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Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records

Artikel i vetenskaplig tidskrift
Författare C. Y. Chen
P. H. Lee
V. M. Castro
J. Minnier
A. W. Charney
E. A. Stahl
D. M. Ruderfer
S. N. Murphy
V. Gainer
T. Cai
I. Jones
C. N. Pato
M. T. Pato
Mikael Landén
P. Sklar
R. H. Perlis
J. W. Smoller
Publicerad i Translational Psychiatry
Volym 8
Nummer/häfte 1
Sidor 1-8
ISSN 2158-3188
Publiceringsår 2018
Publicerad vid Institutionen för neurovetenskap och fysiologi, sektionen för psykiatri och neurokemi
Sidor 1-8
Språk en
Länkar doi.org/10.1038/s41398-018-0133-7
Ämnesord Article, automation, bipolar disorder, case control study, controlled study, electronic health record, genetic algorithm, genetic correlation, genetic screening, genotype, health care system, heritability, human, major clinical study, phenotype, scoring system, single nucleotide polymorphism, validation process
Ämneskategorier Psykiatri

Sammanfattning

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 (h 2 g) and genetic correlation (r g) 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 h 2 g 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 h 2 g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h 2 g) was 0.12 (p = 0.004). These h 2 g were lower or similar to the h 2 g observed by the ICCBD + PGCBD (0.23, p = 3.17E-80, total N = 33,181). However, the r g 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 r g 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.

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