Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth.

While a genetic component of preterm birth (PTB) has long been recognized and recently mapped by genome-wide association studies (GWASs), the molecular determinants underlying PTB remain elusive. This stems in part from an incomplete availability of functional genomic annotations in human cell types relevant to pregnancy and PTB. We generated transcriptome (RNA-seq), epigenome (ChIP-seq of H3K27ac, H3K4me1, and H3K4me3 histone modifications), open chromatin (ATAC-seq), and chromatin interaction (promoter capture Hi-C) annotations of cultured primary decidua-derived mesenchymal stromal/stem cells and in vitro differentiated decidual stromal cells and developed a computational framework to integrate these functional annotations with results from a GWAS of gestational duration in 56,384 women.

Pilot early phase II study of decitabine and carboplatin in patients with advanced melanoma.

Introduction: Resistance to targeted and immune checkpoint blockade treatment remains a major problem in patients with advanced metastatic melanoma. To overcome this problem, there needs to be a decrease in burden of disease as well as re-establishing of immune sensitivity. The aim of this early phase 2 clinical trial is to investigate a novel way of sequencing and combining decitabine and carboplatin to decrease methylation and increase DNA repair resulting in a decrease in the disease burden and to re-establish sensitivity to the immune response.

Publisher Correction: Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

Mendelian randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives.

Detailed modeling of positive selection improves detection of cancer driver genes.

Identifying driver genes from somatic mutations is a central problem in cancer biology. Existing methods, however, either lack explicit statistical models, or use models based on simplistic assumptions. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a model-based approach to driver gene identification.