Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce here a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. In contrast to Approximate Bayesian Computation, another likelihood-free approach widely used in population genetics and other fields, deep learning does not require a distance function on summary statistics or a rejection step, and it is robust to the addition of uninformative statistics. To demonstrate that deep learning can be effectively employed to estimate population genetic parameters and learn informative features of data, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme, likely due in part to the unaccounted impact of selection.

Targeted enrichment of conserved and ultraconserved genomic elements allows universal collection of phylogenomic data from thousands of species. Prior to downstream inference, data from these types of targeted enrichment studies must undergo pre-processing to assemble contigs from sequence data; identify targeted, enriched loci from the off-target background data; align enriched contigs representing conserved loci to one another; and prepare and manipulate these alignments for subsequent phylogenomic inference. PHYLUCE is an efficient and easy-to-install software package that accomplishes these tasks across hundreds of taxa and thousands of enriched loci.

During his well-known debate with Fisher regarding the phenotypic dataset of Panaxia dominula, Wright (1948) suggested fluctuating selection as a potential explanation for the observed change in frequency. This model has since been invoked in a number of analyses, with the focus of discussion centering mainly on random or oscillatory fluctuations of selection intensities. Here, we present a novel method to consider non-random changes in selection intensities using Wright-Fisher approximate Bayesian (ABC)-based approaches, in order to detect and evaluate a change in selection strength from time-sampled data. This novel method jointly estimates the position of a change point as well as the strength of both corresponding selection coefficients (and dominance for diploid cases) from the allele trajectory. The simulation studies of CP-WFABC reveal the combinations of parameter ranges and input values that optimize performance, thus indicating optimal experimental design strategies. We apply this approach to both the historical dataset of Panaxia dominula in order to shed light on this historical debate, as well as to whole-genome time-serial data from influenza virus in order to identify sites with changing selection intensities in response to drug treatment.

Biodiversity loss is a global problem and island species/populations are particularly vulnerable to such loss. Low genetic diversity is one of the factors that can lead a population to extinction. Loss of bee populations is of particular concern because of the knock-on consequences for the pollination guilds that the lost bees once serviced. Here we evaluate the genetic structure of the bumble bee Bombus morio populations on the mainland of South East Brazil and on nearby islands. We analyzed a total of 659 individuals from 24 populations by sequencing two mitochondrial genes (COI and Cytb) and using 14 microsatellite loci. Levels of diversity were high in most of populations and were similar on islands and the mainland. Furthermore, genetic diversity was not significantly correlated with island area, although it was lower in populations from distant islands. Our data suggest that long-term isolation on islands is not affecting the population viability of this species. This may be attributed to the high dispersal ability of B. morio, its capacity to suvive in urban environments, and the characteristics of the studied islands.

Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving population’s mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50,000 generations were better described by a power-law model than by a hyperbolic model. According to the power-law model, the rate of fitness gain declines over time but fitness has no upper limit, whereas the hyperbolic model implies a hard limit. Here, we examine whether the previously estimated power-law model predicts the fitness trajectory for an additional 10,000 generations. To that end, we conducted more than 1100 new competitive fitness assays. Consistent with the previous study, the power-law model fits the new data better than the hyperbolic model. We also analysed the variability in fitness among populations, finding subtle, but significant, heterogeneity in mean fitness. Some, but not all, of this variation reflects differences in mutation rate that evolved over time. Taken together, our results imply that both adaptation and divergence can continue indefinitely-or at least for a long time-even in a constant environment.

Motivation: The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive. Results: We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies. Availability: FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com.

Hybridization is recognized as a powerful mechanism of speciation and a driving force in generating biodiversity. However, only few multicellular species, limited to a handful of plants and animals, have been shown to fulfill all the criteria of homoploid hybrid speciation. This lack of evidence could lead to the misconception that speciation by hybridization has a limited role in eukaryotes, particularly in single-celled organisms. Laboratory experiments have revealed that fungi such as budding yeasts can rapidly develop reproductive isolation and novel phenotypes through hybridization, showing that in principle homoploid speciation could occur in nature. Here we report a case of homoploid hybrid speciation in natural populations of the budding yeast Saccharomyces paradoxus inhabiting the North American forests. We show that the rapid evolution of chromosome architecture and an ecological context that led to secondary contact between nascent species drove the formation of an incipient hybrid species with a potentially unique ecological niche.

Deer farming is a significant international industry. For genetic improvement, using genomic tools, an ordered array of DNA variants and associated flanking sequence across the genome is required. This work reports a comparative assembly of the deer genome and subsequent DNA variant identification. Next generation sequencing combined with an existing bovine reference genome enabled the deer genome to be assembled sufficiently for large-scale SNP discovery. In total, 28 Gbp of sequence data were generated from seven Cervus elaphus (European red deer and Canadian elk) individuals. After aligning sequence to the bovine reference genome build UMD 3.0 and binning reads into one Mbp groups; reads were assembled and analyzed for SNPs. Greater than 99% of the non-repetitive fraction of the bovine genome was covered by deer chromosomal scaffolds. We identified 1.8 million SNPs meeting Illumina InfiniumII SNP chip technical threshold. Markers on the published Red x Pere David deer linkage map were aligned to both UMD3.0 and the new deer chromosomal scaffolds. This enabled deer linkage groups to be assigned to deer chromosomal scaffolds, although the mapping locations remain based on bovine order. Genotyping of 270 SNPs on a Sequenom MS system showed that 88% of SNPs identified could be amplified. Also, inheritance patterns showed no evidence of departure from Hardy-Weinberg equilibrium. A comparative assembly of the deer genome, alignment with existing deer genetic linkage groups and SNP discovery has been successfully completed and validated facilitating application of genomic technologies for subsequent deer genetic improvement.