Genetic and Epidemiological Insights into RAB32-Linked Parkinson's Disease.

Background: The p.Ser71Arg RAB32 variant was recently associated with Parkinson's disease (PD).

Objective: The aim was to investigate the presence of RAB32 variants in a large multiethnic group of individuals affected and unaffected by PD.

Fine-scale subpopulation detection via an SNP-based unsupervised method: A case study on the 1000 Genomes Project resources.

SNP-based information is used in several existing clustering methods to detect shared genetic ancestry or to identify population substructure. Here, we present a methodology, called IPCAPS for unsupervised population analysis using iterative pruning. Our method, which can capture fine-level structure in populations, supports ordinal data, and thus can readily be applied to SNP data.

Benchmarking atlas-level data integration in single-cell genomics.

Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.

EpiScanpy: integrated single-cell epigenomic analysis.

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets.

A different view on fine-scale population structure in Western African populations.

Due to its long genetic evolutionary history, Africans exhibit more genetic variation than any other population in the world. Their genetic diversity further lends itself to subdivisions of Africans into groups of individuals with a genetic similarity of varying degrees of granularity. It remains challenging to detect fine-scale structure in a computationally efficient and meaningful way.