Volume 53 Issue 11, November 2021

A concerning trend in genetics is the common use of the term ‘trans-ethnic’ to describe analyses that combine or compare several ancestrally diverse populations. In this commentary, we discuss how this term is inaccurate and alienating. We propose that geneticists avoid using the term trans-ethnic entirely and that researchers across disciplines reach a new consensus about the best terms to use to describe the populations we study.

Mutational signatures can shed light onto mechanisms of carcinogenesis and reveal the mutagenic impact of novel and established environmental risk factors. A new study examines the mutational spectra of esophageal squamous cell cancer together with exposure information in regions of high and low incidence of the disease, and demonstrates both the limitations and potential of mutational signature analyses.

How somatic and germline mutations interact in cancer remains largely unexplored. A study of 17,152 patients with cancer suggests that the relative contribution of pathogenic germline mutations is governed by lineage and penetrance.

The genomes of cells across human tissues are riddled by changes to their DNA^1–3. Many of these mutations do not alter the properties of a cell, and are neutral passengers. However, a small proportion can change a cell’s fitness, and increase or decrease the progeny that originate from the mutated cell⁴. How many of these alterations under positive selection (drivers) exist in total is unknown. Whether the current list of drivers is almost complete, or whether large proportions of positively selected drivers in the human genome remain undetected is yet to be determined.

Open Targets Genetics is a community resource that provides systematic fine mapping at human GWAS loci, enabling users to prioritize genes at disease-associated regions and assess their potential as drug targets.

Genome-wide association and genetic risk score analyses highlight differences in genetic architecture across five subtypes of diabetes.

Genome-wide association analysis of irritable bowel syndrome identifies genetic susceptibility loci and highlights shared pathways with mood and anxiety disorders.

The incidence of esophageal squamous cell carcinoma varies significantly across different geographical regions. Mutational signature analysis of tumors sampled from high- and low-incidence areas suggests that these variations may not be explained by mutagenic exposures.

A deep-learning framework interprets multiomic data across epidermal differentiation, identifying cooperative DNA sequence rules that regulate gene modules. Massively parallel reporter assay analysis validates temporal dynamics and cis-regulatory logic.

A study of 17,152 patients with cancer identified pathogenic germline variants in cancer predisposition genes. Although tumors showed biallelic inactivation for high-penetrance genes, this was not the case in most patients with pathogenic variants in low-penetrance genes, suggesting alternative routes to tumorigenesis.

Automated and single-cell CUT&Tag is used to characterize the effects of KMT2A fusion proteins on chromatin in human primary leukemia samples, identifying oncogenic networks and fusion-specific therapeutic vulnerabilities.

Synonymous passenger mutations are used to measure levels of positive selection in healthy blood and esophagus. This approach can quantify missing selection due to unidentified drivers.

SNP rs17713054 in the 3p21.31 COVID-19 risk locus is identified as a probable causative variant for disease association. Chromatin conformation and gene expression data indicate that LZTFL1 is impacted by rs17713054 in pulmonary epithelial cells.

FastGWA-GLMM is a fast, scalable generalized linear mixed model method for genetic association testing for binary traits in large cohorts that is robust to variant frequency and case–control imbalance.