Effect of Frequent SARS-CoV-2 Testing on Weekly Case Rates in Long-Term Care Facilities, Florida, USA.

We analyzed 1,292,165 SARS-CoV-2 test results from residents and employees of 361 long-term care facilities in Florida, USA. A 1% increase in testing resulted in a 0.08% reduction in cases 3 weeks after testing began. Increasing SARS-CoV-2 testing frequency is a viable tool for reducing virus transmission in these facilities.

Integrating long-range connectivity information into de Bruijn graphs.

Motivation: The de Bruijn graph is a simple and efficient data structure that is used in many areas of sequence analysis including genome assembly, read error correction and variant calling. The data structure has a single parameter k, is straightforward to implement and is tractable for large genomes with high sequencing depth. It also enables representation of multiple samples simultaneously to facilitate comparison. However, unlike the string graph, a de Bruijn graph does not retain long range information that is inherent in the read data. For this reason, applications that rely on de Bruijn graphs can produce sub-optimal results given their input data. Results: We present a novel assembly graph data structure: the Linked de Bruijn Graph (LdBG). Constructed by adding annotations on top of a de Bruijn graph, it stores long range connectivity information through the graph. We show that with error-free data it is possible to losslessly store and recover sequence from a Linked de Bruijn graph. With assembly simulations we demonstrate that the LdBG data structure outperforms both our de Bruijn graph and the String Graph Assembler (SGA). Finally we apply the LdBG to Klebsiella pneumoniae short read data to make large (12 kbp) variant calls, which we validate using PacBio sequencing data, and to characterize the genomic context of drug-resistance genes. Availability and implementation: Linked de Bruijn Graphs and associated algorithms are implemented as part of McCortex, which is available under the MIT license at https://github.com/mcveanlab/mccortex. Supplementary information: Supplementary data are available at Bioinformatics online.

Contributions of intrinsic mutation rate and selfish selection to levels of de novo HRAS mutations in the paternal germline.

The RAS proto-oncogene Harvey rat sarcoma viral oncogene homolog (HRAS) encodes a small GTPase that transduces signals from cell surface receptors to intracellular effectors to control cellular behavior. Although somatic HRAS mutations have been described in many cancers, germline mutations cause Costello syndrome (CS), a congenital disorder associated with predisposition to malignancy. Based on the epidemiology of CS and the occurrence of HRAS mutations in spermatocytic seminoma, we proposed that activating HRAS mutations become enriched in sperm through a process akin to tumorigenesis, termed selfish spermatogonial selection. To test this hypothesis, we quantified the levels, in blood and sperm samples, of HRAS mutations at the p.G12 codon and compared the results to changes at the p.A11 codon, at which activating mutations do not occur. The data strongly support the role of selection in determining HRAS mutation levels in sperm, and hence the occurrence of CS, but we also found differences from the mutation pattern in tumorigenesis. First, the relative prevalence of mutations in sperm correlates weakly with their in vitro activating properties and occurrence in cancers. Second, specific tandem base substitutions (predominantly GC>TT/AA) occur in sperm but not in cancers; genomewide analysis showed that this same mutation is also overrepresented in constitutional pathogenic and polymorphic variants, suggesting a heightened vulnerability to these mutations in the germline. We developed a statistical model to show how both intrinsic mutation rate and selfish selection contribute to the mutational burden borne by the paternal germline.

High-throughput microbial population genomics using the Cortex variation assembler.

SUMMARY: We have developed a software package, Cortex, designed for the analysis of genetic variation by de novo assembly of multiple samples. This allows direct comparison of samples without using a reference genome as intermediate and incorporates discovery and genotyping of single-nucleotide polymorphisms, indels and larger events in a single framework. We introduce pipelines which simplify the analysis of microbial samples and increase discovery power; these also enable the construction of a graph of known sequence and variation in a species, against which new samples can be compared rapidly. We demonstrate the ease-of-use and power by reproducing the results of studies using both long and short reads. AVAILABILITY: http://cortexassembler.sourceforge.net (GPLv3 license). CONTACT: zam@well.ox.ac.uk, mcvean@well.ox.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association of Lower Socioeconomic Status and SARS-CoV-2 Positivity in Los Angeles, California.

