Local description of phylogenetic group-based models
Marta Casanellas, Jesús Fernández-Sánchez, Mateusz Michałek
(Submitted on 27 Feb 2014)
Motivated by phylogenetics, our aim is to obtain a system of equations that define a phylogenetic variety on an open set containing the biologically meaningful points. In this paper we consider phylogenetic varieties defined via group-based models. For any finite abelian group G, we provide an explicit construction of codimX phylogenetic invariants (polynomial equations) of degree at most |G| that define the variety X on a Zariski open set U. The set U contains all biologically meaningful points when G is the group of the Kimura 3-parameter model. In particular, our main result confirms a conjecture by the third author and, on the set U, a couple of conjectures by Bernd Sturmfels and Seth Sullivant.
DNA methylation modulates transcription factor occupancy chiefly at sites of high intrinsic cell-type variability
Matthew Maurano, Hao Wang, Sam John, Anthony Shafer, Theresa Canfield, Kristen Lee, John A Stamatoyannopoulos
The nuclear genome of every cell harbors millions of unoccupied transcription factor (TF) recognition sequences that harbor methylated cytosines. Although DNA methylation is commonly invoked as a repressive mechanism, the extent to which it actively silences specific TF occupancy sites is unknown. To define the role of DNA methylation in modulating TF binding, we quantified the effect of DNA methyltransferase abrogation on the occupancy patterns of a ubiquitous TF capable of autonomous binding to its target sites in chromatin (CTCF). Here we show that the vast majority of unoccupied, methylated CTCF recognition sequences remain unbound upon depletion of DNA methylation. Rather, methylation-regulated binding is restricted to a small fraction of elements that exhibit high intrinsic variability in CTCF occupancy across cell types. Our results suggest that DNA methylation is not a major groundskeeper of genomic transcription factor occupancy landscapes, but rather a specialized mechanism for stabilizing epigenetically labile sites.
A tug-of-war between driver and passenger mutations in cancer and other adaptive processes
Christopher McFarland, Leonid Mirny, Kirill S. Korolev
(Submitted on 25 Feb 2014)
Cancer progression is an example of a rapid adaptive process where evolving new traits is essential for survival and requires a high mutation rate. Precancerous cells acquire a few key mutations that drive rapid population growth and carcinogenesis. Cancer genomics demonstrates that these few ‘driver’ mutations occur alongside thousands of random ‘passenger’ mutations-a natural consequence of cancer’s elevated mutation rate. Some passengers can be deleterious to cancer cells, yet have been largely ignored in cancer research. In population genetics, however, the accumulation of mildly deleterious mutations has been shown to cause population meltdown. Here we develop a stochastic population model where beneficial drivers engage in a tug-of-war with frequent mildly deleterious passengers. These passengers present a barrier to cancer progression that is described by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers meltdown. We find support for the model in cancer age-incidence and cancer genomics data that also allow us to estimate the fitness advantage of drivers and fitness costs of passengers. We identify two regimes of adaptive evolutionary dynamics and use these regimes to rationalize successes and failures of different treatment strategies. We find that a tumor’s load of deleterious passengers can explain previously paradoxical treatment outcomes and suggest that it could potentially serve as a biomarker of response to mutagenic therapies. Collective deleterious effect of passengers is currently an unexploited therapeutic target. We discuss how their effects might be exacerbated by both current and future therapies.
