Author Archives: cooplab

Multi-locus analysis of genomic time series data from experimental evolution

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Multi-locus analysis of genomic time series data from experimental evolution
Jonathan Terhorst, Yun S. Song

Genomic time series data generated by evolve-and-resequence (E&R) experiments offer a powerful window into the mechanisms that drive evolution. However, standard population genetic inference procedures do not account for sampling serially over time, and new methods are needed to make full use of modern experimental evolution data. To address this problem, we develop a Gaussian process approximation to the multi-locus Wright-Fisher process with selection over a time course of tens of generations. The mean and covariance structure of the Gaussian process are obtained by computing the corresponding moments in discrete-time Wright-Fisher models conditioned on the presence of a linked selected site. This enables our method to account for the effects of linkage and selection, both along the genome and across sampled time points, in an approximate but principled manner. Using simulated data, we demonstrate the power of our method to correctly detect, locate and estimate the fitness of a selected allele from among several linked sites. We also study how this power changes for different values of selection strength, initial haplotypic diversity, population size, sampling frequency, experimental duration, number of replicates, and sequencing coverage depth. In addition to providing quantitative estimates of selection parameters from experimental evolution data, our model can be used by practitioners to design E&R experiments with requisite power. Finally, we explore how our likelihood-based approach can be used to infer other model parameters, including effective population size and recombination rate, and discuss extensions to more complex models.

Long-term balancing selection in LAD1 maintains a missense trans-species polymorphism in humans, chimpanzees and bonobos

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Long-term balancing selection in LAD1 maintains a missense trans-species polymorphism in humans, chimpanzees and bonobos
João C. Teixeira, Cesare de Filippo, Antje Weihmann, Juan R. Meneu, Fernando Racimo, Michael Dannemann, Birgit Nickel, Anne Fischer, Michel Halbwax, Claudine Andre, Rebeca Atencia, Matthias Meyer, Genís Parra, Svante Pääbo, Aida M. Andrés

Balancing selection maintains advantageous genetic and phenotypic diversity in populations. When selection acts for long evolutionary periods selected polymorphisms may survive species splits and segregate in present-day populations of different species. Here, we investigated the role of long-term balancing selection in the evolution of protein-coding sequences in the Pan-Homo clade. We sequenced the exome of 20 humans, 20 chimpanzees and 20 bonobos and detected eight coding trans-species polymorphisms (trSNPs) that are shared among the three species and have segregated for approximately 14 million years of independent evolution. While the majority of these trSNPs were found in three genes of the MHC cluster, we also uncovered one coding trSNP (rs12088790) in the gene LAD1. All these trSNPs show clustering of sequences by allele rather than by species and also exhibit other signatures of long-term balancing selection, such as segregating at intermediate frequency and lying in a locus with high genetic diversity. Here we focus on the trSNP in LAD1, a gene that encodes for Ladinin-1, a collagenous anchoring filament protein of basement membrane that is responsible for maintaining cohesion at the dermal-epidermal junction; the gene is also an autoantigen responsible for linear IgA disease. This trSNP results in a missense change (Leucine257Proline) and, besides altering the protein sequence, is associated with changes in gene expression of LAD1.

The effective founder effect in a spatially expanding population

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The effective founder effect in a spatially expanding population
Benjamin Marco Peter, Montgomery Slatkin

The gradual loss of diversity associated with range expansions is a well known pattern observed in many species, and can be explained with a serial founder model. We show that under a branching process approximation, this loss in diversity is due to the difference in offspring variance between individuals at and away from the expansion front, which allows us to measure the strength of the founder effect, dependant on an effective founder size. We demonstrate that the predictions from the branching process model fit very well with Wright-Fisher forward simulations and backwards simulations under a modified Kingman coalescent, and further show that estimates of the effective founder size are robust to possibly confounding factors such as migration between subpopulations. We apply our method to a data set of Arabidopsis thaliana, where we find that the founder effect is about three times stronger in the Americas than in Europe, which may be attributed to the more recent, faster expansion.

Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling

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Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling
Stinus Lindgreen, Sinan Ugur Umu, Alicia Sook-Wei Lai, Hisham Eldai, Wenting Liu, Stephanie McGimpsey, Nicole Wheeler, Patrick J. Biggs, Nick R. Thomson, Lars Barquist, Anthony M. Poole, Paul P. Gardner
Comments: 16 pages, 4 figures
Subjects: Genomics (q-bio.GN)

Noncoding RNAs are increasingly recognized as integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.

