Yearly Archives: 2014

The relationships among GC content, nucleosome occupancy, and exon size

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The relationships among GC content, nucleosome occupancy, and exon size

Liya Wang, Lincoln Stein, Doreen Ware
(Submitted on 9 Apr 2014)

The average size of internal translated exons, ranging from 120 to 165 nt across metazoans, is approximately the size of the typical mononucleosome (147 nt). Genome-wide study has also shown that nucleosome occupancy is significantly higher in exons than in introns, which might indicate that the evolution of exon size is related to its nucleosome occupancy. By grouping exons by the GC contents of their flanking introns, we show that the average exon size is positively correlated with its GC content. Using the sequencing data from direct mapping of Homo sapiens nucleosomes with limited nuclease digestion, we show that the level of nucleosome occupancy is also positively correlated with the exon GC content in a similar fashion. We then demonstrated that exon size is positively correlated with their nucleosome occupancy. The strong correlation between exon size and the nucleosome occupancy suggests that chromatin organization may be related to the evolution of exon sizes.

Estimating Phylogeny from microRNA Data: A Critical Appraisal

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Estimating Phylogeny from microRNA Data: A Critical Appraisal

Robert Thomson, David Plachetzki, Luke Mahler, Brian Moore

As progress toward a highly resolved tree of life continues to expose nodes that resist resolution, interest in new sources of phylogenetic information that are informative for these most difficult relationships continues to increase. One such potential source of information, the presence and absence of microRNA families, has been vigorously promoted as an ideal phylogenetic marker and has been recently deployed to resolve several long-standing phylogenetic questions. Understanding the utility of such markers for phylogenetic inference hinges on developing a better understanding for how such markers behave under suitable evolutionary models, as well as how they perform in real inference scenarios. However, as yet, no study has rigorously characterized the statistical behavior or utility of these markers. Here we examine the behavior and performance of microRNA presence/absence data under a variety of evolutionary models and reexamine datasets from several previous studies. We find that highly heterogeneous rates of microRNA gain and loss, pervasive secondary loss, and sampling error collectively render microRNA-based inference of phylogeny difficult, and fundamentally alter the conclusions for four of the five studies that we re-examine. Our results indicate that miRNA data have far less phylogenetic utility in resolving the tree of life than is currently recognized and we urge ample caution in their interpretation.

Bias and measurement error in comparative analyses: a case study with the Ornstein Uhlenbeck model

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Bias and measurement error in comparative analyses: a case study with the Ornstein Uhlenbeck model

Gavin Huw Thomas, Natalie Cooper, Chris Venditti, Andrew Meade, Robert P Freckleton

Phylogenetic comparative methods are increasingly used to give new insight into variation, causes and consequences of trait variation among species. The foundation of these methods is a suite of models that attempt to capture evolutionary patterns by extending the Brownian constant variance model. However, the parameters of these models have been hypothesised to be biased and only asymptotically behave in a statistically predictable way as datasets become large. This does not seem to be widely appreciated. We show that a commonly used model in evolutionary biology (the Ornstein-Uhlenbeck model) is biased over a wide range of conditions. Many studies fitting this model use datasets that are small and prone to substantial biases. Our results suggest that simulating fitted models and comparing with empirical results is critical when fitting OU and other extensions of the Brownian model.

Model adequacy and the macroevolution of angiosperm functional traits

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Model adequacy and the macroevolution of angiosperm functional traits
Matthew Pennell, Richard G FitzJohn, William K Cornwell, Luke J Harmon

All models are wrong and sometimes even the best of a set of models is useless. Modern phylogenetic comparative methods (PCMs) are almost exclusively model–based and therefore making robust inferences from PCMs requires using a model of trait evolution that is a good explanation for the data. To date, researchers using PCMs have evaluated the explanatory power of a model only in terms of relative, not absolute, fit. Here we develop a general statistical framework for assessing the absolute fit, or adequacy, of phylogenetic models for the evolution of quantitative traits. We use our approach to test whether commonly used models are adequate descriptors of the macroevolutionary dynamics of real comparative data. We fit models of trait evolution to 337 comparative datasets covering three key Angiosperm functional traits and evaluated the absolute fit of the models to each dataset. Overall, the models we used are very inadequate for the evolution of these traits; this was true for many different groups and at many different scales. Furthermore, the relative support for a model had very little to do with its absolute adequacy. We argue that assessing model adequacy should be a key step in comparative analyses.

