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Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics.Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators. For this we use the model-based estimators introduced by Fu, Futschik et al., and Watterson that minimize the variance or mean square error for no and free recombination. We find that neural networks reproduce these estimators if provided with the appropriate features and training sets. Remarkably, using the model-based estimators to adjust the weights of the training data, only one hidden layer is necessary to obtain a single estimator that performs almost as well as model-based estimators for low and high recombination rates, and at the same time provides a superior estimation method for intermediate recombination rates. We apply the method to simulated data based on the human chromosome 2 recombination map, highlighting its robustness in a realistic setting where local recombination rates vary and/or are unknown.

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introducemsprimeversion 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure andtskitlibrary. We summarisemsprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

How microbes affect host fitness and environmental adaptation has become a fundamental research question in evolutionary biology. We tested for associations of bacterial genomic variation and Drosophila melanogaster offspring number in a microbial Genome Wide Association Study (GWAS). Leveraging strain variation in the genus Gluconobacter, a genus of bacteria that are commonly associated with Drosophila under natural conditions, we pinpoint the thiamine biosynthesis pathway (TBP) as contributing to differences in fitness conferred to the fly host. By tracing the evolutionary history of TBP genes in Gluconobacter, we find that TBP genes were most likely lost and reacquired by horizontal gene transfer (HGT). We suggest that HGT might contribute to microbiome flexibility and speculate that it can also more generally contribute to host adaptation.

The inference of biogeographic ancestry (BGA) has become a focus of forensic genetics. Misinference of BGA can have profound unwanted consequences for investigations and society. We show that recent admixture can lead to misclassification and erroneous inference of ancestry proportions, using state of the art analysis tools with (i) simulations, (ii) 1000 genomes project data, and (iii) two individuals analyzed using the ForenSeq DNA Signature Prep Kit. Subsequently, we extend existing tools for estimation of individual ancestry (IA) by allowing for different IA in both parents, leading to estimates of parental individual ancestry (PIA), and a statistical test for recent admixture. Estimation of PIA outperforms IA in most scenarios of recent admixture. Furthermore, additional information about parental ancestry can be acquired with PIA that may guide casework.HighlightsWe improve statistical methods as used in STRUCTURE and ADMIXTURE for Biogeographical Ancestry (BGA) inference to account for recent admixture, i.e. different admixture of both parents.The resulting recent admixture model has a higher accuracy in estimating individual admixture in most cases.We give a likelihood ratio test for recent admixture, which is both highly specific and sensitive for recent admixture.We find evidence of recent admixture in the 1000 genomes dataset.The self-report on recent admixture of two self-sequenced samples was only confirmed in one case.

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource,stdpopsim, that attempts to rectify this situation.Stdpopsimis a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating howstdpopsimcan be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Inference of the Biogeographical Ancestry (BGA) of a person or trace relies on three ingredients: (1) a reference database of DNA samples including BGA information; (2) a statistical clustering method; (3) a set of loci which segregate dependent on geographical location, i.e. a set of so-called Ancestry Informative Markers (AIMs). We used the theory of feature selection from statistical learning in order to obtain AIMsets for BGA inference. Using simulations, we show that this learning procedure works in various cases, and outperforms ad hoc methods, based on statistics like FST or informativeness for the choice of AIMs. Applying our method to data from the 1000 genomes project (excluding Admixed Americans) we identified an AIMset of 12 SNPs, which gives a vanishing misclassification error on a continental scale, as do other published AIMsets. In fact, cross validation shows that there exists a multitude of sets with comparable performance to the optimal AIMset. On a sub-continental scale, we find a set of 55 SNPs for distinguishing the five European populations. The misclassification error is reduced by a factor of two relative to published AIMsets, but is still 30% and therefore too large in order to be useful in forensic applications.

The Passing-Bablok and Theil-Sen regression are closely related non-parametric methods to estimate the regression coefficients and build tests on the relationship between the dependent and independent variables. Both methods rely on the slopes of the connecting lines between pairwise measurements. While Theil and Sen assume no measurement errors in the independent variable, the method from Passing and Bablok accounts for errors in both variables. Here we consider the case where multiple, e.g. repeated, measurements with errors in both variables are available for m samples. We show that in this case the slopes between repeated measurements need to be excluded to obtain an unbiased estimate. We prove that the resulting Block-Passing-Bablok estimate for grouped data is asymptotically normally distributed. If measurements of the independent variable are without error the variance of the estimate equals the variance of the Theil-Sen method with tied ranks. If both variables are measured with imprecision the result depends on the fraction of measurements between groups that fall within the range of each other. Only if no overlap between measurements of different groups occurs the variance equals again the tied ranks version. Otherwise, the variance is smaller. We explicitly compute this variance and provide a method comparison test for data with repeated measurements based on the method from Passing and Bablok for independent measurements. If repeated measurements are considered this test has a higher power to detect the true relationship between two methods., Comment: 16 pages, 4 figures, 1 table

