As one of the biggest human genome projects, GTEx, aimed to study the genetic variation of human diseases across multiple tissues, has accumulated over thousands of donors for which genotype data, transcriptional data on multiple tissues, and histological data have been collected (GTEx Consortium 2015. We integrate allometric scaling theory (Ye et al, 2019) to express each gene as a function of tissues and estimate net genetic effects of each SNP on tissue-varying gene expression, from which to reconstruct genetic networks across tissues. Another largescale human genome project, known as the Hammington Heart Study, has collected tens of thousands of male and female subjects who were genotyped and phenotyped for many healthrelated traits, such as BMI and blood pressures at different ages (Fox et al, 2007;Andersson et al, 2019).…”