Sharing of Very Short IBD Segments between Humans, Neandertals, and Denisovans

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Sharing of Very Short IBD Segments between Humans, Neandertals, and Denisovans

Gundula Povysil, Sepp Hochreiter

We analyze the sharing of very short identity by descent (IBD) segments between humans, Neandertals, and Denisovans to gain new insights into their demographic history. Short IBD segments convey information about events far back in time because the shorter IBD segments are, the older they are assumed to be. The identification of short IBD segments becomes possible through next generation sequencing (NGS), which offers high variant density and reports variants of all frequencies. However, only recently HapFABIA has been proposed as the first method for detecting very short IBD segments in NGS data. HapFABIA utilizes rare variants to identify IBD segments with a low false discovery rate. We applied HapFABIA to the 1000 Genomes Project whole genome sequencing data to identify IBD segments which are shared within and between populations. Some IBD segments are shared with the reconstructed ancestral genome of humans and other primates. These segments are tagged by rare variants, consequently some rare variants have to be very old. Other IBD segments are also old since they are shared with Neandertals or Denisovans, which explains their shorter lengths compared to segments that are not shared with these ancient genomes. The Denisova genome most prominently matched IBD segments that are shared by Asians. Many of these segments were found exclusively in Asians and they are longer than segments shared between other continental populations and the Denisova genome. Therefore, we could confirm an introgression from Deniosvans into ancestors of Asians after their migration out of Africa. While Neandertal-matching IBD segments are most often shared by Asians, Europeans share a considerably higher percentage of IBD segments with Neandertals compared to other populations, too. Again, many of these Neandertal-matching IBD segments are found exclusively in Asians, whereas Neandertal-matching IBD segments that are shared by Europeans are often found in other populations, too. Neandertal-matching IBD segments that are shared by Asians or Europeans are longer than those observed in Africans. This hints at a gene flow from Neandertals into ancestors of Asians and Europeans after they left Africa. Interestingly, many Neandertal- or Denisova-matching IBD segments are predominantly observed in Africans – some of them even exclusively. IBD segments shared between Africans and Neandertals or Denisovans are strikingly short, therefore we assume that they are very old. This may indicate that these segments stem from ancestors of humans, Neandertals, and Denisovans and have survived in Africans.

Intermediate Migration Yields Optimal Adaptation in Structured, Asexual Populations

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Intermediate Migration Yields Optimal Adaptation in Structured, Asexual Populations

Arthur Covert III, Claus O Wilke

Most evolving populations are subdivided into multiple subpopulations connected to each other by varying levels of gene flow. However, how population structure and gene flow (i.e., migration) affect adaptive evolution is not well understood. Here, we studied the impact of migration on asexually reproducing evolving computer programs (digital organisms). We found that digital organisms evolve the highest fitness values at intermediate migration rates, and we tested three hypotheses that could potentially explain this observation: (i) migration promotes passage through fitness valleys, (ii) migration increases genetic variation, and (iii) migration reduces clonal interference through a process called “leapfrogging”. We found that migration had no appreciable effect on the number of fitness valleys crossed and that genetic variation declined monotonously with increasing migration rates, instead of peaking at the optimal migration rate. However, the number of leapfrogging events, in which a superior beneficial mutation emerges on a genetic background that predates the previously best genotype in the population, did peak at the optimal migration rate. We thus conclude that in structured, asexual populations intermediate migration rates allow for optimal exploration of multiple, distinct fitness peaks, and thus yield the highest long-term adaptive success.

Inferring fitness landscapes by regression produces biased estimates of epistasis

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Inferring fitness landscapes by regression produces biased estimates of epistasis

Jakub Otwinowski, Joshua B. Plotkin
(Submitted on 3 Apr 2014)

The genotype-fitness map plays a fundamental role in shaping the dynamics of evolution. However, it is difficult to directly measure a fitness landscape in practice, because the number of possible genotypes is astronomical. One approach is to sample as many genotypes as possible, measure their fitnesses, and fit a statistical model of the landscape that includes additive and pairwise interactive effects between loci. Here we elucidate the pitfalls of using such regressions, by studying artificial but mathematically convenient fitness landscapes. We identify two sources of bias inherent in these regression procedures that each tends to under-estimate high fitnesses and over-estimate low fitnesses. We characterize these biases for random sampling of genotypes, as well as for samples drawn from a population under selection in the Wright-Fisher model of evolutionary dynamics. We show that common measures of epistasis, such as the number of monotonically increasing paths between ancestral and derived genotypes, the prevalence of sign epistasis, and the number of local fitness maxima, are distorted in the inferred landscape. As a result, the inferred landscape will provide systematically biased predictions for the dynamics of adaptation. We identify the same biases in a computational RNA-folding landscape, as well as in regulatory sequence binding data, treated with the same fitting procedure. Finally, we present a method that may ameliorate these biases in some cases.

