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Identifying how dependence relationships vary across different conditions plays a significant role in many scientific investigations. For example, it is important for the comparison of biological systems to see if relationships between genomic features differ between cases and controls. In this paper, we seek to evaluate whether relationships between two sets of variables are different or not across two conditions. Specifically, we assess: Do two sets of high‐dimensional variables have similar dependence relationships across two conditions? We propose a new kernel‐based test to capture the differential dependence. Specifically, the new test determines whether two measures that detect dependence relationships are similar or not under two conditions. We introduce the asymptotic permutation null distribution of the test statistic, and it is shown to work well under finite samples such that the test is computationally efficient, significantly enhancing its usability in analysing large datasets. We demonstrate through numerical studies that our proposed test has high power for detecting differential linear and non‐linear relationships. The proposed method is implemented in an R$$ \mathtt{R} $$ package kerDAA$$ \mathtt{kerDAA} $$. [ABSTRACT FROM AUTHOR]

Valvular heart disease has recently become an increasing public health concern due to the high prevalence of valve degeneration in aging populations. For patients with severely impacted aortic valves that require replacement, catheter-based bioprosthetic valve deployment offers a minimally invasive treatment option that eliminates many of the risks associated with surgical valve replacement. Although recent percutaneous device advancements have incorporated thinner, more flexible biological tissues to streamline safer deployment through catheters, the impact of such tissues in the complex, mechanically demanding, and highly dynamic valvular system remains poorly understood. The present work utilized a validated computational fluid–structure interaction approach to isolate the behavior of thinner, more compliant aortic valve tissues in a physiologically realistic system. This computational study identified and quantified significant leaflet flutter induced by the use of thinner tissues that initiated blood flow disturbances and oscillatory leaflet strains. The aortic flow and valvular dynamics associated with these thinner valvular tissues have not been previously identified and provide essential information that can significantly advance fundamental knowledge about the cardiac system and support future medical device innovation. Considering the risks associated with such observed flutter phenomena, including blood damage and accelerated leaflet deterioration, this study demonstrates the potentially serious impact of introducing thinner, more flexible tissues into the cardiac system. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) mucosal dysfunction predicts and likely contributes to non-infectious comorbidities and mortality in HIV infection and persists despite antiretroviral therapy. However, the mechanisms underlying this dysfunction remain incompletely understood. Neutrophils are important for containment of pathogens but can also contribute to tissue damage due to their release of reactive oxygen species and other potentially harmful effector molecules. Here we used a flow cytometry approach to investigate increased neutrophil lifespan as a mechanism for GI neutrophil accumulation in chronic, treated HIV infection and a potential role for gastrointestinal dysbiosis. We report that increased neutrophil survival contributes to neutrophil accumulation in colorectal biopsy tissue, thus implicating neutrophil lifespan as a new therapeutic target for mucosal inflammation in HIV infection. Additionally, we characterized the intestinal microbiome of colorectal biopsies using 16S rRNA sequencing. We found that a reduced Lactobacillus: Prevotella ratio associated with neutrophil survival, suggesting that intestinal bacteria may contribute to GI neutrophil accumulation in treated HIV infection. Finally, we provide evidence that Lactobacillus species uniquely decrease neutrophil survival and neutrophil frequency in vitro, which could have important therapeutic implications for reducing neutrophil-driven inflammation in HIV and other chronic inflammatory conditions. [ABSTRACT FROM AUTHOR]

Motivation Spatial proteomics can reveal the spatial organization of immune cells in the tumor immune microenvironment. Relating measures of spatial clustering, such as Ripley's K or Besag's L , to patient outcomes may offer important clinical insights. However, these measures require pre-specifying a radius in which to quantify clustering, yet no consensus exists on the optimal radius which may be context-specific. Results We propose a SPatial Omnibus Test (SPOT) which conducts this analysis across a range of candidate radii. At each radius, SPOT evaluates the association between the spatial summary and outcome, adjusting for confounders. SPOT then aggregates results across radii using the Cauchy combination test, yielding an omnibus P -value characterizing the overall degree of association. Using simulations, we verify that the type I error rate is controlled and show SPOT can be more powerful than alternatives. We also apply SPOT to ovarian and lung cancer studies. Availability and implementation An R package and tutorial are provided at https://github.com/sarahsamorodnitsky/SPOT. [ABSTRACT FROM AUTHOR]

Motivation T cell receptors (TCRs) constitute a major component of our adaptive immune system, governing the recognition and response to internal and external antigens. Studying the TCR diversity via sequencing technology is critical for a deeper understanding of immune dynamics. However, library sizes differ substantially across samples, hindering the accurate estimation/comparisons of alpha diversities. To address this, researchers frequently use an overall rarefying approach in which all samples are sub-sampled to an even depth. Despite its pervasive application, its efficacy has never been rigorously assessed. Results In this paper, we develop an innovative "multi-bin" rarefying approach that partitions samples into multiple bins according to their library sizes, conducts rarefying within each bin for alpha diversity calculations, and performs meta-analysis across bins. Extensive simulations using real-world data highlight the inadequacy of the overall rarefying approach in controlling the confounding effect of library size. Our method proves robust in addressing library size confounding, outperforming competing normalization strategies by achieving better-controlled type-I error rates and enhanced statistical power in association tests. Availability and implementation The code is available at https://github.com/mli171/MultibinAlpha. The datasets are freely available at https://doi.org/10.21417/B7001Z and https://doi.org/10.21417/AR2019NC. [ABSTRACT FROM AUTHOR]

Summary: Kong et al. (2016, Biometrics 72, 364–371) presented a quantile regression kernel machine (QRKM) test for robust analysis of genetic marker‐set association studies. A potential limitation of QRKM is the permutation‐based test design may be unscalable for the massive sizes of modern datasets. In this article, we present an alternative strategy for p‐value calculation of QRKM, which is capable of speeding up the QRKM testing procedure dramatically while maintaining the same testing performance as QRKM. The effectiveness of our approach is demonstrated via simulation studies. [ABSTRACT FROM AUTHOR]

