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MetaSTAAR enables functionally informed rare variant association analysis in biobank-scale cohorts using an efficient, sparse matrix approach for summary statistic storage.
Sort-assisted single-cell chromatin immunocleavage (sortChIC) combines single-cell histone modification profiling with fluorescence-activated cell sorting (FACS), enabling the study of rare cell populations. H3K4me1/H3K4me3, H3K9me3 and H3K27me3 profiling of blood suggest a model of lineage-shared repressive and cell type-specific active chromatin.
CRISPR-Select is a quantitative assay for the functional impact of genetic variants, including pathogenicity, drug response, oncogenicity, cell motility and other cell states.
CRISPR-CATCH is used to isolate extrachromosomal DNA (ecDNA) molecules containing oncogenes from human cancer cells. CRISPR-CATCH followed by nanopore sequencing allows for methylation profiling, highlighting differences from the native chromosomal loci.
Single-cell DNA sequencing data are generated from human neurons using primary template-directed amplification and analyzed using SCAN2, an improved genotyping tool. Indels are enriched in neuronal regulatory elements and may be deleterious.
scDRS associates individual cells in scRNA-seq with disease by scoring single-cell transcriptomes using GWAS gene signatures. Applied to 74 GWAS and 1.3 million single-cell profiles, scDRS identifies specific cellular subpopulations associated with these diseases.
snipar is a software package for imputing missing parental genotypes and estimating direct genetic effects. Application to UK Biobank data shows that effects estimated by standard genome-wide association study designs have confounding bias for some phenotypes.
Orca is a sequence-based deep-learning algorithm that predicts 3D genome architecture from kilobase to whole-chromosome scale, including the impact of structural variants. In silico modeling identifies a putative sequence basis for chromatin compartment formation.
GestaltMatcher uses a deep convolutional neural network to improve recognition of rare disorders based on facial morphology. The framework detects similarities among patients with previously unseen syndromes, aiding discovery of new disease genes.