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Pisanu, C.; Squassina, A. RNA Biomarkers and Bipolar Disorder. Encyclopedia. Available online: https://encyclopedia.pub/entry/46631 (accessed on 27 July 2024).
Pisanu C, Squassina A. RNA Biomarkers and Bipolar Disorder. Encyclopedia. Available at: https://encyclopedia.pub/entry/46631. Accessed July 27, 2024.
Pisanu, Claudia, Alessio Squassina. "RNA Biomarkers and Bipolar Disorder" Encyclopedia, https://encyclopedia.pub/entry/46631 (accessed July 27, 2024).
Pisanu, C., & Squassina, A. (2023, July 11). RNA Biomarkers and Bipolar Disorder. In Encyclopedia. https://encyclopedia.pub/entry/46631
Pisanu, Claudia and Alessio Squassina. "RNA Biomarkers and Bipolar Disorder." Encyclopedia. Web. 11 July, 2023.
RNA Biomarkers and Bipolar Disorder
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This entry is adapted from the peer-reviewed paper 10.3390/ijms241210067

Bipolar disorder (BD) is a severe chronic disorder that represents one of the main causes of disability among young people. To date, no reliable biomarkers are available to inform the diagnosis of BD or clinical response to pharmacological treatment. Studies focused on coding and noncoding transcripts may provide information complementary to genome-wide association studies, allowing to correlate the dynamic evolution of different types of RNAs based on specific cell types and developmental stage with disease development or clinical course. 

bipolar disorder biomarker transcript

1. Introduction

Bipolar disorder (BD) is a severe chronic psychiatric disorder characterized by episodes of mania or hypomania, alternating with depression. Because of its early onset, prevalence of more than 1% of the global population, high rate of psychiatric and medical comorbidities and increased premature mortality, BD represents one of the main causes of disability among young people [1]. Both genetic and environmental factors are known to contribute to the onset of BD, and heritability of this disorder has been estimated at 60–85%. Genome-wide association studies (GWAS) have successfully identified a number of genetic loci implicated in this disorder [2][3]. However, the causes of BD, as well as the biological networks involved in this disorder, are still largely unknown. In addition, an accurate and timely diagnosis is difficult, as no biomarker is available and the clinical presentation of BD is often a depressive episode similar to unipolar depression [1].
Pharmacological treatment represents the mainstay in the long-term management of BD. Among mood stabilizers, lithium represents a first-line option because of its effectiveness in the acute phase of the disorder, in the prevention of recurrences and in the reduction of suicide risk. However, clinical response to lithium presents a high interindividual variability, with approximately 70% of patients showing partial or nonresponse [4]. Lithium response is heritable, and initiatives such as the International Consortium on Lithium Genetics (ConLiGen) are contributing to knowledge on the molecular determinants underlying this trait [5]. The first GWAS conducted by ConLiGen suggested the involvement of long noncoding RNAs (lncRNAs) in lithium response [5], and subsequent secondary analyses pointed to different markers and pathways potentially playing a role in this trait [6][7][8]. However, the molecular players underlying lithium’s complex mechanism of action are still elusive, and no reliable biomarkers are available to identify patients who might respond to this drug [9]. Even less information is available regarding molecular markers involved in the response to other mood stabilizers, such as the anticonvulsants carbamazepine, valproate and lamotrigine. The identification of reliable biomarkers that respond to mood stabilizers is a priority, since demonstration of proven clinical efficacy is one of the most important factors required for the successful implementation of genomic medicine in the health-care system, together with cost-effectiveness, appropriate knowledge and education and appropriate policy and legislation [10].
Transcriptomic studies provide information complementary to GWAS, allowing for the study of the dynamic evolution of different types of RNA markers based on specific tissues or biofluids, cell types and developmental stage. Among the most investigated RNA biomarkers in BD and in response to mood stabilizers, there are messenger RNA (mRNA) and different types of noncoding RNAs, such as microRNAs (miRNAs), circular RNAs (circRNAs) and lncRNAs.