DNA methylation may mediate inter-individual responses to chemical exposure and, thus, modify biomarker levels of exposure and effects. We analyzed inter-individual differences in inhalation and skin exposure to 1,6-hexamethylene diisocyanate (HDI) and urine biomarker 1,6-hexamethylene diamine (HDA) levels in 20 automotive spray-painters. Genome-wide 5-methyl cytosine (CpG) DNA methylation was assessed in each individual’s peripheral blood mononuclear cells (PBMC) DNA using the Illumina 450K CpG array. Mediation analysis using linear regression models adjusted for age, ethnicity, and smoking was conducted to identify and assess the association between HDI exposure, CpG methylation, and urine HDA biomarker levels. We did not identify any CpGs common to HDI exposure and biomarker level suggesting that CpG methylation is a mediator that only partially explains the phenotype. Functional significance of genic- and intergenic-CpG methylation status was tested using protein–protein or protein–DNA interactions and gene-ontology enrichment to infer networks. Combined, the results suggest that methylation has the potential to affect HDI mass transport, permeation, and HDI metabolism. We demonstrate the potential use of PBMC methylation along with quantitative exposure and biomarker data to guide further investigation into the mediators of occupational exposure and biomarkers and its role in risk assessment. [ABSTRACT FROM AUTHOR]

Cluster-correlated data receives a lot of attention in biomedical and longitudinal studies and it is of interest to assess the generalized dependence between two multivariate variables under the cluster-correlated structure. The Hilbert–Schmidt independence criterion (HSIC) is a powerful kernel-based test statistic that captures various dependence between two random vectors and can be applied to an arbitrary non-Euclidean domain. However, the existing HSIC is not directly applicable to cluster-correlated data. Therefore, we propose a HSIC-based test of independence for cluster-correlated data. The new test statistic combines kernel information so that the dependence structure in each cluster is fully considered and exhibits good performance under high dimensions. Moreover, a rapid p value approximation makes the new test fast applicable to large datasets. Numerical studies show that the new approach performs well in both synthetic and real world data. [ABSTRACT FROM AUTHOR]

With development of massively parallel sequencing technologies, there is a substantial need for developing powerful rare variant association tests. Common approaches include burden and non-burden tests. Burden tests assume all rare variants in the target region have effects on the phenotype in the same direction and of similar magnitude. The recently proposed sequence kernel association test (SKAT) (Wu, M. C., and others, 2011. Rare-variant association testing for sequencing data with the SKAT. The American Journal of Human Genetics 89, 82–93], an extension of the C-alpha test (Neale, B. M., and others, 2011. Testing for an unusual distribution of rare variants. PLoS Genetics 7, 161–165], provides a robust test that is particularly powerful in the presence of protective and deleterious variants and null variants, but is less powerful than burden tests when a large number of variants in a region are causal and in the same direction. As the underlying biological mechanisms are unknown in practice and vary from one gene to another across the genome, it is of substantial practical interest to develop a test that is optimal for both scenarios. In this paper, we propose a class of tests that include burden tests and SKAT as special cases, and derive an optimal test within this class that maximizes power. We show that this optimal test outperforms burden tests and SKAT in a wide range of scenarios. The results are illustrated using simulation studies and triglyceride data from the Dallas Heart Study. In addition, we have derived sample size/power calculation formula for SKAT with a new family of kernels to facilitate designing new sequence association studies. [ABSTRACT FROM AUTHOR]

Sequencing studies are increasingly being conducted to identify rare variants associated with complex traits. The limited power of classical single-marker association analysis for rare variants poses a central challenge in such studies. We propose the sequence kernel association test (SKAT), a supervised, flexible, computationally efficient regression method to test for association between genetic variants (common and rare) in a region and a continuous or dichotomous trait while easily adjusting for covariates. As a score-based variance-component test, SKAT can quickly calculate p values analytically by fitting the null model containing only the covariates, and so can easily be applied to genome-wide data. Using SKAT to analyze a genome-wide sequencing study of 1000 individuals, by segmenting the whole genome into 30 kb regions, requires only 7 hr on a laptop. Through analysis of simulated data across a wide range of practical scenarios and triglyceride data from the Dallas Heart Study, we show that SKAT can substantially outperform several alternative rare-variant association tests. We also provide analytic power and sample-size calculations to help design candidate-gene, whole-exome, and whole-genome sequence association studies. [Copyright &y& Elsevier]

The incidence of esophageal adenocarcinoma (EA) has been increasing rapidly, particularly among white males, over the past few decades in the USA. However, the etiology of EA and the striking male predominance is not fully explained by known risk factors. To identify susceptible genes for EA risk, we conducted a pathway-based candidate gene association study on 335 Caucasian EA cases and 319 Caucasian controls. A total of 1330 single-nucleotide polymorphisms (SNPs) selected from 354 genes were analyzed using an Illumina GoldenGate assay. The genotyped common SNPs include missense and exonic SNPs, SNPs within untranslated regions and 2 kb 5′ of the gene, and tagSNPs for genes with little functional information available. Logistic regression adjusted for potential confounders was used to assess the genetic effect of each SNP on EA risk. We also tested gene–gender interactions using the likelihood ratio tests. We found that the genetic variants in the apoptosis pathway were significantly associated with EA risk after correcting for multiple comparisons. SNPs of rs3127075 in Caspase-7 (CASP7) and rs4661636 in Caspase-9 (CASP9) genes that play a critical role in apoptosis were found to be associated with an increased risk of EA. A protective effect of SNP rs572483 in the progesterone receptor (PGR) gene was observed among women carrying the variant G allele [adjusted odds ratio (OR) = 0.19; 95% confidence interval (CI) = 0.08–0.46] but was not observed among men (adjusted OR = 1.38; 95% CI = 0.95–2.00). In conclusion, this study suggests that the genetic variants of CASP7 and CASP9 in the apoptosis pathway may be important predictive markers for EA susceptibility and that PGR in the sex hormone signaling pathway may be associated with the gender differences in EA risk. [ABSTRACT FROM PUBLISHER]

