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Transl Psychiatry. 2018 Apr 18;8(1):86. doi: 10.1038/s41398-018-0133-7.

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

Chen CY1,2,3,4,5, Lee PH1,3,4,5, Castro VM1,6,7, Minnier J8, Charney AW9,10,11, Stahl EA9,10, Ruderfer DM12, Murphy SN7,13,14, Gainer V7, Cai T15, Jones I16, Pato CN17, Pato MT17, Landén M18,19, Sklar P9,10,11, Perlis RH1,3,6, Smoller JW20,21,22.

Author information

1
Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge St., Boston, MA, 02114, USA.
2
Analytic and Translational Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, USA.
3
Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, USA.
4
Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
5
Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA, 02142, USA.
6
Center for Experimental Drugs and Diagnostics, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
7
Partners Research Information Systems and Computing, Partners HealthCare System, One Constitution Center, Charlestown, MA, 02129, USA.
8
Oregon Health & Sciences University, 3181 SW Sam Jackson Park Rd, Portland, OR, 97239, USA.
9
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
10
Department of Genetics and Genomic Sciences, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
11
Friedman Brain Institute, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
12
Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37212, USA.
13
Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
14
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.
15
Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.
16
National Centre for Mental Health, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, CF24 4HQ, UK.
17
SUNY Downstate Medical Center, Brooklyn, NY, 11203, USA.
18
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
19
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
20
Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge St., Boston, MA, 02114, USA. jsmoller@mgh.harvard.edu.
21
Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, USA. jsmoller@mgh.harvard.edu.
22
Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA, 02142, USA. jsmoller@mgh.harvard.edu.

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.

PMID:
29666432
PMCID:
PMC5904248
DOI:
10.1038/s41398-018-0133-7
[Indexed for MEDLINE]
Free PMC Article

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