miRNAs are single-stranded RNA molecules, approximately 20–22 nucleotides in length, that play a substantial role in the regulation of gene expression. Their main mechanism of action consists in promoting gene silencing by guiding argonaute proteins to the 3’ region of a target mRNA, thus allowing for the recruitment of factors that promote translational repression or mRNA decay [11]. Because the majority of protein-coding genes have been shown to present at least one miRNA-binding site, miRNAs are involved in a wide range of cellular functions and biological processes, and their dysregulation has been linked to several human diseases, including psychiatric disorders [11][12]
CircRNAs are single-stranded RNA molecules produced by pre-mRNAs through a process called back-splicing [13]. Unlike linear RNAs, circRNAs are characterized by a covalently closed-loop structure. This conformation makes circRNAs resistant to degradation by exonucleases and more stable compared with other transcripts. CircRNAs were originally considered a result of “splicing noise” with no relevant biological significance. However, in the last few years it has been shown that circRNA molecules are conserved, tissue-specific and involved in relevant biological functions [14]. While their roles are still largely elusive, circRNAs have been suggested to be involved in the regulation of transcription, protein transport and protein–protein interactions. In particular, some circRNAs present miRNA-binding sites and may act as miRNA sponges, sequestering miRNAs and, thus, preventing their interaction with mRNA targets. The prevalence of circRNAs has been largely underestimated until recently because their identification presents technical challenges. In fact, microarrays do not allow the detection of circRNAs or to distinguish the expression of circRNAs from their linear host genes. In addition, since most early RNA sequencing studies focused on measuring levels of RNA with polyA tails, they involved a polyA RNA enrichment step that led to the depletion of circRNAs. Modern RNA sequencing and bioinformatic pipelines are able to identify these markers, which allowed for the realization that circRNAs are much more abundant then first hypothesized. Most circRNAs show specific expression patterns based on tissue, cell type and/or developmental stage and, intriguingly, circRNAs are enriched in brain tissues compared with other tissues [14]. Specifically, in the brain, a significantly greater number of genes, particularly synaptic genes, host circRNAs [14]. In addition, their stability and low turnover rate suggest they might accumulate in postmitotic cells, such as neurons [14]. The abundance of circRNAs in the brain, as well as the peculiar characteristics of these markers, contributes to the increased research interest on these transcripts as potential biomarkers of brain disorders.
LncRNAs are a family of noncoding RNA molecules longer than 200 nucleotides and characterized by substantial differences in terms of length, expression patterns and longevity. Several lncRNAs have been shown to be able to regulate the expression of nearby or distant genes. In addition, lncRNAs can modulate chromatin structure, response to DNA damage and different signaling pathways [15]. As in the case of circRNAs, the measurement of lncRNAs is associated with specific technical challenges because of their generally low abundance. However, while the number of studies exploring these markers is still limited compared with other transcripts, over the last few years there has been increased interest in lncRNAs as disease biomarkers and potential therapeutic targets due to their high specificity in tissue expression patterns, fast turnover and regulation of cellular networks [15].

2. RNA Biomarkers and Bipolar Disorder

The large majority of the studies presented in Table 1 were conducted in whole blood or peripheral blood mononuclear cells (PBMCs), with only a minority of studies exploring RNA levels in plasma/serum [16][17][18][19][20] or plasma-derived extracellular vesicles (EVs) [21][22]. Among EVs, exosomes are membrane vesicles released by different cells into the extracellular matrix that play a pivotal role in intercellular communication and signal transmission through the transfer of bioactive molecules to adjacent or distant recipient cells [23]. Exosomes carry a variety of molecules, including metabolites, lipids and nucleic acids, and are enriched in miRNAs. Intriguingly, neural exosomes can cross the blood–brain barrier and can be detected peripherally. Therefore, the change of peripheral exosomal content in patients with BD might, at least, partly reflect central changes, thus potentially allowing to identify brain-relevant biosignatures of disease and drug response in a noninvasive way. The few available studies that explored miRNA levels in EVs and exosomes reported promising results. Ceylan and colleagues measured genome-wide levels of miRNAs in plasma exosomes from 69 patients with BD (15 depressed, 27 manic and 27 euthymic) and 41 HCs. After multiple testing correction, three miRNAs showed lower levels (miR-484, miR-652-3p and miR-142-3p) and one miRNA higher level (miR-185-5p) in patients with BD compared with HCs [22]. The predicted targets of the four miRNAs were enriched for different pathways, including PI3K/Akt signaling, fatty acid biosynthesis/metabolism, extracellular matrix and adhesion pathways. No miRNA was significantly altered among the different states of BD [22]. Conversely, other studies suggested the potential utility of miRNAs as disease state markers. Namely, Camkurt and colleagues measured the levels of eight candidate miRNAs (selected based on previous evidence of their potential involvement in psychiatric disorders) in whole blood from 58 patients with BD (19 manic and 39 euthymic) and 51 HCs [24]. The levels of miR-07 were found to be significantly higher in patients with BD compared with HCs but also in patients in a manic episode compared with euthymic patients. Another study conducted by Banach and colleagues observed the downregulation of three miRNAs (miR-499, miR-798 and miR-1908) in patients with BD during a depression episode compared with a euthymic state [25].