GWAS have emerged as popular tools for identifying genetic variants that are associated with disease risk. Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects. As an alternative analytical strategy, we propose grouping SNPs together into SNP sets on the basis of proximity to genomic features such as genes or haplotype blocks, then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel-machine-based test, which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility and the ability to naturally adjust for covariate effects are important features of our test that make it appealing in comparison to individual SNP tests and existing multimarker tests. Using simulated data based on the International HapMap Project, we show that SNP-set testing can have improved power over standard individual-SNP analysis under a wide range of settings. In particular, we find that our approach has higher power than individual-SNP analysis when the median correlation between the disease-susceptibility variant and the genotyped SNPs is moderate to high. When the correlation is low, both individual-SNP analysis and the SNP-set analysis tend to have low power. We apply SNP-set analysis to analyze the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS discovery-phase data. [ABSTRACT FROM AUTHOR]

An increasing challenge in analysis of microarray data is how to interpret and gain biological insight of profiles of thousands of genes. This article provides a review of statistical methods for analysis of microarray data by incorporating prior biological knowledge using gene sets and biological pathways, which consist of groups of biologically similar genes. We first discuss issues of individual gene analysis. We compare several methods for analysis of gene sets including over-representation anlaysis, gene set enrichment analysis, principal component analysis, global test and kernel machine. We discuss the assumptions of these methods and their pros and cons. We illustrate these methods by application to a type II diabetes data set. [ABSTRACT FROM AUTHOR]

System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions. [ABSTRACT FROM AUTHOR]

A key goal in the study of visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. One way to guide the search for models is to use a general nonparametric regression algorithm, such as a neural network. We have developed a multilayer feed-forward network algorithm that can be used to characterize nonlinear stimulus-response mapping functions of neurons in primary visual cortex (area V1) using natural image stimuli. The network is capable of extracting several known V1 response properties such as: orientation and spatial frequency tuning, the spatial phase invariance of complex cells, and direction selectivity. We present details of a method for training networks and visualizing their properties. We also compare how well conventional explicit models and those developed using neural networks can predict novel responses to natural scenes. [Copyright &y& Elsevier]

Collagenase-3 (matrix metalloproteinase 13, MMP-13) was employed as a surrogate marker to compare the characteristics of incisional wound repair after surgery with the free-electron laser at 6.1 µm and the scalpel. Using a transgenic mouse strain with the MMP-13 or the COL1A2 promoter driving luciferase expression, we observed MMP-13 and COL1A2 expression, tensile strength, macrophage infiltration, and wound histology for up to 62 d. The scalpel incisions showed higher tensile strength than free-electron laser wounds from days 10 to 22 postwounding, despite minimal collateral thermal damage. After 45 d healing was similar. Trichrome staining confirmed that the scalpel incisions had more dense collagen deposition than free-electron laser incisions up to 36 d postinjury, but at day 45 they became similar. MMP-13 expression was biphasic, with peak activities at days 15 and 37 after injury, whereas free electron laser wounds showed greater luciferase activity than scalpel wounds. Peak COL1A2 activity preceded the MMP-13 maximum. MMP-13 expression localized predominantly to dermal fibroblasts near the epidermis at day 15, and in the region of the deep dermis, muscle, and fascia at day 37 postwounding. Migrating muscle cells, but not all skeletal muscle cells, also expressed MMP-13. Free-electron laser incisions contained more macrophages than scalpel wounds at days 2 and 7 postinjury, suggesting that free-electron laser irradiation exacerbated the inflammatory response and thereby stimulated MMP-13 expression. These results revealed that MMP-13 was involved in a series of coordinated events during wound healing, not only the long-term remodeling of wound connective tissue, but also skeletal muscle repair. MMP-13 activity in vivo may correlate with the extent of tissue damage. [ABSTRACT FROM AUTHOR]

Objective Maternal smoking is associated with as much as a 50% reduced risk of preeclampsia, despite increasing risk of other poor pregnancy outcomes that often co-occur with preeclampsia, such as preterm birth and fetal growth restriction. Researchers have long sought to understand whether this perplexing association is biologically based, or a result of noncausal mechanisms. We examined whether smoking-response genes modify the smoking-preeclampsia association to investigate potential biological explanations. Study Design We conducted a nested case–control study within the Norwegian Mother, Father and Child Birth Cohort (1999–2008) of 2,596 mother–child dyads. We used family-based log-linear Poisson regression to examine modification of the maternal smoking-preeclampsia relationship by maternal and fetal single nucleotide polymorphisms involved in cellular processes related to components of cigarette smoke (n = 1,915 with minor allele frequency ≥10%). We further investigated the influence of smoking cessation during pregnancy. Results Three polymorphisms showed overall (p < 0.001) multiplicative interaction between smoking and maternal genotype. For rs3765692 (TP73) and rs10770343 (PIK3C2G), protection associated with smoking was reduced with two maternal copies of the risk allele and was stronger in continuers than quitters (interaction p = 0.02 for both loci, based on testing 3-level smoking by 3-level genotype). For rs2278361 (APAF1) the inverse smoking-preeclampsia association was eliminated by the presence of a single risk allele, and again the trend was stronger in continuers than in quitters (interaction p = 0.01). Conclusion Evidence for gene–smoking interaction was limited, but differences by smoking cessation warrant further investigation. We demonstrate the potential utility of expanded dyad methods and gene–environment interaction analyses for outcomes with complex relationships between maternal and fetal genotypes and exposures. Key Points Maternal and fetal genotype may differentially influence preeclampsia. Smoking-related genes did not strongly modify smoking–preeclampsia association. Smoking cessation reduced strength of gene by smoking interactions. [ABSTRACT FROM AUTHOR]

