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Two low-input tagmentation-based long-read sequencing methods, single-molecule real-time sequencing by tagmentation (SMRT-Tag), which identifies genetic variation and CpG methylation, and single-molecule adenine-methylated oligonucleosome sequencing assay by tagmentation (SAMOSA-Tag), which detects chromatin accessibility, are presented. Application of SAMOSA-Tag to prostate cancer patient-derived xenograft samples identifies metastasis-associated epigenomic alterations.
MuSiCal is a mutational signature analysis tool combining minimum-volume nonnegative matrix factorization with other algorithmic innovations. Applied to PCAWG data, MuSiCal gives more accurate results, including resolving ambiguous flat signatures.
Causal-TWAS (cTWAS) is a statistical framework that adjusts for genetic confounders in transcriptome-wide association studies. Application of cTWAS on common traits leads to reliable detection of candidate causal genes.
GIFT fine-maps candidate causal genes in a transcription-wide association study by conditioning on predicted expression of nearby genes, leading to improved statistical power and enhanced mapping resolution when applied to complex traits.
A new method allows selection of matched controls from an external pool of samples without genotype sharing. This method has been implemented in an online repository containing 39,472 exome sequencing controls that can be used for association analyses.
MESuSiE extends fine-mapping approaches to multi-ancestry analysis using LD-aware bivariate normal mixture models with a variational algorithm to identify shared and ancestry-specific causal variants.
A powerful Bayesian method, BridgePRS, leverages shared genetic effects across ancestries to increase polygenic risk score portability in non-European populations.
AutoComplete is a deep learning-based method that imputes missing phenotypes in population-scale biobank datasets, increasing effective sample sizes and improving power for genetic discoveries in genome-wide association studies.
CT-SLEB, a powerful and scalable method, improves the performance of multiancestry polygenic prediction by generating polygenic risk scores based on GWAS summary statistics in diverse populations.
Simulations and applications to real data show that adjustment of genome-wide association analyses for polygenic scores increases the statistical power for discovery across all ancestries, suggesting an analytical strategy for future studies in underrepresented populations.
GATK-gCNV uses a probabilistic model and inference framework to discover rare copy number variants (CNVs) from sequencing read-depth information. This algorithm is used to generate a reference catalog of rare coding CNVs in exome sequencing data from UK Biobank.
PhenoScore is an open-source machine-learning tool that combines facial image recognition with Human Phenotype Ontology for genetic syndrome identification without genomic data, with applications to subgroup analysis and variants of unknown significance classification.
A new methodology to study participation genetics via analyzing shared and unshared genotypes is applied to the UK Biobank, suggesting that participating in genetic studies is a complex trait with a meaningful genetic component.
SHAPEIT5, a phasing method that accurately processes large sequencing datasets, was applied on the UK Biobank whole-genome and whole-exome sequencing data to generate reference panels of haplotypes that boost imputation accuracy and enable the detection of compound heterozygous loss-of-function events for 549 genes.
GASPACHO is a statistical method that identifies nonlinear dynamic genetic effects using single-cell RNA-seq data. Analysis of an antiviral response in human fibroblasts identifies 1,275 expression QTLs, many of which colocalize with risk loci for autoimmune and infectious diseases.
Causal robust mapping method in meta-analysis (CARMA) studies incorporates flexible prior distributions, joint modeling of summary statistics and functional annotations and outlier detection for improved causal variant fine-mapping in genome-wide association meta-analyses.
Region Capture Micro-C (RCMC) combines MNase-based 3C with a tiling region-capture method. Profiling mouse embryonic stem cells with RCMC identifies nested microcompartments, which connect enhancers and promoters.
scEC&T-seq profiles extrachromosomal circular DNA and full-length mRNA from single human cancer cells, and may be used to interrogate heterogeneity in both cell lines and primary tumor samples.
AbSplice predicts aberrant splicing for 50 human tissues by integrating sequence-based deep learning models, DNA variation and RNA-seq obtained from accessible tissues.
Concatenating Original Duplex for Error Correction (CODEC) is a method that concatenates both strands of each DNA duplex to enable highly sensitive mutation detection in a range of analytes with fewer reads and lower error rates than current methods.