Genetic and in utero environmental contributions to DNA methylation variation in placenta.

Genetic and prenatal environmental factors shape fetal development and cardiometabolic health in later life. A key target of genetic and prenatal environmental factors is the epigenome of the placenta, an organ that is implicated in fetal growth and diseases in later life. This study had two aims: (1) to identify and functionally characterize placental variably methylated regions (VMRs), which are regions in the epigenome with high inter-individual methylation variability; and (2) to investigate the contributions of fetal genetic loci and 12 prenatal environmental factors (maternal cardiometabolic-,psychosocial-, demographic- and obstetric-related) on methylation at each VMR. Akaike's information criterion was used to select the best model out of four models [prenatal environment only, genotype only, additive effect of genotype and prenatal environment (G + E), and their interaction effect (G × E)]. We identified 5850 VMRs in placenta. Methylation at 70% of VMRs was best explained by G × E, followed by genotype only (17.7%), and G + E (12.3%). Prenatal environment alone best explained only 0.03% of VMRs. We observed that 95.4% of G × E models and 93.9% of G + E models included maternal age, parity, delivery mode, maternal depression or gestational weight gain. VMR methylation sites and their regulatory genetic variants were enriched (P < 0.05) for genomic regions that have known links with regulatory functions and complex traits. This study provided a genome-wide catalog of VMRs in placenta and highlighted that variation in placental DNA methylation at loci with regulatory and trait relevance is best elucidated by integrating genetic and prenatal environmental factors, and rarely by environmental factors alone.

Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions.

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.

Omics community detection using multi-resolution clustering.

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.

Multivariable association discovery in population-scale meta-omics studies.

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.

Differential expression of single-cell RNA-seq data using Tweedie models.

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.

Genomic study of maternal lipid traits in early pregnancy concurs with four known adult lipid loci.

BACKGROUND: Blood lipids during pregnancy are associated with cardiovascular diseases and adverse pregnancy outcomes. Genome-wide association studies (GWAS) in predominantly male European ancestry populations have identified genetic loci associated with blood lipid levels. However, the genetic architecture of blood lipids in pregnant women remains poorly understood. OBJECTIVE: Our goal was to identify genetic loci associated with blood lipid levels among pregnant women from diverse ancestry groups and to evaluate whether previously known lipid loci in predominantly European adults are transferable to pregnant women. METHODS: The trans-ancestry GWAS were conducted on serum levels of total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and triglycerides during first trimester among pregnant women from four population groups (608 European-, 623 African-, 552 Hispanic- and 235 East Asian-Americans) recruited in the NICHD Fetal Growth Studies cohort. The four GWAS summary statistics were combined using trans-ancestry meta-analysis approaches that account for genetic heterogeneity among populations. RESULTS: Loci in CELSR2 and APOE were genome-wide significantly associated (p-value < 5×10-8) with total cholesterol and LDL levels. Loci near CETP and ABCA1 approached genome-wide significant association with HDL (p-value = 2.97×10-7 and 9.71×10-8, respectively). Less than 20% of previously known adult lipid loci were transferable to pregnant women. CONCLUSION: This trans-ancestry GWAS meta-analysis in pregnant women identified associations that concur with four known adult lipid loci. Limited replication of known lipid-loci from predominantly European study populations to pregnant women underlines the need for genomic studies of lipids in ancestrally diverse pregnant women. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT00912132.

Ancestry-Matched and Cross-Ancestry Genetic Risk Scores of Type 2 Diabetes in Pregnant Women and Fetal Growth: A Study in an Ancestrally Diverse Cohort.

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.

Sex-specific placental gene expression signatures of small for gestational age at birth.

INTRODUCTION: Small for gestational age at birth (SGA), often a consequence of placental dysfunction, is a risk factor for neonatal morbidity and later life cardiometabolic diseases. There are sex differences in placental gene expression and fetal growth. Here, we investigated sex-specific associations between gene expression in human placenta measured using RNA sequencing and SGA status using data from ethnic diverse pregnant women in the NICHD Fetal Growth Studies cohort (n = 74). METHODS: Gene expression measures were obtained using RNA-Sequencing and differential gene expression between SGA (birthweight <10th percentile) and appropriate for gestational age (AGA: ≥10th and <90th percentile) was tested separately in males (12 SGA and 27 AGA) and females (9 SGA and 26 AGA) using a weighted mean of log ratios method with adjustment for mode of delivery and ethnicity. RESULTS: At 5% false discovery rate (FDR), we identified 40 differentially expressed genes (DEGs) related to SGA status among males (95% up- and 5% down-regulated) and 314 DEGs among females (32.5% up- and 67.5% down-regulated). Seven female-specific DEGs overlapped with known imprinted genes (AXL, CYP24A1, GPR1, PLAGL1, CMTM1, DLX5, LY6D). The DEGs in males were significantly enriched for immune response and inflammation signaling pathways whereas the DEGs in females were enriched for organ development signaling pathways (FDR<0.05). Sex-combined analysis identified no additional DEGs, rather 98% of the sex-specific DEGs were no longer significant and the remaining 2% were attenuated. DISCUSSION: This study revealed sex-specific human placental gene expression changes and molecular pathways associated with SGA and underscored that unravelling the pathogenesis of SGA warrants consideration of fetal sex as a biological variable. TRIAL REGISTRATION: https://www. CLINICALTRIALS: gov, Unique identifier: NCT00912132.

Placental multi-omics integration identifies candidate functional genes for birthweight.

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.

Recreational physical activity before and during pregnancy and placental DNA methylation-an epigenome-wide association study.

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.

Pleiotropic genetic influence on birth weight and childhood obesity.

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.

Impact of depression and stress on placental DNA methylation in ethnically diverse pregnant women.

Aim: To investigate the association between placental genome-wide methylation at birth and antenatal depression and stress during pregnancy. Methods: We examined the association between placental genome-wide DNA methylation (n = 301) and maternal depression and stress assessed at six gestation periods during pregnancy. Correlation between DNA methylation at the significantly associated CpGs and expression of nearby genes in the placenta was tested. Results: Depression and stress were associated with methylation of 16 CpGs and two CpGs, respectively, at a 5% false discovery rate. Methylation levels at two of the CpGs associated with depression were significantly associated with expression of ADAM23 and CTDP1, genes implicated in neurodevelopment and neuropsychiatric diseases. Conclusion: Placental epigenetic changes linked to antenatal depression suggest potential fetal brain programming. Clinical trial registration number: NCT00912132 (ClinicalTrials.gov).

Lay abstract Our research examined 301 women at six time points during their pregnancies in regard to depression or stress. We then examined samples of the placenta after birth for epigenetic changes and explored whether they were linked to the status of depression or stress observed during pregnancy. We found that 16 epigenetic changes were linked to depression and two were linked to stress. Some of the epigenetic changes in the placenta linked to depression were located close to genes which are known to have important roles in brain development and occurrence of psychiatric disorders. Therefore maternal depression may have implications for the long-term mental health of the child.

Placental Gene Co-expression Network for Maternal Plasma Lipids Revealed Enrichment of Inflammatory Response Pathways.

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.

Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.

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.

We present a novel deep neural network-based artificial intelligence prediction model to help identify a subgroup of patients undergoing carotid artery stenting who are at risk for short-term unplanned readmissions. Prior studies have attempted to develop prediction models but have used mainly logistic regression models and have low prediction ability. The novel model presented in this study boasts 79% capability to accurately predict individuals for unplanned readmissions post carotid artery stenting within 30 days of discharge.