Background: We compared vaginal microbial communities in postmenopausal black and white women.Methods: Shotgun sequencing of vaginal swabs from postmenopausal women self-identified as black or white was compared using MiRKAT.Results: Vaginal community dominance by Lactobacillus crispatus or Lactobacillusgasseri was more common in 44 postmenopausal black women (n = 12, 27%) than among 44 matched white women (n = 2, 5%; P = .01). No individual taxa were significantly more abundant in either group.Conclusions: We identified small overall differences in vaginal microbial communities of black and white postmenopausal women. L. crispatus dominance was more common in black women.Clinical Trials Registration: NCT02516202 (MsFLASH05) and NCT01418209 (MsFLASH03). [ABSTRACT FROM AUTHOR]

A 55-year-old man had phacoemulsification and implantation of a 3-piece acrylic intraocular lens (IOL) (AcrySof MA60AC) in the right eye. One month postoperatively, the intraocular pressure (IOP) was 48 mm Hg and peripheral transillumination defects were noted in the iris circumferentially, with the IOL optic edge visible as a silhouette. Gonioscopy showed dense pigmentation of the trabecular meshwork in the right eye, but in the left eye, only mild trabecular meshwork pigment was seen, along with a concave peripheral iris insertion. At 21 months, the right eye required 3 medications for IOP control. While pigment dispersion has been widely reported after placement of 1-piece acrylic IOLs in the ciliary sulcus, we conclude that in susceptible individuals with a concave peripheral iris insertion, pigment dispersion can occur with sulcus placement of a 3-piece acrylic model despite its thinner optic and angulated haptics. [Copyright &y& Elsevier]

Motivation Cancer is a highly heterogeneous disease, and virtually all types of cancer have subtypes. Understanding the association between cancer subtypes and genetic variations is fundamental to the development of targeted therapies for patients. Somatic mutation plays important roles in tumor development and has emerged as a new type of genetic variations for studying the association with cancer subtypes. However, the low prevalence of individual mutations poses a tremendous challenge to the related statistical analysis. Results In this article, we propose an approach, subtype analysis with somatic mutations (SASOM), for the association analysis of cancer subtypes with somatic mutations. Our approach tests the association between a set of somatic mutations (from a genetic pathway) and subtypes, while incorporating functional information of the mutations into the analysis. We further propose a robust p -value combination procedure, DAPC, to synthesize statistical significance from different sources. Simulation studies show that the proposed approach has correct type I error and tends to be more powerful than possible alternative methods. In a real data application, we examine the somatic mutations from a cutaneous melanoma dataset, and identify a genetic pathway that is associated with immune-related subtypes. Availability and implementation The SASOM R package is available at https://github.com/rksyouyou/SASOM-pkg. R scripts and data are available at https://github.com/rksyouyou/SASOM-analysis. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A recently introduced NURBS mesh generation method for complex-geometry Isogeometric Analysis (IGA) is applied to building a high-quality mesh for a gas turbine. The compressible flow in the turbine is computed using the IGA and a stabilized method with improved discontinuity-capturing, weakly-enforced no-slip boundary-condition, and sliding-interface operators. The IGA results are compared with the results from the stabilized finite element simulation to reveal superior performance of the NURBS-based approach. Free-vibration analysis of the turbine rotor using the structural mechanics NURBS mesh is also carried out and shows that the NURBS mesh generation method can be used also in structural mechanics analysis. With the flow field from the NURBS-based turbine flow simulation, the Courant number is computed based on the NURBS mesh local length scale in the flow direction to show some of the other positive features of the mesh generation framework. The work presented further advances the IGA as a fully-integrated and robust design-to-analysis framework, and the IGA-based complex-geometry flow computation with moving boundaries and interfaces represents the first of its kind for compressible flows. [ABSTRACT FROM AUTHOR]

Intratumoral microbes may have multifunctional roles in carcinogenesis. Microsatellite instability (MSI) is associated with higher tumor immunity and mutational burden. Using whole transcriptome and whole genome sequencing microbial abundance data, we investigated associations of intratumoral microbes with MSI, survival, and MSI-relevant tumor molecular characteristics across multiple cancer types including colorectal cancer (CRC), stomach adenocarcinoma, and endometrial carcinoma. Among 451 CRC patients, our key finding was strong associations of multiple CRC-associated genera, including Dialister and Casatella , with MSI. Dialister and Casatella abundance was associated with improved overall survival (hazard ratiomortality = 0.56, 95% confidence interval = 0.34 to 0.92, and hazard ratiomortality = 0.44, 95% confidence interval = 0.27 to 0.72), respectively, comparing higher relative to lower quantiles. Multiple intratumor microbes were associated with immune genes and tumor mutational burden. Diversity of oral cavity–originating microbes was also associated with MSI among CRC and stomach adenocarcinoma patients. Overall, our findings suggest the intratumor microbiota may differ by MSI status and play a role in influencing the tumor microenvironment. [ABSTRACT FROM AUTHOR]

Background: Microbial communities are known to be closely related to many diseases, such as obesity and HIV, and it is of interest to identify differentially abundant microbial species between two or more environments. Since the abundances or counts of microbial species usually have different scales and suffer from zero-inflation or over-dispersion, normalization is a critical step before conducting differential abundance analysis. Several normalization approaches have been proposed, but it is difficult to optimize the characterization of the true relationship between taxa and interesting outcomes. Results: To avoid the challenge of picking an optimal normalization and accommodate the advantages of several normalization strategies, we propose an omnibus approach. Our approach is based on a Cauchy combination test, which is flexible and powerful by aggregating individual p values. We also consider a truncated test statistic to prevent substantial power loss. We experiment with a basic linear regression model as well as recently proposed powerful association tests for microbiome data and compare the performance of the omnibus approach with individual normalization approaches. Experimental results show that, regardless of simulation settings, the new approach exhibits power that is close to the best normalization strategy, while controling the type I error well. Conclusions: The proposed omnibus test releases researchers from choosing among various normalization methods and it is an aggregated method that provides the powerful result to the underlying optimal normalization, which requires tedious trial and error. While the power may not exceed the best normalization, it is always much better than using a poor choice of normalization. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) classification is a statistical learning method which easily accommodates large numbers of predictors and can discover both linear and nonlinear relationships between the predictors and outcomes. A common challenge is constructing an SVM when the training set includes observations with missing predictor values. In this paper, we identify when missing data can bias an SVM classifier. Because the missing data mechanisms which bias SVMs differ from the traditional framework of missing‐at‐random and missing‐not‐at‐random, we argue for an SVM‐specific framework for understanding missing data. Furthermore, we compare a number of missing data strategies for SVMs in a simulation study and real data example, and we make recommendations for SVM users based on the simulation study. This article is categorized under: Applications of Computational Statistics > Computational Mathematics Statistical Learning and Exploratory Methods of the Data Sciences > Support Vector Machines [ABSTRACT FROM AUTHOR]

