Comparison of Dose-Response Relationships for Two Isolates of SARS-CoV-2 in a Nonhuman Primate Model of Inhalational COVID-19.

Background: As the COVID-19 pandemic has progressed, numerous variants of SARS-CoV-2 have arisen, with several displaying increased transmissibility. Methods: The present study compared dose-response relationships and disease presentation in nonhuman primates infected with aerosols containing an isolate of the Gamma variant of SARS-CoV-2 to the results of our previous study with the earlier WA-1 isolate of SARS-CoV-2. Results: Disease in Gamma-infected animals was mild, characterized by dose-dependent fever and oronasal shedding of virus. Differences were observed in shedding in the upper respiratory tract between Gamma- and WA-1-infected animals that have the potential to influence disease transmission. Specifically, the estimated median doses for shedding of viral RNA or infectious virus in nasal swabs were approximately 10-fold lower for the Gamma variant than the WA-1 isolate. Given that the median doses for fever were similar, this suggests that there is a greater difference between the median doses for viral shedding and fever for Gamma than for WA-1 and potentially an increased range of doses for Gamma over which asymptomatic shedding and disease transmission are possible. Conclusions: These results complement those of previous studies, which suggested that differences in exposure dose may help to explain the range of clinical disease presentations observed in individuals with COVID-19, highlighting the importance of public health measures designed to limit exposure dose, such as masking and social distancing. The dose-response data provided by this study are important to inform disease transmission and hazard modeling, as well as to inform dose selection in future studies examining the efficacy of therapeutics and vaccines in animal models of inhalational COVID-19.

Seroconversion and fever are dose-dependent in a nonhuman primate model of inhalational COVID-19.

While evidence exists supporting the potential for aerosol transmission of SARS-CoV-2, the infectious dose by inhalation remains unknown. In the present study, the probability of infection following inhalation of SARS-CoV-2 was dose-dependent in a nonhuman primate model of inhalational COVID-19. The median infectious dose, assessed by seroconversion, was 52 TCID50 (95% CI: 23-363 TCID50), and was significantly lower than the median dose for fever (256 TCID50, 95% CI: 102-603 TCID50), resulting in a group of animals that developed an immune response post-exposure but did not develop fever or other clinical signs of infection. In a subset of these animals, virus was detected in nasopharyngeal and/or oropharyngeal swabs, suggesting that infected animals without signs of disease are able to shed virus and may be infectious, which is consistent with reports of asymptomatic spread in human cases of COVID-19. These results suggest that differences in exposure dose may be a factor influencing disease presentation in humans, and reinforce the importance of public health measures that limit exposure dose, such as social distancing, masking, and increased ventilation. The dose-response data provided by this study are important to inform disease transmission and hazard modeling, and, ultimately, mitigation strategies. Additionally, these data will be useful to inform dose selection in future studies examining the efficacy of therapeutics and vaccines against inhalational COVID-19, and as a baseline in healthy, young adult animals for assessment of the importance of other factors, such as age, comorbidities, and viral variant, on the infectious dose and disease presentation.

Evidence of directional and stabilizing selection in contemporary humans.

Modern molecular genetic datasets, primarily collected to study the biology of human health and disease, can be used to directly measure the action of natural selection and reveal important features of contemporary human evolution. Here we leverage the UK Biobank data to test for the presence of linear and nonlinear natural selection in a contemporary population of the United Kingdom. We obtain phenotypic and genetic evidence consistent with the action of linear/directional selection. Phenotypic evidence suggests that stabilizing selection, which acts to reduce variance in the population without necessarily modifying the population mean, is widespread and relatively weak in comparison with estimates from other species.

Evolutionary genomics of grape (Vitis vinifera ssp. vinifera) domestication.

We gathered genomic data from grapes (Vitis vinifera ssp. vinifera), a clonally propagated perennial crop, to address three ongoing mysteries about plant domestication. The first is the duration of domestication; archaeological evidence suggests that domestication occurs over millennia, but genetic evidence indicates that it can occur rapidly. We estimated that our wild and cultivated grape samples diverged ∼22,000 years ago and that the cultivated lineage experienced a steady decline in population size (Ne ) thereafter. The long decline may reflect low-intensity management by humans before domestication. The second mystery is the identification of genes that contribute to domestication phenotypes. In cultivated grapes, we identified candidate-selected genes that function in sugar metabolism, flower development, and stress responses. In contrast, candidate-selected genes in the wild sample were limited to abiotic and biotic stress responses. A genomic region of high divergence corresponded to the sex determination region and included a candidate male sterility factor and additional genes with sex-specific expression. The third mystery concerns the cost of domestication. Annual crops accumulate putatively deleterious variants, in part due to strong domestication bottlenecks. The domestication of perennial crops differs from that of annuals in several ways, including the intensity of bottlenecks, and it is not yet clear if they accumulate deleterious variants. We found that grape accessions contained 5.2% more deleterious variants than wild individuals, and these were more often in a heterozygous state. Using forward simulations, we confirm that clonal propagation leads to the accumulation of recessive deleterious mutations but without decreasing fitness.

