The distribution of fitness effects (DFE) encompasses the fraction of deleterious, neutral, and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inferring DFE and α from patterns of polymorphism, as given through the site frequency spectrum (SFS) and divergence data, has been a longstanding goal of evolutionary genetics. A widespread assumption shared by previous inference methods is that beneficial mutations only contribute negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is then predicted by contrasting the SFS with divergence data from an outgroup. We develop a hierarchical probabilistic framework that extends previous methods to infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. While an outgroup is still needed to obtain an unfolded SFS, we show that both a DFE, comprising both deleterious and beneficial mutations, and α can be inferred without using divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α. We compare our framework with one of the most widely used inference methods available and apply it on a recently published chimpanzee exome data set.

The Wright–Fisher model provides an elegant mathematical framework for understanding allele frequency data. In particular, the model can be used to infer the demographic history of species and identify loci under selection. A crucial quantity for inference under the Wright–Fisher model is the distribution of allele frequencies (DAF). Despite the apparent simplicity of the model, the calculation of the DAF is challenging. We review and discuss strategies for approximating the DAF, and how these are used in methods that perform inference from allele frequency data. Various evolutionary forces can be incorporated in the Wright–Fisher model, and we consider these in turn. We begin our review with the basic bi-allelic Wright–Fisher model where random genetic drift is the only evolutionary force. We then consider mutation, migration, and selection. In particular, we compare diffusion-based and moment-based methods in terms of accuracy, computational efficiency, and analytical tractability. We conclude with a brief overview of the multi-allelic process with a general mutation model. [Allele frequency, diffusion, inference, moments, selection, Wright–Fisher.]

Electron transport within living cells is essential for energy conservation in all respiring and photosynthetic organisms. While a few bacteria transport electrons over micrometer distances to their surroundings, filaments of cable bacteria are hypothesized to conduct electric currents over centimeter distances. We used resonance Raman microscopy to analyze cytochrome redox states in living cable bacteria. Cable-bacteria filaments were placed in microscope chambers with sulfide as electron source and oxygen as electron sink at opposite ends. Along individual filaments a gradient in cytochrome redox potential was detected, which immediately broke down upon removal of oxygen or laser cutting of the filaments. Without access to oxygen, a rapid shift toward more reduced cytochromes was observed, as electrons were no longer drained from the filament but accumulated in the cellular cytochromes. These results provide direct evidence for long-distance electron transport in living multicellular bacteria.

The distribution of fitness effects (DFE) encompasses deleterious, neutral and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inference of DFE and α from patterns of polymorphism (SFS) and divergence data has been a longstanding goal of evolutionary genetics. A widespread assumption shared by numerous methods developed so far to infer DFE and α from such data is that beneficial mutations contribute only negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is only predicted by contrasting the SFS with divergence data from an outgroup. Here, we develop a hierarchical probabilistic framework that extends on previous methods and also can infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. We show that both a full DFE, comprising both deleterious and beneficial mutations, and α can be inferred without resorting to divergence data. We demonstrate that inference of DFE from polymorphism data alone can in fact provide more reliable estimates, as it does not rely on strong assumptions about a shared DFE between the outgroup and ingroup species used to obtain the SFS and divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α. We illustrate these points using our newly developed framework, while also comparing to one of the most widely used inference methods available.KEYWORDS distribution of fitness effects, rate of adaptive molecular evolution, beneficial mutations, polymorphism and divergence data, Poisson random field 1 New mutations are the ultimate source of heritable variation. 2The fitness properties of new mutations determine the possible 3

Summary Distribution of fitness effects (DFE) of mutations can be inferred from site frequency spectrum (SFS) data. There is mounting interest to determine whether distinct genomic regions and/or species share a common DFE, or whether evidence exists for differences among them. polyDFEv2.0 fits multiple SFS datasets at once and provides likelihood ratio tests for DFE invariance across datasets. Simulations show that testing for DFE invariance across genomic regions within a species requires models accounting for distinct sources of heterogeneity (chance and genuine difference in DFE) underlying differences in SFS data in these regions. Not accounting for this will result in the spurious detection of DFE differences. Availability and Implementation polyDFEv2.0 is implemented in C and is accompanied by a series of R functions that facilitate post-processing of the output. It is available as source code and compiled binaries under a GNU General Public License v3.0 from https://github.com/paula-tataru/polyDFE. Supplementary information Supplementary data are available at Bioinformatics online.