Some studies identified significant differences in the RNA levels based on BD subtype. D’Addario and colleagues measured the mRNA levels of six candidate genes interacting with the brain-derived neurotrophic factor (BDNF) in PBMCs from 54 patients with BD type 1 (BD I), 45 with BD type 2 (BD II) and 42 controls. The authors reported lower levels of the prodynorphin (PDYN) gene in patients with BD II but not BD I compared with HCs. In addition, this study observed increased methylation at the PDYN promoter, as well as higher levels of genes involved in methylation, such as DNA methyltransferase 3 beta (DNMT3b) and methyl-CpG-binding protein 2 (MECP2) in patients with BD II compared with HCs. Other studies included a sample of patients with different psychiatric disorders, such as MDD [16][26] or SZ [27], aiming to distinguish among RNA markers specifically associated with BD or shared among different psychiatric disorders. Among these studies, Maffioletti and colleagues measured genome-wide miRNA levels in whole blood from 20 patients with BD, 20 with MDD and 20 HCs [26]. The study reported levels of five miRNAs to be increased specifically in patients with BD compared with HCs (hsa-miR-140-3p, hsa-miR-30d-5p, hsa-miR-330-5p, hsa-miR-378a-5p and hsa-miR-21-3p), while hsa-miR-330-3p and hsa-miR-345-5p showed higher levels in patients with either BD or MDD. However, one the miRNAs specifically associated with BD was found to be altered in MDD patients after treatment with antidepressants in a previous study conducted by the same authors [28]. Another study aimed at identifying the biosignatures of bipolar from unipolar depression measured genome-wide plasma miRNA levels in a discovery cohort of seven patients with BD, seven with MDD and six HCs [17]. The study reported higher levels of miR-19b-3p in patients with BD compared with patients with MDD, a result that was validated in a cohort of 27 patients with BD and 32 with MDD. In silico analyses suggested this miRNA to be involved in inflammatory dysregulation associated with experiencing early childhood trauma [17]. As shown in Table 1, a number of studies provided evidence of a good performance of the investigated RNAs in the discrimination of patients with BD from HCs, based, for example, on the area under the curve (AUC) [29]. However, it must be considered that several of these studies did not include a replication cohort.
Table 1. Studies evaluating the association between RNA markers and bipolar disorder in biofluids or peripheral cells.
Ref. Sample RNA Source RNA Type Method Targets Main Findings
[25] 15 patients with BD and 17 with MDD during a depression episode and at remission Whole blood miRNA qPCR miR-499, miR-798 and miR-1908 Lower levels of all miRNAs in the depressive state compared with remission exclusively in patients with BD
[24] 58 patients with BD I (19 manic and 39 euthymic) and 51 HCs Whole blood miRNA qPCR miR-26b-5p, miR-9-5p, miR-29a-3p, miR-106a-5p, miR-106b-5p, miR-107, miR-125a-3p and miR-125b-5p Levels of miR-29a-3p, miR-106b-5p, miR-107 and miR-125a-3p were significantly higher in patients with BD compared with HCs. Levels of miR-106a-5p and miR-107 were significantly higher in manic patients compared to euthymic patients
[22] 69 patients with BD (15 depressed, 27 manic and 27 euthymic) and 41 HCs Plasma exosomes miRNA qPCR Genome wide Levels of miR-484, miR-652-3p and miR-142-3p were lower, while levels of miR-185-5p were higher in patients with BD compared with HCs. No alterations among different states of BD
[17] Discovery cohort: 7 patients with BD, 7 with MDD and 6 HCs; validation cohort: 27 patients with BD and 32 with MDD Plasma miRNA Microarray, qPCR Genome wide Higher levels of miR-19b-3p in patients with BD compared with patients with MDD
