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We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition models for parameters involved in each window to address the dependence structure between windows. The functional difference of groups of individuals at each window can be evaluated by the Bayes factor based on Markov Chain Monte Carlo samples in the analysis. In this paper, we examine the proposed method through simulation studies and apply it to identify differentially methylated genetic regions in TCGA lung adenocarcinoma data. [ABSTRACT FROM AUTHOR]

Background Physical activity (PA) prior to and during pregnancy may have intergenerational effects on offspring health through placental epigenetic modifications. We are unaware of epidemiologic studies on longitudinal PA and placental DNA methylation. Objectives We evaluated the association between PA before and during pregnancy and placental DNA methylation. Methods Placental tissues were obtained at delivery and methylation was measured using HumanMethylation450 Beadchips for participants in the Eunice Kennedy Shriver National Institute of Child Health and Human Development Fetal Growth Studies–Singletons among 298 participants. Using the Pregnancy Physical Activity Questionnaire, women recalled periconception PA (past 12 mo) at 8–13 wk of gestation and PA since last visit at 4 follow-up visits at 16–22, 24–29, 30–33, and 34–37 wk. We conducted linear regression for associations of PA at each visit with methylation controlling for false discovery rate (FDR). Top 100 CpGs were queried for enrichment of functional pathways using Ingenuity Pathway Analysis. Results Periconception PA was significantly associated with 1 CpG site. PA since last visit for visits 1–4 was associated with 2, 2, 8, and 0 CpGs (log fold changes ranging from –0.0319 to 0.0080, after controlling for FDR). The largest change in methylation occurred at a site in TIMP2 , which is known to encode a protein critical for vasodilation, placentation, and uterine expansion during pregnancy (log fold change: –0.05; 95% CI: –0.06, –0.03 per metabolic equivalent of task–h/wk at 30–33 wk). Most significantly enriched pathways include cardiac hypertrophy signaling, B-cell receptor signaling, and netrin signaling. Significant CpGs and enriched pathways varied by visit. Conclusions Recreational PA in the year prior and during pregnancy was associated with placental DNA methylation. The associated CpG sites varied based on timing of PA. If replicated, the findings may inform the mechanisms underlying the impacts of PA on placenta health. This study was registered at clinicaltrials.gov as NCT00912132. [ABSTRACT FROM AUTHOR]

The performance of computational methods and software to identify differentially expressed features in single‐cell RNA‐sequencing (scRNA‐seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA‐seq expression features. To model the technological variability in cross‐platform scRNA‐seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA‐seq expression profiles across experimental platforms induced by platform‐ and gene‐specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero‐inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero‐inflated scRNA‐seq data with excessive zero counts. Using both synthetic and published plate‐ and droplet‐based scRNA‐seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state‐of‐the‐art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open‐source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse. [ABSTRACT FROM AUTHOR]

Abnormal birthweight is associated with increased risk for cardiometabolic diseases in later life. Although the placenta is critical to fetal development and later life health, it has not been integrated into largescale functional genomics initiatives, and mechanisms of birthweight-associated variants identified by genome wide association studies (GWAS) are unclear. The goal of this study is to provide functional mechanistic insight into the causal pathway from a genetic variant to birthweight by integrating placental methylation and gene expression with established GWAS loci for birthweight. We identify placental DNA methylation and gene expression targets for several birthweight GWAS loci. The target genes are broadly enriched in cardiometabolic, immune response, and hormonal pathways. We find that methylation causally influences WNT3A, CTDNEP1, and RANBP2 expression in placenta. Multi-trait colocalization identifies PLEKHA1, FES, CTDNEP1, and PRMT7 as likely functional effector genes. These findings reveal candidate functional pathways that underpin the genetic regulation of birthweight via placental epigenetic and transcriptomic mechanisms. Clinical trial registration; ClinicalTrials.gov, NCT00912132. The placenta plays key roles in fetal development and subsequent health. Here, the authors integrate placental methylation and transcriptome data with genetic loci associated with birthweight to identify functional genes underpinning fetal growth regulation. [ABSTRACT FROM AUTHOR]

