Isaac Turner

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 65 000 (adjusted odds ratio [aOR], 1.77; 95% confidence interval [CI], 1.72 to 1.82), as well as from zip codes with <85% of individuals with health insurance (aOR, 1.29; 95% CI, 1.25 to 1.33), and <60% of individuals employed (aOR, 1.42; 95% CI, 1.41 to 1.44).Conclusions: Residence in zip codes with lower average annual household income, lower rates of employment, or lower rates of health insurance was associated with SARS-CoV-2 positivity. Further research is needed into how those factors increase the spread of SARS-CoV-2 infection among populations of lower socioeconomic status in order to develop targeted public health interventions.

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.

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. 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. 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 data are available at Bioinformatics online.

Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Identifying such interactions can potentially be achieved through genetic association studies. 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. To address these problems, we have developed a Bayesian approach for detecting host influences on pathogen evolution that makes use of 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. Using simulations and empirical analysis of drug-induced selection on the HIV-1 genome we demonstrate the power of the method to recover known associations and show greatly improved 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.

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. 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 the 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 characterise the genomic context of drug-resistance genes. Availability: Linked de Bruijn Graphs and associated algorithms are implemented as part of McCortex, available under the MIT license at https://github.com/mcvean/mccortex . Contact: turner.isaac@gmail.com .

Recombination: The birds and the yeast: Apes and mice have a specific gene, PRDM9 , that is associated with genomic regions with high rates of recombination, called hotspots. In species with PRDM9 , hotspots move rapidly within the genome, varying among populations and closely related species (see the Perspective by Lichten). To investigate recombination hotspots in species lacking PRDM9 , Singhal et al. examined bird genomes, which lack a PRDM9 gene. They looked closely at the genomes of finch species and found that recombination was localized to the promoter regions of genes and highly conserved over millions of years. Similarly, Lam and Keeney examined recombination localization within yeast, which also lacks PRDM9 . They found a similar more-or-less fixed pattern of hotspots. Thus, recombination in species lacking a PRDM9 gene shows similar patterns of hotspot localization and evolution. Science , this issue p. 913 , p. 928 ; see also p. 932

Male chimps evolve faster with age: Chimpanzees are evolving faster than humans. Venn et al. examined the genetics of three generations of western chimpanzees and found that overall the mutation rate is similar between humans and chimpanzees. However, while male humans had three to four times the mutation rate of females, in chimpanzees the sex difference was even higher, with a male mutation rate five to six times that of females. Blame aging dads. For every extra year of the father's age, baby chimpanzees exhibited approximately one extra mutation. This finding will inform future studies of primate evolution. Science , this issue p. 1272

Significance: Harvey rat sarcoma viral oncogene homolog ( HRAS ) occupies an important place in medical history, because it was the first gene in which acquired mutations that led to activation of a normal protein were associated with cancer, making it the prototype of the now canonical oncogene mechanism. Here, we explore what happens when similar HRAS mutations occur in male germ cells, an issue of practical importance because the mutations cause a serious congenital disorder, Costello syndrome, if transmitted to offspring. We provide evidence that the mutant germ cells are positively selected, leading to an increased burden of the mutations as men age. Although there are many parallels between this germline process and classical oncogenesis, there are interesting differences of detail, which are explored in this paper.

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.