Implications of uniformly distributed, empirically informed priors for phylogeographical model selection: A reply to Hickerson et al
Jamie R. Oaks, Charles W. Linkem, Jeet Sukumaran
(Submitted on 26 Feb 2014)
Biogeographers often seek to explain speciation on geographical phenomena. Establishing that a set of population splitting events occurred at the same time can be a persuasive argument that a set of taxa were affected by the same geographic events. Huang et al. (2011) introduced an approximate Bayesian approach (implemented in the software msBayes) to estimate the probabilities of models in which multiple sets of taxa diverge simultaneously. Oaks et al. (2013) used this model-choice framework to study 22 pairs of vertebrates distributed across the Philippines; they also studied the behavior of the approach using simulations. Oaks et al. (2013) found the model was very sensitive to the prior and had low power to detect variation in divergences times. This was not surprising in light of a rich statistical literature showing the marginal likelihood of a model is sensitive to vague priors. Because this sensitivity to prior assumptions affects the crucial insights a researcher who employs msBayes seeks to gain, Oaks et al. (2013) recommended users of the approach carefully assess the robustness of their conclusions to different priors. According to Hickerson et al. (2014), the lack of robustness was due to broad priors leading to inadequate numbers of simulations. They proposed a model-averaging approach using narrow, empirically informed uniform priors. Here, we demonstrate their approach is dangerous in the sense that the empirically-derived priors often exclude the true values of the parameters. We question the value of adopting an empirical-Bayesian stance for this problem, because it can mislead model posterior probabilities. The robust approach of conducting analyses under a variety of priors can reveal sensitivity and communicate assumptions underlying inference. Furthermore, simulations provide insight into the temporal resolution of the method and guide interpretation of results.
Approaching allelic probabilities and Genome-Wide Association Studies from beta distributions
José Santiago García-Cremades, Angel del Río, José A. García, Javier Gayán, Antonio González-Pérez, Agustín Ruiz, O. Sotolongo-Grau, Manuel Ruiz-Marín
(Submitted on 25 Feb 2014)
In this paper we have proposed a model for the distribution of allelic probabilities for generating populations as reliably as possible. Our objective was to develop such a model which would allow simulating allelic probabilities with different observed truncation and de- gree of noise. In addition, we have also introduced here a complete new approach to analyze a genome-wide association study (GWAS) dataset, starting from a new test of association with a statistical distribution and two effect sizes of each genotype. The new methodologi- cal approach was applied to a real data set together with a Monte Carlo experiment which showed the power performance of our new method. Finally, we compared the new method based on beta distribution with the conventional method (based on Chi-Squared distribu- tion) using the agreement Kappa index and a principal component analysis (PCA). Both the analyses show found differences existed between both the approaches while selecting the single nucleotide polymorphisms (SNPs) in association.
Neanderthals had our de novo genes
John Stewart Taylor
In 2009 Knowles and McLysaght reported the discovery of three human genes derived from non-coding DNA. They provided evidence that these genes, CLUU1, C22orf45, and DNAH10OS, were transcribed and translated, they identified orthologous non-coding DNA in chimpanzee (Pan troglodytes) and macaque (Macaca mulatta), and for each gene they located the critical ?enabler? mutations that extended the open reading frames (ORFs) allowing the production of a protein. These genes had no BLASTp hits in any other genome and were considered to be novel human genes, possibly responsible for human-specific traits. Since the discovery of these genes, new high quality Denisovan and Neanderthal genomes have been reported. I used these resources in an effort to determine whether or not CLUU1, C22orf45, and DNAH10OS were truly human-specific.
Genetic drift suppresses bacterial conjugation in spatially structured populations
Peter D. Freese, Kirill S. Korolev, Jose I. Jimenez, Irene A. Chen
(Submitted on 24 Feb 2014)
Conjugation is the primary mechanism of horizontal gene transfer that spreads antibiotic resistance among bacteria. Although conjugation normally occurs in surface-associated growth (e.g., biofilms), it has been traditionally studied in well-mixed liquid cultures lacking spatial structure, which is known to affect many evolutionary and ecological processes. Here we visualize spatial patterns of gene transfer mediated by F plasmid conjugation in a colony of Escherichia coli growing on solid agar, and we develop a quantitative understanding by spatial extension of traditional mass-action models. We found that spatial structure suppresses conjugation in surface-associated growth because strong genetic drift leads to spatial isolation of donor and recipient cells, restricting conjugation to rare boundaries between donor and recipient strains. These results suggest that ecological strategies, such as enforcement of spatial structure and enhancement of genetic drift, could complement molecular strategies in slowing the spread of antibiotic resistance genes.