BACKGROUND: Maternal smoking during pregnancy, especially when sustained, leads to numerous adverse health outcomes in offspring. Pregnant women disproportionately underreport smoking and smokers tend to have lower follow-up rates to repeat questionnaires. Missing, incomplete, or inaccurate data on presence and duration of smoking in pregnancy impairs identification of novel health effects and limits adjustment for smoking in studies of other pregnancy exposures. An objective biomarker in newborns of maternal smoking during pregnancy would be valuable. OBJECTIVES: We developed a biomarker of sustained maternal smoking in pregnancy using common DNA methylation platforms. Methods: Using a dimension reduction method, we developed and tested a numeric score in newborns to reflect sustained maternal smoking in pregnancy from data on cotinine, a short-term smoking biomarker measured mid-pregnancy, and Illumina450K cord blood DNA methylation from newborns in the Norwegian Mother and Child Cohort Study (MoBa). RESULTS: This score reliably predicted smoking status in the training set (n = 1,057; accuracy = 96%, sensitivity = 80%, specificity = 98%). Sensitivity (58%) was predictably lower in the much smaller test set (n = 221), but accuracy (91%) and specificity (97%) remained high. Reduced birth weight, a well-known effect of maternal smoking, was as strongly related to the score as to cotinine. A three-site score had lower, but acceptable, performance (accuracytrain= 82%, accuracytest = 83%). CONCLUSIONS: Our smoking methylation score represents a promising novel biomarker of sustained maternal smoking during pregnancy easily calculated with Illumina450K or IlluminaEPIC data. It may help identify novel health impacts and improve adjustment for smoking when studying other risk factors with more subtle effects. CITATION: Reese SE, Zhao S, Wu MC, Joubert BR, Parr CL, Håberg SE, Ueland PM, Nilsen RM, Midttun Ø, Vollset SE, Peddada SD, Nystad W, London SJ. 2017. DNA methylation score as a biomarker in newborns for sustained maternal smoking during pregnancy. Environ Health Perspect 125:760-766; http://dx.doi.org/10.1289/EHP333 [ABSTRACT FROM AUTHOR]

Background: T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed. Methods: We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined. Results: Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients. Conclusion: The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes. [ABSTRACT FROM AUTHOR]

Background: Bacterial vaginosis (BV) is a common cause of vaginal discharge and associated with vaginal acquisition of BV-associated bacteria (BVAB).Methods: We used quantitative polymerase chain reaction assays to determine whether presence or concentrations of BVAB in the mouth, anus, vagina, or labia before BV predict risk of incident BV in 72 women who have sex with men.Results: Baseline vaginal and extra-vaginal colonization with Gardnerella spp, Megasphaera spp, Sneathia spp, BVAB-2, Dialister sp type 2, and other BVAB was more common among subjects with incident BV.Conclusions: Prior colonization with BVAB is a consistent risk for BV. [ABSTRACT FROM AUTHOR]

We consider in this article testing rare variants by environment interactions in sequencing association studies. Current methods for studying the association of rare variants with traits cannot be readily applied for testing for rare variants by environment interactions, as these methods do not effectively control for the main effects of rare variants, leading to unstable results and/or inflated Type 1 error rates. We will first analytically study the bias of the use of conventional burdenbased tests for rare variants by environment interactions, and show the tests can often be invalid and result in inflated Type 1 error rates. To overcome these difficulties, we develop the interaction sequence kernel association test (iSKAT) for assessing rare variants by environment interactions. The proposed test iSKAT is optimal in a class of variance component tests and is powerful and robust to the proportion of variants in a gene that interact with environment and the signs of the effects. This test properly controls for the main effects of the rare variants using weighted ridge regression while adjusting for covariates. We demonstrate the performance of iSKAT using simulation studies and illustrate its application by analysis of a candidate gene sequencing study of plasma adiponectin levels. [ABSTRACT FROM AUTHOR]

