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Summary There are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis. Availability and implementation LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. [ABSTRACT FROM AUTHOR]

Background Gestational weight gain above Institute of Medicine recommendations is associated with increased risk of pregnancy complications. The goal was to analyze the association between newer HIV antiretroviral regimens (ART) on gestational weight gain. Methods A retrospective cohort study of pregnant women with HIV-1 on ART. The primary outcome was incidence of excess gestational weight gain. Treatment effects were estimated by ART regimen type using log-linear models for relative risk (RR), adjusting for prepregnancy BMI and presence of detectable viral load at baseline. Results Three hundred three pregnant women were included in the analysis. Baseline characteristics, including prepregnancy BMI, viral load at prenatal care entry, and gestational age at delivery were similar by ART, including 53% of the entire cohort had initiated ART before pregnancy (P =  nonsignificant). Excess gestational weight gain occurred in 29% of the cohort. Compared with non–integrase strand transfer inhibitor (-INSTI) or tenofovir alafenamide fumarate (TAF)–exposed persons, receipt of INSTI+TAF showed a 1.7-fold increased RR of excess gestational weight gain (95% CI: 1.18–2.68; P  < .01), while women who received tenofovir disoproxil fumarate had a 0.64-fold decreased RR (95% CI:.41–.99; P  = .047) of excess gestational weight gain. INSTI alone was not significantly associated with excess weight gain in this population. The effect of TAF without INSTI could not be inferred from our data. There was no difference in neonatal, obstetric, or maternal outcomes between the groups. Conclusions Pregnant women receiving ART with a combined regimen of INSTI and TAF have increased risk of excess gestational weight gain. [ABSTRACT FROM AUTHOR]

Motivation PERMANOVA is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence–absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias but at the potential costs of information loss and the introduction of a stochastic component into the analysis. Results Here, we develop a non-stochastic approach to PERMANOVA presence–absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix and averaging the F -statistic. Our simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease in which samples from case participants have systematically smaller library sizes than samples from control participants. Availability and implementation We have implemented all the approaches described above, including the function for calculating the analytical average of the squared or unsquared distance matrix, in our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Many methods for testing association between the microbiome and covariates of interest (e.g. clinical outcomes, environmental factors) assume that these associations are driven by changes in the relative abundance of taxa. However, these associations may also result from changes in which taxa are present and which are absent. Analyses of such presence–absence associations face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias, but at the potential cost of information loss as well as the introduction of a stochastic component into the analysis. Currently, there is a need for robust and efficient methods for testing presence–absence associations in the presence of such confounding, both at the community level and at the individual-taxon level, that avoid the drawbacks of rarefaction. Results We have previously developed the linear decomposition model (LDM) that unifies the community-level and taxon-level tests into one framework. Here, we present an extension of the LDM for testing presence–absence associations. The extended LDM is a non-stochastic approach that repeatedly applies the LDM to all rarefied taxa count tables, averages the residual sum-of-squares (RSS) terms over the rarefaction replicates, and then forms an F -statistic based on these average RSS terms. We show that this approach compares favorably to averaging the F -statistic from R rarefaction replicates, which can only be calculated stochastically. The flexible nature of the LDM allows discrete or continuous traits or interactions to be tested while allowing confounding covariates to be adjusted for. Our simulations indicate that our proposed method is robust to any systematic differences in library size and has better power than alternative approaches. We illustrate our method using an analysis of data on inflammatory bowel disease (IBD) in which cases have systematically smaller library sizes than controls. Availabilityand implementation The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary We previously developed the LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. The LDM can be applied to relative abundance data and presence–absence data separately, which work well when associated taxa are abundant and rare, respectively. Here, we propose LDM-omni3 that combines LDM analyses at the relative abundance and presence–absence data scales, thereby offering optimal power across scenarios with different association mechanisms. The new LDM-omni3 test is available for the wide range of data types and analyses that are supported by the LDM. Availability and implementation The LDM-omni3 test has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR). Results We introduce the linear decomposition model (LDM), that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g. clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based P -values that can control for sample correlation. The LDM can also be applied to transformed data, and an 'omnibus' test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach. For global testing, our simulations indicate the LDM provided correct type I error and can have comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations. Availability and implementation The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows. Contact yijuan.hu@emory.edu Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across different groups of samples (i.e. sequencing groups). This situation can arise in population genetics when combining sequencing data from different studies, or in genetic epidemiology when using historical controls such as samples from the 1000 Genomes Project. Results: To allow inference on population structure using PCA in these situations, we provide an approach that is based on using sequencing reads directly without calling genotypes. Our approach is to adjust the data from different sequencing groups to have the same read depth and error rate so that PCA does not generate spurious components representing sequencing quality. To accomplish this, we have developed a subsampling procedure to match the depth distributions in different sequencing groups and a read-flipping procedure to match the error rates. We average over subsamples and read flips to minimize loss of information. We demonstrate the utility of our approach using two datasets from 1000 Genomes and further evaluate it using simulation studies. [ABSTRACT FROM AUTHOR]

