Volume 52 Issue 6, June 2020

Many normal tissues are populated by clonal expansions that compete for space. Colom et al. now show that mutant clones keep other mutant clones in check by effectively annulling one another’s advantages.

TET2 and DNMT3A mutations lead to similar long-term outcomes in blood cancers despite the antagonistic biochemical functions of their encoded proteins. A new study highlights the opposing effects of TET2 and DNMT3A mutations in shaping the early erythroid or myeloid bias of hematopoietic progenitors.

Genome-wide association analysis for morphological traits across 350 elite maize inbred lines in Chinese and US germplasm identifies loci and genomic regions representing the targets of selection during modern maize breeding.

Genome-wide analysis identifies 32 loci associated with breast cancer susceptibility, accounting for estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade.

Immunogenomic analyses of advanced high-grade serous ovarian cancer samples before and after neoadjuvant chemotherapy show that the tumor–immune microenvironment is intrinsically heterogeneous and that chemotherapy induces local immune activation.

Single-cell RNA sequencing of colorectal tumors highlights associations between lineage-related gene expression signatures and specific stromal and immune cell populations.

Deep sequencing and lineage tracing analysis of esophageal epithelium of mutagen-treated aging mice leads to a model in which the proliferative advantage of positively selected mutations depends on the competitive fitness of neighboring cells.

A CRISPR screen for MLL2-dependent transcription identifies a compensatory repressive mechanism involving PRC2 and DNA methylation. PRC2 inactivation or DNA demethylation activates gene expression in the absence of H3K4me3.

Mediated expression score regression (MESC) is a new method that estimates disease heritability mediated by the cis genetic component of gene expression levels by using summary statistics from GWAS and eQTL studies.

SAIGE-GENE is a scalable generalized mixed-model region-based association test that can analyze large datasets while accounting for sample relatedness and unbalanced case–control ratios for binary traits.