Polygenic architecture of rare coding variation across 394,783 exomes.

Both common and rare genetic variants influence complex traits and common diseases. Genome-wide association studies have identified thousands of common-variant associations, and more recently, large-scale exome sequencing studies have identified rare-variant associations in hundreds of genes. However, rare-variant genetic architecture is not well characterized, and the relationship between common-variant and rare-variant architecture is unclear.

Treatment-associated remodeling of the pancreatic cancer endothelium at single-cell resolution.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most treatment refractory and lethal malignancies. The diversity of endothelial cell (EC) lineages in the tumor microenvironment (TME) impacts the efficacy of antineoplastic therapies, which in turn remodel EC states and distributions. Here, we present a single-cell resolution framework of diverse EC lineages in the PDAC TME in the context of neoadjuvant chemotherapy, radiotherapy, and losartan.

SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests.

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics.

Genome-wide association studies (GWAS) provide a powerful means to identify loci and genes contributing to disease, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. Here, we introduce sc-linker, a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease.

Avoiding genetic racial profiling in criminal DNA profile databases.

DNA profiling has become an essential tool for crime solving and prevention, and CODIS (Combined DNA Index System) criminal investigation databases have flourished at the national, state and even local level. However, reports suggest that the DNA profiles of all suspects searched in these databases are often retained, which could result in racial profiling. Here, we devise an approach to both enable broad DNA profile searches and preserve exonerated citizens' privacy through a real-time privacy-preserving procedure to query CODIS databases.

Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology.

AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature.

The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication(s) supporting a single gene that best explains a patient's disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process.

AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literature.

Purpose: Both monogenic pathogenic variant cataloging and clinical patient diagnosis start with variant-level evidence retrieval followed by expert evidence integration in search of diagnostic variants and genes. Here, we try to accelerate pathogenic variant evidence retrieval by an automatic approach.

Methods: Automatic VAriant evidence DAtabase (AVADA) is a novel machine learning tool that uses natural language processing to automatically identify pathogenic genetic variant evidence in full-text primary literature about monogenic disease and convert it to genomic coordinates.

CRISPR/Cas9 Genome Engineering in Engraftable Human Brain-Derived Neural Stem Cells.

Human neural stem cells (NSCs) offer therapeutic potential for neurodegenerative diseases, such as inherited monogenic nervous system disorders, and neural injuries. Gene editing in NSCs (GE-NSCs) could enhance their therapeutic potential. We show that NSCs are amenable to gene targeting at multiple loci using Cas9 mRNA with synthetic chemically modified guide RNAs along with DNA donor templates.

S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing.

Exome analysis of patients with a likely monogenic disease does not identify a causal variant in over half of cases. Splice-disrupting mutations make up the second largest class of known disease-causing mutations. Each individual (singleton) exome harbors over 500 rare variants of unknown significance (VUS) in the splicing region.

Phrank measures phenotype sets similarity to greatly improve Mendelian diagnostic disease prioritization.

Purpose: Exome sequencing and diagnosis is beginning to spread across the medical establishment. The most time-consuming part of genome-based diagnosis is the manual step of matching the potentially long list of patient candidate genes to patient phenotypes to identify the causative disease.

Methods: We introduce Phrank (for phenotype ranking), an information theory-inspired method that utilizes a Bayesian network to prioritize candidate diseases or genes, as a stand-alone module that can be run with any underlying knowledgebase and any variant filtering scheme.

Biallelic loss-of-function WNT5A mutations in an infant with severe and atypical manifestations of Robinow syndrome.

Robinow syndrome (RS) is a well-recognized Mendelian disorder known to demonstrate both autosomal dominant and autosomal recessive inheritance. Typical manifestations include short stature, characteristic facies, and skeletal anomalies. Recessive inheritance has been associated with mutations in ROR2 while dominant inheritance has been observed for mutations in WNT5A, DVL1, and DVL3.

Deriving genomic diagnoses without revealing patient genomes.

Patient genomes are interpretable only in the context of other genomes; however, genome sharing enables discrimination. Thousands of monogenic diseases have yielded definitive genomic diagnoses and potential gene therapy targets. Here we show how to provide such diagnoses while preserving participant privacy through the use of secure multiparty computation.

M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity.

Variant pathogenicity classifiers such as SIFT, PolyPhen-2, CADD, and MetaLR assist in interpretation of the hundreds of rare, missense variants in the typical patient genome by deprioritizing some variants as likely benign. These widely used methods misclassify 26 to 38% of known pathogenic mutations, which could lead to missed diagnoses if the classifiers are trusted as definitive in a clinical setting. We developed M-CAP, a clinical pathogenicity classifier that outperforms existing methods at all thresholds and correctly dismisses 60% of rare, missense variants of uncertain significance in a typical genome at 95% sensitivity.