A retrospective review of the association between maternal body mass index and the risk of congenital anomalies.

The objective of the study was to examine the association of congenital anomalies with the specific classes of pre-pregnancy BMI. An IRB-approved retrospective cohort study was performed using the data from the Natality Public Use File from the National Center for Health Statistics (2019). We included all singleton live births and excluded pregnancies with suspected or confirmed chromosomal abnormalities and people with pre-existing diabetes mellitus and missing pertinent data.

A retrospective review of pregnancy outcomes in women with uncomplicated mild to moderate chronic hypertension.

Objective: To examine pregnancy complications in women with uncomplicated mild-moderate chronic hypertension (CHTN) treated with antihypertensives prior to 20 weeks compared to those not on antihypertensives.

Study Design: This retrospective cohort study examined singleton pregnancies of women with mild-moderate CHTN who delivered from 01/2014-3/2019. Pregnancies complicated by hypertension at ≥ 20 weeks, end organ damage, preexisting diabetes mellitus, early-onset gestational diabetes, multifetal gestation, and fetal anomalies were excluded.

Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics.

Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results.

Interpretation of machine learning predictions for patient outcomes in electronic health records.

Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of seven patient outcomes using three state-of-the-art machine learning methods. Our primary goal is to validate the models by interpreting the importance of predictors in the final models.

Defining Major Depressive Disorder Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders.

Background: Major Depressive Disorder (MDD) is one of the most common mental illnesses and a leading cause of disability worldwide. Electronic Health Records (EHR) allow researchers to conduct unprecedented large-scale observational studies investigating MDD, its disease development and its interaction with other health outcomes. While there exist methods to classify patients as clear cases or controls, given specific data requirements, there are presently no simple, generalizable, and validated methods to classify an entire patient population into varying groups of depression likelihood and severity.

EUS-guided gastroenterostomy for the management of gastric outlet obstruction: A systematic review and meta-analysis.

Gastric outlet obstruction (GOO) is characterized by epigastric pain and postprandial vomiting secondary to mechanical obstruction. Management of GOO is usually focused on alleviating the symptoms of obstruction and can be achieved by surgical gastrojejunostomy or enteral stenting. Recent studies have shown success with EUS-guided gastroenterostomy (EUS-GE) in the management of GOO but data is limited.

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy.

Exome-Wide Rare Variant Analysis From the DiscovEHR Study Identifies Novel Candidate Predisposition Genes for Endometrial Cancer.

Endometrial cancer is the fourth most commonly diagnosed cancer in women. Family history is a known risk factor for endometrial cancer. The incidence of endometrial cancer in a first-degree relative elevates the relative risk to range between 1.

A -Ethnic Genome-Wide Association Study of Uterine Fibroids.

Uterine fibroids affect up to 77% of women by menopause and account for up to $34 billion in healthcare costs each year. Although fibroid risk is heritable, genetic risk for fibroids is not well understood. We conducted a two-stage case-control meta-analysis of genetic variants in European and African ancestry women with and without fibroids classified by a previously published algorithm requiring pelvic imaging or confirmed diagnosis.

A catalog of genetic loci associated with kidney function from analyses of a million individuals.

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178).

Differential gene expression induced by Verteporfin in endometrial cancer cells.

Endometrial cancer (EMCA) is a clinically heterogeneous disease. Previously, we tested the efficacy of Verteporfin (VP) in EMCA cells and observed cytotoxic and anti-proliferative effects. In this study, we analyzed RNA sequencing data to investigate the comprehensive transcriptomic landscape of VP treated Type 1 EMCA cell lines, including HEC-1-A and HEC-1-B.

Artificial Intelligence Transforms the Future of Health Care.

Life sciences researchers using artificial intelligence (AI) are under pressure to innovate faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed.

Identification of exon skipping events associated with Alzheimer's disease in the human hippocampus.

Background: At least 90% of human genes are alternatively spliced. Alternative splicing has an important function regulating gene expression and miss-splicing can contribute to risk for human diseases, including Alzheimer's disease (AD).

Methods: We developed a splicing decision model as a molecular mechanism to identify functional exon skipping events and genetic variation affecting alternative splicing on a genome-wide scale by integrating genomics, transcriptomics, and neuroimaging data in a systems biology approach.

Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico.

Background: The development of sequencing techniques and statistical methods provides great opportunities for identifying the impact of rare genetic variation on complex traits. However, there is a lack of knowledge on the impact of sample size, case numbers, the balance of cases vs controls for both burden and dispersion based rare variant association methods. For example, Phenome-Wide Association Studies may have a wide range of case and control sample sizes across hundreds of diagnoses and traits, and with the application of statistical methods to rare variants, it is important to understand the strengths and limitations of the analyses.