A dynamic framework for quantifying the genetic architecture of phenotypic plasticity

Zhong Wang, Xiaoming Pang, Yafei Lv, Fang Xu, Tao Zhou, Xin Li, Sisi Feng, Jiahan Li, Zhikang Li, Rongling Wu

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations

Abstract

Despite its central role in the adaptation and microevolution of traits, the genetic architecture of phenotypic plasticity, i.e. multiple phenotypes produced by a single genotype in changing environments, remains elusive. We know little about the genes that underlie the plastic response of traits to the environment, their number, chromosomal locations and genetic interactions as well as environment impact on their effects. Here we review key statistical approaches for analyzing the genetic variation of phenotypic plasticity due to genotype-environment interactions and describe the implementation of a dynamic model to map specific quantitative trait loci (QTLs) that affect the gradient expression of a quantitative trait across a range of environments. This dynamic model is distinct by incorporating mathematical aspects of phenotypic plasticity into a QTL mapping framework, thereby better unraveling the quantitative attribute of trait response to the environment. By testing the curve parameters that specify environment-dependent trajectories of the trait, the model allows a series of fundamental hypotheses to be tested in a quantitative way about the interplay between gene action/interaction and environmental sensitivity. The model can also make the dynamic prediction of genetic control over phenotypic plasticity within the context of changing environments. We demonstrate the usefulness of the model by reanalyzing a QTL data set for rice, gleaning new insights into the genetic basis for phenotypic plasticity in plant height growth.

Original languageEnglish (US)
Article numberbbs009
Pages (from-to)82-95
Number of pages14
JournalBriefings in bioinformatics
Volume14
Issue number1
DOIs
StatePublished - Jan 2013

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Molecular Biology

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