Explore the vast range of titles available.

The current rate of species extinction is rapidly approaching unprecedented highs, and life on Earth presently faces a sixth mass extinction event driven by anthropogenic activity, climate change, and ecological collapse. The field of conservation genetics aims at preserving species by using their levels of genetic diversity, usually measured as neutral genome-wide diversity, as a barometer for evaluating population health and extinction risk. A fundamental assumption is that higher levels of genetic diversity lead to an increase in fitness and long-term survival of a species. Here, we argue against the perceived importance of neutral genetic diversity for the conservation of wild populations and species. We demonstrate that no simple general relationship exists between neutral genetic diversity and the risk of species extinction. Instead, a better understanding of the properties of functional genetic diversity, demographic history, and ecological relationships is necessary for developing and implementing effective conservation genetic strategies.

Comparative genomic approaches have been used to identify sites where mutations are under purifying selection and of functional consequence by searching for sequences that are conserved across distantly related species. However, the performance of these approaches has not been rigorously evaluated under population genetic models. Further, short-lived functional elements may not leave a footprint of sequence conservation across many species. We use simulations to study how one measure of conservation, the Genomic Evolutionary Rate Profiling (GERP) score, relates to the strength of selection (Nes). We show that the GERP score is related to the strength of purifying selection. However, changes in selection coefficients or functional elements over time (i.e. functional turnover) can strongly affect the GERP distribution, leading to unexpected relationships between GERP and Nes. Further, we show that for functional elements that have a high turnover rate, adding more species to the analysis does not necessarily increase statistical power. Finally, we use the distribution of GERP scores across the human genome to compare models with and without turnover of sites where mutations under purifying selection. We show that mutations in 4.51% of the noncoding human genome are under purifying selection and that most of this sequence has likely experienced changes in selection coefficients throughout mammalian evolution. Our work reveals limitations to using comparative genomic approaches to identify deleterious mutations. Commonly used GERP score thresholds miss over half of the noncoding sites in the human genome where mutations are under purifying selection.

While it is appreciated that population size changes can impact patterns of deleterious variation in natural populations, less attention has been paid to how gene flow affects and is affected by the dynamics of deleterious variation. Here we use population genetic simulations to examine how gene flow impacts deleterious variation under a variety of demographic scenarios, mating systems, dominance coefficients, and recombination rates. Our results show that admixture between populations can temporarily reduce the genetic load of smaller populations and cause increases in the frequency of introgressed ancestry, especially if deleterious mutations are recessive. Additionally, when fitness effects of new mutations are recessive, between-population differences in the sites at which deleterious variants exist creates heterosis in hybrid individuals. Together, these factors lead to an increase in introgressed ancestry, particularly when recombination rates are low. Under certain scenarios, introgressed ancestry can increase from an initial frequency of 5% to 30-75% and fix at many loci, even in the absence of beneficial mutations. Further, deleterious variation and admixture can generate correlations between the frequency of introgressed ancestry and recombination rate or exon density, even in the absence of other types of selection. The direction of these correlations is determined by the specific demography and whether mutations are additive or recessive. Therefore, it is essential that null models of admixture include both demography and deleterious variation before invoking other mechanisms to explain unusual patterns of genetic variation.

Storage pressures of magma chambers influence the style, frequency and magnitude of volcanic eruptions. Neutral buoyancy or rheological transitions are commonly assumed to control where magmas accumulate and form such chambers. However, the density of volatile-rich silicic magmas is typically lower than that of the surrounding crust, and the rheology of the crust alone does not define the depth of the brittle–ductile transition around a magma chamber. Yet, typical storage pressures inferred from geophysical inversions or petrological methods seem to cluster around 2 ± 0.5 kbar in all tectonic settings and crustal compositions. Here, we use thermomechanical modelling to show that storage pressure is controlled by volatile exsolution and crustal rheology. At pressures \\[\\lesssim\\]1.5 kbar, and for geologically realistic water contents, chamber volumes and recharge rates, the presence of an exsolved magmatic volatile phase hinders chamber growth because eruptive volumes are typically larger than recharges feeding the system during periods of dormancy. At pressures \\[>rsim\\]2.5 kbar, the viscosity of the crust in long-lived magmatic provinces is sufficiently low to inhibit most eruptions. Sustainable eruptible magma reservoirs are able to develop only within a relatively narrow range of pressures around 2 ± 0.5 kbar, where the amount of exsolved volatiles fosters growth while the high viscosity of the crust promotes the necessary overpressurization for eruption.

Ancient DNA has emerged as a powerful tool for studying human migration through the detection of admixture signatures. Here, we present the theoretical principles and methodologies for admixture analysis, with an emphasis on f -statistics and qpAdm. We review case studies from the literature demonstrating how these methods uncover patterns of human mobility, and discuss challenges related to data quality, demographic complexity, and sample representativeness on admixture and migration inferences. Finally, we highlight promising advancements in admixture analysis and underscore the importance of integrating genetic, archaeological, and historical data to achieve a more interdisciplinary and nuanced reconstruction of human history.