Author post: Predicting evolution from the shape of genealogical trees

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This guest post by Richard Neher discusses his preprint Predicting evolution from the shape of genealogical trees. Richard A. Neher, Colin A. Russell, Boris I. Shraiman. arXived here. This is cross-posted from the Neher lab website.

In this preprint — a collaboration with Colin Russell and Boris Shraiman — we show that it is possible to predict which individual from a population is most closely related to future populations. To this end, we have developed a method that uses the branching pattern of genealogical trees to estimate which part of the tree contains the “fittest” sequences, where fit means rapidly multiplying. Those that multiply rapidly, are most likely to take over the population. We demonstrate the power of our method by predicting the evolution of seasonal influenza viruses.

How does it work?
Individuals adapt to a changing environment by accumulating beneficial mutations, while avoiding deleterious mutations. We model this process assuming that there are many such mutations which change fitness in small increments. Using this model, we calculate the probability that an individual that lived in the past at time t leaves n descendants in the present. This distributions depends critically on the fitness of the ancestral individual. We then extend this calculation to the probability of observing a certain branch in a genealogical tree reconstructed from a sample of sequences. A branch in a tree connects an individual A that lived at time tA and had fitness xA and with an individual B that lived at a later time tB with fitness xB as illustrated in the figure. B has descendants in the sample, otherwise the branch would not be part of the tree. Furthermore, all sampled descendants of A are also descendants of B, otherwise the connection between A and B would have branched between tA and tB. We call the mathematical object describing fitness evolution between A and B “branch propagator” and propagatordenote it by g(xB,tB|xA,tA). The joint probability distribution of fitness values of all nodes of the tree is given by a product of branch propagators. We then calculate the expected fitness of each node and use it to rank the sampled sequences. The top ranked sequence is our prediction for the sequence of the progenitor of the future population.

Why do we care?
flu_tree Being able to predict evolution could have immediate applications. The best example is the seasonal influenza vaccine, that needs to be updated frequently to keep up with the evolving virus. Vaccine strains are chosen among sampled virus strains, and the more closely this strain matches the future influenza virus population, the better the vaccine is going to be. Hence by predicting a likely progenitor of the future, our method could help to improve influenza vaccines. One of our predictions is shown in the figure, with the top ranked sequence marked by a black arrow. Influenza is not the only possible application. Since the algorithm only requires a reconstructed tree as input, it can be applied to other rapidly evolving pathogens or cancer cell populations. In addition, to being useful, the ability to predict also implies that the model captures an essential aspect of evolutionary dynamics: influenza evolution is to a substantial degree — enough to enable prediction — dependent on the accumulation of small effect mutations.

Comparison to other approaches
Given the importance of good influenza vaccines, there has been a number of previous efforts to anticipate influenza virus evolution, typically based on using patterns of molecular evolution from historical data. Along these lines, Luksza and Lässig have recently presented an explicit fitness model for influenza virus evolution that rewards mutations at positions known to convey antigenic novelty and penalizes likely deleterious mutations (+a few other things). By using molecular influenza specific signatures, this model is complementary to ours that uses only the tree reconstructed from nucleotide sequences. Interestingly, the two models do more or less equally well and combining different methods of prediction should result in more reliable results.

Author post: Inferring human population size and separation history from multiple genome sequences

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This guest post is by Stephan Schiffels (@stschiff) on his paper with Richard Durbin Inferring human population size and separation history from multiple genome sequences biorxived here

In our paper, we study genome sequences to learn about human history and how human populations are related to each other. Remarkably, we only need a few individuals for this, because once we look sufficiently many generations into the past, every single genome contains fragments from a very large number of ancestors. This means that given only two genomes, say one individual from Africa and one individual from Europe, we typically find shared fragments from common ancestors (great great … great grandparents) from 2,000 or more generations ago. This trace of shared segments in our genomes can be detected and enables us to make inference about human history.

A few years ago, Heng Li and Richard Durbin introduced the PSMC method which is based on estimating this shared common ancestry in a single diploid genome to infer population sizes. We now introduced a major extension to this approach, called MSMC (Multiple Sequentially Markovian Coalescent), which is able to find and date traces of shared ancestry across multiple genome sequences. This is generally a hard problem because of the complex way of how sequences relate with each other through recombination and mutation (see an excellent blog post by Adam Siepel). In our method, we therefore made a choice to focus only on the pair of segments which coalesce first, i.e. share the most recent common ancestor of all pairs. Because of ancestral recombinations, this changes along the sequences.

