Estimating Disease Heritability from Electronic Healthcare Records

Objective: Chronic diseases are a major driver of rising healthcare costs. Accurate predictions of disease risk are integral to effective disease prevention initiatives and patient treatment strategies. A family history of a chronic disease, which reflects both genetics and shared environments, often predicts disease risk, with predictive value determined by heritability, the proportion of variation in risk explained by inherited genetic factors. Electronic healthcare records (EHRs) are frequently used to study chronic diseases, and when linked to familial relationship information could also been used to measure family disease histories for disease risk prediction. Our objective was to assess the validity of disease heritability estimates from EHRs that capture familial relationships and disease diagnoses.

Methods: A population-based investigation was conducted using healthcare records from Manitoba, Canada for 1970 to 2021. We constructed family relationships for up to four generations using health insurance registration information containing unique family and individual identifiers. Health histories for family members were created using diagnosis codes in hospital and physician visit records. Linear mixed-effects models were used to estimate heritability (h) for 130 chronic health conditions using open-source Clinical Classifications Software (CCS) that defines clinically-meaningful disease categories. Comparisons between EHR-derived estimates and genetically-derived estimates from published studies were used to assess validity of the methodology.

Results: Health insurance registration data were used to construct relationships for 10,000 families that included 116,879 individuals. Median family size was 9 (interquartile range: 8). Median observation time was 39.6 years (interquartile range: 25.7). Males comprised half (51.0%) of family members. A total of 272,114 familial relationships were identified; slightly more than half (53%) were first degree (i.e., child and parent) relationships. One-third (33.2%) of families were comprised of four generations; only 15.3% were comprised of two generations. Heritability estimates were consistent with published genetically-derived estimates for several conditions, including diabetes (EHR h = 0.29 vs. 0.22), anemia (EHR h = 0.21 vs. 0.20), and asthma (EHR h = 0.34 vs. 0.33). However, inconsistencies in heritability estimates were identified for pancreatic disorders, gastrointestinal conditions, some mental health conditions, and heart disease.

Conclusion: EHRs provide a promising and novel approach to explore heritability of selected health conditions in large and diverse populations, which is of value for producing disease risk predictions with high generalizability. Such risk predictions are essential for informing chronic disease prevention and treatment policies. However, inconsistencies between EHR-derived and genetically-derived estimates are indicative of the limitations of diagnoses recorded for administrative purposes. Future research will explore sex-specific heritability estimates, effects of change in disease diagnosis coding over time on heritability estimates, and utility of family health histories in risk prediction models for diseases with high heritability.

administrative data

chronic disease

family health history

mixed-effect models

International Classification of Diseases

genetics