Maternal genetic variants associated with offspring birth weight and adult type 2 diabetes (T2D) risk loci show some overlap. Whether T2D genetic risk influences longitudinal fetal weight and the gestational timing when these relationships begin is unknown. We investigated the associations of T2D genetic risk scores (GRS) with longitudinal fetal weight and birth weight among 1,513 pregnant women from four ancestral groups. Women had up to five ultrasonography examinations. Ancestry-matched GRS were constructed separately using 380 European- (GRSeur), 104 African- (GRSafr), and 189 East Asian- (GRSeas) related T2D loci discovered in different population groups. Among European Americans, the highest quartile GRSeur was significantly associated with 53.8 g higher fetal weight (95% CI 19.2-88.5) over the pregnancy. The associations began at gestational week 24 and continued through week 40, with a 106.8 g (95% CI 6.5-207.1) increase in birth weight. The findings were similar in analysis further adjusted for maternal glucose challenge test results. No consistent association was found using ancestry-matched or cross-ancestry GRS in non-Europeans. In conclusion, T2D genetic susceptibility may influence fetal growth starting at midsecond trimester among Europeans. Absence of similar associations in non-Europeans urges the need for further genetic T2D studies in diverse ancestries. [ABSTRACT FROM AUTHOR]

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles. Author summary: Recently, several statistical methods have been proposed to identify phenotypic or environmental associations with features (e.g., taxa, genes, pathways, chemicals, etc.) from molecular profiles of microbial communities. Particularly for human microbiome epidemiology, however, most of these are primarily focused on univariable associations that analyze only one or a few environmental covariates. This is a critical gap to address, given the growing commonality of population-scale microbiome research and the complexity of associated study designs, including dietary, pharmaceutical, clinical, and environmental covariates, often with samples from multiple time points or tissues. Surprisingly, there have been no systematic evaluations of statistical analysis methods appropriate for such studies, nor consensus on appropriate methods for scalable microbiome epidemiology. To this end, we developed and validated a statistical model (MaAsLin) that provides both the first unified method and the first large-scale, comprehensive benchmarking of multivariable associations in population-scale microbial community studies. We hope that the MaAsLin 2 implementation, validated through extensive simulations and an application to HMP2 IBD multi-omics, will be helpful for researchers in future analysis of both human-associated and environmental microbial communities. [ABSTRACT FROM AUTHOR]

Maternal dyslipidemia during pregnancy has been associated with suboptimal fetal growth and increased cardiometabolic diseasse risk in offspring. Altered placental function driven by placental gene expression is a hypothesized mechanism underlying these associations. We tested the relationship between maternal plasma lipid concentrations and placental gene expression. Among 64 pregnant women from the NICHD Fetal Growth Studies–Singleton cohort with maternal first trimester plasma lipids we extracted RNA-Seq on placental samples obtained at birth. Placental gene co-expression networks were validated by regulatory network analysis that integrated transcription factors and gene expression, and genome-wide transcriptome analysis. Network analysis detected 24 gene co-expression modules in placenta, of which one module was correlated with total cholesterol (r = 0.27, P-value = 0.03) and LDL-C (r = 0.31, P-value = 0.01). Genes in the module (n = 39 genes) were enriched in inflammatory response pathways. Out of the 39 genes in the module, three known lipid-related genes (MPO , PGLYRP1 and LTF) and MAGEC2 were validated by the regulatory network analysis, and one known lipid-related gene (ALX4) and two germ-cell development-related genes (MAGEC2 and LUZP4) were validated by genome-wide transcriptome analysis. Placental gene expression signatures associated with unfavorable maternal lipid concentrations may be potential pathways underlying later life offspring cardiometabolic traits. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT00912132. [ABSTRACT FROM AUTHOR]

