Coalescent Simulation with msprime

The genetic basis of a recent transition to live-bearing in marine snails

Key innovations are fundamental to biological diversification, but their genetic basis is poorly understood. A recent transition from egg-laying to live-bearing in marine snails (Littorina spp.) provides the opportunity to study the genetic architecture of an innovation that has evolved repeatedly across animals. Individuals do not cluster by reproductive mode in a genome-wide phylogeny, but local genealogical analysis revealed numerous small genomic regions where all live-bearers carry the same core haplotype. Candidate regions show evidence for live-bearer-specific positive selection and are enriched for genes that are differentially expressed between egg-laying and live-bearing reproductive systems. Ages of selective sweeps suggest that live-bearer-specific alleles accumulated over more than 200,000 generations. Our results suggest that new functions evolve through the recruitment of many alleles rather than in a single evolutionary step.

Genomic diversity landscapes in outcrossing and selfing Caenorhabditis nematodes

Caenorhabditis nematodes form an excellent model for studying how the mode of reproduction affects genetic diversity, as some species reproduce via outcrossing whereas others can self-fertilize. Currently, chromosome-level patterns of diversity and recombination are only available for self-reproducing Caenorhabditis, making the generality of genomic patterns across the genus unclear given the profound potential influence of reproductive mode. Here we present a whole-genome diversity landscape, coupled with a new genetic map, for the outcrossing nematode C. remanei. We demonstrate that the genomic distribution of recombination in C. remanei, like the model nematode C. elegans, shows high recombination rates on chromosome arms and low rates toward the central regions. Patterns of genetic variation across the genome are also similar between these species, but differ dramatically in scale, being tenfold greater for C. remanei. Historical reconstructions of variation in effective population size over the past million generations echo this difference in polymorphism. Evolutionary simulations demonstrate how selection, recombination, mutation, and selfing shape variation along the genome, and that multiple drivers can produce patterns similar to those observed in natural populations. The results illustrate how genome organization and selection play a crucial role in shaping the genomic pattern of diversity whereas demographic processes scale the level of diversity across the genome as a whole.

Mutation rates and adaptive variation among the clinically dominant clusters of Mycobacterium abscessus

Mycobacterium abscessus (Mab) is a multidrug-resistant pathogen increasingly responsible for severe pulmonary infections. Analysis of whole-genome sequences (WGS) of Mab demonstrates dense genetic clustering of clinical isolates collected from disparate geographic locations. This has been interpreted as supporting patient-to-patient transmission, but epidemiological studies have contradicted this interpretation. Here, we present evidence for a slowing of the Mab molecular clock rate coincident with the emergence of phylogenetic clusters. We performed phylogenetic inference using publicly available WGS from 483 Mab patient isolates. We implement a subsampling approach in combination with coalescent analysis to estimate the molecular clock rate along the long internal branches of the tree, indicating a faster long-term molecular clock rate compared to branches within phylogenetic clusters. We used ancestry simulation to predict the effects of clock rate variation on phylogenetic clustering and found that the degree of clustering in the observed phylogeny is more easily explained by a clock rate slowdown than by transmission. We also find that phylogenetic clusters are enriched in mutations affecting DNA repair machinery and report that clustered isolates have lower spontaneous mutation rates in vitro. We propose that Mab adaptation to the host environment through variation in DNA repair genes affects the organism's mutation rate and that this manifests as phylogenetic clustering. These results challenge the model that phylogenetic clustering in Mab is explained by person-to-person transmission and inform our understanding of transmission inference in emerging, facultative pathogens.

Dispersal inference from population genetic variation using a convolutional neural network

The geographic nature of biological dispersal shapes patterns of genetic variation over landscapes, making it possible to infer properties of dispersal from genetic variation data. Here, we present an inference tool that uses geographically distributed genotype data in combination with a convolutional neural network to estimate a critical population parameter: the mean per-generation dispersal distance. Using extensive simulation, we show that our deep learning approach is competitive with or outperforms state-of-the-art methods, particularly at small sample sizes. In addition, we evaluate varying nuisance parameters during training-including population density, demographic history, habitat size, and sampling area-and show that this strategy is effective for estimating dispersal distance when other model parameters are unknown. Whereas competing methods depend on information about local population density or accurate inference of identity-by-descent tracts, our method uses only single-nucleotide-polymorphism data and the spatial scale of sampling as input. Strikingly, and unlike other methods, our method does not use the geographic coordinates of the genotyped individuals. These features make our method, which we call "disperseNN," a potentially valuable new tool for estimating dispersal distance in nonmodel systems with whole genome data or reduced representation data. We apply disperseNN to 12 different species with publicly available data, yielding reasonable estimates for most species. Importantly, our method estimated consistently larger dispersal distances than mark-recapture calculations in the same species, which may be due to the limited geographic sampling area covered by some mark-recapture studies. Thus genetic tools like ours complement direct methods for improving our understanding of dispersal.

Inferring the Demographic History and Inheritance Mode of Tetraploid Species Using ABC

Genomic patterns of diversity and divergence are impacted by certain life history traits, reproductive systems, and demographic history. The latter is characterized by fluctuations in population sizes over time, as well as by temporal patterns of introgression. For a given organism, identifying a demographic history that deviates from the standard neutral model allows a better understanding of its evolution but also helps to reduce the risk of false positives when screening for molecular targets of natural selection. Tetraploid organisms and beyond have demographic histories that are complicated by the mode of polyploidization, the mode of inheritance, and different scenarios of gene flow between sub-genomes and diploid parental species. Here we provide guidelines for experimenters wishing to address these issues through a flexible statistical framework: approximate Bayesian computation (ABC). The emphasis is on the general philosophy of the approach to encourage future users to exploit the enormous flexibility of ABC beyond the limitations imposed by generalist data analysis pipelines.