Consider again the example of an African and a European individual, each of them carrying two copies of a chromosome. In one part of their genomes, the most recent ancestor of any two chromosomes may be shared between the two European chromosomes, in other parts it may be shared between the two African chromosomes, and in some cases it may actually be found across a European and an African chromosome. The relative frequency of how often we observe each of the three cases, and the distribution of times to the most recent common ancestor, give information about when the separation happened, and how long it took for the ancestral people to part fully from each other. In the case of West-Africans and Europeans, we found that the two populations started to separate from each other (at least genetically) long before the known out-of-Africa emigration 50,000 years ago. And we see the same thing if we compare West-Africans to Asians or Americans instead of Europeans. We can also see clearly how ancestors of Native Americans separated from Asians around 20,000 years ago, consistently preceding the known first arrival of people in the New World around 15,000 years ago.

Our method can also estimate effective population size changes through time. One consequence of our approach to look only for the first common ancestor is that we can now look into the much more recent past than was previously possible with similar methods, such as PSMC. For example, we can now see a deep bottleneck in Native American ancestors around 15,000 years ago which fits with the separation and immigration history described above, and we can see recent expansions that are consistent with the spread of agriculture in Africa.

We believe that MSMC is a useful tool for estimating population history from whole genome sequences. But more ideas and development are still needed in the future to expand this approach to more genomes and to look into the past even more recently than 2,000 years ago, which is our current limit with MSMC. Closely related approaches are currently developed by Yun Song, Thomas Mailund and others, which will complement MSMC. This is a great time to work in this field, given that many more high quality individual genome sequences are being generated, and in many cases from populations that we have not covered at all in our paper. All of this will help to greatly expand our knowledge of human population history.

High performance computation of landscape genomic models integrating local indices of spatial association

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High performance computation of landscape genomic models integrating local indices of spatial association
Sylvie Stucki, Pablo Orozco-terWengel, Michael W. Bruford, Licia Colli, Charles Masembe, Riccardo Negrini, Pierre Taberlet, Stéphane Joost, the NEXTGEN Consortium
Comments: 1 figure in text, 1 figure in supplementary material
Subjects: Populations and Evolution (q-bio.PE)

Motivation: The increasing availability of high-throughput datasets requires powerful methods to support the detection of signatures of selection in landscape genomics. Results: We present an integrated approach to study signatures of local adaptation, providing rapid processing of whole genome data and enabling assessment of spatial association using molecular markers. Availabilty: Sam{\ss}ada is an open source software written in C++ available at http:lasig.epfl.ch/sambada (under the license GNU GPL 3). Compiled versions are provided for Windows, Linux and MacOS X. Contact: stephane.joost@epfl.ch, sylvie.stucki@a3.epfl.ch. Supplementary material is available online.

High-resolution transcriptome analysis with long-read RNA sequencing

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High-resolution transcriptome analysis with long-read RNA sequencing
Hyunghoon Cho, Joe Davis, Xin Li, Kevin S. Smith, Alexis Battle, Stephen B. Montgomery
Comments: 29 pages, 8 figures, 11 supplementary figures
Subjects: Genomics (q-bio.GN)

RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2×75 bp and 2×262 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals.

Cis-regulatory elements and human evolution

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Cis-regulatory elements and human evolution
Adam Siepel, Leonardo Arbiza

Modification of gene regulation has long been considered an important force in human evolution, particularly through changes to cis-regulatory elements (CREs) that function in transcriptional regulation. For decades, however, the study of cis-regulatory evolution was severely limited by the available data. New data sets describing the locations of CREs and genetic variation within and between species have now made it possible to study CRE evolution much more directly on a genome-wide scale. Here, we review recent research on the evolution of CREs in humans based on large-scale genomic data sets. We consider inferences based on primate divergence,human polymorphism, and combinations of divergence and polymorphism. We then consider “new frontiers” in this field stemming from recent research on transcriptional regulation.

Quantifying the effects of anagenetic and cladogenetic evolution

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Quantifying the effects of anagenetic and cladogenetic evolution
Krzysztof Bartoszek

An ongoing debate in evolutionary biology is whether phenotypic change occurs predominantly around the time of speciation or whether it instead accumulates gradually over time. In this work I propose a general framework incorporating both types of change, quantify the effects of speciational change via the correlation between species and attribute the proportion of change to each type. I discuss results of parameter estimation of Hominoid body size in this light. I derive mathematical formulae related to this problem, the probability generating functions of the number of speciation events along a randomly drawn lineage and from the most recent common ancestor of two randomly chosen tip species for a conditioned Yule tree. Additionally I obtain in closed form the variance of the distance from the root to the most recent common ancestor of two randomly chosen tip species.