The stratification score for a case-control study is the probability of disease modeled as a function of potential confounders. The authors show that the stratification score is a retrospective balancing score and thus plays a similar role in case-control studies as the propensity score plays in prospective studies. The authors further show how standardization using the stratification score can be used to compare the distributions of exposures that would be found among case and control participants if both groups had the same distribution of confounding covariables. The authors illustrate these results using data from a genome-wide association study, the GAIN (Genetic Association Information Network) study of schizophrenia among African Americans (2006–2008). [ABSTRACT FROM PUBLISHER]

A novel mass spectral fingerprinting and proteomics approach using MALDI-TOF MS was applied to detect and identify protein biomarkers of group A Streptococcus (GAS) strains. Streptococcus pyogenes ATCC 700294 genome strain was compared with eight GAS clinical isolates to explore the ability of MALDI-TOF MS to differentiate isolates. Reference strains of other bacterial species were also analyzed and compared with the GAS isolates. MALDI preparations were optimized by varying solvents, matrices, plating techniques, and mass ranges for S. pyogenes ATCC 700294. Spectral variability was tested. A subset of common, characteristic, and reproducible biomarkers in the range of 2000–14 000 Da were detected, and they appeared to be independent of the culture media. Statistical analysis confirmed method reproducibility. Random Forest analysis of all selected GAS isolates revealed differences among most of them, and summed spectra were used for hierarchical cluster analysis. Specific biomarkers were found for each strain, and invasive GAS isolates could be differentiated. GAS isolates from cases of necrotizing fasciitis were clustered together and were distinct from isolates associated with noninvasive infections, despite their sharing the same emm type. Almost 30% of the biomarkers detected were tentatively identified as ribosomal proteins. [ABSTRACT FROM AUTHOR]

We consider the problem of comparing two outcome measures when the pairs are clustered. Using the general principle of within-cluster resampling, we obtain a novel signed-rank test for clustered paired data. We show by a simple informative cluster size simulation model that only our test maintains the correct size under a null hypothesis of marginal symmetry compared to four other existing signed rank tests; further, our test has adequate power when cluster size is noninformative. In general, cluster size is informative if the distribution of pair-wise differences within a cluster depends on the cluster size. An application of our method to testing radiation toxicity trend is presented. [ABSTRACT FROM AUTHOR]

Modelling human genetic variation is critical to understanding the genetic basis of complex disease. The Human Genome Project has discovered millions of binary DNA sequence variants, called single nucleotide polymorphisms, and millions more may exist. As coding for proteins takes place along chromosomes, organisation of polymorphisms along each chromosome, the haplotype phase structure, may prove to be most important in discovering genetic variants associated with disease. As haplotype phase is often uncertain, procedures that model the distribution of parental haplotypes can, if this distribution is misspecified, lead to substantial bias in parameter estimates even when complete genotype information is available. Using a geometric approach to estimation in the presence of nuisance parameters, we address this problem and develop locally-efficient estimators of the effect of haplotypes on disease that are robust to incorrect estimates of haplotype frequencies. The methods are demonstrated with a simulation study of a case-parent design. [ABSTRACT FROM AUTHOR]

Summary. We propose a new approach to fitting marginal models to clustered data when cluster size is in- formative. This approach uses a generalized estimating equation (GEE) that is weighted inversely with the cluster size. We show that our approach is asymptotically equivalent to within-cluster resampling (Hoffman, Sen, and Weinberg, 2001, Biometrika 73, 13–22), a computationally intensive approach in which replicate data sets containing a randomly selected observation from each cluster are analyzed, and the resulting estima- tes averaged. Using simulated data and an example involving dental health, we show the superior performa- nce of our approach compared to unweighted GEE, the equivalence of our approach with WCR for large sam- ple sizes, and the superior performance of our approach compared with WCR when sample sizes are small. [ABSTRACT FROM AUTHOR]