The distribution of fitness effects (DFE) has considerable importance in population genetics. To date, estimates of the DFE come from studies using a small number of individuals. Thus, estimates of the proportion of moderately to strongly deleterious new mutations may be unreliable because such variants are unlikely to be segregating in the data. Additionally, the true functional form of the DFE is unknown, and estimates of the DFE differ significantly between studies. Here we present a flexible and computationally tractable method, called Fit∂a∂i, to estimate the DFE of new mutations using the site frequency spectrum from a large number of individuals. We apply our approach to the frequency spectrum of 1300 Europeans from the Exome Sequencing Project ESP6400 data set, 1298 Danes from the LuCamp data set, and 432 Europeans from the 1000 Genomes Project to estimate the DFE of deleterious nonsynonymous mutations. We infer significantly fewer (0.38–0.84 fold) strongly deleterious mutations with selection coefficient |s| > 0.01 and more (1.24–1.43 fold) weakly deleterious mutations with selection coefficient |s| < 0.001 compared to previous estimates. Furthermore, a DFE that is a mixture distribution of a point mass at neutrality plus a gamma distribution fits better than a gamma distribution in two of the three data sets. Our results suggest that nearly neutral forces play a larger role in human evolution than previously thought.

Dominance is a fundamental concept in molecular genetics and has implications for understanding patterns of genetic variation, evolution, and complex traits. However, despite its importance, the degree of dominance in natural populations is poorly quantified. Here, we leverage multiple mating systems in natural populations of Arabidopsis to co-estimate the distribution of fitness effects and dominance coefficients of new amino acid changing mutations. We find that more deleterious mutations are more likely to be recessive than less deleterious mutations. Further, this pattern holds across gene categories, but varies with the connectivity and expression patterns of genes. Our work argues that dominance arises as a consequence of the functional importance of genes and their optimal expression levels. Dominance is difficult to measure in natural populations as it is confounded with fitness. Here, Huber et al. developed a new approach to co-estimate dominance and selection coefficients, and found that the observed relationship is best fit by a new model of dominance based on gene expression level.

The Neolithic Revolution initiated a pivotal change in human society, marking the shift from foraging to farming. Historically, the underlying mechanisms of agricultural expansion have been a topic of debate, centered around two primary models: cultural diffusion, involving the transfer of knowledge and practices, and demic diffusion, characterized by the migration and replacement of populations. More recently, ancient DNA analyses have revealed significant ancestry changes during Europe’s Neolithic transition, suggesting a primarily demic expansion. Nonetheless, the presence of 10-15% hunter-gatherer ancestry in modern Europeans indicates cultural transmission and between-group mating were additional contributing factors. Here, we integrate mathematical models, agent-based simulations, and ancient DNA analysis to dissect and quantify the roles of cultural diffusion and between-group mating in farming’s expansion. Our findings indicate limited cultural transmission and predominantly within-group mating. Additionally, we challenge the assumption that demic expansion always leads to ancestry turnover. These results offer insights into early agricultural society through the integration of ancient DNA with archaeological models. The Neolithic Revolution marked an important shift from foraging to farming in human society. Here, the authors show that in Europe the spread of farming involved mostly within-group mating and limited cultural transmission.

The monoclonal anti-CD20 IgG1 antibody rituximab is used as a first-line treatment for B cell lymphoma. Like all therapeutic antibodies, it is a complex protein for which both safety and efficacy heavily depend on the integrity of its three-dimensional structure. Aptamers, short oligonucleotides with a distinct fold, can be used to detect minor modifications or structural variations of a molecule or protein. To detect antibody molecules in a fold state occurring prior to protein precipitation, we generated DNA aptamers that were selected for extensively heat-treated rituximab. Using the magnetic bead-based systematic evolution of ligands by exponential enrichment (SELEX), we obtained six DNA aptamer sequences (40-mers) specific for 80°C heat-treated rituximab. In silico fold prediction and circular dichroism analysis revealed a G-quadruplex structure for one aptamer, while all others exhibited a B-DNA helix. Binding affinities ranging from 8.8–86.7 nM were determined by an enzyme-linked apta-sorbent assay (ELASA). Aptamers additionally detected structural changes in rituximab treated for 5 min at 70°C, although with lower binding activity. Notably, none of the aptamers recognized rituximab in its native state nor did they detect the antibody after it was exposed to lower temperatures or different physical stressors. Aptamers also reacted with the therapeutic antibody adalimumab incubated at 80°C suggesting similar aptamer binding motifs located on extensively heat-treated IgG1 antibodies. Within this work, we obtained the first aptamer panel, which is specific for an antibody fold state specifically present prior to protein aggregation. This study demonstrates the potential of aptamer selection for specific stress-based protein variants, which has potential impact for quality control of biopharmaceuticals.