GA4GH Phenopackets: A Practical Introduction

A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery

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. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 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.

Consensus reporting guidelines to address gaps in descriptions of ultra-rare genetic conditions

Genome-wide sequencing and genetic matchmaker services are propelling a new era of genotype-driven ascertainment of novel genetic conditions. The degree to which reported phenotype data in discovery-focused studies address informational priorities for clinicians and families is unclear. We identified reports published from 2017 to 2021 in 10 genetics journals of novel Mendelian disorders. We adjudicated the quality and detail of the phenotype data via 46 questions pertaining to six priority domains: (I) Development, cognition, and mental health; (II) Feeding and growth; (III) Medication use and treatment history; (IV) Pain, sleep, and quality of life; (V) Adulthood; and (VI) Epilepsy. For a subset of articles, all subsequent published follow-up case descriptions were identified and assessed in a similar manner. A modified Delphi approach was used to develop consensus reporting guidelines, with input from content experts across four countries. In total, 200 of 3243 screened publications met inclusion criteria. Relevant phenotypic details across each of the 6 domains were rated superficial or deficient in >87% of papers. For example, less than 10% of publications provided details regarding neuropsychiatric diagnoses and "behavioural issues", or about the type/nature of feeding problems. Follow-up reports (n = 95) rarely contributed this additional phenotype data. In summary, phenotype information relevant to clinical management, genetic counselling, and the stated priorities of patients and families is lacking for many newly described genetic diseases. The PHELIX (PHEnotype LIsting fiX) reporting guideline checklists were developed to improve phenotype reporting in the genomic era.

Improving prenatal diagnosis through standards and aggregation

Advances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and phenotypic data permits dissection of the genomic architecture of prenatal presentations of disease and provides a dataset on which data analysis algorithms can be tuned to the prenatal space. Here we discuss the importance of generating aggregate data sets and how the prenatal space is driving the development of interoperable standards and phenotype-driven tools.

Convert-Pheno: A software toolkit for the interconversion of standard data models for phenotypic data

Efficient sharing and integration of phenotypic data is crucial for advancing biomedical research and enhancing patient outcomes in precision medicine and public health. To achieve this, the health data community has developed standards to promote the harmonization of variable names and values. However, the use of diverse standards across different research centers can hinder progress. Here we present Convert-Pheno, an open-source software toolkit that enables the interconversion of common data models for phenotypic data such as Beacon v2 Models, CDISC-ODM, OMOP-CDM, Phenopackets v2, and REDCap. Along with the software, we have created a detailed documentation that includes information on deployment and installation.

Current aspects in biobanking for personalized oncology investigations and treatments

Background/Aim

A biobank is an organization that gathers, refines, preserves and provides access to biospecimens along with relevant clinical data that can be used in applied or clinical research. Biobanking is a critical component of the scientific foundation for personalized medicine; this implies the accessibility of high-quality human biospecimens, such as blood, tissue, and other body fluids, along with the patient clinical data that goes with them.

Methods

This paper summarizes the function of biobanks in oncology and the requirements for biobank development in translational and clinical research.

Results

Biobanks raise numerous ethical issues that government agencies address by enacting particular laws. To develop personalized medicine, biobanks are crucial, given that the availability of an extensive collection of patient samples with thoroughly annotated clinical and pathological data is an essential necessity. Also, data related to biobanking raises complex ethical, legal, and social issues, particularly concerning the protection of donor privacy and the appropriate use of collected samples. International standards have been developed to address these issues to ensure biobanking practices' quality, safety, and integrity.

Conclusions

Biobanking is vital in advancing biomedical research, supporting clinical applications, and enhancing our understanding of human health and disease. Using real-world data and biobanking can accelerate medical research, support personalized medicine initiatives, and improve patient care.