Cumulative All-Cause Mortality in Diverse Hispanic/Latino Adults : A Prospective, Multicenter Cohort Study.

BACKGROUND: All-cause mortality among diverse Hispanic/Latino groups in the United States and factors underlying mortality differences have not been examined prospectively. OBJECTIVE: To describe cumulative all-cause mortality (and factors underlying differences) by Hispanic/Latino background, before and during the COVID-19 pandemic. DESIGN: Prospective, multicenter cohort study. SETTING: Hispanic Community Health Study/Study of Latinos. PARTICIPANTS: 15 568 adults aged 18 to 74 years at baseline (2008 to 2011) of Central American, Cuban, Dominican, Mexican, Puerto Rican, South American, and other backgrounds from the Bronx, New York; Chicago, Illinois; Miami, Florida; and San Diego, California. MEASUREMENTS: Sociodemographic, acculturation-related, lifestyle, and clinical factors were assessed at baseline, and vital status was ascertained through December 2021 (969 deaths; 173 444 person-years of follow-up). Marginally adjusted cumulative all-cause mortality risks (11-year before the pandemic and 2-year during the pandemic) were examined using progressively adjusted Cox regression. RESULTS: Before the pandemic, 11-year cumulative mortality risks adjusted for age and sex were higher in the Puerto Rican and Cuban groups (6.3% [95% CI, 5.2% to 7.6%] and 5.7% [CI, 5.0% to 6.6%], respectively) and lowest in the South American group (2.4% [CI, 1.7% to 3.5%]). Differences were attenuated with adjustment for lifestyle and clinical factors. During the pandemic, 2-year cumulative mortality risks adjusted for age and sex ranged from 1.1% (CI, 0.6% to 2.0%; South American) to 2.0% (CI, 1.4% to 3.0%; Central American); CIs overlapped across groups. With adjustment for lifestyle factors, 2-year cumulative mortality risks were highest in persons of Central American and Mexican backgrounds and lowest among those of Puerto Rican and Cuban backgrounds. LIMITATION: Lack of data on race and baseline citizenship status; correlation between Hispanic/Latino background and site. CONCLUSION: Differences in prepandemic mortality risks across Hispanic/Latino groups were explained by lifestyle and clinical factors. Mortality patterns changed during the pandemic, with higher risks in persons of Central American and Mexican backgrounds than in those of Puerto Rican and Cuban backgrounds. PRIMARY FUNDING SOURCE: National Institutes of Health.

Quantifying the HIV reservoir with dilution assays and deep viral sequencing.

People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. The quantitative viral outgrowth assay (QVOA) is commonly used to estimate the reservoir size, that is, the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. A new variation of the QVOA, the ultra deep sequencing assay of the outgrowth virus (UDSA), was recently developed that further quantifies the number of viral lineages within a subset of infected wells. Performing the UDSA on a subset of wells provides additional information that can improve IUPM estimation. This paper considers statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Methods are proposed to accommodate assays with wells sequenced at multiple dilution levels and with imperfect sensitivity and specificity, and a novel bias-corrected estimator is included for small samples. The proposed methods are evaluated in a simulation study, applied to data from the University of North Carolina HIV Cure Center, and implemented in the open-source R package SLDeepAssay.

Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR.

Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures.

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.

Comparisons of statistical methods for handling attrition in a follow-up visit with complex survey sampling.

Design-based analysis, which accounts for the design features of the study, is commonly used to conduct data analysis in studies with complex survey sampling, such as the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). In this type of longitudinal study, attrition has often been a problem. Although there have been various statistical approaches proposed to handle attrition, such as inverse probability weighting (IPW), non-response cell weighting (NRCW), multiple imputation (MI), and full information maximum likelihood (FIML) approach, there has not been a systematic assessment of these methods to compare their performance in design-based analyses. In this article, we perform extensive simulation studies and compare the performance of different missing data methods in linear and generalized linear population models, and under different missing data mechanism. We find that the design-based analysis is able to produce valid estimation and statistical inference when the missing data are handled appropriately using IPW, NRCW, MI, or FIML approach under missing-completely-at-random or missing-at-random missing mechanism and when the missingness model is correctly specified or over-specified. We also illustrate the use of these methods using data from HCHS/SOL.

Efficient detection and classification of epigenomic changes under multiple conditions.

Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP-seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, epigraHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.

Differential requirement for the Polycomb repressor complex 2 in dendritic cell and tissue-resident myeloid cell homeostasis.