Motivation The discovery of biologically interpretable and clinically actionable communities in heterogeneous omics data is a necessary first step toward deriving mechanistic insights into complex biological phenomena. Here, we present a novel clustering approach, omeClust , for community detection in omics profiles by simultaneously incorporating similarities among measurements and the overall complex structure of the data. Results We show that omeClust outperforms published methods in inferring the true community structure as measured by both sensitivity and misclassification rate on simulated datasets. We further validated omeClust in diverse, multiple omics datasets, revealing new communities and functionally related groups in microbial strains, cell line gene expression patterns and fetal genomic variation. We also derived enrichment scores attributable to putatively meaningful biological factors in these datasets that can serve as hypothesis generators facilitating new sets of testable hypotheses. Availability and implementation omeClust is open-source software, and the implementation is available online at http://github.com/omicsEye/omeClust. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-term readmissions. The prediction of unplanned readmissions after index CAS remains challenging. There is a need to leverage deep machine learning algorithms in order to develop robust prediction tools for early readmissions.Methods: Patients undergoing inpatient CAS during the year 2017 in the US Nationwide Readmission Database (NRD) were evaluated for the rates, predictors, and costs of unplanned 30-day readmission. Logistic regression, support vector machine (SVM), deep neural network (DNN), random forest, and decision tree models were evaluated to generate a robust prediction model.Results: We identified 16,745 patients who underwent CAS, of whom 7.4% were readmitted within 30 days. Depression [p < 0.001, OR 1.461 (95% CI 1.231-1.735)], heart failure [p < 0.001, OR 1.619 (95% CI 1.363-1.922)], cancer [p < 0.001, OR 1.631 (95% CI 1.286-2.068)], in-hospital bleeding [p = 0.039, OR 1.641 (95% CI 1.026-2.626)], and coagulation disorders [p = 0.007, OR 1.412 (95% CI 1.100-1.813)] were the strongest predictors of readmission. The artificial intelligence machine learning DNN prediction model has a C-statistic value of 0.79 (validation 0.73) in predicting the patients who might have all-cause unplanned readmission within 30 days of the index CAS discharge.Conclusions: Machine learning derived models may effectively identify high-risk patients for intervention strategies that may reduce unplanned readmissions post carotid artery stenting.Central Illustration: Figure 2: ROC and AUPRC analysis of DNN prediction model with other classification models on 30-day readmission data for CAS subjects. [ABSTRACT FROM AUTHOR]

Childhood obesity is a global public health problem. Understanding the molecular mechanisms that underlie early origins of childhood obesity can facilitate interventions. Consistent phenotypic and genetic correlations have been found between childhood obesity traits and birth weight (a proxy for in-utero growth), suggesting shared genetic influences (pleiotropy). We aimed to (1) investigate whether there is significant shared genetic influence between birth weight and childhood obesity traits, and (2) to identify genetic loci with shared effects. Using a statistical approach that integrates summary statistics and functional annotations for paired traits, we found strong evidence of pleiotropy (P < 3.53 × 10–127) and enrichment of functional annotations (P < 1.62 × 10–39) between birth weight and childhood body mass index (BMI)/obesity. The pleiotropic loci were enriched for regulatory features in skeletal muscle, adipose and brain tissues and in cell lines derived from blood lymphocytes. At 5% false discovery rate, 6 loci were associated with birth weight and childhood BMI and 13 loci were associated with birth weight and childhood obesity. Out of these 19 loci, one locus (EBF1) was novel to childhood obesity and one locus (LMBR1L) was novel to both birth weight and childhood BMI/obesity. These findings give evidence of substantial shared genetic effects in the regulation of both fetal growth and childhood obesity. [ABSTRACT FROM AUTHOR]

It is well documented that maternal type 2 diabetes (T2D) is related to fetal overgrowth. Further, maternal T2D genetic risk score (GRS) has been associated with increased offspring birthweight. However, the earliest gestational age when fetal growth starts to be associated with T2D GRS is unknown. We investigated the associations between maternal T2D GRS and longitudinal ultrasound measured fetal weight. We included 1,513 women of diverse race/ethnicity from the NICHD Fetal Growth Studies-singleton cohort. Women were enrolled at gestational weeks 8-13 and randomly assigned to four ultrasound schedules to capture weekly fetal growth. Genome-wide single nucleotide polymorphisms (SNPs) previously known to be associated with T2D in European-, African-, and Asian- ancestry populations (380, 104, and 19 SNPs, respectively) were used to calculate weighted GRS. The analysis involved European GRS for Whites and Hispanics, African GRS for Blacks and Asian GRS for Asians. Associations between quartiles of GRS and fetal weight were adjusted for pre-pregnancy BMI, population structure, fetal sex and gestational weeks. Among Whites, the highest quartile of European GRS was related to a 53.8 g increment in fetal weight (95% CI 19.2-88.5 g) over the pregnancy, and across 24 to 40 weeks (from 15.8 g increase at week 24 to 196.0 g increase at week 40). Similar findings were observed in analysis that excluded women with impaired glucose tolerance. African-, European- and Asian-GRSs were not significantly associated with fetal weight among other race/ethnic groups. Genetic susceptibility to T2D based on European-specific GRS among Whites was related to increased fetal weight starting at week 24 even among normal glycemia women, suggesting an early influence of genetic factors on fetal growth. Absence of similar associations in non-Whites is likely due to the small number of established T2D genetic loci warranting the need for genetic studies of T2D in diverse populations. Disclosure: M. Ouidir: None. X. Zeng: None. S. Chatterjee: None. C. Zhang: None. F. Tekola-ayele: None. [ABSTRACT FROM AUTHOR]