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Multiple system atrophy (MSA) is a neurodegenerative disease with unclear cellular drivers and mechanisms. Here, the authors use single nucleus RNAseq and show dysfunctional, immune tolerant microglia in MSA distinguishing it from Parkinson’s disease.

The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 cancer whole genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

Analyses of 2,658 whole genomes across 38 types of cancer identify the contribution of non-coding point mutations and structural variants to driving cancer.

With the generation of large pan-cancer whole-exome and whole-genome sequencing projects, a question remains about how comparable these datasets are. Here, using The Cancer Genome Atlas samples analysed as part of the Pan-Cancer Analysis of Whole Genomes project, the authors explore the concordance of mutations called by whole exome sequencing and whole genome sequencing techniques.

Genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders identifies five underlying genomic factors that explain the majority of the genetic variance of the individual disorders.

Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.

Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.

Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer.

In this study the authors consider the structural variants (SVs) present within cancer cases of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. They report hundreds of genes, including known cancer-associated genes for which the nearby presence of a SV breakpoint is associated with altered expression.

The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.

The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

Analyses of genome and exome sequencing data identify rare coding and structural variants associated with atrial fibrillation and highlight genetic links between atrial fibrillation and cardiomyopathies.

An analysis of 2,954 genomes from 38 cancer subtypes identified 19,166 retrotransposition events in 35% of samples. Aberrant LINE-1 retrotranspositions can lead to the deletion of tumor-suppressor genes as well as the amplification of oncogenes.

Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors across 38 cancer types is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.

There’s an emerging body of evidence to show how biological sex impacts cancer incidence, treatment and underlying biology. Here, using a large pan-cancer dataset, the authors further highlight how sex differences shape the cancer genome.

In somatic cells the mechanisms maintaining the chromosome ends are normally inactivated; however, cancer cells can re-activate these pathways to support continuous growth. Here, the authors characterize the telomeric landscapes across tumour types and identify genomic alterations associated with different telomere maintenance mechanisms.

Whole-genome sequencing data from more than 2,500 cancers of 38 tumour types reveal 16 signatures that can be used to classify somatic structural variants, highlighting the diversity of genomic rearrangements in cancer.

A pan-cancer genomic analysis reports the effects of structural variations on chromatin domains (TADs). Most TAD disruptions do not result in appreciable changes in expression of nearby genes.

Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

The advantage of living in cities compared with rural areas with respect to height and BMI in children and adolescents has generally become smaller globally from 1990 to 2020, except in sub-Saharan Africa.

Viral pathogen load in cancer genomes is estimated through analysis of sequencing data from 2,656 tumors across 35 cancer types using multiple pathogen-detection pipelines, identifying viruses in 382 genomic and 68 transcriptome datasets.

Many tumours exhibit hypoxia (low oxygen) and hypoxic tumours often respond poorly to therapy. Here, the authors quantify hypoxia in 1188 tumours from 27 cancer types, showing elevated hypoxia links to increased mutational load, directing evolutionary trajectories.

This study identifies protein signatures of pediatric obesity linked to cardiometabolic risk, shows treatment-related changes, and highlights a three-protein panel predictive of steatotic liver disease for early diagnosis

A covalent inhibitor based on an electron-deficient olefin scaffold reversibly and selectively inhibits the p90 ribosomal protein S6 kinase RSK.

Here, the authors characterize the gut virome of 647 one-year-old children in the COPSAC2010 cohort, finding that anelloviruses are highly prevalent and associated with daycare attendance, gestational age, and FUT2 secretor status.

Metabolomic profiling of 581 mother–child pairs revealed patterns associated with neurodevelopmental disorders (NDD). Maternal metabolomic profiles at gestational week 24 showed the strongest predictive value, with quinolinate consistently associated with NDD across time points and linking maternal inflammation to NDD risk.

Large-scale genome-wide analyses identify hundreds of genetic loci associated with hypothyroidism and thyroid hormone levels, demonstrating the potential of using polygenic risk scores to predict disease onset and progression.

The first genome sequence of an ancient human is reported. It comes from an approximately 4,000-year-old permafrost-preserved hair from a male from the first known culture to settle in Greenland. Functional single-nucleotide polymorphism (SNP) assessment is used to assign possible phenotypic characteristics and high-confidence SNPs are compared to those of contemporary populations to find those most closely related to the individual.

Feuerer and colleagues use multiomic analyses to identify DNA methylation, chromatin accessibility and gene expression programs that govern the tissue adaptation of human skin T_reg cells.

Analyses of 34 ancient genomes from northeastern Siberia, dating to between 31,000 and 600 years ago, reveal at least three major migration events in the late Pleistocene population history of the region.

Efficient, large-scale genomic analysis is facilitated on the cloud by a computational tool with error-diagnosing and self-healing capabilities.

Systematic analysis and machine learning coupled with in vitro and in vivo validation link gut microbiome-mediated transformation of dietary phytonutrients to health outcomes.

Stratified medicine promises to tailor treatment for individual patients, however it remains a major challenge to leverage genetic risk data to aid patient stratification. Here the authors introduce an approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue-specific gene expression levels, and highlight its ability to identify biologically meaningful and clinically actionable patient subgroups, supporting the notion of different patient ‘biotypes’ characterized by partially distinct disease mechanisms.

Experimental measurements of high-order out-of-time-order correlators on a superconducting quantum processor show that these correlators remain highly sensitive to the quantum many-body dynamics in quantum computers at long timescales.

Analysis of whole-genome sequencing data across 2,658 tumors spanning 38 cancer types shows that chromothripsis is pervasive, with a frequency of more than 50% in several cancer types, contributing to oncogene amplification, gene inactivation and cancer genome evolution.

Analysis of mitochondrial genomes (mtDNA) by using whole-genome sequencing data from 2,658 cancer samples across 38 cancer types identifies hypermutated mtDNA cases, frequent somatic nuclear transfer of mtDNA and high variability of mtDNA copy number in many cancers.

In this Stage 2 Registered Report, Buchanan et al. show evidence confirming the phenomenon of semantic priming across speakers of 19 diverse languages.

Moreno-Justicia and colleagues report transcriptomic and proteomic pipelines to explore heterogeneity within human skeletal muscle fibers, identifying sources of fiber heterogeneity in healthy individuals and children with nemaline myopathy.

The authors defined a roadmap for investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders. Their proof-of-concept study using the largest available common variant data sets for schizophrenia and volumes of several (mainly subcortical) brain structures did not find evidence of genetic overlap.

Genome-wide association meta-analyses identify more than 350 loci associated with atrial fibrillation. A polygenic score derived from these results improves atrial fibrillation risk prediction compared to published clinical and polygenic scores.

On the electrocardiogram, the PR interval reflects conduction from the atria to ventricles and also serves as risk indicator of cardiovascular morbidity and mortality. Here, the authors perform genome-wide meta-analyses for PR interval in multiple ancestries and identify 141 previously unreported genetic loci.