HIV, asymptomatic STI, and the rectal mucosal immune environment among young men who have sex with men.

Young men who have sex with men (YMSM) are disproportionately affected by HIV and bacterial sexually transmitted infections (STI) including gonorrhea, chlamydia, and syphilis; yet research into the immunologic effects of these infections is typically pursued in siloes. Here, we employed a syndemic approach to understand potential interactions of these infections on the rectal mucosal immune environment among YMSM. We enrolled YMSM aged 18-29 years with and without HIV and/or asymptomatic bacterial STI and collected blood, rectal secretions, and rectal tissue biopsies. YMSM with HIV were on suppressive antiretroviral therapy (ART) with preserved blood CD4 cell counts. We defined 7 innate and 19 adaptive immune cell subsets by flow cytometry, the rectal mucosal transcriptome by RNAseq, and the rectal mucosal microbiome by 16S rRNA sequencing and examined the effects of HIV and STI and their interactions. We measured tissue HIV RNA viral loads among YMSM with HIV and HIV replication in rectal explant challenge experiments among YMSM without HIV. HIV, but not asymptomatic STI, was associated with profound alterations in the cellular composition of the rectal mucosa. We did not detect a difference in the microbiome composition associated with HIV, but asymptomatic bacterial STI was associated with a higher probability of presence of potentially pathogenic taxa. When examining the rectal mucosal transcriptome, there was evidence of statistical interaction; asymptomatic bacterial STI was associated with upregulation of numerous inflammatory genes and enrichment for immune response pathways among YMSM with HIV, but not YMSM without HIV. Asymptomatic bacterial STI was not associated with differences in tissue HIV RNA viral loads or in HIV replication in explant challenge experiments. Our results suggest that asymptomatic bacterial STI may contribute to inflammation particularly among YMSM with HIV, and that future research should examine potential harms and interventions to reduce the health impact of these syndemic infections.

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.

Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.

eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects.

Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.

Compositional analysis of microbiome data using the linear decomposition model (LDM).

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.

MERIT: Controlling Monte-Carlo error rate in large-scale Monte-Carlo hypothesis testing.

The use of Monte-Carlo (MC) p $$ p $$ -values when testing the significance of a large number of hypotheses is now commonplace. In large-scale hypothesis testing, we will typically encounter at least some p $$ p $$ -values near the threshold of significance, which require a larger number of MC replicates than p $$ p $$ -values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg, 1 0 6 $$ 1{0}^6 $$ ) of MC replicates may not be enough to guarantee conclusions reached using MC p $$ p $$ -values. Gandy and Hahn (GH)6-8 have developed the only method that directly addresses this problem. They defined a Monte-Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p $$ p $$ -values are different from decisions based on ideal p $$ p $$ -values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large-scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the ideal p $$ p $$ -values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study.

Interaction between genetics and smoking in determining risk of coronary artery diseases.

Coronary artery disease (CAD) is a preeminent cause of death, and smoking is a strong risk factor for CAD. Genetic factors contribute to the development of CAD, but the interplay between genetic predisposition and smoking history in CAD remains unclear. Using data from the UK Biobank, we constructed several genetic risk scores (GRSs) based on known CAD loci and assessed their interactions with smoking for the development of incident CAD in 307,147 participants of European ancestry who were free of CAD. We fitted Cox proportional hazard models and assessed gene-smoking interaction on both multiplicative and additive scales. Overall, we found no multiplicative interactions, but observed a synergistic additive interaction of GRS with both smoking status and pack-years of smoking, finding that the absolute CAD risk due to smoking was higher for those with high genetic risk. Trait-based sub-GRSs suggested smoking status and smoking intensity measured by pack-years might confer gene-smoking interaction effects with different intermediate risk factors for CAD. Our study results suggest that genetics could modify the effects of smoking on CAD and highlight the value of addressing gene-lifestyle interactions on both additive and multiplicative scales.

A new approach to testing mediation of the microbiome at both the community and individual taxon levels.