Dendritic cells (DCs) and macrophages are at the forefront of immune responses, modifying their transcriptional programs in response to their tissue environment or immunological challenge. Posttranslational modifications of histones, such as histone H3 lysine-27 trimethylation (H3K27me3) by the Polycomb repressive complex 2 (PRC2), are tightly associated with epigenetic regulation of gene expression. To explore whether H3K27me3 is involved in either the establishment or function of the mononuclear phagocyte system, we selectively deleted core components of PRC2, either EZH2 or SUZ12, in CD11c-expressing myeloid cells. Unexpectedly, EZH2 deficiency neither prevented the deposition and maintenance of H3K27me3 in DCs nor hindered DC/macrophage homeostasis. In contrast, SUZ12 deficiency markedly impaired the capacity of DCs and macrophages to maintain H3K27me3. SUZ12 ablation induced a rapid loss of the alveolar macrophage and Langerhans cell networks under both steady state and inflammatory conditions because these cells could no longer proliferate to facilitate their self-renewal. Despite the reduced H3K27me3, DC development and function were unaffected by SUZ12 ablation, suggesting that PRC2-mediated gene repression was dispensable for DC homeostasis. Thus, the role of SUZ12 highlights the fundamentally different homeostatic mechanisms used by tissue-resident myeloid cells versus DCs.

On the Use of Regression Calibration in a Complex Sampling Design With Application to the Hispanic Community Health Study/Study of Latinos.

Regression calibration is the most widely used method to adjust regression parameter estimates for covariate measurement error. Yet its application in the context of a complex sampling design, for which the common bootstrap variance estimator can be less straightforward, has been less studied. We propose 2 variance estimators for a multistage probability-based sampling design, a parametric and a resampling-based multiple imputation approach, where a latent mean exposure needed for regression calibration is the target of imputation. This work was motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) data from 2008 to 2011, for which relationships between several outcomes and diet, an error-prone self-reported exposure, are of interest. We assessed the relative performance of these variance estimation strategies in an extensive simulation study built on the HCHS/SOL data. We further illustrate the proposed estimators with an analysis of the cross-sectional association of dietary sodium intake with hypertension-related outcomes in a subsample of the HCHS/SOL cohort. We have provided guidelines for the application of regression models with regression-calibrated exposures. Practical considerations for implementation of these 2 variance estimators in the setting of a large multicenter study are also discussed. Code to replicate the presented results is available online.

Current Smoking Raises Risk of Incident Hypertension: Hispanic Community Health Study-Study of Latinos.

BACKGROUND: Hypertension has been implicated as a smoking-related risk factor for cardiovascular disease but the dose-response relationship is incompletely described. Hispanics, who often have relatively light smoking exposures, have been understudied in this regard. METHODS: We used data from a 6-year follow-up study of US Hispanic adults aged 18-76 to address the dose-response linking cigarette use with incident hypertension, which was defined by measured blood pressure above 140/90 mm Hg or initiation of antihypertensive medications. Adjustment was performed for potential confounders and mediators, including urinary albumin-to-creatinine ratio which worsened over time among smokers. RESULTS: Current smoking was associated with incident hypertension, with a threshold effect above 5 cumulative pack-years of smoking (vs. never smokers, hazard ratio for hypertension [95% confidence interval] of 0.95 [0.67, 1.35] for 0-5 pack-years, 1.47 [1.05, 2.06] for 5-10 pack-years, 1.40 [1.00, 1.96] for 10-20 pack-years, and 1.34 [1.09, 1.66] for ≥20 pack-years, P = 0.037). In contrast to current smokers, former smokers did not appear to have increased risk of hypertension, even at the highest cumulative pack-years of past exposure. CONCLUSIONS: The results confirm that smoking constitutes a hypertension risk factor in Hispanic adults. A relatively modest cumulative dose of smoking, above 5 pack-years of exposure, raises risk of hypertension by over 30%. The increased hypertension risk was confined to current smokers, and did not increase further with higher pack-year levels. The lack of a smoking-hypertension association in former smokers underscores the value of smoking cessation.

Incidence of Hypertension Among US Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos, 2008 to 2017.

Background Among US Hispanics/Latinos, the largest ethnic minority population in the United States, hypertension incidence has not been thoroughly reported. The goal of this study was to describe the incidence of hypertension among US Hispanic/Latino men and women of diverse Hispanic/Latino background. Methods and Results We studied 6171 participants of the Hispanic Community Health Study/Study of Latinos, a diverse group of self-identified Hispanics/Latinos from 4 US urban communities, aged 18 to 74 years, and free from hypertension in 2008 to 2011 and re-examined in 2014 to 2017. Hypertension was defined as self-reported use of anti-hypertension medication, or measured systolic blood pressure ≥130 mm Hg, or diastolic blood pressure ≥80 mm Hg. Results were weighted given the complex survey design to reflect the target population. Among men, the 6-year age-adjusted probability of developing hypertension was 21.7% (95% CI, 19.5-24.1) and differed by Hispanic/Latino background. Specifically, the probability was significantly higher among men of Cuban (27.1%; 95% CI, 20.2-35.2) and Dominican (28.1%; 95% CI, 19.5-38.8) backgrounds compared with Mexican Americans (17.6%; 95% CI: 14.5-21.2). Among women, the 6-year age-adjusted probability of developing hypertension was 19.7% (95% CI, 18.1-21.5) and also differed by Hispanic/Latino background. Specifically, the probability was significantly higher among women of Cuban (22.6%; 95% CI, 18.3-27.5), Dominican (23.3%; 95% CI, 18.0-29.5), and Puerto Rican (28.2%; 95% CI, 22.7-34.4) backgrounds compared with Mexican Americans (16.0%; 95% CI, 13.9-18.4). Conclusions Hypertension incidence varies by Hispanic/Latino background, with highest incidence among those of Caribbean background.

