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Cell migration depends on controlled adhesion turnover, but its regulation remains unclear. Here the authors show that AAK1 promotes focal adhesion disassembly via PDLIM5 and Talin1 phosphorylation, enabling efficient cell migration.

cellSTAAR advances noncoding rare variant association analysis by integrating single-cell genomics data.

Geospatial estimates of the prevalence of anemia in women of reproductive age across 82 low-income and middle-income countries reveals considerable heterogeneity and inequality at national and subnational levels, with few countries on track to meet the WHO Global Nutrition Targets by 2030.

The anterior cingulate cortex encodes affective pain behaviours modulated by opioids; targeting opioid-sensitive neurons through a new chemogenetic gene therapy replicates the analgesic effects of morphine, providing precise chronic pain relief without affecting sensory detection.

Many genetic loci have been identified to be associated with kidney disease, but the molecular mechanisms are not well understood. Here, the authors perform epigenome-wide association studies on kidney function measures to identify epigenetic marks and pathways involved in kidney function.

Serum urate concentration can be studied in large datasets to find genetic and epigenetic loci that may be related to cardiometabolic traits. Here the authors identify and replicate 100 urate-associated CpGs, which provide insights into urate GWAS loci and shared CpGs of urate and cardiometabolic traits.

Here the authors use a genome-wide approach to identify genetic variants associated with RNA isoform variation, uncovering new links between transcriptional regulation and complex traits. The findings can advance understanding of disease mechanisms.

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.

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.

Here the authors develop a novel statistical method for quantifying mutation burden from whole genome sequencing data and use it to discover the genetic, genomic, and phenotypic correlates of clonal hematopoiesis without known driver genetic lesions.

Polygenic scores for body mass index and obesity constructed using data from 5 million people with different ancestries were associated with distinct weight gain trajectories from early childhood to adulthood.

Here the authors provide an explanation for 95% of examined predicted loss of function variants found in disease-associated haploinsufficient genes in the Genome Aggregation Database (gnomAD), underscoring the power of the presented analysis to minimize false assignments of disease risk.

A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.

A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.

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.

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.

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.

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.

Data from over 700,000 individuals reveal the identity of 83 sequence variants that affect human height, implicating new candidate genes and pathways as being involved in growth.

A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

A multi-ancestry genome-wide gene–smoking interaction study identifies 13 new loci associated with serum lipids.

Bhattacharjee and Schaeffer et al. map exclusive breastfeeding (EBF) in 94 low- and middle-income countries (LMICs), finding increased EBF practice and reduced subnational variation across the majority of LMICs from 2000 to 2018. However, only six LMICs will meet WHO’s target of ≥70% EBF by 2030 nationally, and only three will achieve this in all districts.

Multi-ancestry meta-analyses of genome-wide association studies for self-reported physical activity during leisure time, leisure screen time, sedentary commuting and sedentary behavior at work identify 99 loci associated with at least one of these traits.

Although the common genetic variants contributing to blood lipid levels have been studied, the contribution of rare variants is less understood. Here, the authors perform a rare coding and noncoding variant association study of blood lipid levels using whole genome sequencing data.

STAARpipeline is a comprehensive framework for flexible and scalable rare-variant association analysis using whole-genome sequencing data and annotation information.

Exome-wide analysis identifies rare and low-frequency coding variants associated with body mass index. Gene-based meta-analysis and functional studies implicate 13 genes, eight of which are novel, and neuronal pathways as factors in human obesity.

Here, the authors use passenger mutations to quantify expansion rate in ~6,000 people with mosaic chromosomal alterations in the NHLBI TOPMed cohort, finding associations between growth rate and blood counts along with germline genetic modulators of growth rate.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

Sleep duration is associated with an adverse lipid profile. Here, the authors perform genome-wide gene-by-sleep interaction analysis and find 49 previously unreported lipid loci when considering short or long total sleep time.

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.

Mitochondrial DNA is known to exhibit heterogeneity of variants, even within a single cell. Here, the authors assessed this heteroplasmy of mitochondrial DNA within the UK Biobank cohort and showed that the presence of heteroplasmy and a functional score generated from heteroplasmic SNVs were associated with all-cause mortality and certain cancers.

Timothy Frayling, Joel Hirschhorn, Peter Visscher and colleagues report a meta-analysis of genome-wide association studies for adult height in 253,288 individuals. They identify 697 variants in 423 loci significantly associated with adult height and find that these variants cluster in pathways involved in growth and together explain one-fifth of the heritability for this trait.

A multi-ancestry meta-regression study analyses diverse genome-wide association studies and genome loci associated with tobacco and alcohol use.

Most genetic association studies have been done on single nucleotide polymorphisms and small indels, while other types of variants have been less studied. Here, the authors use whole genome sequencing in a diverse population to identify and provide experimental evidence for associations between structural variants and blood-cell traits.

Here, using a dynamic modelling approach, the authors find that the spread of dengue through Mexico and Brazil is shaped by specific interactions between human mobility, climate, and the environment. Their models can also be applied to predict future spread in these geographic areas.

Statistical mapping techniques provide insights into the spread of two key arbovirus vectors in Europe and the United States, and predict the future distributions of both mosquitoes in response to accelerating urbanization, connectivity and climate change.

Using large-scale data, Kraemer et al. find that human mobility patterns vary across the globe and in scale by environmental and sociodemographic contexts. There are tenfold differences in mobility patterns depending on the countries’ economic development.