MOTIVATION: Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null, most existing methods treat the microbes as if they were all under the same type of null, leading to excessive false positive results. RESULTS: We propose a new approach based on inverse regression that regresses the microbiome data at each taxon on the exposure and the exposure-adjusted outcome. Then, the P-values for testing the coefficients are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method LDM-med, implemented in the LDM framework, enjoys all the features of the LDM, e.g. allowing an arbitrary number of taxa to be tested simultaneously, supporting continuous, discrete, or multivariate exposures and outcomes (including survival outcomes), and so on. Using extensive simulations, we showed that LDM-med always preserved the FDR of testing individual taxa and had adequate sensitivity; LDM-med always controlled the type I error of the global test and had compelling power over existing methods. The flexibility of LDM-med for a variety of mediation analyses is illustrated by an application to a murine microbiome dataset, which identified several plausible mediating taxa. AVAILABILITY AND IMPLEMENTATION: Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome.

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.

Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM.

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.

Testing microbiome associations with survival times at both the community and individual taxon levels.

BACKGROUND: Finding microbiome associations with possibly censored survival times is an important problem, especially as specific taxa could serve as biomarkers for disease prognosis or as targets for therapeutic interventions. The two existing methods for survival outcomes, MiRKAT-S and OMiSA, are restricted to testing associations at the community level and do not provide results at the individual taxon level. An ad hoc approach testing each taxon with a survival outcome using the Cox proportional hazard model may not perform well in the microbiome setting with sparse count data and small sample sizes. METHODS: We have previously developed the linear decomposition model (LDM) for testing continuous or discrete outcomes that unifies community-level and taxon-level tests into one framework. Here we extend the LDM to test survival outcomes. We propose to use the Martingale residuals or the deviance residuals obtained from the Cox model as continuous covariates in the LDM. We further construct tests that combine the results of analyzing each set of residuals separately. Finally, we extend PERMANOVA, the most commonly used distance-based method for testing community-level hypotheses, to handle survival outcomes in a similar manner. RESULTS: Using simulated data, we showed that the LDM-based tests preserved the false discovery rate for testing individual taxa and had good sensitivity. The LDM-based community-level tests and PERMANOVA-based tests had comparable or better power than MiRKAT-S and OMiSA. An analysis of data on the association of the gut microbiome and the time to acute graft-versus-host disease revealed several dozen associated taxa that would not have been achievable by any community-level test, as well as improved community-level tests by the LDM and PERMANOVA over those obtained using MiRKAT-S and OMiSA. CONCLUSIONS: Unlike existing methods, our new methods are capable of discovering individual taxa that are associated with survival times, which could be of important use in clinical settings.

Oral microbiome of electronic cigarette users: A cross-sectional exploration.

OBJECTIVE: Electronic cigarettes have increased in popularity globally. Vaping may be associated with oral symptoms and pathologies including dental and periodontal damage, both of which have an underlying microbial etiology. The primary aim of this pilot study, therefore, was to compare the oral microbiome of vapers and non-vapers. SUBJECTS AND METHODS: This secondary data analysis had a cross-sectional comparative descriptive design and included data for 36 adults. Bacterial 16S rRNA genes were extracted and amplified from soft tissue oral swab specimens and taxonomically classified using the Human Oral Microbiome Database. RESULTS: Data for 18 vapers and 18 non-vapers were included in this study. Almost 56% of the vapers also smoked conventional cigarettes. Beta diversity differences were identified between vapers and non-vapers. Vapers had a significantly higher relative abundance of an unclassified species of Veillonella compared with non-vapers. Dual users had higher alpha diversity compared with exclusive vapers. Beta diversity was also associated with dual use. Multiple OTUs were identified to be associated with dual use of e-cigarettes and conventional cigarettes. CONCLUSIONS: Vapers exhibit an altered oral microbiome. Dual use of electronic cigarettes and conventional cigarettes is associated with the presence of several known pathogenic microbes.

A robust and transformation-free joint model with matching and regularization for metagenomic trajectory and disease onset.

