Gene Regulatory Evolution During Speciation in a Songbird

Gene Regulatory Evolution During Speciation in a Songbird

Christopher N. Balakrishnan, John H. Davidson

The dissection of expression quantitative trait locus hotspots

The dissection of expression quantitative trait locus hotspots
Jianan Tian, Mark P. Keller, Aimee Teo Broman, Christina Kendziorski, Brian S. Yandell, Alan D. Attie, Karl W. Broman

Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effect on gene expression: expression quantitative trait locus (eQTL) hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression traits that map to the locus: the direction of the effects, as well as the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (such as with linear discriminant analysis) and compare the phenotype distribution in the non-recombinants to that in the recombinant individuals: If the recombinant individuals display a different expression pattern than the non-recombinants, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues.

Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1

Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1
Matteo Figliuzzi, Hervé Jacquier, Alexander Schug, Olivier Tenaillon, Martin Weigt

The quantitative characterization of mutational landscapes is a task of outstanding importance in evolutionary and medical biology: It is, e.g., of central importance for our understanding of the phenotypic effect of mutations related to disease and antibiotic drug resistance. Here we develop a novel inference scheme for mutational landscapes, which is based on the statistical analysis of large alignments of homologs of the protein of interest. Our method is able to capture epistatic couplings between residues, and therefore to assess the dependence of mutational effects on the sequence context where they appear. Compared to recent large-scale mutagenesis data of the beta-lactamase TEM-1, a protein providing resistance against beta-lactam antibiotics, our method leads to an increase of about 40% in explicative power as compared to approaches neglecting epistasis. We find that the informative sequence context extends to residues at native distances of about 20{\AA} from the mutated site, reaching thus far beyond residues in direct physical contact.

Genetic structure of the stingless bee Tetragonisca angustula

Genetic structure of the stingless bee Tetragonisca angustula
Flavio O Francisco, Leandro R Santiago, Yuri M Mizusawa, Benjamin P Oldroyd, Maria C Arias

Evaluating the performance of selection scans to detect selective sweeps in domestic dogs

Evaluating the performance of selection scans to detect selective sweeps in domestic dogs

Florencia Schlamp, Julian van der Made, Rebecca Stambler, Lewis Chesebrough, Adam R Boyko, Philipp W Messer

A segregating inversion generates fitness variation in a yellow monkeyflower (Mimulus guttatus) population

A segregating inversion generates fitness variation in a yellow monkeyflower (Mimulus guttatus) population

John Kelly, John Willis, Young Wha Lee, Lila Fishman

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast

Joshua S Bloom, Iulia Kotenko, Meru J Sadhu, Sebastian Treusch, Frank W Albert, Leonid Kruglyak

Biogeographic dating of speciation times using paleogeographically informed processes

Biogeographic dating of speciation times using paleogeographically informed processes

Michael J Landis

Life history evolution in response to changes in metapopulation structure in an arthropod herbivore

Life history evolution in response to changes in metapopulation structure in an arthropod herbivore

Annelies De Roissart, Nicky Wybouw, David Renault, Thomas Van Leeuwen, Dries Bonte

3D RNA from evolutionary couplings

3D RNA from evolutionary couplings
Caleb Weinreb, Torsten Gross, Chris Sander, Debora S. Marks

RNA genes are ubiquitous in cell physiology, with a diverse repertoire of known functions. In fact, the majority of the eukaryotic genome does not code for proteins, and thousands of conserved RNAs of currently unknown function have been identified. Knowledge of 3D structure could can help elucidate the function of these RNAs but despite outstanding word using X-ray crystallography, NMR and cryoEM, structure determination remains low-throughput. RNA structure prediction in silico is a promising alternative. However, 3D structure prediction for large RNAs requires tertiary contacts between distant secondary structural elements that are difficult to infer with existing methods. Here, based only on sequences, we use a global statistical probability model of co-variation to detect 3D contacts, in analogy to recently developed breakthrough methods for computational protein folding. In blinded tests on 22 known RNA structures ranging in size from 65 to 1800 nucleotides, the predicted contacts matched physical interactions with 65-95% prediction accuracy. Importantly, we infer many long-range tertiary contacts, including non-Watson-Crick interactions. When used as restraints in molecular dynamics simulations, the inferred contacts improve RNA 3D structure prediction to a coordinate error as low as 6 to 10 Angstrom rmsd with potential for use with other constraints. These contacts include functionally important interactions, such as those that distinguish the active and inactive conformations of four riboswitches. In blind prediction mode, we present evolutionary couplings for 180 RNAs of unknown structure (available at this https URL). We anticipate that this approach will shed light on the structure and function of as yet less known RNA genes.