Motivation Most existing microbiome association analyses focus on the association between microbiome and conditional mean of health or disease-related outcomes, and within this vein, vast computational tools and methods have been devised for standard binary or continuous outcomes. However, these methods tend to be limited either when the underlying microbiome-outcome association occurs somewhere other than the mean level, or when distribution of the outcome variable is irregular (e.g. zero-inflated or mixtures) such that conditional outcome mean is less meaningful. We address this gap by investigating association analysis between microbiome compositions and conditional outcome quantiles. Results We introduce a new association analysis tool named MiRKAT-IQ within the Mi crobiome R egression-based K ernel A ssociation T est framework using I ntegrated Q uantile regression models to examine the association between microbiome and the distribution of outcome. For an individual quantile, we utilize the existing kernel machine regression framework to examine the association between that conditional outcome quantile and a group of microbial features (e.g. microbiome community compositions). Then, the goal of examining microbiome association with the whole outcome distribution is achieved by integrating all outcome conditional quantiles over a process, and thus our new MiRKAT-IQ test is robust to both the location of association signals (e.g. mean, variance, median) and the heterogeneous distribution of the outcome. Extensive numerical simulation studies have been conducted to show the validity of the new MiRKAT-IQ test. We demonstrate the potential usefulness of MiRKAT-IQ with applications to actual biological data collected from a previous microbiome study. Availability and implementation R codes to implement the proposed methodology is provided in the MiRKAT package, which is available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Moyamoya disease (MMD) is characterized by narrowing of the distal internal carotid artery and the circle of Willis (CoW) and leads to recurring ischemic and hemorrhagic stroke. A retrospective review of data from 50 pediatric MMD patients revealed that among the 24 who had a unilateral stroke and were surgically treated, 11 (45.8%) had a subsequent, contralateral stroke. There is no reliable way to predict these events. After a pilot study in Acta−/− mice that have features of MMD, we hypothesized that local hemodynamics are predictive of contralateral strokes and sought to develop a patient-specific analysis framework to noninvasively assess this stroke risk. A pediatric MMD patient with an occlusion in the right middle cerebral artery and a right-sided stroke, who was surgically treated and then had a contralateral stroke, was selected for analysis. By using an unsteady Navier–Stokes solver within an isogeometric analysis framework, blood flow was simulated in the CoW model reconstructed from the patient's postoperative imaging data, and the results were compared with those from an age- and sex-matched control subject. A wall shear rate (WSR) > 60,000 s−1 (about 12 × higher than the coagulation threshold of 5000 s−1 and 9 × higher than control) was measured in the terminal left supraclinoid artery; its location coincided with that of the subsequent postsurgical left-sided stroke. A parametric study of disease progression revealed a strong correlation between the degree of vascular morphology altered by MMD and local hemodynamic environment. The results suggest that an occlusion in the CoW could lead to excessive contralateral WSRs, resulting in thromboembolic ischemic events, and that WSR could be a predictor of future stroke. [ABSTRACT FROM AUTHOR]

Problem Inflammatory biomarkers are associated with preeclampsia ( PE) and poor fetal growth; however, genetic epidemiologic studies have been limited by reduced gene coverage and the exclusion of African American mothers. Method of study Cases and controls ( N = 1646) from a pregnancy cohort were genotyped for 503 tag SNPs in 40 genes related to inflammation. Gene-set analyses were stratified by race and were followed by a single SNP analysis within significant gene sets. Results Gene-level associations were found for IL6 and KLRD1 for term small for gestational age ( SGA) among African Americans. LTA/ TNF and TBX21 were associated with PE among European Americans. The strongest association was for PE among European Americans for an upstream regulator of TNF with RR = 1.8 (95% CI 1.1-2.7). Conclusion Although previous studies have suggested null associations, increased tagging and stratification by genetic ancestry suggests important associations between IL6 and term SGA for African Americans, and a TNF regulator and PE among European Americans ( N = 149). [ABSTRACT FROM AUTHOR]

Although epigenetic regulation plays a critical role in embryonic development, few studies have examined the relationship of epigenome-wide methylation with fetal growth. Using the Infinium HumanMethylation450 BeadChip (Illumina, Inc., San Diego, California) in a substudy of 1,046 infants from the Norwegian Mother and Child Cohort Study (MoBa) enrolled between 1999 and 2008, we examined epigenome-wide cord blood DNA methylation in relation to birth weight. In multivariable-adjusted robust linear regression models, we identified differential methylation at 19 cytosine-guanine dinucleotides (CpGs) associated with either decreased (AT-rich interactive domain 5B (MRF1-like) (ARID5B), 2 CpGs) or increased (x-ray repair complementing defective repair in Chinese hamster cells 3 (XRCC3), 4 CpGs) birth weight. ARID5B knockout mice have less adipose tissue and significantly lower weight in the postnatal period. XRCC3 plays a key role in the maintenance of chromosome stability and the repair of DNA damage. Although there are fewer data on the other implicated genes, many of these genes have been shown to have roles in developmental processes. This constitutes the largest and most robust study of birth weight using an epigenome-wide methylation platform and offers potential insights into epigenetic mechanisms of fetal growth. [ABSTRACT FROM AUTHOR]

An earlier version of this article included a number of typesetting mistakes. These were corrected on October 16, 2020. The publisher apologizes for the errors made during production. The symbol "Λn" was incorrectly published as "Γn" in equations 46 and 47. The correct equations are provided in this correction. [ABSTRACT FROM AUTHOR]

The Nugent score is the reference standard for bacterial vaginosis (BV) diagnosis but has not been validated in postmenopausal women. We compared relative abundances from 16S ribosomal RNA gene sequencing of vaginal microbiota with Nugent score in cohorts of premenopausal (n = 220) and postmenopausal (n = 144) women. In premenopausal women, 33 taxa were significantly correlated with Nugent score, including the classic BV-associated taxa Gardnerella, Atopobium, Sneathia, Megasphaera, and Prevotella. In postmenopausal women, 11 taxa were significantly associated with Nugent score, including Prevotella but no other BV-associated genera. High Nugent scores should not be used to infer BV in postmenopausal women. [ABSTRACT FROM AUTHOR]

A number of isomer structures can be formed in hydrogen-bondedclusters, reflecting the essential variety of structural motifs ofhydrogen bond networks. Control of isomer distribution of a clusteris important not only in practical use for isomer-specific spectroscopybut also in understanding of isomerization processes of hydrogen bondnetworks. Protonated methanol clusters have relatively simple networksand they are model systems suitable to investigate isomer distributionchanges. In this paper, isomer distribution of H+(CH3OH)7is studied by size-selective infrared spectroscopyin the OH and CH stretching vibrational region and density functionaltheory calculations. While the clusters produced by a supersonic jetexpansion combined with electron ionization were predominantly isomershaving open hydrogen bond networks such as a linear chain, the Aror Ne attachment (so-called rare gas tagging) entirely switches theisomer structures to compactly folded ones, which are composed onlyof closed multiple rings. The origin of the isomer switching is discussedin terms of thermal effects and specific isomer preference. [ABSTRACT FROM AUTHOR]