We consider the problem of matched studies with a binary outcome that are analysed using conditional logistic regression, and for which data on some covariates are missing for some study participants. Methods for this problem involve either modelling the distribution of missing covariates or modelling the probability of data being missing. For this second approach, the previously proposed method did not make use of data for those persons with missing covariate data except in the model for the missingness. We propose a new class of estimators that use outcome and available covariate data for all study participants, and show that a particular member of this class always has better efficiency than the previously proposed estimator. We illustrate the efficiency gains that are possible with our approach using simulated data. [ABSTRACT FROM PUBLISHER]

The Bangkok (Thailand) Metropolitan Administration cohort of injecting drug users (IDUs) consisted of 1,209 IDUs initially seronegative for human immunodeficiency virus (HIV) who were followed from 1995 to 1998 at 15 Administration drug treatment clinics. At enrollment and approximately every 4 months thereafter, participants were assessed for HIV seropositivity. As of December 1998, there were 133 HIV type 1 seroconversions and approximately 2,300 person-years of follow-up. Of the 133 observed seroconversions, specimens from 126 persons were available for subtyping (27 subtype B, 99 subtype E). In this analysis, the authors assessed differences in subtype-specific transmission while controlling for important risk factors. The methodology used accounts for left truncation, interval censoring, and competing risks as well as for time-varying covariates such as each IDU's history of reported frequency of injection and of incarceration. Using plausible epidemiologic assumptions and controlling for behavioral risks, the authors found that a significantly higher transmission probability was associated with subtype E compared with subtype B in this population. Since many epidemiologic, virologic, and host factors can influence HIV transmission, it was difficult to conclude whether these differences in transmission probabilities were due to biologic properties associated with subtype. [ABSTRACT FROM PUBLISHER]

Left-truncated and interval-censored data, termed dynamic cohort data, arise in longitudinal studies with rolling admissions and only occasional follow-up. The authors compared four approaches for analyzing such data: a constant hazard model; maximum likelihood estimation with flexible parametric models; the midpoint method, in which the midpoint of the last negative and first positive test result is used in a Cox proportional hazards model that accounts for left truncation; and a semiparametric method that uses imputed failure times in the Cox model. By using a simulation study, they assessed the performance of these approaches under conditions that can arise in observational studies: changes in disease incidence and changes in the underlying population. The simulation results indicated that the constant hazard model and midpoint method were inadequate and that the flexible parametric model was useful when enough parameters were used in modeling the baseline hazard. The semiparametric method ensured correct parameter (odds ratio) estimation when the baseline hazard was misspecified, but the trade-off increased computational complexity. In this paper, a study of the incidence of human immunodeficiency virus in patients repeatedly tested for the virus at a sexually transmitted disease clinic in New Orleans, Louisiana, illustrates the methods used. [ABSTRACT FROM PUBLISHER]

SUMMARY A marginal likelihood approach to fitting the proportional hazards model to interval censored or grouped data is proposed; this approach maximises a likelihood that is the sum over all rankings of the data that are consistent with the observed censoring intervals. As in the usual proportional hazards model, the method does not require specification of the bascline hazard function. The score equations determining the maximum marginal likelihood estimator can be written as the expected value of the score of the usual proportional hazards model, with respect to a certain distribution of rankings. A Gibbs sampling scheme is given to generate rankings from this distribution, and stochastic approximation is used to solve the score equations. Simulation results under various censoring schemes give point estimates that are close to estimates obtained using actual failure times. [ABSTRACT FROM AUTHOR]

A Markvo chain is a useful way of describing cohort data. Longitudinal observations of a marker of the progression of the human immunodeficiency virus (HIV), such as CD4 cell count, measured on members of a cohort study, can be analysed as a continuous time Markov chain by categorizing the CD4 cell counts into stages. Unfortunately, CD4 cell counts are subject to substantial measurement error and short timescale variability. Thus, fitting a Markov chain to raw CD4 cell count measurements does not determine the transition probabilities for the true or underlying CD4 cell counts; the measurement error results in a process that is too tough. Assuming independent measurement errors, we propose a likelihood-based method for estimating the 'true' or underlying transition probabilities. The Markov structure allows efficient calculation of the likelihood by using hidden Markov model methodology. As an example, we consider CD4 cell count data from 430 HIV-infected participants in the San Francisco Mens Health Study by categorizing the marker data into seven stages; up to 17 observations are available for each individual. We find that including measurement error both produces a significantly better fit and provides a model for CD4 progression that is more biologically reasonable. [ABSTRACT FROM AUTHOR]