The distribution of fitness effects (DFE) is central to many questions in evolutionary biology. However, little is known about the differences in DFE between closely related species. We use .9000 coding genes orthologous one-to-one across great apes, gibbons, and macaques to assess the stability of the DFE across great apes. We use the unfolded site frequency spectrum of polymorphic mutations (n = 8 haploid chromosomes per population) to estimate the DFE. We find that the shape of the deleterious DFE is strikingly similar across great apes. We confirm that effective population size (N e) is a strong predictor of the strength of negative selection, consistent with the nearly neutral theory. However, we also find that the strength of negative selection varies more than expected given the differences in N e between species. Across species, mean fitness effects of new deleterious mutations covaries with N e , consistent with positive epistasis among deleterious mutations. We find that the strength of negative selection for the smallest populations, bonobos and western chimpanzees, is higher than expected given their N e. This may result from a more efficient purging of strongly deleterious recessive variants in these populations. Forward simulations confirm that these findings are not artifacts of the way we are inferring N e and DFE parameters. All findings are replicated using only GC-conservative mutations, thereby confirming that GC-biased gene conversion is not affecting our conclusions.

The large amount and high quality of genomic data available today enable, in principle, accurate inference of evolutionary histories of observed populations. The Wright-Fisher model is one of the most widely used models for this purpose. It describes the stochastic behavior in time of allele frequencies and the influence of evolutionary pressures, such as mutation and selection. Despite its simple mathematical formulation, exact results for the distribution of allele frequency (DAF) as a function of time are not available in closed analytical form. Existing approximations build on the computationally intensive diffusion limit or rely on matching moments of the DAF. One of the moment-based approximations relies on the beta distribution, which can accurately describe the DAF when the allele frequency is not close to the boundaries (0 and 1). Nonetheless, under a Wright-Fisher model, the probability of being on the boundary can be positive, corresponding to the allele being either lost or fixed. Here we introduce the beta with spikes, an extension of the beta approximation that explicitly models the loss and fixation probabilities as two spikes at the boundaries. We show that the addition of spikes greatly improves the quality of the approximation. We additionally illustrate, using both simulated and real data, how the beta with spikes can be used for inference of divergence times between populations with comparable performance to an existing state-of-the-art method.KEYWORDS Wright-Fisher; beta; pure genetic drift; linear evolutionary pressures; divergence times A DVANCES in sequencing technologies have revolutionized the collection of genomic data, increasing both the volume and quality of available sequenced individuals from a large variety of populations and species (Romiguier et al. 2014;Gudbjartsson et al. 2015). These data, which may involve up to millions of single-nucleotide polymorphisms (SNPs), contain information about the evolutionary history of the observed populations. There has been a great focus in the recent years on inferring such histories, and to this end, one of the most widely used models is the Wright-Fisher model (Gautier et al. 2010; Sirén et al. 2011; Malaspinas et al. 2012; Pickrell and Pritchard 2012; Steinrücken et al. 2014; Terhorst et al. 2015).The Wright-Fisher model characterizes the evolution of a randomly mating population of finite size in discrete nonoverlapping generations. The model describes the stochastic behavior in time of the number of copies (frequency) of alleles at a locus. The frequency is influenced by a series of factors, such as random genetic drift, mutations, migrations, selection, and changes in population size. When inferring the evolutionary history of a population, the effects of the different factors have to be untangled. Mutation, migration, and selection affect the allele frequency in a deterministic manner (Ewens 2004). We collectively refer to these as evolutionary pressures. The frequency also varies from one generation to the next as a resul...

Summary:We present a tool, diCal-IBD, for detecting identity-bydescent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.