We describe the Berkeley wavelet transform (BWT), a two-dimensional triadic wavelet transform. The BWT comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and the other has even symmetry. By translation and scaling of the whole set (plus a single constant term), the wavelets form a complete, orthonormal basis in two dimensions. The BWT shares many characteristics with the receptive fields of neurons in mammalian primary visual cortex (V1). Like these receptive fields, BWT wavelets are localized in space, tuned in spatial frequency and orientation, and form a set that is approximately scale invariant. The wavelets also have spatial frequency and orientation bandwidths that are comparable with biological values. Although the classical Gabor wavelet model is a more accurate description of the receptive fields of individual V1 neurons, the BWT has some interesting advantages. It is a complete, orthonormal basis and is therefore inexpensive to compute, manipulate, and invert. These properties make the BWT useful in situations where computational power or experimental data are limited, such as estimation of the spatiotemporal receptive fields of neurons. [ABSTRACT FROM AUTHOR]

Histone H3 lysine 4 methylation (H3K4me) is extensively regulated by numerous writer and eraser enzymes in mammals. Nine H3K4me enzymes are associated with neurodevelopmental disorders to date, indicating their important roles in the brain. However, interplay among H3K4me enzymes during brain development remains largely unknown. Here, we show functional interactions of a writer-eraser duo, KMT2A and KDM5C, which are responsible for Wiedemann-Steiner Syndrome (WDSTS), and mental retardation X-linked syndromic Claes-Jensen type (MRXSCJ), respectively. Despite opposite enzymatic activities, the two mouse models deficient for either Kmt2a or Kdm5c shared reduced dendritic spines and increased aggression. Double mutation of Kmt2a and Kdm5c clearly reversed dendritic morphology, key behavioral traits including aggression, and partially corrected altered transcriptomes and H3K4me landscapes. Thus, our study uncovers common yet mutually suppressive aspects of the WDSTS and MRXSCJ models and provides a proof of principle for balancing a single writer-eraser pair to ameliorate their associated disorders. Vallianatos et al. study the functional interactions of KMT2A and KDM5C, H3K4me enzymes known to be involved in neurodevelopmental disorders. Using genetic mouse models, neuronal structure analysis, neurobehavior, and epigenomic profiling, they demonstrate a mutually suppressive relationship between KMT2A and KDM5C during neurodevelopment. [ABSTRACT FROM AUTHOR]

Motivation The human microbiome is notoriously variable across individuals, with a wide range of 'healthy' microbiomes. Paired and longitudinal studies of the microbiome have become increasingly popular as a way to reduce unmeasured confounding and to increase statistical power by reducing large inter-subject variability. Statistical methods for analyzing such datasets are scarce. Results We introduce a paired UniFrac dissimilarity that summarizes within-individual (or within-pair) shifts in microbiome composition and then compares these compositional shifts across individuals (or pairs). This dissimilarity depends on a novel transformation of relative abundances, which we then extend to more than two time points and incorporate into several phylogenetic and non-phylogenetic dissimilarities. The data transformation and resulting dissimilarities may be used in a wide variety of downstream analyses, including ordination analysis and distance-based hypothesis testing. Simulations demonstrate that tests based on these dissimilarities retain appropriate type 1 error and high power. We apply the method in two real datasets. Availability and implementation The R package pldist is available on GitHub at https://github.com/aplantin/pldist. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels. Availability and implementation MiRKAT is available on CRAN as an R package. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Bardet-Biedl syndrome (BBS), a ciliopathy, is a rare genetic condition characterised by retinal degeneration, obesity, kidney failure, and cognitive impairment. In spite of progress made in our general understanding of BBS aetiology, the molecular and cellular mechanisms underlying cognitive impairment in BBS remain elusive. Here, we report that the loss of BBS proteins causes synaptic dysfunction in principal neurons, providing a possible explanation for the cognitive impairment phenotype observed in BBS patients. Using synaptosomal proteomics and immunocytochemistry, we demonstrate the presence of Bbs proteins in the postsynaptic density (PSD) of hippocampal neurons. Loss of Bbs results in a significant reduction of dendritic spines in principal neurons of Bbs mouse models. Furthermore, we show that spine deficiency correlates with events that destabilise spine architecture, such as impaired spine membrane receptor signalling, known to be involved in the maintenance of dendritic spines. Our findings suggest a role for BBS proteins in dendritic spine homeostasis that may be linked to the cognitive phenotype observed in BBS. In Bardet-Biedl syndrome (BBS), mutations that affect the function of primary cilia cause retinal degeneration, obesity, kidney failure, and cognitive impairment. The cognitive impairment has yet to be explained, but this study shows that loss of BBS proteins causes synaptic aberrations in principal neurons and can be partially rescued by aerobic exercise. [ABSTRACT FROM AUTHOR]

Bardet-Biedl syndrome (BBS), a ciliopathy, is a rare genetic condition characterised by retinal degeneration, obesity, kidney failure, and cognitive impairment. In spite of progress made in our general understanding of BBS aetiology, the molecular and cellular mechanisms underlying cognitive impairment in BBS remain elusive. Here, we report that the loss of BBS proteins causes synaptic dysfunction in principal neurons, providing a possible explanation for the cognitive impairment phenotype observed in BBS patients. Using synaptosomal proteomics and immunocytochemistry, we demonstrate the presence of Bbs proteins in the postsynaptic density (PSD) of hippocampal neurons. Loss of Bbs results in a significant reduction of dendritic spines in principal neurons of Bbs mouse models. Furthermore, we show that spine deficiency correlates with events that destabilise spine architecture, such as impaired spine membrane receptor signalling, known to be involved in the maintenance of dendritic spines. Our findings suggest a role for BBS proteins in dendritic spine homeostasis that may be linked to the cognitive phenotype observed in BBS. [ABSTRACT FROM AUTHOR]

Most common human diseases are a result from the combined effect of genes, the environmental factors, and their interactions such that including gene–environment (GE) interactions can improve power in gene mapping studies. The standard strategy is to test the SNPs, one‐by‐one, using a regression model that includes both the SNP effect and the GE interaction. However, the SNP‐by‐SNP approach has serious limitations, such as the inability to model epistatic SNP effects, biased estimation, and reduced power. Thus, in this article, we develop a kernel machine regression framework to model the overall genetic effect of a SNP‐set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function andwe model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. Extensive simulations and real data analysis show that our proposed method has correct type I error and can have higher power than score‐based approaches under many situations. [ABSTRACT FROM AUTHOR]