[30] 54 patients with BD I, 45 with BD II and 42 HCs PBMC mRNA qPCR COMT, DNMT, GAD67, MECP2, PDYN and SERT Lower PDYN expression in patients with BD II but not BD I compared with HCs. Higher levels of genes involved in methylation, such as DNMT3b and MECP2, in patients with BD II compared with HCs
[31] 169 patients with BD and 211 HCs Whole blood mRNA qPCR NOTCH4 Higher NOTCH4 levels in patients with BD compared with HC
[32] 50 patients with BD I and 50 HCs Whole blood mRNA, lncRNA qPCR SNHG6, MALAT1, Linc00511, Linc00346, VDR and CYP27B1 Levels of VDR, SNHG6, CYP27B1, MALAT1 and Linc00346 were higher in patients compared with HCs
[21] 20 patients with BD I and 21 HCs Plasma EV miRNA Microarray Genome wide 33 nominally significant microRNAs altered in patients with BD
[33] Discovery cohort: 4 patients with BD and 4 HCs; validation cohort: 16 patients with BD and 16 controls Whole blood mRNA, circRNA NGS and qPCR Genome wide 50 circRNAs and 244 mRNAs showed nominally significant higher levels, while 44 circRNAs and 294 mRNAs lower levels in patients with BD compared with HCs
[16] Discovery cohort: 18 patients with BD, 44 with MDD, and 31 HCs; validation cohort: 26 patients with BD, 84 with MDD and 74 HCs Plasma mRNA, miRNA NGS and qPCR Genome wide Higher levels of hsa-let-7e-5p and hsa-miR-125a-5p in patients with either BD or MDD compared with HCs
[34] Discovery cohort: 4 patients with BD and 4 HCs; validation cohort: 130 patients with BD and 116 HCs Whole blood mRNA, lncRNA NGS and qPCR Genome wide Higher levels of the NR_028138.1 lncRNA in patients with BD compared with HCs
[18] Discovery cohort: 3 patients with BD II and 3 HCs; validation cohort: 99 patients with BD II and 115 HCs Serum miRNA NGS and qPCR Genome wide Levels of miR-7-5p, miR-23b-3p, miR-142-3p, miR-221-5p and miR-370-3p were higher in patients with BD II compared with HCs
[29] 51 patients with BD and 116 HCs White blood mRNA qPCR COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4 and SRR A logistic ridge regression model including age, gender and mRNA expression levels of the 7 NMDAR genes showed an AUC of 0.92 in differentiating patients with BD from HCs
[26] 20 patients with BD, 20 with MDD and 20 HCs Whole blood miRNA Microarray and qPCR Genome wide 5 miRNAs showed higher levels specifically in patients with BD compared with HCs (hsa-miR-140-3p, hsa-miR-30d-5p, hsa-miR-330-5p, hsa-miR-378a-5p and hsa-miR-21-3p), while hsa-miR-330-3p and hsa-miR-345-5p showed higher levels in both BD and MDD
[27] 19 patients with BD, 20 with SZ and 20 HCs PBMC circRNA NGS and qPCR Genome wide Levels of 30 circRNAs were specifically altered in patients with BD compared with HCs
[35] 50 patients with BD and 50 HCs PBMC lncRNA qPCR 3 lncRNAs related to oxidative stress (lincRNA-p21, lincRNA-ROR and lincRNA-PINT) Levels of all lncRNAs were lower in patients with BD, and specifically in male patients with BD, compared with HCs
[36] 50 patients with BD and 50 HCs PBMC lncRNA qPCR 5 lncRNAs related to oxidative stress (H19, LUCAT1, RMST, MEG3 and MT1DP) Levels of LUCAT1, RMST and MEG3 were lower in patients with BD, specifically in male patients with BD, compared with HCs
[37] 63 patients with BD, 42 with MDD and 55 HCs PBMC miRNA qPCR miR-499-5p Higher levels of miR-499-5p in patients with BD (regardless of disease phase) compared with HCs
[19] Serum: 41 patients with BD, 43 with MDD and 93 HCs; fibroblasts: 12 patients with BD, 23 with MDD and 15 HCs Serum and fibroblasts mRNA qPCR IGFBP2 Lower IGFBP2 levels exclusively in patients with BD compared with HCs
