Category Archives: Population genetics analysis

Epidemic clones, oceanic gene pools and epigenotypes in the free living marine pathogen Vibrio parahaemolyticus

Posted on by
Reply

Epidemic clones, oceanic gene pools and epigenotypes in the free living marine pathogen Vibrio parahaemolyticus
Yujun Cui, Xianwei Yang, Xavier Didelot, Chenyi Guo, Dongfang Li, Yanfeng Yan, Yiquan Zhang, Yanting Yuan, Huanming Yang, Jian Wang, Jun Wang, Yajun Song, Dongsheng Zhou, Daniel Falush, Ruifu Yang
Subjects: Populations and Evolution (q-bio.PE)

In outbreeding organisms, genetic variation is reassorted each generation, leading to geographic gene pools. By contrast bacterial clones can spread and adapt independently leading to a wide variety of possible genetic structures. Here we investigated global patterns of variation in 157 whole genome sequences of Vibrio parahaemolyticus, a free living and seafood associated marine bacterium. Pandemic clones, responsible for recent outbreaks of gastroenteritis in humans have spread globally. However, there are oceanic gene pools, one located in the oceans surrounding Asia and another in the Mexican Gulf. Frequent recombination means that most isolates have acquired the genetic profile of their current location. Within oceanic gene pools, there is nevertheless the opportunity for substructure, for example due to niche partitioning by different clones. We investigated this structure by calculating the effective population size in two different ways. Under standard population genetic models, the two estimates should give similar answers but we found a 30 fold difference. This discrepancy provides evidence for an ‘epigenotype’ model in which distinct ecotypes are maintained by selection on an otherwise homogeneous genetic background. To investigate the genetic factors involved, we used 54 unrelated isolates to conduct a genome wide scan for epistatically interacting loci. We found a single example of strong epistasis between distant genome regions. One of the genes involved in this interaction has previously been implicated in biofilm formation, while the other is a hypothetical protein. Further work will allow a detailed understanding of how selection acts to structure the pattern of variation within natural bacterial populations.

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

Posted on by
Reply

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

Anand Bhaskar, Y.X. Rachel Wang, Yun S. Song

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal which is difficult to pick up with small sample sizes. Lastly, we apply our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing dataset of tens of thousands of individuals assayed at a few hundred genic regions.

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

Posted on by
Reply

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.

Approximation to the distribution of fitness effects across functional categories in human segregating polymorphisms

Posted on by
Reply

Approximation to the distribution of fitness effects across functional categories in human segregating polymorphisms

Fernando Racimo, Joshua G Schraiber

Quantifying the proportion of polymorphic mutations that are deleterious or neutral is of fundamental importance to our understanding of evolution, disease genetics and the maintenance of variation genome-wide. Here, we develop an approximation to the distribution of fitness effects (DFE) of segregating single-nucleotide mutations in humans. Unlike previous methods, we do not assume that synonymous mutations are neutral or not strongly selected, and we do not rely on fitting the DFE of all new nonsynonymous mutations to a single probability distribution, which is poorly motivated on a biological level. We rely on a previously developed method that utilizes a variety of published annotations (including conservation scores, protein deleteriousness estimates and regulatory data) to score all mutations in the human genome based on how likely they are to be affected by negative selection, controlling for mutation rate. We map this score to a scale of fitness coefficients via maximum likelihood using diffusion theory and a Poisson random field model on SNP data. Our method serves to approximate the deleterious DFE of mutations that are segregating, regardless of their genomic consequence. We can then compare the proportion of mutations that are negatively selected or neutral across various categories, including different types of regulatory sites. We observe that the distribution of intergenic polymorphisms is highly peaked at neutrality, while the distribution of nonsynonymous polymorphisms is bimodal, with a neutral peak and a second peak at s ≈ −10^(−4). Other types of polymorphisms have shapes that fall roughly in between these two. We find that transcriptional start sites, strong CTCF-enriched elements and enhancers are the regulatory categories with the largest proportion of deleterious polymorphisms.

Parametric Inference using Persistence Diagrams: A Case Study in Population Genetics

Posted on by
Reply

Parametric Inference using Persistence Diagrams: A Case Study in Population Genetics

Kevin Emmett, Daniel Rosenbloom, Pablo Camara, Raul Rabadan
(Submitted on 18 Jun 2014)

Persistent homology computes topological invariants from point cloud data. Recent work has focused on developing statistical methods for data analysis in this framework. We show that, in certain models, parametric inference can be performed using statistics defined on the computed invariants. We develop this idea with a model from population genetics, the coalescent with recombination. We apply our model to an influenza dataset, identifying two scales of topological structure which have a distinct biological interpretation.