OBJECTIVES: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads heterogeneously, disproportionately impacting poor and minority communities. The relationship between poverty and race is complex, with a diverse set of structural and systemic factors driving higher rates of poverty among minority populations. The factors that specifically contribute to the disproportionate rates of SARS-CoV-2 infection, however, are not clearly understood. METHODS: We evaluated SARS-CoV-2 test results from community-based testing sites in Los Angeles, California, between June and December, 2020. We used tester zip code data to link those results with United States Census report data on average annual household income, rates of healthcare coverage, and employment status by zip code. RESULTS: We analyzed 2 141 127 SARS-CoV-2 test results, of which 245 154 (11.4%) were positive. Multivariable modeling showed a higher likelihood of SARS-CoV-2 test positivity among Hispanic communities than among other races. We found an increased risk for SARS-CoV-2 positivity among individuals from zip codes with an average annual household income

Mapping the drivers of within-host pathogen evolution using massive data sets.

Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Genetic association studies can potentially identify such interactions. However, extensive and correlated genetic population structure in hosts and pathogens presents a substantial risk of confounding analyses. Moreover, the multiple testing burden of interaction scanning can potentially limit power. We present a Bayesian approach for detecting host influences on pathogen evolution that exploits vast existing data sets of pathogen diversity to improve power and control for stratification. The approach models key processes, including recombination and selection, and identifies regions of the pathogen genome affected by host factors. Our simulations and empirical analysis of drug-induced selection on the HIV-1 genome show that the method recovers known associations and has superior precision-recall characteristics compared to other approaches. We build a high-resolution map of HLA-induced selection in the HIV-1 genome, identifying novel epitope-allele combinations.

Stable recombination hotspots in birds.

The DNA-binding protein PRDM9 has a critical role in specifying meiotic recombination hotspots in mice and apes, but it appears to be absent from other vertebrate species, including birds. To study the evolution and determinants of recombination in species lacking the gene that encodes PRDM9, we inferred fine-scale genetic maps from population resequencing data for two bird species: the zebra finch, Taeniopygia guttata, and the long-tailed finch, Poephila acuticauda. We found that both species have recombination hotspots, which are enriched near functional genomic elements. Unlike in mice and apes, most hotspots are shared between the two species, and their conservation seems to extend over tens of millions of years. These observations suggest that in the absence of PRDM9, recombination targets functional features that both enable access to the genome and constrain its evolution.

Nonhuman genetics. Strong male bias drives germline mutation in chimpanzees.

Germline mutation determines rates of molecular evolution, genetic diversity, and fitness load. In humans, the average point mutation rate is 1.2 × 10(-8) per base pair per generation, with every additional year of father's age contributing two mutations across the genome and males contributing three to four times as many mutations as females. To assess whether such patterns are shared with our closest living relatives, we sequenced the genomes of a nine-member pedigree of Western chimpanzees, Pan troglodytes verus. Our results indicate a mutation rate of 1.2 × 10(-8) per base pair per generation, but a male contribution seven to eight times that of females and a paternal age effect of three mutations per year of father's age. Thus, mutation rates and patterns differ between closely related species.

De novo assembly and genotyping of variants using colored de Bruijn graphs.

Detecting genetic variants that are highly divergent from a reference sequence remains a major challenge in genome sequencing. We introduce de novo assembly algorithms using colored de Bruijn graphs for detecting and genotyping simple and complex genetic variants in an individual or population. We provide an efficient software implementation, Cortex, the first de novo assembler capable of assembling multiple eukaryotic genomes simultaneously. Four applications of Cortex are presented. First, we detect and validate both simple and complex structural variations in a high-coverage human genome. Second, we identify more than 3 Mb of sequence absent from the human reference genome, in pooled low-coverage population sequence data from the 1000 Genomes Project. Third, we show how population information from ten chimpanzees enables accurate variant calls without a reference sequence. Last, we estimate classical human leukocyte antigen (HLA) genotypes at HLA-B, the most variable gene in the human genome.