Brief. Bioinformatics - A dynamic framework for quantifying the genetic architecture of phenotypic plasticity.

Tópicos

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Resumo

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.

Resumo Limpo

despit central role adapt microevolut trait genet architectur phenotyp plastic ie multipl phenotyp produc singl genotyp chang environ remain elus know littl gene underli plastic respons trait environ number chromosom locat genet interact well environ impact effect review key statist approach analyz genet variat phenotyp plastic due genotypeenviron interact describ implement dynam model map specif quantit trait loci qtls affect gradient express quantit trait across rang environ dynam model distinct incorpor mathemat aspect phenotyp plastic qtl map framework therebi better unravel quantit attribut trait respons environ test curv paramet specifi environmentdepend trajectori trait model allow seri fundament hypothes test quantit way interplay gene actioninteract environment sensit model can also make dynam predict genet control phenotyp plastic within context chang environ demonstr use model reanalyz qtl data set rice glean new insight genet basi phenotyp plastic plant height growth

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