A Model of Compound Heterozygous, Loss-of-Function Alleles Is Broadly Consistent with Observations from Complex-Disease GWAS Datasets.

The genetic component of complex disease risk in humans remains largely unexplained. A corollary is that the allelic spectrum of genetic variants contributing to complex disease risk is unknown. Theoretical models that relate population genetic processes to the maintenance of genetic variation for quantitative traits may suggest profitable avenues for future experimental design. Here we use forward simulation to model a genomic region evolving under a balance between recurrent deleterious mutation and Gaussian stabilizing selection. We consider multiple genetic and demographic models, and several different methods for identifying genomic regions harboring variants associated with complex disease risk. We demonstrate that the model of gene action, relating genotype to phenotype, has a qualitative effect on several relevant aspects of the population genetic architecture of a complex trait. In particular, the genetic model impacts genetic variance component partitioning across the allele frequency spectrum and the power of statistical tests. Models with partial recessivity closely match the minor allele frequency distribution of significant hits from empirical genome-wide association studies without requiring homozygous effect sizes to be small. We highlight a particular gene-based model of incomplete recessivity that is appealing from first principles. Under that model, deleterious mutations in a genomic region partially fail to complement one another. This model of gene-based recessivity predicts the empirically observed inconsistency between twin and SNP based estimated of dominance heritability. Furthermore, this model predicts considerable levels of unexplained variance associated with intralocus epistasis. Our results suggest a need for improved statistical tools for region based genetic association and heritability estimation.

Efficient Software for Multi-marker, Region-Based Analysis of GWAS Data.

Genome-wide association studies (GWAS) have associated many single variants with complex disease, yet the better part of heritable complex disease risk remains unexplained. Analytical tools designed to work under specific population genetic models are needed. Rare variants are increasingly shown to be important in human complex disease, but most existing GWAS data do not cover rare variants. Explicit population genetic models predict that genes contributing to complex traits and experiencing recurrent, unconditionally deleterious, mutation will harbor multiple rare, causative mutations of subtle effect. It is difficult to identify genes harboring rare variants of large effect that contribute to complex disease risk via the single marker association tests typically used in GWAS. Gene/region-based association tests may have the power detect associations by combining information from multiple markers, but have yielded limited success in practice. This is partially because many methods have not been widely applied. Here, we empirically demonstrate the utility of a procedure based on the rank truncated product (RTP) method, filtered to reduce the effects of linkage disequilibrium. We apply the procedure to the Wellcome Trust Case Control Consortium (WTCCC) data set, and uncover previously unidentified associations, some of which have been replicated in much larger studies. We show that, in the absence of significant rare variant coverage, RTP based methods still have the power to detect associated genes. We recommend that RTP-based methods be applied to all existing GWAS data to maximize the usefulness of those data. For this, we provide efficient software implementing our procedure.

Revised annotations, sex-biased expression, and lineage-specific genes in the Drosophila melanogaster group.

Here, we provide revised gene models for D. ananassae, D. yakuba, and D. simulans, which include untranslated regions and empirically verified intron-exon boundaries, as well as ortholog groups identified using a fuzzy reciprocal-best-hit blast comparison. Using these revised annotations, we perform differential expression testing using the cufflinks suite to provide a broad overview of differential expression between reproductive tissues and the carcass. We identify thousands of genes that are differentially expressed across tissues in D. yakuba and D. simulans, with roughly 60% agreement in expression patterns of orthologs in D. yakuba and D. simulans. We identify several cases of putative polycistronic transcripts, pointing to a combination of transcriptional read-through in the genome as well as putative gene fusion and fission events across taxa. We furthermore identify hundreds of lineage specific genes in each species with no blast hits among transcripts of any other Drosophila species, which are candidates for neofunctionalized proteins and a potential source of genetic novelty.