Although recent genome-wide association studies (GWASs) have identified genetic variants associated with Helicobacter pylori (HP)-induced gastric cancer, few studies have examined the genetic traits associated with the risk of HP -induced gastric precancerous conditions. This study aimed to elucidate genetic variants associated with these conditions using a genome-wide approach. Data from four sites of the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study were used in the discovery phase (Stage I); two datasets from the Hospital-based Epidemiologic Research Program at Aichi Cancer Center 2 (HERPACC2) study were used in the replication phases (Stages II and III) and SKAT (SNP-set Kernel Association Test) and single variant-based GWASs were conducted for the risks of gastric atrophy (GA) and severe GA defined by serum pepsinogen (PG) levels, and PG1 and PG1/2 ratios. In the gene-based SKAT in Stage I, prostate stem cell antigen (PSCA) was significantly associated with the risks of GA and severe GA, and serum PG1/2 level by linear kernel [false discovery rate (FDR) = 0.011, 0.230 and 7.2 × 10−7, respectively]. The single variant-based GWAS revealed that nine PSCA single nucleotide polymorphisms (SNPs) fulfilled the genome-wide significance level (P < 5 × 10−8) for the risks of both GA and severe GA in the combined study, although most of these associations did not reach genome-wide significance in the discovery or validation cohort on their own. GWAS for serum PG1 levels and PG1/2 ratios revealed that the PSCA rs2920283 SNP had a striking P -value of 4.31 × 10−27 for PG1/2 ratios. The present GWAS revealed the genetic locus of PSCA as the most significant locus for the risk of HP -induced GA, which confirmed the recently reported association in Europeans. [ABSTRACT FROM AUTHOR]

Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a rte structure guided local est (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data. [ABSTRACT FROM AUTHOR]

This work couples high-fidelity moving-domain finite element compressible flow modeling with a Surrogate Management Framework (SMF) for optimization to effectively design a variable speed gas turbine stage. The superior accuracy of high-fidelity modeling, however, comes with relatively high computational costs, which are further amplified in the iterative design process that relies on parametric sweeps. An innovative approach is developed to reduce the number of iterations needed for optimal design, leading to a significant reduction in the computational cost without sacrificing the high fidelity of the analysis. The proposed design optimization approach is applied to a novel incidence-tolerant turbomachinery blade technology that articulates the stator- and rotor-blade positions of an annular single-stage high pressure turbine to achieve peak performance. This work also extends our understanding of rotor–stator interactions by simulating complex internal flows occurring during multi-speed turbine operation. Potential variable-speed gas turbine stage designs and the proposed optimization approach are presented to provide valuable insight into this new turbomachinery technology that can positively impact future propulsion systems. [ABSTRACT FROM AUTHOR]

Background: Preeclampsia is thought to originate during placentation, with incomplete remodelling and perfusion of the spiral arteries leading to reduced placental vascular capacity. Nitric oxide (NO) and carbon monoxide (CO) are powerful vasodilators that play a role in the placental vascular system. Although family clustering of preeclampsia has been observed, the existing genetic literature is limited by a failure to consider both mother and child.Methods: We conducted a nested case-control study within the Norwegian Mother and Child Birth Cohort of 1545 case-pairs and 995 control-pairs from 2540 validated dyads (2011 complete pairs, 529 missing mother or child genotype). We selected 1518 single-nucleotide polymorphisms (SNPs) with minor allele frequency >5% in NO and CO signalling pathways. We used log-linear Poisson regression models and likelihood ratio tests to assess maternal and child effects.Results: One SNP met criteria for a false discovery rate Q-value <0.05. The child variant, rs12547243 in adenylate cyclase 8 (ADCY8), was associated with an increased risk (relative risk [RR] 1.42, 95% confidence interval [CI] 1.20, 1.69 for AG vs. GG, RR 2.14, 95% CI 1.47, 3.11 for AA vs. GG, Q = 0.03). The maternal variant, rs30593 in PDE1C was associated with a decreased risk for the subtype of preeclampsia accompanied by early delivery (RR 0.45, 95% CI 0.27, 0.75 for TC vs. CC; Q = 0.02). None of the associations were replicated after correction for multiple testing.Conclusions: This study uses a novel approach to disentangle maternal and child genotypic effects of NO and CO signalling genes on preeclampsia. [ABSTRACT FROM AUTHOR]

Background. Graft-versus-host disease (GVHD) is common after allogeneic hematopoietic cell transplantation (HCT). Risk for death from GVHD has been associated with low bacterial diversity in the stool microbiota early after transplant; however, the specific species associated with GVHD risk remain poorly defined. Methods. We prospectively collected serial weekly stool samples from 66 patients who underwent HCT, starting pre-transplantation and continuing weekly until 100 days post-transplant, a total of 694 observations in HCT recipients. We used 16S rRNA gene polymerase chain reaction with degenerate primers, followed by high-throughput sequencing to assess the relative abundance of sequence reads from bacterial taxa in stool samples over time. Results. The gut microbiota was highly dynamic in HCT recipients, with loss and appearance of taxa common on short time scales. As in prior studies, GVHD was associated with lower alpha diversity of the stool microbiota. At neutrophil recovery post-HCT, the presence of oral Actinobacteria and oral Firmicutes in stool was positively correlated with subsequent GVHD; Lachnospiraceae were negatively correlated. A gradient of bacterial species (difference of the sum of the relative abundance of positive correlates minus the sum of the relative abundance of negative correlates) was most predictive (receiver operator characteristic area under the curve of 0.83) of subsequent severe acute GVHD. Conclusions. The stool microbiota around the time of neutrophil recovery post-HCT is predictive of subsequent development of severe acute GVHD in this study. [ABSTRACT FROM AUTHOR]