A, The association of PGS for SCZ and lithium treatment response defined as a categorical and continuous scale, at different SCZ genome-wide association study (GWAS) P value thresholds. The x-axis refers to the percentage of variance in treatment response to lithium accounted for by the PGSs of SCZ at a particular P value threshold. On the y-axis, plotted from top to bottom, are the GWAS P value thresholds used to group single-nucleotide polymorphisms for PGSs. On the right of each bar are the P values of the association between the PGS for SCZ and lithium treatment response. B, Trends in the odds ratios (ORs) for favorable treatment response to lithium for patients with BPAD in the low SCZ deciles (first to ninth) compared with patients in the highest SCZ PGS decile (10th), estimated at the most significant P value thresholds (P < 5 x 10−2) (n = 2586). The open circles on the line plot indicate that the association is not statistically significant at that particular decile. ALDA indicates Retrospective Criteria of Long-term Treatment Response in Research Subjects With Bipolar Disorder.
eTable 1. Combined list of eGenes and hGenes used as an input in the Ingenuity Pathway Analysis (IPA)
eTable 2. The top canonical signaling pathways enriched for genes identified in the cross-trait meta-analyses
eTable 3. Top IPA protein networks, molecules in network and top diseases functionally related to the network
eFigure 1. Scatter plot that assesses the linearity between the PGS for SCZ and total score on the ALDA subscale A
eFigure 2. Manhattan plot showing the result of cross-trait meta-analysis of GWASs on SCZ and the GWASs on lithium treatment response ALDA, highlighting the loci that showed genome-wide significance (orange), and the nearest genes (top)
eFigure 3. Top networks of molecules in IPA, in which TNFα, IL-4, and IFNγ represent the main functional nodes mediating the genetic interaction between lithium response and SCZ
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International Consortium on Lithium Genetics (ConLi+Gen). Association of Polygenic Score for Schizophrenia and HLA Antigen and Inflammation Genes With Response to Lithium in Bipolar Affective Disorder: A Genome-Wide Association Study. JAMA Psychiatry. 2018;75(1):65–74. doi:10.1001/jamapsychiatry.2017.3433
Is a polygenic score for schizophrenia associated with response to lithium in patients with bipolar affective disorder, and, if so, what are the molecular drivers of this association?
This genome-wide association study found an inverse association between genetic loading for schizophrenia risk variants and response to lithium in patients with bipolar affective disorder. Genetic variants in the HLA antigen region and the antigen presentation pathway point to the molecular underpinnings of schizophrenia and lithium treatment response.
For patients with bipolar affective disorder, assessment of a polygenic load for schizophrenia risk variants, in conjunction with clinical data, may assist in determining whether they would respond to lithium treatment.
Lithium is a first-line mood stabilizer for the treatment of bipolar affective disorder (BPAD). However, the efficacy of lithium varies widely, with a nonresponse rate of up to 30%. Biological response markers are lacking. Genetic factors are thought to mediate treatment response to lithium, and there is a previously reported genetic overlap between BPAD and schizophrenia (SCZ).
To test whether a polygenic score for SCZ is associated with treatment response to lithium in BPAD and to explore the potential molecular underpinnings of this association.
Design, Setting, and Participants
A total of 2586 patients with BPAD who had undergone lithium treatment were genotyped and assessed for long-term response to treatment between 2008 and 2013. Weighted SCZ polygenic scores were computed at different P value thresholds using summary statistics from an international multicenter genome-wide association study (GWAS) of 36 989 individuals with SCZ and genotype data from patients with BPAD from the Consortium on Lithium Genetics. For functional exploration, a cross-trait meta-GWAS and pathway analysis was performed, combining GWAS summary statistics on SCZ and response to treatment with lithium. Data analysis was performed from September 2016 to February 2017.
Main Outcomes and Measures
Treatment response to lithium was defined on both the categorical and continuous scales using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. The effect measures include odds ratios and the proportion of variance explained.
Of the 2586 patients in the study (mean [SD] age, 47.2 [13.9] years), 1478 were women and 1108 were men. The polygenic score for SCZ was inversely associated with lithium treatment response in the categorical outcome, at a threshold P < 5 × 10−2. Patients with BPAD who had a low polygenic load for SCZ responded better to lithium, with odds ratios for lithium response ranging from 3.46 (95% CI, 1.42-8.41) at the first decile to 2.03 (95% CI, 0.86-4.81) at the ninth decile, compared with the patients in the 10th decile of SCZ risk. In the cross-trait meta-GWAS, 15 genetic loci that may have overlapping effects on lithium treatment response and susceptibility to SCZ were identified. Functional pathway and network analysis of these loci point to the HLA antigen complex and inflammatory cytokines.
Conclusions and Relevance
This study provides evidence for a negative association between high genetic loading for SCZ and poor response to lithium in patients with BPAD. These results suggest the potential for translational research aimed at personalized prescribing of lithium.
