Phenotypes

Development and validation of the All of Us baseline surveys covering demographics, lifestyle, overall health, healthcare access, and physical activity. Surveys designed for broad accessibility including low-literacy populations.

Cross-sectional analysis of baseline clinical data from 200,000 BBJ participants across 47 common diseases, documenting prevalence, comorbidity patterns, and clinical characteristics of the Japanese patient cohort.

Linkage to hospital episode statistics (England 1996+, Scotland 1997+, Wales 1998+), death registers (2006+), and cancer registers. Repeated in-person assessments for ~20k participants.

137k participants completed online mental health assessment covering subjective well-being, psychotic experiences, self-harm, traumatic events, cannabis and alcohol use (PHQ-9, GAD-7).

Web-based dietary questionnaire (176k participants) and online cognitive function tests (120k, including symbol digit substitution and trail making).

Hierarchical system for classifying drugs by anatomical site, therapeutic and pharmacological subgroup, and chemical substance. Maintained by the WHO Collaborating Centre for Drug Statistics Methodology; widely used in prescription and claims data for exposure phenotypes.

ATC; drugs; pharmacoepidemiology; medication coding

WHO standard for coding diagnoses, symptoms, and procedures in health records and vital statistics. Widely used for epidemiology, billing, and EHR-based research (including mapping to other systems such as Phecodes).

ICD-10; clinical coding; diagnosis; mortality; morbidity

WHO's successor to ICD-10 for mortality and morbidity statistics (ICD-11 for MMS), with a digital-first structure, updated clinical detail, and tooling for implementation in health systems and research.

ICD-11; clinical coding; MMS; mortality; morbidity

Earlier WHO disease and injury classification still present in many historical EHR and claims datasets (e.g. ICD-9-CM in US data). Useful for harmonization with ICD-10/11 and aggregated phenotypes such as Phecodes.

ICD-9; ICD-9-CM; clinical coding; legacy EHR

Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions.

PheWAS; EHR; ICD; phenotype grouping

OBJECTIVE: To compare three groupings of Electronic Health Record (EHR) billing codes for their ability to represent clinically meaningful phenotypes and to replicate known genetic associations. The three tested coding systems were the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, the Agency for Healthcare Research and Quality Clinical Classification Software for ICD-9-CM (CCS), and manually curated "phecodes" designed to facilitate phenome-wide association studies (PheWAS) in EHRs. METHODS AND MATERIALS: We selected 100 disease phenotypes and compared the ability of each coding system to accurately represent them without performing additional groupings. The 100 phenotypes included 25 randomly-chosen clinical phenotypes pursued in prior genome-wide association studies (GWAS) and another 75 common disease phenotypes mentioned across free-text problem lists from 189,289 individuals. We then evaluated the performance of each coding system to replicate known associations for 440 SNP-phenotype pairs. RESULTS: Out of the 100 tested clinical phenotypes, phecodes exactly matched 83, compared to 53 for ICD-9-CM and 32 for CCS. ICD-9-CM codes were typically too detailed (requiring custom groupings) while CCS codes were often not granular enough. Among 440 tested known SNP-phenotype associations, use of phecodes replicated 153 SNP-phenotype pairs compared to 143 for ICD-9-CM and 139 for CCS. Phecodes also generally produced stronger odds ratios and lower p-values for known associations than ICD-9-CM and CCS. Finally, evaluation of several SNPs via PheWAS identified novel potential signals, some seen in only using the phecode approach. Among them, rs7318369 in PEPD was associated with gastrointestinal hemorrhage. CONCLUSION: Our results suggest that the phecode groupings better align with clinical diseases mentioned in clinical practice or for genomic studies. ICD-9-CM, CCS, and phecode groupings all worked for PheWAS-type studies, though the phecode groupings produced superior results.