PubMed Health⌕ Search

PubMed · 11908665

Mapping epigenetic quantitative trait loci (QTL) altering a developmental trajectory.

Abstract

Genetic variation in a quantitative trait that changes with age is important to both evolutionary biologists and breeders. A traditional analysis of the dynamics of genetic variation is based on the genetic variance-covariance matrix among different ages estimated from a quantitative genetic model. Such an analysis, however, cannot reveal the mechanistic basis of the genetic variation for a growth trait during ontogeny. Age-specific genetic variance at time t conditional on the causal genetic effect at time t - 1 implies the generation of episodes of new genetic variation arising during the interval t - 1 to t. In the present paper, the conditional genetic variance estimated from Zhu's (1995) conditional model was partitioned into its underlying individual quantitative trait loci (QTL) using molecular markers in an F2 progeny of poplars (Populus trichocarpa and Populus deltoides). These QTL, defined as epigenetic QTL, govern the alterations of growth trajectory in a population. Three epigenetic QTL were detected to contribute significantly to variation in growth trajectory during the period from the establishment year to the subsequent year in the field. It is suggested that the activation and expression of epigenetic QTL are influenced by the developmental status of trees and the environment in which they are grown.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rongling Wu, Chang-Xing Ma, Jun Zhu, George Casella. 2002. Mapping epigenetic quantitative trait loci (QTL) altering a developmental trajectory.. https://doi.org/10.1139/g01-118

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Molecular heterochrony and the evolution of sociality in bumblebees (Bombus terrestris).

Sibling care is a hallmark of social insects, but its evolution remains challenging to explain at the molecular level. The hypothesis that sibling care evolved from ancestral maternal care in primitively eusocial insects has been elaborated to involve heterochronic changes in gene expression. This elaboration leads to the prediction that workers in these species will show patterns of gene expression more similar to foundress queens, who express maternal care behaviour, than to established queens engaged solely in reproductive behaviour. We tested this idea in bumblebees (Bombus terrestris) using a microarray platform with approximately 4500 genes. Unlike the wasp Polistes metricus, in which support for the above prediction has been obtained, we found that patterns of brain gene expression in foundress and queen bumblebees were more similar to each other than to workers. Comparisons of differentially expressed genes derived from this study and gene lists from microarray studies in Polistes and the honeybee Apis mellifera yielded a shared set of genes involved in the regulation of related social behaviours across independent eusocial lineages. Together, these results suggest that multiple independent evolutions of eusociality in the insects might have involved different evolutionary routes, but nevertheless involved some similarities at the molecular level.

Analysis of Variance↗

Confidence intervals for the standardized effect arising in the comparison of two normal populations.

Confidence intervals for a standardized effect are derived after stabilizing the variance of the Welch t-statistic. Simulation studies demonstrate the viability of the resulting intervals for a wide range of parameter values and sample sizes as small as five. The methodology is extended to the combination of results from several studies, so as to obtain a confidence interval for a representative standardized effect for all the studies. The methods are illustrated on a recent meta-analytic study of systolic blood pressure reduction during a weight reducing regime, as well as the classical Mumford data on psychological intervention and hospital length of stay.

Analysis of Variance↗