Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease | Nature Genetics
To elucidate the genetics of coronary artery disease (CAD) in the Japanese population, researchers conducted a large-scale genome-wide association study of 168,228 individuals of Japanese ancestry (25,892 cases and 142,336 controls) with genotype imputation using a newly developed reference panel of Japanese haplotypes including 1,781 CAD cases and 2,636 controls. They detected eight new susceptibility loci and Japanese-specific rare variants contributing to disease severity and increased cardiovascular mortality. They then conducted a trans-ancestry meta-analysis and discovered 35 additional new loci. Using the meta-analysis results, they derived a polygenic risk score (PRS) for CAD, which outperformed those derived from either Japanese or European genome-wide association studies.
An analysis of tissue-specific alternative splicing at the protein level | PLOS Computational Biology
Researchers manually curated a set of 255 splice events detected in a large-scale tissue-based proteomics experiment and found that more than a third had evidence of significant tissue-specific differences.
Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis | Nature Machine Intelligence
Clustering and cell type classification are important steps in single-cell RNA-seq (scRNA-seq) analysis. As more and more scRNA-seq data are becoming available, supervised cell type classification methods that utilize external well-annotated source data start to gain popularity over unsupervised clustering algorithms; however, the performance of existing supervised methods is highly dependent on source data quality and they often have limited accuracy to classify cell types that are missing in the source data. Researchers developed ItClust to overcome these limitations, a transfer learning algorithm that borrows ideas from supervised cell type classification algorithms, but also leverages information in target data to ensure sensitivity in classifying cells that are only present in the target data.