[38] 37 rapid cycling patients with BD in different affective states and 40 HCs PBMC mRNA qPCR 19 candidate genes Lower levels of POLG and OGG1 in patients with BD compared with HCs; higher levels of NDUFV2 in patients in a depressed state compared with those in an euthymic state. A gene expression score including all genes showed an AUC of 0.73 in separating patients from HCs
[39] 50 patients with BD and 50 HCs PBMC lncRNA qPCR DISC1 and DISC2 Lower levels of DISC1 and higher levels of DISC2 in patients with BD compared with HCs (AUC: 0.76 and 0.68, respectively)
[40] 50 patients with BD and 50 HCs PBMC lncRNA qPCR 6 apoptosis-related lncRNAs (CCAT2, TUG1, PANDA, NEAT1, FAS-AS1 and OIP5-AS1) Levels of CCAT2, TUG1 and PANDA were higher and levels of OIP5-AS1 were lower in patients with BD patients compared with HCs. CCAT2 and TUG1 expression levels were only different in male subgroups
[20] 11 drug-free manic psychotic patients with BD and 9 HCs Plasma miRNA Nanostring and qPCR Genome wide Higher levels of hsa-miR-25-3p and hsa-miR-144-3p, and lower levels of hsa-miR-6721-5p in patients with BD compared with HCs
[41] 66 patients with BD and 66 HCs Plasma miRNA qPCR 15 miRNAs A model including levels of miR-15b-5p, miR-155-5p, miR-134-5p and miR-652-3p showing a 83.3% sensitivity and 78.8% specificity
[42] 56 patients with BD I and 52 HCs Whole blood miRNA qPCR hsa-miR-145-5p, hsa-miR-376a-3p, hsa-miR-3680-5p, hsa-miR-4253-5p, hsa-miR-4482-3p and hsa-miR-4725 Higher levels of hsa-miR-376a-3p, hsa-miR-3680-5p, hsa-miR-4253-5p, hsa-miR-4482-3p and lower levels of hsa-miR-145-5p in patients with BD compared with HCs
[43] 50 patients with BD and 50 HCs Whole blood lncRNA qPCR Five NF-κB-associated lncRNAs (ANRIL, CEBPA-DT, H19, NKILA and HNF1A-AS1) Lower levels of ANRIL, CEBPA-DT and HNF1-AS1 and higher levels of NKILA in patients with BD compared with HC. HFN1A-AS1 showed the best diagnostic parameters (AUC: 0.86).
ANRIL, CDKN2B antisense RNA 1; AUC, area under the curve; BD I, bipolar disorder type 1; BD II, bipolar disorder type II; CCAT2, colon-cancer-associated transcript 2; CEBPA-DT, CEBPA-divergent transcript; COMT, catechol-O-methyltransferase; CYP27B1, cytochrome P450 family 27 subfamily B member 1; DISC1, DISC1 scaffold protein; DISC2, DISC2 scaffold protein; DNMTs, DNA methyltransferases; GAD67, glutamate decarboxylase; EVs, extracellular vesicles; FAT-AS1, FAS antisense RNA 1; GCAT, glycine C-acetyltransferase; H19, H19-imprinted maternally expressed transcript; HNF1A-AS1, HNF1A antisense RNA 1; IGFBP2, insulin-like growth factor-binding protein 2; LUCAT1, lung cancer-associated transcript 1; MALAT1, metastasis-associated lung adenocarcinoma transcript 1; MDD, major depressive disorder; MECP2, methyl CpG-binding protein 2; MEG3, maternally expressed 3; MT1DP, metallothionein 1D, pseudogene; NDUFV2, NADH:Ubiquinone oxidoreductase core subunit V2; NGS, next-generation sequencing; NEAT1, nuclear paraspeckle assembly transcript 1; NMDAR, N-methyl-D-aspartate receptor; NKILA, NF-KappaB interacting LncRNA; NOTCH4, notch receptor 4; NRG1, neuregulin 1; OGG1, 8-oxoguanine DNA glycosylase; OIP5-AS1, OIP5 antisense RNA 1; PANDA, ATPase copper transporting beta; PBMC, peripheral blood mononuclear cells; PDYN, prodynorphin; POLG, DNA Polymerase Gamma, Catalytic Subunit; PSAT1, phosphoserine aminotransferase 1; qPCR, quantitative PCR; RMST, rhabdomyosarcoma 2-associated transcript; SERT, serotonin transporter; SHMT2, serine hydroxymethyltransferase 2; SNHG6, small nucleolar RNA host gene 6; SLC1A4, solute carrier family 1 member 4; SRR, serine racemase; TUG1, taurine upregulated 1; VDR, vitamin D receptor.

