Study sample sizes in human genetics are growing rapidly, and in due course it will become routine to analyze samples with hundreds of thousands, if not millions, of individuals. In addition to posing computational challenges, such large sample sizes call for carefully reexamining the theoretical foundation underlying commonly used analytical tools. Here, we study the accuracy of the coalescent, a central model for studying the ancestry of a sample of individuals. The coalescent arises as a limit of a large class of random mating models, and it is an accurate approximation to the original model provided that the population size is sufficiently larger than the sample size. We develop a method for performing exact computation in the discrete-time Wright-Fisher (DTWF) model and compare several key genealogical quantities of interest with the coalescent predictions. For recently inferred demographic scenarios, we find that there are a significant number of multiple- and simultaneous-merger events under the DTWF model, which are absent in the coalescent by construction. Furthermore, for large sample sizes, there are noticeable differences in the expected number of rare variants between the coalescent and the DTWF model. To balance the trade-off between accuracy and computational efficiency, we propose a hybrid algorithm that uses the DTWF model for the recent past and the coalescent for the more distant past. Our results demonstrate that the hybrid method with only a handful of generations of the DTWF model leads to a frequency spectrum that is quite close to the prediction of the full DTWF model.

Early embryogenesis is a unique developmental stage where genetic control of development is handed off from mother to zygote. Yet the contribution of this transition to the evolution of gene expression is poorly understood. Here we study two aspects of gene expression specific to early embryogenesis in Drosophila: sex-biased gene expression prior to the onset of canonical X chromosomal dosage compensation, and the contribution of maternally supplied mRNAs. We sequenced mRNAs from individual unfertilized eggs and precisely staged and sexed blastoderm embryos, and compared levels between D. melanogaster, D. yakuba, D. pseudoobscura and D. virilis. First, we find that mRNA content is highly conserved for a given stage and that studies relying on pooled embryos likely systematically overstate the degree of gene expression divergence. Unlike studies done on larvae and adults where most species show a larger proportion of genes with male-biased expression, we find that transcripts in Drosophila embryos are largely female-biased in all species, likely due to incomplete dosage compensation prior to the activation of the canonical dosage compensation mechanism. The divergence of sex-biased gene expression across species is observed to be often due to lineage-specific decrease of expression; the most drastic example of which is the overall reduction of male expression from the neo-X chromosome in D. pseudoobscura, leading to a pervasive female-bias on this chromosome. We see no evidence for a faster evolution of expression on the X chromosome in embryos (no "faster-X" effect), unlike in adults, and contrary to a previous study on pooled non-sexed embryos. Finally, we find that most genes are conserved in regard to their maternal or zygotic origin of transcription, and present evidence that differences in maternal contribution to the blastoderm transcript pool may be due to species-specific divergence of transcript degradation rates.

Human diets differ from those of non-human primates. Among few obvious differences, humans consume more meat than most non-human primates and regularly cook their food. It is hypothesized that a dietary shift during human evolution has been accompanied by molecular adaptations in metabolic pathways. Consistent with this notion, comparative studies of gene expression levels in primates have found that the regulation of genes with metabolic functions tend to evolve rapidly in the human lineage. The metabolic consequences of these regulatory differences, however, remained unknown. To address this gap, we performed a comparative study using a combination of gene expression and metabolomic profiling in livers from humans, chimpanzees, and rhesus macaques. We show that dietary differences between species have a strong effect on metabolic concentrations. In addition, we found that differences in metabolic concentration across species are correlated with inter-species differences in the expression of the corresponding enzymes, which control the same metabolic reaction. We identified a number of metabolic compounds with lineage-specific profiles, including examples of human-species metabolic differences that may be directly related to dietary differences.

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: "What can causal networks tell us about metabolic pathways?". Using data from an Arabidopsis Bay[Formula: see text]Sha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies.

Duplications play a significant role in both extremes of the phenotypic spectrum of newly arising mutations: they can have severe deleterious effects (e.g. duplications underlie a variety of diseases) but can also be highly advantageous. The phenotypic potential of newly arisen duplications has stimulated wide interest in both the mutational and selective processes shaping these variants in the genome. Here we take advantage of the Drosophila simulans-Drosophila melanogaster genetic system to further our understanding of both processes. Regarding mutational processes, the study of two closely related species allows investigation of the potential existence of shared duplication hotspots, and the similarities and differences between the two genomes can be used to dissect its underlying causes. Regarding selection, the difference in the effective population size between the two species can be leveraged to ask questions about the strength of selection acting on different classes of duplications. In this study, we conducted a survey of duplication polymorphisms in 14 different lines of D. simulans using tiling microarrays and combined it with an analogous survey for the D. melanogaster genome. By integrating the two datasets, we identified duplication hotspots conserved between the two species. However, unlike the duplication hotspots identified in mammalian genomes, Drosophila duplication hotspots are not associated with sequences of high sequence identity capable of mediating non-allelic homologous recombination. Instead, Drosophila duplication hotspots are associated with late-replicating regions of the genome, suggesting a link between DNA replication and duplication rates. We also found evidence supporting a higher effectiveness of selection on duplications in D. simulans than in D. melanogaster. This is also true for duplications segregating at high frequency, where we find evidence in D. simulans that a sizeable fraction of these mutations is being driven to fixation by positive selection.

