A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery.

Daniel Danis, The Jackson Laboratory
Michael J Bamshad
Yasemin Bridges
Andrés Caballero-Oteyza
Pilar Cacheiro
Leigh Carmody, The Jackson Laboratory
Leonardo Chimirri
Jessica X Chong
Ben D Coleman, The Jackson Laboratory
Raymond Dalgleish
Peter J Freeman
Adam S L Graefe
Tudor Groza
Peter Hansen
Julius O B Jacobsen
Adam Klocperk
Maaike Kusters
Markus S Ladewig
Anthony J Marcello
Teresa Mattina
Christopher J Mungall
Monica C Munoz-Torres
Justin T Reese
Filip Rehburg
Bárbara C S Reis
Catharina Schuetz
Damian Smedley
Timmy Strauss
Jagadish Chandrabose Sundaramurthi, The Jackson Laboratory
Sylvia Thun
Kyran Wissink
John F Wagstaff
David Zocche
Melissa A Haendel
Peter N Robinson, The Jackson Laboratory

Abstract

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Phenopacket Store v.0.1.19 includes 6,668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3,834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.