Long-Term Safety of Roflumilast in Patients with Chronic Obstructive Pulmonary Disease, a Multinational Observational Database Cohort Study.

Purpose: This study evaluated the long-term safety of roflumilast in patients with chronic obstructive pulmonary disease or chronic bronchitis using electronic healthcare databases from Germany, Norway, Sweden, and the United States (US).

Patients And Methods: The study population consisted of patients aged ≥40 years who had been exposed to roflumilast and a matched cohort unexposed to roflumilast. The matching was based on sex, age, calendar year of cohort entry date (2010-2011, 2012, or 2013), and a propensity score that included variables such as demographics, markers of chronic obstructive pulmonary disease (COPD) severity and morbidity, and comorbidities.

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions.

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10).

chromMAGMA: regulatory element-centric interrogation of risk variants.

Candidate causal risk variants from genome-wide association studies reside almost exclusively in noncoding regions of the genome and innovative approaches are necessary to understand their biological function. Multi-marker analysis of genomic annotation (MAGMA) is a widely used program that nominates candidate risk genes by mapping single-nucleotide polymorphism summary statistics from genome-wide association studies to gene bodies. We augmented MAGMA to create chromatin-MAGMA (chromMAGMA), a method to nominate candidate risk genes based on the presence of risk variants within noncoding regulatory elements (REs).

Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction.