The Clinical Genome and Ancestry Report: An interactive web application for prioritizing clinically implicated variants from genome sequencing data with ancestry composition. Issue 2 (15th November 2019)

• Record Type: Journal Article
• Authors: Lee, In‐Hee; Negron, Jose A.; Hernandez‐Ferrer, Carles; Alvarez, William Jefferson; Mandl, Kenneth D.; Kong, Sek Won
• Is Part Of: Human mutation. Volume 41:Issue 2(2020)
• Page Start: 387
• Page End: 396
• Publication Date: 2019-11-15
• Subjects: ancestry -- cancer -- clinical interpretation -- pharmacogenomics -- variant annotation -- whole‐genome sequencing; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.23942
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract: Genome sequencing is positioned as a routine clinical work‐up for diverse clinical conditions. A commonly used approach to highlight candidate variants with potential clinical implication is to search over locus‐ and gene‐centric knowledge databases. Most web‐based applications allow a federated query across diverse databases for a single variant; however, sifting through a large number of genomic variants with combination of filtering criteria is a substantial challenge. Here we describe the Clinical Genome and Ancestry Report (CGAR), an interactive web application developed to follow clinical interpretation workflows by organizing variants into seven categories: (1) reported disease‐associated variants, (2) rare‐ and high‐impact variants in putative disease‐associated genes, (3) secondary findings that the American College of Medical Genetics and Genomics recommends reporting back to patients, (4) actionable pharmacogenomic variants, (5) focused reports for candidate genes, (6) de novo variant candidates for trio analysis, and (7) germline and somatic variants implicated in cancer risk, diagnosis, treatment and prognosis. For each variant, a comprehensive list of external links to variant‐centric and phenotype databases are provided. Furthermore, genotype‐derived ancestral composition is used to highlight allele frequencies from a matched population since some disease‐associated variants show a wide variation between populations. CGAR is an open‐source software and is available at https://tom.tch.harvard.edu/apps/cgar/ .