Volume 51 Issue 12, December 2019

A new study presents a powerful experimental approach, CRISPRi-FlowFISH, for mapping regulatory interactions, and uses it to characterize thousands of putative enhancer–gene pairs. The results suggest that most current approaches for predicting enhancer–gene interactions perform poorly, but a simple mathematical model combining distance with enhancer activity shows promise.

Combining CRISPRi-FlowFISH to perturb enhancers with an activity-by-contact model to predict complex connections allows systematic mapping of enhancer–gene connections in a given cell type, on the basis of chromatin-state measurements.

Genome-wide meta-analysis with individuals of East Asian or European ancestry identifies 176 loci associated with schizophrenia. Despite consistent genetic effects across populations, polygenic risk models trained in one population have reduced performance in the other population.

Transcriptome and functional studies in human iPSC-derived neurons suggest that the phenotypic effects of NRXN1 deletions can occur through reduction in wild-type NRXN1α isoform levels and expression of mutant NRXN1α isoforms.

Short- and long-term cultures of human stem-cell-derived neurons reveal that a pattern of restricted selection of clustered protocadherin isoforms, pre-established in pluripotent cells, distinguishes immature from mature neurons.

A single-cell transcriptomic atlas from embryonal pons and forebrain provides insights into the developmental origins of pediatric brain tumors. The study identifies impaired differentiation of specific neural progenitors as a common mechanism underlying these cancers.

High levels of histone acetylation at rhabdomyosarcoma SEs, including SOX8, are detrimental to transcription via exclusion of RNA Pol II, but not BRD4, from phase condensates.

Oncogenic MYC expression involves super-enhancer-mediated tethering of MYC alleles to nuclear pores, thus increasing messenger RNA export. This is regulated by AHCTF1 and β-catenin.

Analysis of whole-genome sequences from more than 3,500 metastatic tumors identifies mutational signatures associated with different chemotherapies and provides estimates of the relative contribution of different treatments to tumor mutational burden.

Pan-cancer genomic analyses based on HLA affinity predictions show that apparent neoantigen depletion signals in untreated tumors become negligible after correction for trinucleotide-based mutational signatures.

fastGWA is a mixed linear model–based approach for performing genome-wide association analyses at biobank scale, while controlling for population stratification and relatedness.