Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Area of Science:

• Genetics
• Bioinformatics
• Epidemiology

Background:

• Heritability studies are crucial for understanding disease etiology but traditionally require extensive patient data collection.
• Electronic Health Records (EHRs) offer a vast, passively collected data resource for biomedical research.
• Next-of-kin information within EHRs has been an underutilized research asset.

Purpose of the Study:

• To investigate the utility of EHR-derived familial relationships for genetic heritability studies.
• To assess the feasibility of using EHR data for calculating heritability estimates across numerous disease phenotypes.
• To validate EHRs as a scalable resource for genetic and disease research.

Main Methods:

• Mining emergency contact data from three academic medical centers to identify familial relationships.
• Utilizing identified relationships to compute heritability estimates for 500 disease phenotypes.
• Comparing EHR-derived heritability estimates with existing literature and across different sites.

Main Results:

• Successfully identified 7.4 million familial relationships from EHRs while preserving patient privacy.
• Familial relationships derived from EHRs demonstrated consistency with genetically inferred relatedness.
• Heritability estimates for 500 disease phenotypes were largely consistent with prior research and between institutions.

Conclusions:

• EHRs, particularly next-of-kin data, represent a valuable and scalable resource for genetic research.
• The study validates the use of EHRs for calculating disease heritability, complementing traditional methods.
• Findings highlight the potential of EHRs to advance our understanding of the genetic basis of diseases.