Abstract
Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363–372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency—“coded-strict”, “coded-broad”, and “coded-broad based on a single clinical encounter” (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p = 0.015), 0.09 (p = 0.064), 0.13 (p = 0.003), 0.00 (p = 0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p = 0.004). These h2g were lower or similar to the h2g observed by the ICCBD + PGCBD (0.23, p = 3.17E−80, total N = 33,181). However, the rg between ICCBD + PGCBD and the EHR-based cases were high for 95-NLP (0.66, p = 3.69 × 10–5), coded-strict (1.00, p = 2.40 × 10−4), and coded-broad (0.74, p = 8.11 × 10–7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.
Highlights
Twin studies first documented the high heritability of bipolar disorder (BD) decades ago, only recently have robustly associated genetic risk loci been identified through genomewide association studiesChen et al Translational Psychiatry (2018)8:86(GWAS)[1,2,3,4,5,6,7,8]
The rg between electronic health records (EHRs)-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods
Introduction twin studies first documented the high heritability of bipolar disorder (BD) decades ago, only recently have robustly associated genetic risk loci been identified through genomewide association studies
Summary
Twin studies first documented the high heritability of bipolar disorder (BD) decades ago, only recently have robustly associated genetic risk loci been identified through genomewide association studiesChen et al Translational Psychiatry (2018)8:86(GWAS)[1,2,3,4,5,6,7,8]. The widespread adoption of longitudinal electronic health records (EHRs) has provided a vast and growing repository of phenotypic data that can be leveraged for psychiatric research[9]. When linked to sample collections through biobanks and other efforts, EHR data provide a relatively untapped opportunity to enhance the power of genetic research. In an effort to rapidly increase available samples for genomewide studies of BD, we established the International Cohort Collection for Bipolar Disorder (ICCBD) through which we applied high-throughput phenotyping methods at sites in the United States (US), United Kingdom (UK), and Sweden[7]. At the US site (Partners Healthcare), we developed and applied EHR phenotyping algorithms to identify approximately 4500 cases and 5000 controls for whom DNA was obtained from discarded blood samples. We have undertaken systematic efforts to evaluate the validity of our EHR-based phenotyping algorithms
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