Interactions Between Natural Selection and Recombination Shape the Genomic Landscape of Introgression.

Hybridization between lineages that have not reached complete reproductive isolation appears more and more like a common phenomenon. Indeed, speciation genomic studies have now extensively shown that many species' genomes have hybrid ancestry. However, genomic patterns of introgression are often heterogeneous across the genome. In many organisms, a positive correlation between introgression levels and recombination rate has been observed. It is usually explained by the purging of deleterious introgressed material due to incompatibilities. However, the opposite relationship was observed in a North American population of Drosophila melanogaster with admixed European and African ancestry. In order to explore how directional and epistatic selection can impact the relationship between introgression and recombination, we performed forward simulations of whole D. melanogaster genomes reflecting the North American population's history. Our results revealed that the simplest models of positive selection often yield negative correlations between introgression and recombination such as the one observed in D. melanogaster. We also confirmed that incompatibilities tend to produce positive introgression-recombination correlations. And yet, we identify parameter space under each model where the predicted correlation is reversed. These findings deepen our understanding of the evolutionary forces that may shape patterns of ancestry across genomes, and they strengthen the foundation for future studies aimed at estimating genome-wide parameters of selection in admixed populations.

Fitness consequences and ancestry loss in the Apennine brown bear after a simulated genetic rescue intervention.

Reduction in population size, with its predicted effects on population fitness, is the most alarming anthropogenic impact on endangered species. By introducing compatible individuals, genetic rescue (GR) is a promising but debated approach for reducing the genetic load unmasked by inbreeding and for restoring the fitness of declining populations. Although GR can improve genetic diversity and fitness, it can also produce loss of ancestry, hampering local adaptation, or replace with introduced variants the unique genetic pools evolved in endemic groups. We used forward genetic simulations based on empirical genomic data to assess fitness benefits and loss of ancestry risks of GR in the Apennine brown bear (Ursus arctos marsicanus). There are approximately 50 individuals of this isolated subspecies, and they have lower genetic diversity and higher inbreeding than other European brown bears, and GR has been suggested to reduce extinction risks. We compared 10 GR scenarios in which the number and genetic characteristics of migrants varied with a non-GR scenario of simple demographic increase due to nongenetic factors. The introduction of 5 individuals of higher fitness or lower levels of deleterious mutations than the target Apennine brown bear from a larger European brown bear population produced a rapid 10-20% increase in fitness in the subspecies and up to 22.4% loss of ancestry over 30 generations. Without a contemporary demographic increase, fitness started to decline again after a few generations. Doubling the population size without GR gradually increased fitness to a comparable level, but without losing ancestry, thus resulting in the best strategy for the Apennine brown bear conservation. Our results highlight the importance for management of endangered species of realistic forward simulations grounded in empirical whole-genome data.

Consecuencias en la aptitud y pérdida de ascendencia del oso pardo de los Apeninos después de un rescate genético simulado Resumen La reducción del tamaño poblacional, con los previsibles efectos sobre su aptitud, es el impacto antropogénico más alarmante sobre las especies amenazadas. Mediante la introducción de individuos compatibles, el rescate genético (RG) es una estrategia prometedora para reducir la carga genética revelada por la endogamia y restaurar la aptitud de las poblaciones en declive, aunque todavía se debate la eficiencia de esta. Aunque el RG puede mejorar la diversidad genética y la aptitud, también puede producir pérdida de ascendencia, lo que puede dificultar la adaptación local, o sustituir con variantes introducidas por los migrantes los acervos genéticos únicos que han evolucionado en grupos endémicos. En este trabajo realizamos simulaciones genéticas a futuro basadas en datos genómicos empíricos para evaluar los beneficios del RG en términos de aptitud y los riesgos de la pérdida de ascendencia en el oso pardo de los Apeninos (Ursus arctos marsicanus). Quedan aproximadamente 50 individuos de esta subespecie aislada que cuentan con una menor diversidad genética y un mayor nivel de endogamia comparado con otros osos pardos europeos y se ha sugerido que el RG podria reducir el riesgo de extinción de esta población. Comparamos 10 escenarios de RG en los que variaban el número y las características genéticas de los osos migrantes con un escenario sin RG con aumento demográfico causado por factores no genéticos. La introducción de 5 individuos procedentes de una población europea de oso pardo con mayor aptitud o niveles menores de mutaciones deletéreas que el oso pardo de los Apeninos produjo un rápido aumento de la aptitud del 10-20% en la subespecie y hasta un 22.4% de pérdida de ascendencia durante 30 generaciones. En las simulaciones sin un aumento demográfico, la aptitud empezó a disminuir de nuevo después de unas pocas generaciones. La duplicación del tamaño de la población sin RG aumentó gradualmente la aptitud hasta un nivel comparable al de algunos escenarios de RG, pero sin pérdida de ascendencia, por lo que parece ser la mejor estrategia para la conservación del oso pardo de los Apeninos. Nuestros resultados resaltan la importancia que tienen las simulaciones realistas a futuro basadas en datos empíricos del genoma completo para la gestión de especies amenazadas.

Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies.

Evolve and resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and nonselected populations using next-generation sequencing (NGS), the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations has been examined in previous works. However, the influence of the selection regime, that is the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a genome-wide association study, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.

Dynamics of Transposable Element Invasions with piRNA Clusters.

In mammals and invertebrates, the proliferation of an invading transposable element (TE) is thought to be stopped by an insertion into a piRNA cluster. Here, we explore the dynamics of TE invasions under this trap model using computer simulations. We found that piRNA clusters confer a substantial benefit, effectively preventing extinction of host populations from a proliferation of deleterious TEs. TE invasions consist of three distinct phases: first, the TE amplifies within the population, next TE proliferation is stopped by segregating cluster insertions, and finally the TE is inactivated by fixation of a cluster insertion. Suppression by segregating cluster insertions is unstable and bursts of TE activity may yet occur. The transposition rate and the population size mostly influence the length of the phases but not the amount of TEs accumulating during an invasion. Solely, the size of piRNA clusters was identified as a major factor influencing TE abundance. We found that a single nonrecombining cluster is more efficient in stopping invasions than clusters distributed over several chromosomes. Recombination among cluster sites makes it necessary that each diploid carries, on the average, four cluster insertions to stop an invasion. Surprisingly, negative selection in a model with piRNA clusters can lead to a novel equilibrium state, where TE copy numbers remain stable despite only some individuals in a population carrying a cluster insertion. In Drosophila melanogaster, the trap model accounts for the abundance of TEs produced in the germline but fails to predict the abundance of TEs produced in the soma.

Population genetic simulations of complex phenotypes with implications for rare variant association tests.

Demographic events and natural selection alter patterns of genetic variation within populations and may play a substantial role in shaping the genetic architecture of complex phenotypes and disease. However, the joint impact of these basic evolutionary forces is often ignored in the assessment of statistical tests of association. Here, we provide a simulation-based framework for generating DNA sequences that incorporates selection and demography with flexible models for simulating phenotypic variation (sfs_coder). This tool also allows the user to perform locus-specific simulations by automatically querying annotated genomic functional elements and genetic maps. We demonstrate the effects of evolutionary forces on patterns of genetic variation by simulating recently inferred models of human selection and demography. We use these simulations to show that the demographic model and locus-specific features, such as the proportion of sites under selection, may have practical implications for estimating the statistical power of sequencing-based rare variant association tests. In particular, for some phenotype models, there may be higher power to detect rare variant associations in African populations compared to non-Africans, but power is considerably reduced in regions of the genome with rampant negative selection. Furthermore, we show that existing methods for simulating large samples based on resampling from a small set of observed haplotypes fail to recapitulate the distribution of rare variants in the presence of rapid population growth (as has been observed in several human populations).

piRNA Clusters Need a Minimum Size to Control Transposable Element Invasions.

piRNA clusters are thought to repress transposable element (TE) activity in mammals and invertebrates. Here, we show that a simple population genetics model reveals a constraint on the size of piRNA clusters: The total size of the piRNA clusters of an organism must exceed 0.2% of a genome to repress TE invasions. Moreover, larger piRNA clusters accounting for up to 3% of the genome may be necessary when populations are small, transposition rates are high, and TE insertions are recessive. If piRNA clusters are too small, the load of deleterious TE insertions that accumulate during a TE invasion may drive populations extinct before an effective piRNA-based defense against the TE can be established. Our findings are solely based on three well-supported assumptions: 1) TEs multiply within genomes, 2) TEs are mostly deleterious, and 3) piRNA clusters act as transposon traps, where a single insertion in a cluster silences all TE copies in trans. Interestingly, the piRNA clusters of some species meet our observed minimum size requirements, whereas the clusters of other species do not. Species with small piRNA clusters, such as humans and mice, may experience severe fitness reductions during invasions of novel TEs, which is possibly even threatening the persistence of some populations. This work also raises the important question of how piRNA clusters evolve. We propose that the size of piRNA clusters may be at an equilibrium between evolutionary forces that act to expand and contract piRNA clusters.

Combining population genomics and forward simulations to investigate stocking impacts: A case study of Muskellunge (Esox masquinongy) from the St. Lawrence River basin.

