8 Result(s)
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Open AccessPublisher Correction: SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests
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Open AccessSAIGE-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 inflat...
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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq wit...
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Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics
Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain u...
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Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or ther...
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Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity
Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link reg...
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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 mutati...
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M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity
Gill Bejerano and colleagues present M-CAP, a classifier that estimates variant pathogenicity in clinical exome data sets. They show that M-CAP outperforms other existing methods at all thresholds and correctl...
