224,188 research outputs found
Variable selection and regression analysis for graph-structured covariates with an application to genomics
Graphs and networks are common ways of depicting biological information. In
biology, many different biological processes are represented by graphs, such as
regulatory networks, metabolic pathways and protein--protein interaction
networks. This kind of a priori use of graphs is a useful supplement to the
standard numerical data such as microarray gene expression data. In this paper
we consider the problem of regression analysis and variable selection when the
covariates are linked on a graph. We study a graph-constrained regularization
procedure and its theoretical properties for regression analysis to take into
account the neighborhood information of the variables measured on a graph. This
procedure involves a smoothness penalty on the coefficients that is defined as
a quadratic form of the Laplacian matrix associated with the graph. We
establish estimation and model selection consistency results and provide
estimation bounds for both fixed and diverging numbers of parameters in
regression models. We demonstrate by simulations and a real data set that the
proposed procedure can lead to better variable selection and prediction than
existing methods that ignore the graph information associated with the
covariates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS332 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The evolution of genetic architectures underlying quantitative traits
In the classic view introduced by R. A. Fisher, a quantitative trait is
encoded by many loci with small, additive effects. Recent advances in QTL
mapping have begun to elucidate the genetic architectures underlying vast
numbers of phenotypes across diverse taxa, producing observations that
sometimes contrast with Fisher's blueprint. Despite these considerable
empirical efforts to map the genetic determinants of traits, it remains poorly
understood how the genetic architecture of a trait should evolve, or how it
depends on the selection pressures on the trait. Here we develop a simple,
population-genetic model for the evolution of genetic architectures. Our model
predicts that traits under moderate selection should be encoded by many loci
with highly variable effects, whereas traits under either weak or strong
selection should be encoded by relatively few loci. We compare these
theoretical predictions to qualitative trends in the genetics of human traits,
and to systematic data on the genetics of gene expression levels in yeast. Our
analysis provides an evolutionary explanation for broad empirical patterns in
the genetic basis of traits, and it introduces a single framework that unifies
the diversity of observed genetic architectures, ranging from Mendelian to
Fisherian.Comment: Minor changes in the text; Added supplementary materia
Negative feedback and transcriptional overshooting in a regulatory network for horizontal gene transfer
Horizontal gene transfer (HGT) is a major force driving bacterial evolution. Because of their ability to cross inter-species barriers, bacterial plasmids are essential agents for HGT. This ability, however, poses specific requisites on plasmid physiology, in particular the need to overcome a multilevel selection process with opposing demands. We analyzed the transcriptional network of plasmid R388, one of the most promiscuous plasmids in Proteobacteria. Transcriptional analysis by fluorescence expression profiling and quantitative PCR revealed a regulatory network controlled by six transcriptional repressors. The regulatory network relied on strong promoters, which were tightly repressed in negative feedback loops. Computational simulations and theoretical analysis indicated that this architecture would show a transcriptional burst after plasmid conjugation, linking the magnitude of the feedback gain with the intensity of the transcriptional burst. Experimental analysis showed that transcriptional overshooting occurred when the plasmid invaded a new population of susceptible cells. We propose that transcriptional overshooting allows genome rebooting after horizontal gene transfer, and might have an adaptive role in overcoming the opposing demands of multilevel selection
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