The rugged adaptive landscape of an emerging plant RNA virus

Posted on by
Reply

The rugged adaptive landscape of an emerging plant RNA virus

Jasna Lalic, Santiago F. Elena

RNA viruses are the main source of emerging infectious diseases owed to the evolutionary potential bestowed by their fast replication, large population sizes and high mutation and recombination rates. However, an equally important parameter, which is usually neglected, is the topography of the fitness landscape, that is, how many fitness maxima exist and how well connected they are, which determines the number of accessible evolutionary pathways. To address this question, we have reconstructed the fitness landscape describing the adaptation of Tobacco etch potyvirus to its new host, Arabidopsis thaliana. Fitness was measured for most of the genotypes in the landscape, showing the existence of peaks and holes. We found prevailing epistatic effects between mutations, with cases of reciprocal sign epistasis being common at latter stages. Therefore, results suggest that the landscape was rugged and holey, with several local fitness peaks and a very limited number of potential neutral paths. The viral genotype fixed at the end of the evolutionary process was not on the global fitness optima but stuck into a suboptimal peak.

Natural selection helps explain the small range of genetic variation within species

Posted on by
2

Natural selection helps explain the small range of genetic variation within species

Russell B. Corbett-Detig, Daniel L. Hartl, Timothy B. Sackton

The range of genetic diversity observed within natural populations is much more narrow than expected based on models of neutral molecular evolution. Although the increased efficacy of natural selection in larger populations has been invoked to explain this paradox, to date no tests of this hypothesis have been conducted. Here, we present an analysis of whole-genome polymorphism data and genetic maps from 39 species to estimate for each species the reduction in genetic variation attributable to the operation of natural selection on the genome. We find that species with larger population sizes do in fact show greater reductions in genetic variation. This finding provides the first experimental support for the hypothesis that natural selection contributes to the restricted range of within-species genetic diversity.

Recombination impacts damaging and disease mutations accumulation in human populations

Posted on by
Reply

Recombination impacts damaging and disease mutations accumulation in human populations

Julie Hussin, Alan Hodgkinson, Youssef Idaghdour, Jean-Christophe Grenier, Jean-Philippe Goulet, Elias Gbeha, Elodie Hip-Ki, Philip Awadalla

Many decades of theory have demonstrated that in non-recombining systems, slightly deleterious mutations accumulate non-reversibly, potentially driving the extinction of many asexual species. Non-recombining chromosomes in sexual organisms are thought to have degenerated in a similar fashion, however it is not clear the extent to which these processes operate along recombining chromosomes with highly variable rates of crossing over. Using high coverage sequencing data from over 1400 individuals, we show that recombination rate modulates the genomic distribution of putatively deleterious variants across the entire human genome. We find that exons in regions of low recombination are significantly enriched for deleterious and disease variants, a signature that varies in strength across worldwide human populations with different demographic histories. As low recombining regions are enriched for highly conserved genes with essential cellular functions and show an excess of mutations with demonstrated effect on health, this phenomenon likely affects disease susceptibility in humans.

Author post: Predicting evolution from the shape of genealogical trees

Posted on by
1

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.

Genomic, transcriptomic and phenomic variation reveals the complex adaptation of modern maize breeding

Posted on by
Reply

Genomic, transcriptomic and phenomic variation reveals the complex adaptation of modern maize breeding

Haijun Liu, Xiaqing Wang, Marilyn Warburton, Weiwei Wen, Minliang Jin, Min Deng, Jie Liu, Hao Tong, Qingchun Pan, Xiaohong Yang, Jianbing Yan

The temperate-tropical division of early maize germplasm to different agricultural environments was arguably the greatest adaptation process associated with the success and near ubiquitous importance of global maize production. Deciphering this history is challenging, but new insight has been gained from the genomic, transcriptomic and phenotypic variation collected from 368 diverse temperate and tropical maize inbred lines in this study. This is the first attempt to systematically explore the mechanisms of the adaptation process. Our results indicated that divergence between tropical and temperate lines seem occur 3,400-6,700 years ago. A number of genomic selection signals and transcriptomic variants including differentially expressed individual genes and rewired co-expression networks of genes were identified. These candidate signals were found to be functionally related to stress response and most were associated with directionally selected traits, which may have been an advantage under widely varying environmental conditions faced by maize as it was migrated away from its domestication center. It?s also clear in our study that such stress adaptation could involve evolution of protein-coding sequences as well as transcriptome-level regulatory changes. This latter process may be a more flexible and dynamic way for maize to adapt to environmental changes over this dramatically short evolutionary time frame.