2.2. Levels of RNA Markers in Cellular Models Derived from Patients with BD

A number of studies investigating RNA biosignatures of BD (or of response to mood stabilizers) were conducted in lymphoblastoid cell lines (LCLs). While some studies did not identify significant differences in RNA markers between LCLs derived from patients with BD and HCs [44], a recent study including 37 euthymic patients with BD I and 20 HCs suggested a potential role for circadian genes, showing lower levels of aryl hydrocarbon receptor nuclear translocator-like protein 1 (ARNTL) and higher levels of circadian-associated repressor of transcription (CIART) and basic helix–loop–helix family member E41 (BHLHE41) in patients with BD compared with HCs [45]. Interestingly, genes related to the regulation of circadian rhythms was also implicated in a study conducted by the same authors in response to lithium in patients with cluster headache [46]. Other studies aimed at identifying biomarkers of specific endophenotypes of BD such as suicide risk. Squassina and colleagues investigated differences at baselines, as well as after in vitro lithium treatment in LCLs, from 9 patients with BD who died by suicide, 17 at low risk of suicide, 17 at high risk of suicide and 21 HCs [47]. In this study, in vitro treatment with lithium chloride (LiCl) 1 mM for 1 week, increased expression of the spermidine/spermine N1-acetyltransferase 1 (SAT1) gene in LCLs from HCs or from patients with BD at low or high risk of suicide but not in those from patients with BD who died by suicide. The enzyme encoded by this gene is a key regulator of cellular content of polyamines, a system of ubiquitous molecules involved in cell growth, differentiation and stress response, previously suggested to be altered in suicide [48]. In a subsequent study, the same group conducted a miRnome analysis showing higher levels of miR-4286 and lower levels of miR-186-5p in LCLs from patients who died by suicide compared with patients at low risk of suicide and HCs. Based on an in silico analysis, this study also suggested that a higher expression of miR-4286 might be responsible for a reduction in the expression of several genes involved in glucose metabolism [49]. The use of LCLs as a cellular model provides a number of advantages that contributes to their widespread use, such as the possibility to minimize variability by growing the cells under strictly similar conditions, as well as testing the effect of in vitro treatment. However, LCLs also present some criticisms that are still under debate, mainly concerning the effects of immortalization on the host genome. While LCLs are still used, over the last few years, cellular models for the identification of RNA biosignatures of BD shifted to NPCs, differentiated neurons or brain organoids derived from iPSCs. In some of these studies, cellular models were used to explore mechanistic aspects and corroborate hypotheses developed based on post mortem brain samples. Among these, Bavamian and colleagues conducted a candidate miRNA–mRNA study, investigating the levels of miR-34a and predicted targets [50]. The authors showed increased levels of this miRNA in post mortem cerebellum samples from 29 patients with BD and 34 HCs and subsequently explored the effect of the enhancement of miR-34a expression in iPSC-derived NPCs from 1 patient with BD and 1 HC. Increased expression of this miRNA was associated with impaired neuronal differentiation, expression of synaptic proteins and neuronal morphology [50]. A transcriptomic study with a relatively large sample size was conducted by Kathuria and colleagues and included brain organoids from eight patients with BD I and eights HCs [51]. This study reported the downregulation of pathways involved in cell adhesion, neurodevelopment and synaptic biology and the upregulation of genes involved in immune signaling in organoids from patients with BD compared with HCs. A network analysis conducted on differentially expressed genes showed as the central hub the neurocan (NCAN) gene, which was significantly downregulated in brain organoids from patients with BD. NCAN encodes a proteoglycan component of the neuronal extracellular matrix, which is involved in remodeling of neuronal tissue, neural adhesion and migration. Interestingly, this locus has been previously found to be implicated in BD by GWAS [2].

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Subjects: Genetics & Heredity
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Claudia Pisanu
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