Simulation-Based Evaluation of Methods, Data Types, and Temporal Sampling Schemes for Detecting Recent Population Declines

Understanding recent population trends is critical to quantifying species vulnerability and implementing effective management strategies. To evaluate the accuracy of genomic methods for quantifying recent declines (beginning <120 generations ago), we simulated genomic data using forward-time methods (SLiM) coupled with coalescent simulations (msprime) under a number of demographic scenarios. We evaluated both site frequency spectrum (SFS)-based methods (momi2, Stairway Plot) and methods that employ linkage disequilibrium information (NeEstimator, GONE) with a range of sampling schemes (contemporary-only samples, sampling two time points, and serial sampling) and data types (RAD-like data and whole-genome sequencing). GONE and momi2 performed best overall, with >80% power to detect severe declines with large sample sizes. Two-sample and serial sampling schemes could accurately reconstruct changes in population size, and serial sampling was particularly valuable for making accurate inferences when genotyping errors or minor allele frequency cutoffs distort the SFS or under model mis-specification. However, sampling only contemporary individuals provided reliable inferences about contemporary size and size change using either site frequency or linkage-based methods, especially when large sample sizes or whole genomes from contemporary populations were available. These findings provide a guide for researchers designing genomics studies to evaluate recent demographic declines.

XSI-a genotype compression tool for compressive genomics in large biobanks

MOTIVATION

Generation of genotype data has been growing exponentially over the last decade. With the large size of recent datasets comes a storage and computational burden with ever increasing costs. To reduce this burden, we propose XSI, a file format with reduced storage footprint that also allows computation on the compressed data and we show how this can improve future analyses.

RESULTS

We show that xSqueezeIt (XSI) allows for a file size reduction of 4-20× compared with compressed BCF and demonstrate its potential for 'compressive genomics' on the UK Biobank whole-genome sequencing genotypes with 8× faster loading times, 5× faster run of homozygozity computation, 30× faster dot products computation and 280× faster allele counts.

AVAILABILITY AND IMPLEMENTATION

The XSI file format specifications, API and command line tool are released under open-source (MIT) license and are available at https://github.com/rwk-unil/xSqueezeIt.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Haplotype and population structure inference using neural networks in whole-genome sequencing data

Accurate inference of population structure is important in many studies of population genetics. Here we present HaploNet, a method for performing dimensionality reduction and clustering of genetic data. The method is based on local clustering of phased haplotypes using neural networks from whole-genome sequencing or dense genotype data. By using Gaussian mixtures in a variational autoencoder framework, we are able to learn a low-dimensional latent space in which we cluster haplotypes along the genome in a highly scalable manner. We show that we can use haplotype clusters in the latent space to infer global population structure using haplotype information by exploiting the generative properties of our framework. Based on fitted neural networks and their latent haplotype clusters, we can perform principal component analysis and estimate ancestry proportions based on a maximum likelihood framework. Using sequencing data from simulations and closely related human populations, we show that our approach is better at distinguishing closely related populations than standard admixture and principal component analysis software. We further show that HaploNet is fast and highly scalable by applying it to genotype array data of the UK Biobank.

Efficient ancestry and mutation simulation with msprime 1.0

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.

fastsimcoal2: demographic inference under complex evolutionary scenarios

Motivation

fastsimcoal2 extends fastsimcoal, a continuous time coalescent-based genetic simulation program, by enabling the estimation of demographic parameters under very complex scenarios from the site frequency spectrum under a maximum-likelihood framework.

Results

Other improvements include multi-threading, handling of population inbreeding, extended input file syntax facilitating the description of complex demographic scenarios, and more efficient simulations of sparsely structured populations and of large chromosomes.

Availability and implementation

fastsimcoal2 is freely available on http://cmpg.unibe.ch/software/fastsimcoal2/. It includes console versions for Linux, Windows and MacOS, additional scripts for the analysis and visualization of simulated and estimated scenarios, as well as a detailed documentation and ready-to-use examples.

Imprints of Past Habitat Area Reduction on Extant Taxonomic, Functional, and Phylogenetic Composition

Past environmental changes have shaped the evolutionary and ecological diversity of extant organisms. Specifically, climatic fluctuations have made environmental conditions alternatively common or rare over time. Accordingly, most taxa have undergone restriction of their distribution to local refugia during habitat contraction, from which they could expand when suitable habitat became more common. Assessing how past restrictions in refugia have shaped species distributions and genetic diversity has motivated much research in evolutionary biology and biogeography. But there is still lack of clear synthesis on whether and how the taxonomic, functional and phylogenetic composition of extant multispecies assemblages retains the imprint of past restriction in refugia. We devised an original eco-evolutionary model to investigate the temporal dynamics of a regional species pool inhabiting a given habitat today, and which have experienced habitat reduction in the past. The model includes three components: (i) a demographic component driving stochastic changes in population sizes and extinctions due to habitat availability, (ii) a mutation and speciation component representing how divergent genotypes emerge and define new species over time, and (iii) a trait evolution component representing how trait values have changed across descendants over time. We used this model to simulate dynamics of multispecies assemblages that occupied a restricted refugia in the past and could expand their distribution subsequently. We characterized the past restriction in refugia in terms of two parameters representing the ending time of past refugia, and the extent of habitat restriction in the refugia. We characterized extant patterns of taxonomic, functional and phylogenetic diversity depending on these parameters. We found that extant relative abundances reflect the lasting influence of more recent refugia on demographic dynamics, while phylogenetic composition reflects the influence of more ancient habitat change. Extant functional diversity depends on the interplay between diversification dynamics and trait evolution, offering new options to jointly infer current trait adaptation and past trait evolution dynamics.