33 research outputs found
Changes in Selective Effects Over Time Facilitate Turnover of Enhancer Sequences
No full textAbstract
Correct gene expression is often critical and consequently stabilizing selection on expression is widespread. Yet few genes possess highly conserved regulatory DNA, and for the few enhancers that have been carefully characterized, substantial functional reorganization has often occurred. Given that natural selection removes mutations of even very small deleterious effect, how can transcription factor binding evolve so readily when it underlies a conserved phenotype? As a first step toward addressing this question, I combine a computational model for regulatory function that incorporates many aspects of our present biological knowledge with a model for the fitness effects of misexpression. I then use this model to study the evolution of enhancers. Several robust behaviors emerge: First, the selective effects of mutations at a site change dramatically over time due to substitutions elsewhere in the enhancer, and even the overall degree of constraint across the enhancer can change considerably. Second, many of the substitutions responsible for changes in binding occur at sites where previously the mutation would have been strongly deleterious, suggesting that fluctuations in selective effects at a site are important for functional turnover. Third, most substitutions contributing to the repatterning of binding and constraint are effectively neutral, highlighting the importance of genetic drift—even for enhancers underlying conserved phenotypes. These findings have important implications for phylogenetic inference of function and for interpretations of selection coefficients estimated for regulatory DNA.</jats:p
The evolution of gene regulatory architecture: Insights from modeling natural selection and biological function
No full textUnderstanding the evolution of biological systems involves understanding (i) how mutational changes to the genetic encoding of the system alter system function (ii) how these functional changes impact fitness, and (iii) the population processes dictating the fates of these changes. These basic components of functional evolution are interdependent, although they are often studied individually. At the crux of these components is the extensive epistasis exhibited by biological systems; both the effect of a mutation on the traits comprising the system, as well as the fitness effect of altering the traits, may be highly dependent on other aspects of the system (i.e., the genetic background). Therefore, understanding the evolution of function requires understanding the epistasis intrinsic to the systems of interest, and the evolutionary implications of that epistasis. Fortunately, our understanding of the function and encoding of biological systems is reaching the point that realistic models of these can be combined with population genetic models in order to shed light on many outstanding questions in evolutionary biology. I pursue this approach of uniting functional and population genetic models to address several questions related to gene regulatory evolution. The first question I examine is, how can the encoding of regulatory control systems evolve in the presence of efficient stabilizing selection that preserves functional output? I combine a mechanistically-motivated model of cis-regulatory function that has been shown to be predictive in Drosophila melanogaster, with a model of stabilizing selection on a multi-dimensional expression phenotype. I show that functional turnover is due in a large part to shifting patterns of constraint and that even the overall level of constraint evolves considerably. I also show that the substitutions most responsible for functional repatterning often would have been deleterious had they occurred on earlier haplotypic backgrounds. This study highlights the functional importance of nearly-neutral mutations and reveals a high degree of historical contingency in the evolution of regulatory DNA. In the second study, I examine whether it is plausible that substitutions of opposing functional effect can be individually adaptive given the epistasis likely to characterize regulatory networks. As a case study, I employ a mathematical model of a three-gene network, the type I coherent feed-forward loop, which is ubiquitous in nature, accurately described by the model, and has an interpretable basis for fitness. I show that during a single bout of adaptation, substitutions that affect a trait in one direction can be beneficial while subsequent substitutions that affect the trait in the opposite direction can also be beneficial. The existence and apparent prevalence of such adaptive reversals has important implications for interpreting comparative genomics data and for detecting adaptation. These two studies, which comprise the main part of my dissertation, exemplify the utility of combining functional and population genetic models to study evolution. In particular, the epistasis present in these model systems stems directly from the functional properties of the models, rather than an arbitrary specification of epistatic effects (as is often modeled). In turn, the evolutionary dynamics are heavily influenced by the epistasis, and the modeling yields new, detailed insights regarding functional evolution
MULTIDIMENSIONAL ADAPTIVE EVOLUTION OF A FEED-FORWARD NETWORK AND THE ILLUSION OF COMPENSATION
No full textExpression quantitative trait loci detected in cell lines are often present in primary tissues
No full text
Recommended from our members
A population genetic interpretation of GWAS findings for human quantitative traits
No full textHuman genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes—notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around s = 10−3
Nucleotide diversity of transcription factor binding site motif variants across <i>Saccharomyces sensu stricto</i> species in comparison to other sites.
No full text<p>Nucleotide diversity of transcription factor binding site motif variants across <i>Saccharomyces sensu stricto</i> species in comparison to other sites.</p
Distribution of the number of within and between variant comparisons between gene expression profiles of positions with binding site motif variants (BSMVs).
No full text<p>For each motif position, the lower of either the number of within-BSMV comparisons or between BSMV expression comparisons was counted. The blue line and blue bars represent the distribution of all counts, while the orange line and orange dots represent the distribution of only the positions that are functionally variable. Triangles indicate the median of the two distributions. The distribution suggests that there are a reasonable number of comparisons available for most positions.</p
Variable and highly variable binding site motif positions are evolutionarily constrained.
No full text<p>The relative evolutionary rate of binding site motif positions that are variable (>1 bit of information) and highly variable (≤bit of information) evolve more slowly than putatively neutral sites: third codon positons, introns, and intergenic regions. First and second positions, which are more functionally constrained, are also shown. Rates were calcualted from a whole-genome alignment of <i>Saccharomyces sensu stricto</i> species using emperical Bayesian estimation.</p