Next-generation sequencing technology has facilitated the discovery of millions of genetic variants in human genomes. A sizeable fraction of these variants are predicted to be deleterious. Here, we review the pattern of deleterious alleles as ascertained in genome sequencing data sets and ask whether human populations differ in their predicted burden of deleterious alleles - a phenomenon known as mutation load. We discuss three demographic models that are predicted to affect mutation load and relate these models to the evidence (or the lack thereof) for variation in the efficacy of purifying selection in diverse human genomes. We also emphasize why accurate estimation of mutation load depends on assumptions regarding the distribution of dominance and selection coefficients - quantities that remain poorly characterized for current genomic data sets.

Next-generation sequencing technology has facilitated the discovery of millions of genetic variants in human genomes. A sizeable fraction of these variants are predicted to be deleterious. Here, we review the pattern of deleterious alleles as ascertained in genome sequencing data sets and ask whether human populations differ in their predicted burden of deleterious alleles - a phenomenon known as mutation load. We discuss three demographic models that are predicted to affect mutation load and relate these models to the evidence (or the lack thereof) for variation in the efficacy of purifying selection in diverse human genomes. We also emphasize why accurate estimation of mutation load depends on assumptions regarding the distribution of dominance and selection coefficients - quantities that remain poorly characterized for current genomic data sets.

The importance of epistasis--non-additive interactions between alleles--in shaping population fitness has long been a controversial topic, hampered in part by lack of empirical evidence. Traditionally, epistasis is inferred on the basis of non-independence of genotypic values between loci for a given trait. However, epistasis for fitness should also have a genomic footprint. To capture this signal, we have developed a simple approach that relies on detecting genotype ratio distortion as a sign of epistasis, and we apply this method to a large panel of Drosophila melanogaster recombinant inbred lines. Here we confirm experimentally that instances of genotype ratio distortion represent loci with epistatic fitness effects; we conservatively estimate that any two haploid genomes in this study are expected to harbour 1.15 pairs of epistatically interacting alleles. This observation has important implications for speciation genetics, as it indicates that the raw material to drive reproductive isolation is segregating contemporaneously within species and does not necessarily require, as proposed by the Dobzhansky-Muller model, the emergence of incompatible mutations independently derived and fixed in allopatry. The relevance of our result extends beyond speciation, as it demonstrates that epistasis is widespread but that it may often go undetected owing to lack of statistical power or lack of genome-wide scope of the experiments.

The trillions of microbes living in the gut-the gut microbiota-play an important role in human biology and disease. While much has been done to explore its diversity, a full understanding of our microbiomes demands an evolutionary perspective. In this review, we compare microbiomes from human populations, placing them in the context of microbes from humanity's near and distant animal relatives. We discuss potential mechanisms to generate host-specific microbiome configurations and the consequences of disrupting those configurations. Finally, we propose that this broader phylogenetic perspective is useful for understanding the mechanisms underlying human-microbiome interactions.

A central goal of population genetics is to understand how genetic drift, natural selection, and gene flow shape allele frequencies through time. However, the actual processes underlying these changes-variation in individual survival, reproductive success, and movement-are often difficult to quantify. Fully understanding these processes requires the population pedigree, the set of relationships among all individuals in the population through time. Here, we use extensive pedigree and genomic information from a long-studied natural population of Florida Scrub-Jays (Aphelocoma coerulescens) to directly characterize the relative roles of different evolutionary processes in shaping patterns of genetic variation through time. We performed gene dropping simulations to estimate individual genetic contributions to the population and model drift on the known pedigree. We found that observed allele frequency changes are generally well predicted by accounting for the different genetic contributions of founders. Our results show that the genetic contribution of recent immigrants is substantial, with some large allele frequency shifts that otherwise may have been attributed to selection actually due to gene flow. We identified a few SNPs under directional short-term selection after appropriately accounting for gene flow. Using models that account for changes in population size, we partitioned the proportion of variance in allele frequency change through time. Observed allele frequency changes are primarily due to variation in survival and reproductive success, with gene flow making a smaller contribution. This study provides one of the most complete descriptions of short-term evolutionary change in allele frequencies in a natural population to date.