BACKGROUND: To identify operational taxonomy units (OTUs) signaling disease onset in an observational study, a powerful strategy was selecting participants by matched sets and profiling temporal metagenomes, followed by trajectory analysis. Existing trajectory analyses modeled individual OTU or microbial community without adjusting for the within-community correlation and matched-set-specific latent factors. RESULTS: We proposed a joint model with matching and regularization (JMR) to detect OTU-specific trajectory predictive of host disease status. The between- and within-matched-sets heterogeneity in OTU relative abundance and disease risk were modeled by nested random effects. The inherent negative correlation in microbiota composition was adjusted by incorporating and regularizing the top-correlated taxa as longitudinal covariate, pre-selected by Bray-Curtis distance and elastic net regression. We designed a simulation pipeline to generate true biomarkers for disease onset and the pseudo biomarkers caused by compositionality. We demonstrated that JMR effectively controlled the false discovery and pseudo biomarkers in a simulation study generating temporal high-dimensional metagenomic counts with random intercept or slope. Application of the competing methods in the simulated data and the TEDDY cohort showed that JMR outperformed the other methods and identified important taxa in infants' fecal samples with dynamics preceding host disease status. CONCLUSION: Our method JMR is a robust framework that models taxon-specific trajectory and host disease status for matched participants without transformation of relative abundance, improving the power of detecting disease-associated microbial features in certain scenarios. JMR is available in R package mtradeR at https://github.com/qianli10000/mtradeR.

A rarefaction-based extension of the LDM for testing presence-absence associations in the microbiome.

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.

Obesity and patient-reported sexual health outcomes in gynecologic cancer survivors: A systematic review.

As obesity prevalence among gynecologic cancer (GC) survivors is expected to increase, the role of obesity in sexual health needs to be understood. This systematic review examined the impact of obesity on patient-reported sexual health outcomes (SHOs) in this population. PubMed, Embase, Web of Science, CINAHL, and PsycINFO were searched for original studies published between 2015 and 2020 following the Preferred Reporting Items for Systematic Review and Meta-Analyses guideline. We performed a narrative synthesis of findings via cancer type, cancer treatment, sexual health measures, and countries. Eleven observational studies were included. Most were conducted in European countries (n = 7), reported on endometrial cancer survivors (n = 7), and defined obesity as body mass index ≥30 kg/m2 (n = 10). Studies about cervical cancer survivors reported negative effects of obesity on sexual activity and body image while studies about endometrial cancer survivors reported positive effects of obesity on vaginal/sexual symptoms. Findings suggested interaction effects of radiotherapy and obesity on SHOs. Sexual functioning measured by the Female Sexual Function Index was less likely to be associated with obesity than other SHOs. A positive effect of obesity on SHOs was only found in studies conducted in European countries. Current evidence on the association between obesity and sexual health in GC survivors lacks in both quantity and quality. To better understand the effect of obesity on SHOs in the population, more studies are needed with critical evaluations of obesity and sexual health measures, careful considerations of cancer type and treatment, and a focus on the cultural context of obesity.

Testing hypotheses about the microbiome using the linear decomposition model (LDM).

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.

Exploring the Vaginal Microbiome and Intravaginal Practices in Postmenopausal Women.

