Estimating the temporal and spatial extent of gene flow among sympatric lizard populations (genus Sceloporus) in the southern Mexican highlands

Estimating the temporal and spatial extent of gene flow among sympatric lizard populations (genus Sceloporus) in the southern Mexican highlands

Jared A Grummer, Martha L. Calderón, Adrián Nieto Montes-de Oca, Eric N Smith, Fausto Mendez-de la Cruz, Adam Leache
doi: http://dx.doi.org/10.1101/008623

Interspecific gene flow is pervasive throughout the tree of life. Although detecting gene flow between populations has been facilitated by new analytical approaches, determining the timing and geography of hybridization has remained difficult, particularly for historical gene flow. A geographically explicit phylogenetic approach is needed to determine the ancestral population overlap. In this study, we performed population genetic analyses, species delimitation, simulations, and a recently developed approach of species tree diffusion to infer the phylogeographic history, timing and geographic extent of gene flow in the Sceloporus spinosus group. The two species in this group, S. spinosus and S. horridus, are distributed in eastern and western portions of Mexico, respectively, but populations of these species are sympatric in the southern Mexican highlands. We generated data consisting of three mitochondrial genes and eight nuclear loci for 148 and 68 individuals, respectively. We delimited six lineages in this group, but found strong evidence of mito-nuclear discordance in sympatric populations of S. spinosus and S. horridus owing to mitochondrial introgression. We used coalescent simulations to differentiate ancestral gene flow from secondary contact, but found mixed support for these two models. Bayesian phylogeography indicated more than 60% range overlap between ancestral S. spinosus and S. horridus populations since the time of their divergence. Isolation-migration analyses, however, revealed near-zero levels of gene flow between these ancestral populations. Interpreting results from both simulations and empirical data indicate that despite a long history of sympatry among these two species, gene flow in this group has only recently occurred.

Continuous and Discontinuous Phase Transitions in Quantitative Genetics: the role of stabilizing selective pressure

Continuous and Discontinuous Phase Transitions in Quantitative Genetics: the role of stabilizing selective pressure

Annalisa Fierro, Sergio Cocozza, Antonella Monticelli, Giovanni Scala, Gennaro Miele
(Submitted on 2 Sep 2014)

By using the tools of statistical mechanics, we have analyzed the evolution of a population of N diploid hermaphrodites in random mating regime. The population evolves under the effect of drift, selective pressure in form of viability on an additive polygenic trait, and mutation. The analysis allows to determine a phase diagram in the plane of mutation rate and strength of selection. The involved pattern of phase transitions is characterized by a line of critical points for weak selective pressure (smaller than a threshold), whereas discontinuous phase transitions characterized by metastable hysteresis are observed for strong selective pressure. A finite size scaling analysis suggests the analogy between our system and the mean field Ising model for selective pressure approaching the threshold from weaker values. In this framework, the mutation rate, which allows the system to explore the accessible microscopic states, is the parameter controlling the transition from large heterozygosity (disordered phase) to small heterozygosity (ordered one).

Rate and cost of adaptation in the Drosophila genome

Rate and cost of adaptation in the Drosophila genome

Stephan Schiffels, Michael Lässig, Ville Mustonen
doi: http://dx.doi.org/10.1101/008680

Recent studies have consistently inferred high rates of adaptive molecular evolution between Drosophila species. At the same time, the Drosophila genome evolves under different rates of recombination, which results in partial genetic linkage between alleles at neighboring genomic loci. Here we analyze how linkage correlations affect adaptive evolution. We develop a new inference method for adaptation that takes into account the effect on an allele at a focal site caused by neighboring deleterious alleles (background selection) and by neighboring adaptive substitutions (hitchhiking). Using complete genome sequence data and fine-scale recombination maps, we infer a highly heterogeneous scenario of adaptation in Drosophila. In high-recombining regions, about 50% of all amino acid substitutions are adaptive, together with about 20% of all substitutions in proximal intergenic regions. In low-recombining regions, only a small fraction of the amino acid substitutions are adaptive, while hitchhiking accounts for the majority of these changes. Hitchhiking of deleterious alleles generates a substantial collateral cost of adaptation, leading to a fitness decline of about 30/2N per gene and per million years in the lowest-recombining regions. Our results show how recombination shapes rate and efficacy of the adaptive dynamics in eukaryotic genomes.

Segregation distorters are not a primary source of Dobzhansky-Muller incompatibilities in house mouse hybrids

Segregation distorters are not a primary source of Dobzhansky-Muller incompatibilities in house mouse hybrids

Russ Corbett-Detig, Emily Jacobs-Palmer, Daniel Hartl, Hopi Hoekstra
doi: http://dx.doi.org/10.1101/008672

Understanding the molecular basis of species formation is an important goal in evolutionary genetics, and Dobzhansky-Muller incompatibilities are thought to be a common source of postzygotic reproductive isolation between closely related lineages. However, the evolutionary forces that lead to the accumulation of such incompatibilities between diverging taxa are poorly understood. Segregation distorters are an important source of Dobzhansky-Muller incompatibilities between Drosophila species and crop plants, but it remains unclear if the contribution of these selfish genetic elements to reproductive isolation is prevalent in other species. Here, we genotype millions of single nucleotide polymorphisms across the genome from viable sperm of first-generation hybrid male progeny in a cross between Mus musculus castaneus and M. m. domesticus, two subspecies of rodent in the earliest stages of speciation. We then search for a skew in the allele frequencies of the gametes and show that segregation distorters are not measurable contributors to observed infertility in these hybrid males, despite sufficient statistical power to detect even weak segregation distortion with our novel method. Thus, reduced hybrid male fertility in crosses between these nascent species is attributable to other evolutionary forces.

