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Determining the identities, frequencies, and memberships of ecotypes inProchlorococcusand other superabundant microbes (SAMs) is essential to studies of their evolution and ecology. This is challenging, however, because the extremely large population sizes of SAMs likely cause violations of foundational assumptions made by standard methods used in molecular evolution and phylogenetics. Here we present a tree-free likelihood method to identify ecotypes, which we define as populations with genome sequences whose high similarity is maintained by purifying selection. We applied the method to 96 genomes of the superabundant marine cyanobacteriumProchlorococcusand find that this sample is comprised of about 24 ecotypes, substantially more than the five major ecotypes that are generally recognized. The method presented here may prove useful with other superabundant microbes.

In model-based inference of population structure from individual-level genetic data, individuals are assigned membership coefficients in a series of statistical clusters generated by clustering algorithms. Distinct patterns of variability in membership coefficients can be produced for different groups of individuals, for example, representing different predefined populations, sampling sites, or time periods. Such variability can be difficult to capture in a single numerical value; membership coefficient vectors are multivariate and potentially incommensurable across groups, as the number of clusters over which individuals are distributed can vary among groups of interest. Further, two groups might share few clusters in common, so that membership coefficient vectors are concentrated on different clusters. We introduce a method for measuring the variability of membership coefficients of individuals in a predefined group, making use of an analogy between variability across individuals in membership coefficient vectors and variation across populations in allele frequency vectors. We show that in a model in which membership coefficient vectors in a population follow a Dirichlet distribution, the measure increases linearly with a parameter describing the variance of a specified component of the membership vector. We apply the approach, which makes use of a normalized FST statistic, to data on inferred population structure in three example scenarios. We also introduce a bootstrap test for equivalence of two or more groups in their level of membership coefficient variability. Our methods are implemented in the R package FSTruct.

As COVID-19 spreads across the United States, people experiencing homelessness (PEH) are among the most vulnerable to the virus. To mitigate transmission, municipal governments are procuring isolation facilities for PEH to utilize following possible exposure to the virus. Here we describe the framework for anticipating isolation bed demand in PEH communities that we developed to support public health planning in Austin, Texas during March 2020. Using a mathematical model of COVID-19 transmission, we projected that, under no social distancing orders, a maximum of 299 (95% Confidence Interval: 223, 321) PEH may require isolation rooms in the same week. Based on these analyses, Austin Public Health finalized a lease agreement for 205 isolation rooms on March 27th 2020. As of October 7th 2020, a maximum of 130 rooms have been used on a single day, and a total of 602 PEH have used the facility. As a general rule of thumb, we expect the peak proportion of the PEH population that will require isolation to be roughly triple the projected peak daily incidence in the city. This framework can guide the provisioning of COVID-19 isolation and post-acute care facilities for high risk communities throughout the United States.

Background Humans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and therapeutic opportunities. Methods We conducted a comprehensive study including genome-wide and transcriptome-wide association analyses to identify genetic loci associated with immunoglobulin G antibody response to 28 antigens for 16 viruses using serological data from 7924 European ancestry participants in the UK Biobank cohort. Results Signals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. We identified specific amino acid (AA) residues that are associated with seroreactivity, the strongest associations presented in a range of AA positions within DRβ1 at positions 11, 13, 71, and 74 for Epstein-Barr virus (EBV), Varicella zoster virus (VZV), human herpesvirus 7, (HHV7), and Merkel cell polyomavirus (MCV). Genome-wide association analyses discovered 7 novel genetic loci outside the HLA associated with viral antibody response (P −8), including FUT2 (19q13.33) for human polyomavirus BK (BKV), STING1 (5q31.2) for MCV, and CXCR5 (11q23.3) and TBKBP1 (17q21.32) for HHV7. Transcriptome-wide association analyses identified 114 genes associated with response to viral infection, 12 outside of the HLA region, including ECSCR: P = 5.0 × 10−15 (MCV), NTN5: P = 1.1 × 10−9 (BKV), and P2RY13: P = 1.1 × 10−8 EBV nuclear antigen. We also demonstrated pleiotropy between viral response genes and complex diseases, from autoimmune disorders to cancer to neurodegenerative and psychiatric conditions. Conclusions Our study confirms the importance of the HLA region in host response to viral infection and elucidates novel genetic determinants beyond the HLA that contribute to host-virus interaction.