BACKGROUND: Evidence suggests that intravaginal practices (IVPs) women use to cleanse their vagina or enhance sexual pleasure may be associated with unhealthy changes in the vaginal microbiome (VM). However, the effects of these practices in postmenopausal women are unknown. OBJECTIVES: The objective of this pilot study was to characterize the VM communities of postmenopausal women, identify types and frequency of IVPs, and explore associations between the VM and IVPs in postmenopausal women. METHODS: We analyzed the VM data of 21 postmenopausal women in Atlanta, Georgia, from vaginal swabs collected at a routine gynecological visit. 16S rRNA gene sequencing in the V3-V4 region was used to characterize the VM. In addition, we described the IVPs of these women, identified by using our newly developed instrument: the Vaginal Cleansing Practices Questionnaire. The associations between the VM and IVPs were explored by comparing the alpha diversities, beta diversities, and the relative abundances at both the community level and individual genus level. RESULTS: The most abundant known bacterial genus found in the VM samples was Lactobacillus (35.7%), followed by Prevotella (21.4%). Eleven women (52%) reported using at least one type of IVP since menopause. The most common type of IVP was soap and water to clean inside the vagina. The use of IVPs was not associated with any alpha diversity metric, including Shannon index, inverse Simpson index, and Chao1 index; beta diversity metric, including Bray-Curtis and Jaccard distances; nor relative abundances at the community and individual genus level. Sociodemographic factors were also not associated with any alpha diversity metric. DISCUSSION: Clinicians must assess IVPs and other vaginal and sexual hygiene practices of women of all ages to educate and promote healthy behaviors. More than half of the postmenopausal women in this pilot study use IVPs. Understanding the reasoning behind participants' use of IVPs and their perceptions of the possible effects of these practices will require further research. Although the small sample did not show associations with the VM, more extensive studies are warranted.

Exploring the Maternal and Infant Oral Microbiomes: A Pilot Study.

Setting the stage for good oral health early in life is critical to long-term oral and overall health. This exploratory study aimed to characterize and compare maternal and newborn oral microbiota among mother-infant pairs. Oral samples were collected from 34 pregnant African American women and their infants at 1 to 3 months of age. Extracted 16SrRNA genes were matched to the Human Oral Microbiome Database. Alpha and beta diversity differed significantly between overall maternal and infant microbiomes. Maternal or infant alpha diversity, however, was not differentiated by maternal gingival status. Several demographic and behavioral variables were associated with, but not predictive of, maternal oral microbiome alpha diversity. There was no association, however, among birth mode, feeding mode, and the infant oral microbiome. Megasphaera micronuciformis was the only periodontal pathogen detected among the infants. Notably, maternal gingival status was not associated with the presence/absence of most periodontal pathogens. This study provides an initial description of the maternal and infant oral microbiomes, laying the groundwork for future studies. The perinatal period presents an important opportunity where perinatal nurses and providers can provide oral assessment, education, and referral to quality dental care.

Robust inference of population structure from next-generation sequencing data with systematic differences in sequencing.

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. Availability and implementation: TASER-PC software is publicly available at http://web1.sph.emory.edu/users/yhu30/software.html. Contact: yijuan.hu@emory.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls.

Next-generation sequencing of DNA provides an unprecedented opportunity to discover rare genetic variants associated with complex diseases and traits. However, the common practice of first calling underlying genotypes and then treating the called values as known is prone to false positive findings, especially when genotyping errors are systematically different between cases and controls. This happens whenever cases and controls are sequenced at different depths, on different platforms, or in different batches. In this article, we provide a likelihood-based approach to testing rare variant associations that directly models sequencing reads without calling genotypes. We consider the (weighted) burden test statistic, which is the (weighted) sum of the score statistic for assessing effects of individual variants on the trait of interest. Because variant locations are unknown, we develop a simple, computationally efficient screening algorithm to estimate the loci that are variants. Because our burden statistic may not have mean zero after screening, we develop a novel bootstrap procedure for assessing the significance of the burden statistic. We demonstrate through extensive simulation studies that the proposed tests are robust to a wide range of differential sequencing qualities between cases and controls, and are at least as powerful as the standard genotype calling approach when the latter controls type I error. An application to the UK10K data reveals novel rare variants in gene BTBD18 associated with childhood onset obesity. The relevant software is freely available.

PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies.

A fundamental challenge in analyzing next-generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base-calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too high threshold may lose data, while a too low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The approach, which we call PhredEM, uses the expectation-maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. It also includes a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be nonmonomorphic require application of the EM algorithm. Like GATK, PhredEM can be used together with a linkage-disequilibrium-based method such as Beagle, which can further improve genotype calling as a refinement step. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project and the 1000 Genomes project. The results demonstrate that PhredEM performs better than either GATK or SeqEM, and that PhredEM is an improved, robust, and widely applicable genotype-calling approach for NGS studies. The relevant software is freely available.