Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone

CFH, C3 and ARMS2 Are Significant Risk Loci for Susceptibility but Not for Disease Progression of Geographic Atrophy Due to AMD

Age-related macular degeneration (AMD) is a prevalent cause of blindness in Western societies. Variants in the genes encoding complement factor H (CFH), complement component 3 (C3) and age-related maculopathy susceptibility 2 (ARMS2) have repeatedly been shown to confer significant risks for AMD; however, their role in disease progression and thus their potential relevance for interventional therapeutic approaches remains unknown. Here, we analyzed association between variants in CFH, C3 and ARMS2 and disease progression of geographic atrophy (GA) due to AMD. A quantitative phenotype of disease progression was computed based on longitudinal observations by fundus autofluorescence imaging. In a subset of 99 cases with pure bilateral GA, variants in CFH (Y402H), C3 (R102G), and ARMS2 (A69S) are associated with disease (P = 1.6x10(-9), 3.2x10(-3), and P = 2.6x10(-12), respectively) when compared to 612 unrelated healthy control individuals. In cases, median progression rate of GA over a mean follow-up period of 3.0 years was 1.61 mm(2)/year with high concordance between fellow eyes. No association between the progression rate and any of the genetic risk variants at the three loci was observed (P>0.13). This study confirms that variants at CFH, C3, and ARMS2 confer significant risks for GA due to AMD. In contrast, our data indicate no association of these variants with disease progression which may have important implications for future treatment strategies. Other, as yet unknown susceptibilities may influence disease progression

Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC's performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics

Semantic Heterogeneity in the Formal Development of Complex Systems: An Introduction

International audienceSystem engineering is a complex discipline[1], which is becoming more and more complicated by the heterogeneity of the subsystem components[2] and of the models involved in their design. This complexity can be managed only through the use of formal methods[3]. However, in general the engineering of software in such systems leads to a need for a mix of modelling languages and semantics; and this often leads to unexpected and undesirable interactions between components at all levels of abstraction[4]. There are currently no generally applicable tools for dealing with this heterogeneity of interactions in the engineering of complex systems

The potential for a released autosomal X-shredder becoming a driving-Y chromosome and invasively suppressing wild populations of malaria mosquitoes

Abstract Synthetic sex-ratio distorters based on X-chromosome shredding are predicted to be more efficient than sterile males for population suppression of malaria mosquitoes using genetic control. X-chromosome shredding operates through the targeted elimination of X-chromosome-bearing gametes during male spermatogenesis, resulting in males that have a high fraction of male offspring. Strains harboring autosomal constructs containing a modified endonuclease I- Ppo I have now been developed in the malaria mosquito Anopheles gambiae , resulting in strong sex-ratio distortion towards males. Data are being gathered for these strains for submission of regulatory dossiers for contained use and subsequent field release in West Africa. Since autosomal X-shredders are transmitted in a Mendelian fashion and can be selected against their frequency in the population is expected to decline once releases are halted. However, any unintended transfer of the X-shredder to the Y-chromosome could theoretically change these dynamics: This could lead to 100% transmission of the newly Y-linked X-shredder to the predominant male-biased offspring and its insulation from negative selection in females, resulting in its potential spread in the population and ultimately to suppression. Here, we analyze plausible mechanisms whereby an autosomal X-shredder could become linked to the Y-chromosome after release and provide data regarding its potential for activity should it become linked to the Y-chromosome. Our results strongly suggest that Y-chromosome linkage through remobilization of the transposon used for the initial genetic transformation is unlikely, and that, in the unexpected event that the X-shredder becomes linked to the Y-chromosome, expression and activity of the X-shredder would likely be inhibited by meiotic sex chromosome inactivation. We conclude that a functioning X-shredding-based Y-drive resulting from a naturally induced transposition or translocation of the transgene onto the Y-chromosome is unlikely