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Objective: Vulnerability indices use quantitative indicators and geospatial data to examine the level of vulnerability to morbidity in a community. The Centers for Disease Control and Prevention (CDC) uses 3 indices for the COVID-19 response: the CDC Social Vulnerability Index (CDC-SVI), the US COVID-19 Community Vulnerability Index (CCVI), and the Pandemic Vulnerability Index (PVI). The objective of this review was to describe these tools and explain the similarities and differences between them. Methods: We described the 3 indices, outlined the underlying data sources and metrics for each, and discussed their use by CDC for the COVID-19 response. We compared the percentile score for each county for each index by calculating Spearman correlation coefficients (Spearman ρ). Results: These indices have some, but not all, component metrics in common. The CDC-SVI is a validated metric that estimates social vulnerability, which comprises the underlying population-level characteristics that influence differences in health risk among communities. To address risk specific to the COVID-19 pandemic, the CCVI and PVI build on the CDC-SVI and include additional variables. The 3 indices were highly correlated. Spearman ρ for comparisons between the CDC-SVI score and the CCVI and between the CCVI and the PVI score was 0.83. Spearman ρ for the comparison between the CDC-SVI score and PVI score was 0.73. Conclusion: The indices can empower local and state public health officials with additional information to focus resources and interventions on disproportionately affected populations to combat the ongoing pandemic and plan for future pandemics. [ABSTRACT FROM AUTHOR]

The author reflects on the study that evaluates the relationship between common genetic variants and human immunodeficiency virus type 1 (HIV-1) set point in African Americans. She contends that the authors of the study present excellent results that demonstrates key aspects of study design and analysis. She adds that the study is well designed and has a reasonable sample size and reliable genotyping with careful quality control procedures.

Background: Chronic exposure to microorganisms inside homes can impact respiratory health. Few studies have used advanced sequencing methods to examine adult respiratory outcomes, especially continuous measures. We aimed to identify metagenomic profiles in house dust related to the quantitative traits of pulmonary function and airway inflammation in adults. Microbial communities, 1264 species (389 genera), in vacuumed bedroom dust from 779 homes in a US cohort were characterized by whole metagenome shotgun sequencing. We examined two overall microbial diversity measures: richness (the number of individual microbial species) and Shannon index (reflecting both richness and relative abundance). To identify specific differentially abundant genera, we applied the Lasso estimator with high-dimensional inference methods, a novel framework for analyzing microbiome data in relation to continuous traits after accounting for all taxa examined together., Results: Pulmonary function measures (forced expiratory volume in one second (FEV 1 ), forced vital capacity (FVC), and FEV 1 /FVC ratio) were not associated with overall dust microbial diversity. However, many individual microbial genera were differentially abundant (p-value < 0.05 controlling for all other microbial taxa examined) in relation to FEV 1 , FVC, or FEV 1 /FVC. Similarly, fractional exhaled nitric oxide (FeNO), a marker of airway inflammation, was unrelated to overall microbial diversity but associated with differential abundance for many individual genera. Several genera, including Limosilactobacillus, were associated with a pulmonary function measure and FeNO, while others, including Moraxella to FEV 1 /FVC and Stenotrophomonas to FeNO, were associated with a single trait., Conclusions: Using state-of-the-art metagenomic sequencing, we identified specific microorganisms in indoor dust related to pulmonary function and airway inflammation. Some were previously associated with respiratory conditions; others were novel, suggesting specific environmental microbial components contribute to various respiratory outcomes. The methods used are applicable to studying microbiome in relation to other continuous outcomes. Video Abstract., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: The goal of this study was to perform candidate gene association with cytotoxicity of chemotherapeutics in cell line models through resequencing and discovery of rare and low frequency variants along with common variations. Here, an association study of cytotoxicity response to 30 FDA approved drugs was conducted and we applied next generation targeted sequencing technology to discover variants from 103 candidate genes in 95 lymphoblastoid cell lines from 14 CEPH pedigrees. In this article, we called variants across 95 cell lines and performed association analysis for cytotoxic response using the Family Based Association Testing method and software., Results: We called 2281 variable SNP genotypes across the 103 genes for these cell lines and identified three genes of significant association within this marker set. Specifically, ATP-binding cassette, sub-family C, member 5 (ABCC5), metallothionein 1A (MT1A) and NAD(P)H dehydrogenase quinone1 (NQO1) were significantly associated with oxaliplatin drug response. The significant SNP on NQO1 (rs1800566) has been linked with poor survival rates in patients with non-small cell lung cancer treated with cisplatin (which belongs to the same class of drugs as oxaliplatin). A SNP (rs1846692) near the 5' region of MT1A was associated with arsenic trioxide., Conclusions: The results from this study are promising and this serves as a proof-of-principle demonstration of the use of sequencing data in the cytotoxicity models of human cell lines. With increased sample sizes, such studies will be a fast and powerful way to associate common and rare variants with drug response; while overcoming the cost and time limitations to recruit cohorts for association study.