Understanding the genetic and evolutionary impacts of stocking on wild fish populations has long been of interest as negative consequences such as reduced fitness and loss of genetic diversity are commonly reported outcomes. In an attempt to sustain a fishery, managers implemented nearly five decades of extensive stocking of over a million Muskellunge (Esox masquinongy), a native species in the Lower St. Lawrence River (Québec, Canada). We investigated the effect of this stocking on population genetic structure and allelic diversity in the St. Lawrence River in addition to tributaries and several stocked inland lakes. Using genotype by sequencing, we genotyped 643 individuals representing 22 locations and combined this information with forward simulations to investigate the genetic consequences of long-term stocking. Individuals native to the St. Lawrence watershed were genetically differentiated from stocking sources and tributaries, and inland lakes were naturally differentiated from the main river. Empirical data and simulations within the St. Lawrence River revealed weak stocking effects on admixture patterns. Our data suggest that the genetic structure associated with stocked fish was diluted into its relatively large effective population size. This interpretation is also consistent with a hypothesis that selection against introgression was in operation and relatively efficient within the large St. Lawrence River system. In contrast, smaller populations from adjacent tributaries and lakes displayed greater stocking-related admixture that resulted in comparatively higher heterozygosity than the St. Lawrence. Finally, individuals from inland lakes that were established by stocking maintained a close affinity with their source populations. This study illustrated a benefit of combining extensive genomic data with forward simulations for improved inference regarding population-level genetic effects of long-term stocking, and its relevance for fishery management decision making.

Contribution of muscle short-range stiffness to initial changes in joint kinetics and kinematics during perturbations to standing balance: A simulation study.

Simulating realistic musculoskeletal dynamics is critical to understanding neural control of muscle activity evoked in sensorimotor feedback responses that have inherent neural transmission delays. Thus, the initial mechanical response of muscles to perturbations in the absence of any change in muscle activity determines which corrective neural responses are required to stabilize body posture. Muscle short-range stiffness, a history-dependent property of muscle that causes a rapid and transient rise in muscle force upon stretch, likely affects musculoskeletal dynamics in the initial mechanical response to perturbations. Here we identified the contributions of short-range stiffness to joint torques and angles in the initial mechanical response to support surface translations using dynamic simulation. We developed a dynamic model of muscle short-range stiffness to augment a Hill-type muscle model. Our simulations show that short-range stiffness can provide stability against external perturbations during the neuromechanical response delay. Assuming constant muscle activation during the initial mechanical response, including muscle short-range stiffness was necessary to account for the rapid rise in experimental sagittal plane knee and hip joint torques that occurs simultaneously with very small changes in joint angles and reduced root mean square errors between simulated and experimental torques by 56% and 47%, respectively. Moreover, forward simulations lacking short-range stiffness produced unreasonably large joint angle changes during the initial response. Using muscle models accounting for short-range stiffness along with other aspects of history-dependent muscle dynamics may be important to advance our ability to simulate inherently unstable human movements based on principles of neural control and biomechanics.

Robust forward simulations of recurrent hitchhiking.

Evolutionary forces shape patterns of genetic diversity within populations and contribute to phenotypic variation. In particular, recurrent positive selection has attracted significant interest in both theoretical and empirical studies. However, most existing theoretical models of recurrent positive selection cannot easily incorporate realistic confounding effects such as interference between selected sites, arbitrary selection schemes, and complicated demographic processes. It is possible to quantify the effects of arbitrarily complex evolutionary models by performing forward population genetic simulations, but forward simulations can be computationally prohibitive for large population sizes (>10(5)). A common approach for overcoming these computational limitations is rescaling of the most computationally expensive parameters, especially population size. Here, we show that ad hoc approaches to parameter rescaling under the recurrent hitchhiking model do not always provide sufficiently accurate dynamics, potentially skewing patterns of diversity in simulated DNA sequences. We derive an extension of the recurrent hitchhiking model that is appropriate for strong selection in small population sizes and use it to develop a method for parameter rescaling that provides the best possible computational performance for a given error tolerance. We perform a detailed theoretical analysis of the robustness of rescaling across the parameter space. Finally, we apply our rescaling algorithms to parameters that were previously inferred for Drosophila and discuss practical considerations such as interference between selected sites.

Interplay of interlocus gene conversion and crossover in segmental duplications under a neutral scenario.

Interlocus gene conversion is a major evolutionary force that drives the concerted evolution of duplicated genomic regions. Theoretical models successfully have addressed the effects of interlocus gene conversion and the importance of crossover in the evolutionary fate of gene families and duplications but have not considered complex recombination scenarios, such as the presence of hotspots. To study the interplay between interlocus gene conversion and crossover, we have developed a forward-time simulator that allows the exploration of a wide range of interlocus gene conversion rates under different crossover models. Using it, we have analyzed patterns of nucleotide variation and linkage disequilibrium within and between duplicate regions, focusing on a neutral scenario with constant population size and validating our results with the existing theoretical models. We show that the interaction of gene conversion and crossover is nontrivial and that the location of crossover junctions is a fundamental determinant of levels of variation and linkage disequilibrium in duplicated regions. We also show that if crossover activity between duplications is strong enough, recurrent interlocus gene conversion events can break linkage disequilibrium within duplicates. Given the complex nature of interlocus gene conversion and crossover, we provide a framework to explore their interplay to help increase knowledge on molecular evolution within segmental duplications under more complex scenarios, such as demographic changes or natural selection.