Stability and response of polygenic traits to stabilizing selection and mutation

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Stability and response of polygenic traits to stabilizing selection and mutation

Harold P. de Vladar, Nick Barton
(Submitted on 3 Apr 2014)

When polygenic traits are under stabilizing selection, many different combinations of alleles allow close adaptation to the optimum. If alleles have equal effects, all combinations that result in the same deviation from the optimum are equivalent. Furthermore, the genetic variance that is maintained by mutation-selection balance is 2μ/S per locus, where μ is the mutation rate and S the strength of stabilizing selection. In reality, alleles vary in their effects, making the fitness landscape asymmetric, and complicating analysis of the equilibria. We show that that the resulting genetic variance depends on the fraction of alleles near fixation, which contribute by 2μ/S, and on the total mutational effects of alleles that are at intermediate frequency. The interplay between stabilizing selection and mutation leads to a sharp transition: alleles with effects smaller than a threshold value of 2μ/S‾‾‾‾√ remain polymorphic, whereas those with larger effects are fixed. The genetic load in equilibrium is less than for traits of equal effects, and the fitness equilibria are more similar. We find that if the optimum is displaced, alleles with effects close to the threshold value sweep first, and their rate of increase is bounded by μS‾‾‾√. Long term response leads in general to well-adapted traits, unlike the case of equal effects that often end up at a sub-optimal fitness peak. However, the particular peaks to which the populations converge are extremely sensitive to the initial states, and to the speed of the shift of the optimum trait value.

Taxator-tk: Fast and Precise Taxonomic Assignment of Metagenomes by Approximating Evolutionary Neighborhoods

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Taxator-tk: Fast and Precise Taxonomic Assignment of Metagenomes by Approximating Evolutionary Neighborhoods

J. Dröge, I. Gregor, A. C. McHardy
(Submitted on 3 Apr 2014)

Metagenomics characterizes microbial communities by random shotgun sequencing of DNA isolated directly from an environment of interest. An essential step in computational metagenome analysis is taxonomic sequence assignment, which allows us to identify the sequenced community members and to reconstruct taxonomic bins with sequence data for the individual taxa. We describe an algorithm and the accompanying software, taxator-tk, which performs taxonomic sequence assignments by fast approximate determination of evolutionary neighbors from sequence similarities. Taxator-tk was precise in its taxonomic assignment across all ranks and taxa for a range of evolutionary distances and for short sequences. In addition to the taxonomic binning of metagenomes, it is well suited for profiling microbial communities from metagenome samples becauseit identifies bacterial, archaeal and eukaryotic community members without being affected by varying primer binding strengths, as in marker gene amplification, or copy number variations of marker genes across different taxa. Taxator-tk has an efficient, parallelized implementation that allows the assignment of 6 Gb of sequence data per day on a standard multiprocessor system with ten CPU cores and microbial RefSeq as the genomic reference data.

An experimentally informed evolutionary model improves phylogenetic fit to divergent lactamase homologs

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An experimentally informed evolutionary model improves phylogenetic fit to divergent lactamase homologs

Jesse D Bloom

Phylogenetic analyses of molecular data require a quantitative model for how sequences evolve. Traditionally, the details of the site-specific selection that governs sequence evolution are unknown, and so most phylogenetic models treat this selection crudely with a variety of free parameters designed to represent general features of mutation and selection. However, recent advances in high-throughput experiments have made it possible to quantify the effects of all single mutations on gene function. I have previously shown that such high-throughput experiments can be combined with knowledge of underlying mutation rates to create a parameter-free evolutionary model that describes the phylogeny of influenza nucleoprotein far better than existing models. Here I extend this work by showing that published experimental data on TEM-1 beta-lactamase (Firnberg et al, 2014) can be combined with a few mutation rate parameters to create an evolutionary model that describes beta-lactamase phylogenies much better than existing models. This experimentally informed evolutionary model is superior even for homologs that are substantially diverged (about 35% divergence at the protein level) from the TEM-1 parent that was the subject of the experimental study. These results suggest that experimental measurements can inform phylogenetic evolutionary models that are applicable to homologs that span a substantial range of sequence divergence.