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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Using a prospective, multicenter cohort (the Hispanic Community Health Study/Study of Latinos), the authors describe all-cause mortality across different U.S. Hispanic/Latino groups and how it is affected when sociodemographic, acculturation-related, lifestyle, and clinical factors are accounted for. Visual Abstract. Cumulative All-Cause Mortality in Diverse Hispanic/Latino Adults: Using a prospective, multicenter cohort (the Hispanic Community Health Study/Study of Latinos), the authors describe all-cause mortality across different U.S. Hispanic/Latino groups and how it is affected when sociodemographic, acculturation-related, lifestyle, and clinical factors are accounted for. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 P (X < Y) ≡ p as a measure of effect size, where X and Y denote random observations from two distributions hypothesized to be equal under the null. Effect size p fosters productive communications because researchers understand p = 0.5 is analogous to a fair coin toss, and p 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]