Precise and accurate power of the rank-sum test for a continuous outcome.

Accurate power calculations are essential in small studies containing expensive experimental units or high-stakes exposures. Herein, power of the Wilcoxon Mann-Whitney rank-sum test of a continuous outcome is formulated using a Monte Carlo approach and defining [Formula: see text] as a measure of effect size, where [Formula: see text] and [Formula: see text] denote random observations from two distributions hypothesized to be equal under the null. Effect size [Formula: see text] fosters productive communications because researchers understand [Formula: see text] is analogous to a fair coin toss, and [Formula: see text] near 0 or 1 represents a large effect. This approach is feasible even without background data. Simulations were conducted comparing the empirical power approach to existing approaches by Rosner & Glynn, Shieh and colleagues, Noether, and O'Brien-Castelloe. Approximations by Noether and O'Brien-Castelloe are shown to be inaccurate for small sample sizes. The Rosner & Glynn and Shieh, Jan & Randles approaches performed well in many small sample scenarios, though both are restricted to location-shift alternatives and neither approach is theoretically justified for small samples. The empirical method is recommended and available in the R package wmwpow.

Improved detection of epigenomic marks with mixed-effects hidden Markov models.

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) is a technique to detect genomic regions containing protein-DNA interaction, such as transcription factor binding sites or regions containing histone modifications. One goal of the analysis of ChIP-seq experiments is to identify genomic loci enriched for sequencing reads pertaining to DNA bound to the factor of interest. The accurate identification of such regions aids in the understanding of epigenomic marks and gene regulatory mechanisms. Given the reduction of massively parallel sequencing costs, methods to detect consensus regions of enrichment across multiple samples are of interest. Here, we present a statistical model to detect broad consensus regions of enrichment from ChIP-seq technical or biological replicates through a class of zero-inflated mixed-effects hidden Markov models. We show that the proposed model outperforms existing methods for consensus peak calling in common epigenomic marks by accounting for the excess zeros and sample-specific biases. We apply our method to data from the Encyclopedia of DNA Elements and Roadmap Epigenomics projects and also from an extensive simulation study.

SLDAssay: A software package and web tool for analyzing limiting dilution assays.

Serial limiting dilution (SLD) assays are used in many areas of infectious disease related research. This paper presents SLDAssay, a free and publicly available R software package and web tool for analyzing data from SLD assays. SLDAssay computes the maximum likelihood estimate (MLE) for the concentration of target cells, with corresponding exact and asymptotic confidence intervals. Exact and asymptotic goodness of fit p-values, and a bias-corrected (BC) MLE are also provided. No other publicly available software currently implements the BC MLE or the exact methods. For validation of SLDAssay, results from Myers et al. (1994) are replicated. Simulations demonstrate the BC MLE is less biased than the MLE. Additionally, simulations demonstrate that exact methods tend to give better confidence interval coverage and goodness-of-fit tests with lower type I error than the asymptotic methods. Additional advantages of using exact methods are also discussed.

Quantification of the Latent HIV-1 Reservoir Using Ultra Deep Sequencing and Primer ID in a Viral Outgrowth Assay.

BACKGROUND: In this study, we measured the latent HIV-1 reservoir harboring replication-competent HIV-1 in resting CD4 T cells in participants on highly active antiretroviral therapy, quantitating the frequency of latent infection through the use of a Primer ID-based Ultra Deep Sequencing Assay (UDSA), in comparison to the readout of the quantitative viral outgrowth assay (QVOA). METHODS: Viral RNA derived from culture wells of QVOA that scored as HIV-1 p24 capsid antigen positive were tagged with a specific barcode during cDNA synthesis, and the sequences within the V1-V3 region of the HIV-1 env gene were analyzed for diversity using the Primer ID-based paired-end MiSeq platform. We analyzed samples from a total of 19 participants, 2 initially treated with highly active antiretroviral therapy in acute infection and 17 treated during chronic infection. Phylogenetic trees were generated with all viral lineages detected from culture wells derived from each participant to determine the number of distinct viral lineages growing out in each well, thus capturing another level of information beyond the well being positive for viral antigen. The infectious units per million (IUPM) cell values estimated using a maximum likelihood approach, based on the number of distinct viral lineages detected (VOA-UDSA), were compared with those obtained from QVOA measured using limiting dilution. RESULTS: IUPM estimates determined by VOA-UDSA ranged from 0.14 to 3.66 and strongly correlated with the IUPM estimates determined by QVOA (r = 0.94; P < 0.0001). CONCLUSIONS: VOA-UDSA may be an alternative readout for that currently used for QVOA.