An algorithm for constructing principal geodesics in phylogenetic treespace

An algorithm for constructing principal geodesics in phylogenetic treespace

Tom M. W. Nye
(Submitted on 2 Sep 2014)

Most phylogenetic analyses result in a sample of trees, but summarizing and visualizing these samples can be challenging. Consensus trees often provide limited information about a sample, and so methods such as consensus networks, clustering and multidimensional scaling have been developed and applied to tree samples. This paper describes a stochastic algorithm for constructing a principal geodesic or line through treespace which is analogous to the first principal component in standard Principal Components Analysis. A principal geodesic summarizes the most variable features of a sample of trees, in terms of both tree topology and branch lengths, and it can be visualized as an animation of smoothly changing trees. The algorithm performs a stochastic search through parameter space for a geodesic which minimises the sum of squared projected distances of the data points. This procedure aims to identify the globally optimal principal geodesic, though convergence to locally optimal geodesics is possible. The methodology is illustrated by constructing principal geodesics for experimental and simulated data sets, demonstrating the insight into samples of trees that can be gained and how the method improves on a previously published approach. A java package called GeoPhytter for constructing and visualising principal geodesics is freely available from http://www.ncl.ac.uk/~ntmwn/geophytter.

MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

Mark D Robinson, Abdullah Kahraman, Charity W Law, Helen Lindsay, Malgorzata Nowicka, Lukas M Weber, Xiaobei Zhou
doi: http://dx.doi.org/10.1101/007120

DNA methylation, and specifically the reversible addition of methyl groups at CpG dinucleotides genome-wide, represents an important layer that is associated with the regulation of gene expression. In particular, aberrations in the methylation status have been noted across a diverse set of pathological states, including cancer. With the rapid development and uptake of large scale sequencing of short DNA fragments, there has been an explosion of data analytic methods for processing and discovering changes in DNA methylation across diverse data types. In this mini-review, we aim to condense many of the salient challenges, such as experimental design, statistical methods for differential methylation detection and critical considerations such as cell type composition and the potential confounding that can arise from batch effects, into a compact and accessible format. Our main interests, from a statistical perspective, include the practical use of empirical Bayes or hierarchical models, which have been shown to be immensely powerful and flexible in genomics and the procedures by which control of false discoveries are made. Of course, there are many critical platform-specific data preprocessing aspects that we do not discuss here. In addition, we do not make formal performance comparisons of the methods, but rather describe the commonly used statistical models and many of the pertinent issues; we make some recommendations for further study.

Quantitative trait evolution with arbitrary mutational models

Quantitative trait evolution with arbitrary mutational models

Joshua G. Schraiber, Michael J. Landis
doi: http://dx.doi.org/10.1101/008540

When models of quantitative genetic variation are built from population ge- netic first principles, several assumptions are often made. One of the most important assumptions is that traits are controlled by many genes of small effect. This leads to a prediction of a Gaussian trait distribution in the population, via the Central Limit Theorem. Since these biological assumptions are often unknown or untrue, we charac- terized how finite numbers of loci or large mutational effects can impact the sampling distribution of a quantitative trait. To do so, we developed a neutral coalescent-based framework, allowing us to experiment freely with the number of loci and the underlying mutational model. Through both analytical theory and simulation we found the nor- mality assumption was highly sensitive to the details of the mutational process, with the greatest discrepancies arising when the number of loci was small or the mutational kernel was heavy-tailed. In particular, fat-tailed mutational kernels result in multimodal sampling distributions for any number of loci. Since selection models and robust neutral models may produce qualitatively similar sampling distributions, we advise extra caution should be taken when interpreting model-based results for poorly understood systems of quantitative traits.

Nuclear stability and transcriptional directionality separate functionally distinct RNA species

Nuclear stability and transcriptional directionality separate functionally distinct RNA species

Robin Andersson, Peter Refsing Andersen, Eivind Valen, Leighton Core, Jette Bornholdt, Mette Boyd, Torben Heick Jensen, Albin Sandelin
doi: http://dx.doi.org/10.1101/005447

Mammalian genomes are pervasively transcribed, yielding a complex transcriptome with high variability in composition and cellular abundance. While recent efforts have identified thousands of new long non-coding (lnc) RNAs and demonstrated a complex transcriptional repertoire produced by protein-coding (pc) genes, limited progress has been made in distinguishing functional RNA from spurious transcription events. This is partly due to present RNA classification, which is typically based on technical rather than biochemical criteria. Here we devise a strategy to systematically categorize human RNAs by their sensitivity to the ribonucleolytic RNA exosome complex and by the nature of their transcription initiation. These measures are surprisingly effective at correctly classifying annotated transcripts, including lncRNAs of known function. The approach also identifies uncharacterized stable lncRNAs, hidden among a vast majority of unstable transcripts. The predictive power of the approach promises to streamline the functional analysis of known and novel RNAs.

Most viewed on Haldane’s Sieve: August 2014

The most viewed posts on Haldane’s Sieve this month were: