Showing papers by "Simon Jupp published in 2020"
TL;DR: Using the PGS Catalog, it is demonstrated that multiple PGS can be systematically evaluated to generate comparable performance metrics and provide the community with an open platform for polygenic score research and translation.
Abstract: Polygenic [risk] scores (PGS) can enhance prediction and understanding of common diseases and traits. However, the reproducibility of PGS and their subsequent applications in biological and clinical research have been hindered by several factors, including: inadequate and incomplete reporting of PGS development, heterogeneity in evaluation techniques, and inconsistent access to, and distribution of, the information necessary to calculate the scores themselves. To address this we present the PGS Catalog (www.PGSCatalog.org), an open resource for polygenic scores. The PGS Catalog currently contains 192 published PGS from 78 publications for 86 diverse traits, including diabetes, cardiovascular diseases, neurological disorders, cancers, as well as traits like BMI and blood lipids. Each PGS is annotated with metadata required for reproducibility as well as accurate application in independent studies. Using the PGS Catalog, we demonstrate that multiple PGS can be systematically evaluated to generate comparable performance metrics. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with an open platform for polygenic score research and translation.
153 citations
TL;DR: The eQTL Catalogue is presented, a resource which contains quality controlled, uniformly recomputed QTLs from 21 eQtl studies, and it is found that for matching cell types and tissues, the eZTL effect sizes are highly reproducible between studies, enabling the integrative analysis of these data.
Abstract: An increasing number of gene expression quantitative trait locus (QTL) studies have made summary statistics publicly available, which can be used to gain insight into human complex traits by downstream analyses such as fine-mapping and colocalisation. However, differences between these datasets in their variants tested, allele codings, and in the transcriptional features quantified are a barrier to their widespread use. Here, we present the eQTL Catalogue, a resource which contains quality controlled, uniformly re-computed QTLs from 19 eQTL publications. In addition to gene expression QTLs, we have also identified QTLs at the level of exon expression, transcript usage, and promoter, splice junction and 3ʹ end usage. Our summary statistics can be downloaded by FTP or accessed via a REST API and are also accessible via the Open Targets Genetics Portal. We demonstrate how the eQTL Catalogue and GWAS Catalog APIs can be used to perform colocalisation analysis between GWAS and QTL results without downloading and reformatting summary statistics. New datasets will continuously be added to the eQTL Catalogue, enabling systematic interpretation of human GWAS associations across a large number of cell types and tissues. The eQTL Catalogue is available at https://www.ebi.ac.uk/eqtl/.
62 citations