Quincunx: an R package to query, download and wrangle PGS catalog data

• Autor(a) principal: Magno, Ramiro
• Data de Publicação: 2022
• Outros Autores: Duarte, Isabel, Maia, Ana-Teresa
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/17482

For two decades, GWAS identified individual variants associated with risk for complex diseases. These associations can be combined into polygenic scores (PGS) aiming at quantifying an individual’s risk to disease, inform on prognosis and even treatment response (Lambert et al., 2019). Broadly, PGS use summary statistics produced by GWAS to calculate a weighted sum of trait-associated alleles carried by each individual, in which the weights correspond to the per-allele size effects. Initially used to validate associations with disease and uncover interactions between variants, PGS have been more challenging to implement in the clinic. In 2020, over 1400 publications on PGS appeared in PubMed, raising the need for a standardized distribution of studies’ key data, assuring their wide evaluation and accurate use. The Polygenic Score (PGS) Catalog, created in 2019, is a publicly available, manually curated database of PGS and relevant metadata, that responds to this need (Lambert et al., 2020). Its current release [date 2021-02-03] includes data from 133 publications and 721 PGS associated with 194 traits. Currently, data is accessed via three ways: (i) the web graphical user interface (GUI); (ii) by downloading database dumps; and (iii) the recent PGS Catalog representational state transfer (REST) application programming interface (API), the preferred method for batch analyses.