Adam Siepel and colleagues develop a statistical method, fitCons, which combines comparative and functional genomic data to estimate the probability that a point mutation will influence fitness. They generate fitCons scores for three human cell types from ENCODE data sets and demonstrate improved prediction power for cis regulatory elements in comparison to conventional conservation-based scores. We describe a new computational method for estimating the probability that a point mutation at each position in a genome will influence fitness. These 'fitness consequence' (fitCons) scores serve as evolution-based measures of potential genomic function. Our approach is to cluster genomic positions into groups exhibiting distinct 'fingerprints' on the basis of high-throughput functional genomic data, then to estimate a probability of fitness consequences for each group from associated patterns of genetic polymorphism and divergence. We have generated fitCons scores for three human cell types on the basis of public data from ENCODE. In comparison with conventional conservation scores, fitCons scores show considerably improved prediction power for cis regulatory elements. In addition, fitCons scores indicate that 4.2–7.5% of nucleotides in the human genome have influenced fitness since the human-chimpanzee divergence, and they suggest that recent evolutionary turnover has had limited impact on the functional content of the genome.

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. Stdpopsim is 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 how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Genome admixture in two endemic North American wolf species.

Significance: Genome-wide scans can identify differentiated loci between species that may have promoted speciation. So-called “islands of differentiation” have generally been identified and characterized using standard population genetic summary statistics (e.g., F _ST and d _xy ), which are limited in distinguishing among possible causes of differentiation, such as selection against gene flow and selective sweeps. We introduce a powerful strategy for analyzing such islands, combining new methods for inferring the full ancestral recombination graph and machine learning methods for identifying selective sweeps. We applied our methods to genomic sequences from closely related southern capuchino seedeaters (Neotropical tanagers) and found signatures of recent selective sweeps around pigmentation genes, including many soft sweeps that acted on standing variation.

The maritime Phoenician civilization from the Levant transformed the entire Mediterranean during the first millennium bce^1,2,3. However, the extent of human movement between the Levantine Phoenician homeland and Phoenician–Punic settlements in the central and western Mediterranean has been unclear in the absence of comprehensive ancient DNA studies. Here, we generated genome-wide data for 210 individuals, including 196 from 14 sites traditionally identified as Phoenician and Punic in the Levant, North Africa, Iberia, Sicily, Sardinia and Ibiza, and an early Iron Age individual from Algeria. Levantine Phoenicians made little genetic contribution to Punic settlements in the central and western Mediterranean between the sixth and second centuries bce, despite abundant archaeological evidence of cultural, historical, linguistic and religious links⁴. Instead, these inheritors of Levantine Phoenician culture derived most of their ancestry from a genetic profile similar to that of Sicily and the Aegean. Much of the remaining ancestry originated from North Africa, reflecting the growing influence of Carthage⁵. However, this was a minority contributor of ancestry in all of the sampled sites, including in Carthage itself. Different Punic sites across the central and western Mediterranean show similar patterns of high genetic diversity. We also detect genetic relationships across the Mediterranean, reflecting shared demographic processes that shaped the Punic world.

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here, we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsim’s core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.