Background As conscientious vaccination exemption (CVE) percentages rise across the United States, so does the risk and occurrence of outbreaks of vaccine-preventable diseases such as measles. In the state of Texas, the median CVE percentage across school systems more than doubled between 2012 and 2018. During this period, the proportion of schools surpassing a CVE percentage of 3% rose from 2% to 6% for public schools, 20% to 26% for private schools, and 17% to 22% for charter schools. The aim of this study was to investigate this phenomenon at a fine scale. Methods and findings Here, we use beta regression models to study the socioeconomic and geographic drivers of CVE trends in Texas. Using annual counts of CVEs at the school system level from the 2012–2013 to the 2017–2018 school year, we identified county-level predictors of median CVE percentage among public, private, and charter schools, the proportion of schools below a high-risk threshold for vaccination coverage, and five-year trends in CVEs. Since the 2012–2013 school year, CVE percentages have increased in 41 out of 46 counties in the top 10 metropolitan areas of Texas. We find that 77.6% of the variation in CVE percentages across metropolitan counties is explained by median income, the proportion of the population that holds a bachelor's degree, the proportion of the population that self-reports as ethnically white, the proportion of the population that is English speaking, and the proportion of the population that is under the age of five years old. Across the 10 top metropolitan areas in Texas, counties vary considerably in the proportion of school systems reporting CVE percentages above 3%. Sixty-six percent of that variation is explained by the proportion of the population that holds a bachelor’s degree and the proportion of the population affiliated with a religious congregation. Three of the largest metropolitan areas—Austin, Dallas–Fort Worth, and Houston—are potential vaccination exemption "hotspots," with over 13% of local school systems above this risk threshold. The major limitations of this study are inconsistent school-system-level CVE reporting during the study period and a lack of geographic and socioeconomic data for individual private schools. Conclusions In this study, we have identified high-risk communities that are typically obscured in county-level risk assessments and found that public schools, like private schools, are exhibiting predictable increases in vaccination exemption percentages. As public health agencies confront the reemerging threat of measles and other vaccine-preventable diseases, findings such as ours can guide targeted interventions and surveillance within schools, cities, counties, and sociodemographic subgroups., Maike Morrison and coworkers study vaccination exemptions in Texas schools to assess risks of infectious disease outbreaks., Author summary Why was this study done? Nonmedical vaccination exemptions for childhood preventable diseases have been rising in the US, presumably fueled by declining health literacy and increasing distrust in medical authority. Studies in “hotspot” states have found that vaccine hesitancy is positively correlated with both the educational level of the population and the proportion of the population that self-reports as ethnically white. Recent population growth and declining vaccination percentages in Texas put the state at clear risk for outbreaks of vaccine-preventable diseases. However, the risk is highly variable, and its socioeconomic and geographic determinants of risk are largely unknown. This research aims to provide actionable insight for policy makers into trends in vaccine exemptions across Texas at a granular scale. What did the research do and find? We analyzed publicly available reports of the number of conscientious vaccination exemptions (CVEs) for 318 private, 818 public, and 60 charter school systems in Texas from the 2012–2013 to 2017–2018 school years. We used regression methods to relate CVE percentages at the school and county scales to 115 socioeconomic and demographic variables available from the US Census Bureau and the Texas Education Agency. Between the 2012–2013 and 2017–2018 school years, median CVE percentages increased from 0.38% to 0.79%, resulting in more than 24,000 additional vaccination-exempt students. Increases were highest in suburban school districts. The 2017–2018 statewide public school exemption percentages were best explained by school system resources, the percentage of the students that self-report as ethnically white, and whether the school system was in a metropolitan county. In metropolitan areas, vaccine exemptions were positively correlated with wealth and attained educational level. What do these findings mean? Metropolitan communities are at higher risk than rural communities for high exemption percentages across Texas. County-level averaging of CVE percentages obfuscates pockets of low vaccine coverage; the proportion of high-risk schools is a more sensitive indicator of local risk. The findings of the study—both the improved metric for detecting high risk communities and the robust socioeconomic predictors of declining CVEs—can inform targeted interventions to combat the rising but heterogeneous risks of disease emergence across Texas.