A dissection model for mapping complex traits

Abstract

Summary Many quantitative traits are composites of other traits that contribute differentially to genetic variation. Quantitative trait locus (QTL) mapping of these composite traits can benefit by incorporating the mechanistic process of how their formation is mediated by the underlying components. We propose a dissection model by which to map these interconnected components traits under a joint likelihood setting. The model can test how a composite trait is determined by pleiotropic QTLs for its component traits or jointly by different sets of QTLs each responsible for a different component. The model can visualize the pattern of time‐varying genetic effects for individual components and their impacts on composite traits. The dissection model was used to map two composite traits, stemwood volume growth decomposed into its stem height, stem diameter and stem form components for Euramerican poplar adult trees, and total lateral root length constituted by its average lateral root length and lateral root number components for Euphrates poplar seedlings. We found the pattern of how QTLs for different components contribute to phenotypic variation in composite traits. The detailed understanding of the genetic machineries of composite traits will not only help in the design of molecular breeding in plants and animals, but also shed light on the evolutionary processes of quantitative traits under natural selection.

Inferring multilayer interactome networks shaping phenotypic plasticity and evolution

“…However, as have been shown above, the characterization of dependent effects is not dependent on sample size but on net genetic effect curves. The estimation of main genetic effect curves does rely on sample size, but both simulation and experimental studies suggest that a sample size of 100 to 400 can reasonably well estimate such curves if the number of time points is two or more times the number of curve parameters 97 , 98 . Furthermore, traditionally defined epistasis is the genetic interaction between different loci, without knowing the direction and sign of epistasis.…”

“…FunMap is a statistical model, proven to be powerful for mapping growth traits by integrating biologically meaningful growth equations 97 , 108 – 110 . More recently, Sang et al 98 . have extended FunMap to coFunMap designed to map a composite trait mathematically constituted by different traits.…”

“…The statistical power of coFunMap can also attribute to the covariance modeling 97 , 98 , 109 , 110 . The residual covariance matrix of phenotypic plasticity at SNP s has the following structure: where the time-dependent variance (and covariance =1, …, T ) of phenotypic plasticity are the sum of the time-variances and (and covariances and ) of the trait in two environments, respectively, under the assumption that two environments are independent of one another.…”

Functional principal component based time-series genome-wide association in sorghum

“…Simulation studies based on fitting parametric models have demonstrated that integrating longitudinal measures of phenotypes in a single population can provide increased resolution and power to identify quantitative trait loci 16 . Here we employed nonparametric models which required fewer initial assumptions about the pattern of how phenotypes change over time 47 .…”

“…Plant growth and development is a dynamic process, responding to environmental perturbations and regulated by different suites of genes at different times and in different environments 4,[10][11][12][13][14][15] . The availability and use of time-series trait data from mapping and association populations has the potential to increase both the accuracy and power of gene mapping studies 4,16 . It may also provide greater insight into the biologically distinct roles different loci pay in determining final phenotypes.…”