Erratum: Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder

Chen, D. T.; Jiang, Xunping; Akula, Nirmala; Shugart, Yin Yao; Wendland, Jens R.; Steele, C. J.M.; Kassem, Layla; Park, J. H.; Chatterjee, Nilanjan; Jamain, Stéphane; Cheng, Anqi; Leboyer, M.; Muglia, Pierandrea; Schulze, Thomas G.; Cichon, Sven; Nöthen, M. M.; Rietschel, M.; McMahon, Francis J.

doi:10.1038/mp.2012.45

Cited by 3 publications

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“…A study conducted by Jiang et al in 2019 showed that VPA retrieves the expression of TRANK1 in iPSC-derived neural cells. Prior studies by the same group reported that VPA increased the expression of TRANK1 from shallow baseline levels in immortalized, non-neural cell lines [160]. In this study, they derived neurons and astrocytes from iPSCs generated from fibroblasts of two BD patients, one spinomuscular atrophy I patient, and eight healthy controls.…”

Section: Vpa Treatment In Cellular Models Of Bdmentioning

confidence: 91%

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Mood Stabilizers in Psychiatric Disorders and Mechanisms Learnt from In Vitro Model Systems

Nayak

Rosh

Kustanovich

et al. 2021

IJMS

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Bipolar disorder (BD) and schizophrenia are psychiatric disorders that manifest unusual mental, behavioral, and emotional patterns leading to suffering and disability. These disorders span heterogeneous conditions with variable heredity and elusive pathophysiology. Mood stabilizers such as lithium and valproic acid (VPA) have been shown to be effective in BD and, to some extent in schizophrenia. This review highlights the efficacy of lithium and VPA treatment in several randomized, controlled human trials conducted in patients suffering from BD and schizophrenia. Furthermore, we also address the importance of using induced pluripotent stem cells (iPSCs) as a disease model for mirroring the disease’s phenotypes. In BD, iPSC-derived neurons enabled finding an endophenotype of hyperexcitability with increased hyperpolarizations. Some of the disease phenotypes were significantly alleviated by lithium treatment. VPA studies have also reported rescuing the Wnt/β-catenin pathway and reducing activity. Another significant contribution of iPSC models can be attributed to studying the molecular etiologies of schizophrenia such as abnormal differentiation of patient-derived neural stem cells, decreased neuronal connectivity and neurite number, impaired synaptic function, and altered gene expression patterns. Overall, despite significant advances using these novel models, much more work remains to fully understand the mechanisms by which these disorders affect the patients’ brains.

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Section: Vpa Treatment In Cellular Models Of Bdmentioning

confidence: 91%

“…Previous GWAS studies have already identified the TRANK1 gene loci to be significantly associated with BD [85,160,161]. A study conducted by Jiang et al in 2019 showed that VPA retrieves the expression of TRANK1 in iPSC-derived neural cells.…”

Section: Vpa Treatment In Cellular Models Of Bdmentioning

confidence: 99%

Mood Stabilizers in Psychiatric Disorders and Mechanisms Learnt from In Vitro Model Systems

Nayak

Rosh

Kustanovich

et al. 2021

IJMS

View full text Add to dashboard Cite

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

et al. 2018

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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.

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

Chen

Lee

Castro

et al. 2017

Preprint

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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. 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 3,330 BD cases and 3,952 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 3 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, 1,968 coded-strict, 2,581 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=3.69x10-5), coded-strict (1.00, p=2.40x10-4), and coded-broad (0.74, p=8.11x10-7). The rg between EHR-based BDs 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.

show abstract

Erratum: Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder

Cited by 3 publications

References 0 publications

Mood Stabilizers in Psychiatric Disorders and Mechanisms Learnt from In Vitro Model Systems

Mood Stabilizers in Psychiatric Disorders and Mechanisms Learnt from In Vitro Model Systems

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

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

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