The differences between DNA-sequences within a population are the basis to infer the ancestral relationship of the individuals. Within the classical infinitely many sites model, it is possible to estimate the mutation rate based on the site frequency spectrum, which is comprised by the numbers $C_1,...,C_{n-1}$, where n is the sample size and $C_s$ is the number of site mutations (Single Nucleotide Polymorphisms, SNPs) which are seen in $s$ genomes. Classical results can be used to compare the observed site frequency spectrum with its neutral expectation, $E[C_s]= \theta_2/s$, where $\theta_2$ is the scaled site mutation rate. In this paper, we will relax the assumption of the infinitely many sites model that all individuals only carry homologous genetic material. Especially, it is today well-known that bacterial genomes have the ability to gain and lose genes, such that every single genome is a mosaic of genes, and genes are present and absent in a random fashion, giving rise to the dispensable genome. While this presence and absence has been modeled under neutral evolution within the infinitely many genes model in previous papers, we link presence and absence of genes with the numbers of site mutations seen within each gene. In this work we derive a formula for the expectation of the joint gene and site frequency spectrum, denotes $G_{k,s}$ the number of mutated sites occurring in exactly $s$ gene sequences, while the corresponding gene is present in exactly $k$ individuals. We show that standard estimators of $\theta_2$ for dispensable genes are biased and that the site frequency spectrum for dispensable genes differs from the classical result., Comment: 24 pages, 8 figures

Today, the CRISPR (clustered regularly interspaced short palindromic repeats) region within bacterial and archaeal genomes is known to encode an adaptive immune system. We rely on previous results on the evolution of the CRISPR arrays, which led to the ordered independent loss model, introduced by Kupczok and Bollback (2013). When focusing on the spacers (between the repeats), new elements enter a CRISPR array at rate $\theta$ at the leader end of the array, while all spacers present are lost at rate $\rho$ along the phylogeny relating the sample. Within this model, we compute the distribution of distances of spacers which are present in all arrays in samples of size $n=2$ and $n=3$. We use these results to estimate the loss rate $\rho$ from spacer array data., Comment: 20 pages, 3 figures, added a more general result and a discussion section, changed the title

Horizontal transfer, gene loss, and duplication result in dynamic bacterial genomes shaped by a complex mixture of different modes of evolution. Closely related strains can differ in the presence or absence of many genes, and the total number of distinct genes found in a set of related isolates – the pan-genome – is often many times larger than the genome of individual isolates. We have developed a pipeline that efficiently identifies orthologous gene clusters in the pan-genome. This pipeline is coupled to a powerful yet easy-to-use web-based visualization software for interactive exploration of the pan-genome. The visualization consists of connected components that allow rapid filtering and searching of genes and inspection of their evolutionary history. For each gene cluster, panX displays an alignment, a phylogenetic tree, maps mutations within that cluster to the branches of the tree and infers gain and loss of genes on the core-genome phylogeny. PanX is available at pangenome.de. Custom pan-genomes can be visualized either using a webserver or by serving panX locally as a browser-based application.

The genome of bacterial species is much more flexible than that of eukaryotes. Moreover, the distributed genome hypothesis for bacteria states that the total number of genes present in a bacterial population is greater than the genome of every single individual. The pangenome, i.e. the set of all genes of a bacterial species (or a sample), comprises the core genes which are present in all living individuals, and accessory genes, which are carried only by some individuals. In order to use accessory genes for adaptation to environmental forces, genes can be transferred horizontally between individuals. Here, we extend the infinitely many genes model from Baumdicker, Hess and Pfaffelhuber (2010) for horizontal gene transfer. We take a genealogical view and give a construction -- called the Ancestral Gene Transfer Graph -- of the joint genealogy of all genes in the pangenome. As application, we compute moments of several statistics (e.g. the number of differences between two individuals and the gene frequency spectrum) under the infinitely many genes model with horizontal gene transfer., Comment: 31 pages, 3 figures

The distributed genome hypothesis states that the set of genes in a population of bacteria is distributed over all individuals that belong to the specific taxon. It implies that certain genes can be gained and lost from generation to generation. We use the random genealogy given by a Kingman coalescent in order to superimpose events of gene gain and loss along ancestral lines. Gene gains occur at a constant rate along ancestral lines. We assume that gained genes have never been present in the population before. Gene losses occur at a rate proportional to the number of genes present along the ancestral line. In this infinitely many genes model we derive moments for several statistics within a sample: the average number of genes per individual, the average number of genes differing between individuals, the number of incongruent pairs of genes, the total number of different genes in the sample and the gene frequency spectrum. We demonstrate that the model gives a reasonable fit with gene frequency data from marine cyanobacteria., Published in at http://dx.doi.org/10.1214/09-AAP657 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)