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scHLApers is an analysis pipeline that quantifies single-cell expression of HLA genes using a personalized genomic reference. Mapping of HLA expression quantitative trait loci at single-cell resolution identifies dynamic effects across cell states.
In single-cell studies, combining healthy reference atlases and designed control datasets allows more precise identification of disease-associated cell states.
Genes encoding members of mammalian SWI/SNF (BAF) complexes are frequently mutated in individuals with neurodevelopmental disorders (NDDs). Mutant NDD residues include some unique to NDD and those shared with human cancers, impacting key structural hubs.
Re-analysis of published RNA-sequencing samples finds that unannotated splicing events predict, with high sensitivity, the activation of exon skipping and cryptic splicing by splice-site variants.
The cattle Genotype–Tissue Expression atlas of expression and splicing QTLs is generated from 7,180 uniformly re-processed RNA-seq samples. Integration with GWAS identifies candidate genes and variants associated with economically important traits.
Association analyses using resting-state functional magnetic resonance images identify common genetic variants influencing intrinsic brain activity. Variation in brain function is genetically correlated with several neuropsychiatric traits.
Analyses on the global diversity of SARS-CoV-2 genomic surveillance across 118 countries and the extent of public availability of genomic data provide evidence to better inform SARS-CoV-2 surveillance policy.
A high-resolution reference panel based on whole-genome sequencing data enables accurate imputation of HLA alleles across diverse populations and fine-mapping of HLA association signals for HIV-1 host response.
Imputation of rare coding variants in the UK Biobank enables association and fine-mapping analyses of rare (minor allele frequency (MAF) = 0.00005) genotypes, identifying 600 new variant–trait associations, including long allelic series in individual genes.
Radiotherapy induces small and large deletions as well as inversions across the genome in multiple cancer types. The genomic changes associated with radiotherapy correlate with poorer clinical outcomes.
Analysis of RNA-seq datasets from seven organs across seven species generates an alternative splicing (AS) atlas and shows that AS events provide functional gene diversification through generation of tissue- and time-specific transcript isoforms.
Genome-wide meta-analysis, fine-mapping and integrative prioritization using expression quantitative trait loci, protein interaction networks and tissue-specific expression implicate new candidate susceptibility genes for Alzheimer’s disease.
Mendelian randomization (MR) and colocalization analyses are used to estimate causal effects of 1,002 plasma proteins on 225 phenotypes. Evidence from drug developmental programs shows that target-indication pairs with MR and colocalization support were more likely to be approved, highlighting the value of this approach for prioritizing therapeutic targets.
A new statistical framework to classify mutagenesis clusters identifies a novel, diffuse hypermutation pattern, named omikli, that is induced by APOBEC3 and associated with mismatch-repair activity.
Application of a new k-mer-based genome-wide association approach to 2,000 phenotypes in Arabidopsisthaliana, tomato and maize detects new associations with structural variants and with regions missing from reference genomes.
This analysis presents a harmonized meta-knowledgebase to facilitate clinical interpretation of somatic genomic variants in cancer. This community-based project highlights the need for cooperative efforts to curate clinical interpretations of somatic variants for robust practice of precision oncology.
Genetic analyses of depression based on minimal phenotyping identify nonspecific genetic risk factors shared between major depressive disorder (MDD) and other psychiatric conditions, suggesting that this approach may have limited ability to identify pathways specific to MDD.
Assessing heritability models using summary statistics from genome-wide association studies of 31 human traits shows that the Baseline LD model is realistic and can be improved by incorporating features from the LDAK model.
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.