Bipolar affective disorder (BPAD) is a severe and often disabling psychiatric condition characterized by recurrent dysregulation of mood, with episodes of mania and depression. With an early onset and an estimated lifetime prevalence of 1%1 to 4.4%,2 BPAD is associated with high levels of personal impairment and high societal costs, accounting for 9.9 million years of life lived with disability worldwide,3 increased all-cause mortality, and risk of suicide.4 The possible causes of BPAD are complex, and both genetic and environmental factors contribute to its pathogenesis.5 The estimated heritability of BPAD ranges from 60% to 85%,6 and candidate gene7 and genome-wide association studies (GWASs)8-12 have successfully identified genetic loci implicated in the illness.
Lithium’s mood-stabilizing properties were discovered in 1949.13 It has retained a status as the criterion standard mood stabilizer,14,15 possessing unique protective effects against both manic and depressive episodes,16 as well as for suicide prevention.17 Consequently, lithium is recommended as first-line maintenance treatment for BPAD by several clinical practice guidelines.18-21 However, there is significant interindividual variation between those who do and those who do not respond to treatment with lithium. About 30% of patients are only partially responsive, and more than one-fourth show no clinical response.22 Although clinical studies report a combination of demographic and clinical characteristics as potential factors determining response to lithium treatment,23 genetic factors also appear to be highly involved.22,24-26 So far, 3 GWASs have successfully identified single-nucleotide polymorphisms (SNPs) associated with treatment response to lithium in BPAD pointing to different genetic loci.22,26,27
To improve our understanding of the molecular mechanisms underlying the therapeutic effects of lithium, alternative genomic approaches that can complement GWASs deserve consideration. One such approach is polygenic analysis, which quantifies the combined effects of genetic variants across the whole genome on a given clinical outcome, computed as a weighted summation of effect sizes of multiple independent polymorphisms. An accurate and successful polygenic model may assist early screening for disease risk, clinical diagnosis, and the determination of treatment response and prognosis. In the present study, we aimed to investigate whether patients with BPAD who had a high genetic susceptibility for schizophrenia (SCZ), expressed by their SCZ polygenic score (PGS), would respond better or more poorly to lithium compared with patients with BPAD who had a low PGS for SCZ. In addition, we set out to explore the genetic and molecular underpinnings of any identified association between SCZ and treatment response to lithium.
Several previous observations motivated this approach. First, there is increasing evidence for a substantial genetic overlap between BPAD and SCZ. The Psychiatric Genomics Consortium (PGC; http://www.med.unc.edu/pgc/) estimated a shared genetic variation between BPAD and SCZ of approximately 68%, which is the highest among all pairs of psychiatric diagnoses,28 and several shared risk genes and shared biological pathways associated with both disorders have been identified.29-31 Second, despite these genetic and molecular commonalities, lithium is not an effective medication for people with SCZ,32 and increased SCZ trait loading in those with BPAD might be expected to be associated with poor treatment response to lithium. An earlier family study found an association between family history of SCZ and poor response to lithium.33 Third, during acute episodes of illness, BPAD and SCZ are often difficult to distinguish clinically because of overlapping psychotic symptoms such as hallucinations, delusions, and disorganization, as well as some common behavioral disturbances such as irritability or anger.34 Aiming to determine response to lithium, which could potentially confer advantages for patients and their treating physicians,35 we sought to evaluate the aggregated outcome of genome-wide SNPs for SCZ on treatment response to lithium in patients with BPAD using a PGS approach that was based on the results of the largest SCZ GWAS to date.36 Furthermore, to explore potential genetic and molecular drivers of any detected association, we carried out a cross-trait GWAS meta-analysis, combining the summary statistics from the largest available GWAS for both SCZ36 and response to lithium.22
In the present study, conducted from 2008 to 2013, we first tested whether a PGS for SCZ is associated with treatment response to lithium in patients with BPAD; 2043 patients (79.0%) had BPAD type I and 543 (21.0%) had BPAD type II.22 In a second step, we applied a cross-trait GWAS meta-analysis approach to identify individual genetic variants shared between SCZ and treatment response to lithium. In a third step, we characterized the genetic variants identified in the second step and explored the shared biological pathways underlying genetic susceptibility to SCZ and treatment response in BPAD. We built the PGS using the discovery GWAS outcome estimates (logs of odds ratio [OR]) of 36 989 patients with SCZ36 and the targeted genetic data of 2586 patients from the International Consortium on Lithium Genetics (ConLi+Gen).22,37 The cross-trait meta-analysis and pathway analysis were based on GWAS summary statistics from GWASs of SCZ36 and treatment response to lithium from ConLi+Gen.22 Overlapping SNPs that met genome-wide significance in the meta-GWAS were subsequently analyzed for biological context using the Ingenuity Pathway Analysis platform (IPA; QIAGEN [http://www.ingenuity.com]). This study used consortium data through an international collaboration. The University of Heidelberg Ethics Committee provided central ethics approval for the consortium. Written consent was obtained from each patient according to the study protocols of the participating cohorts.
Lithium treatment outcome was assessed using the Retrospective Criteria of Long-term Treatment Response in Research Subjects With Bipolar Disorder scale, also known as the ALDA scale.38,39 The ALDA scale quantifies symptom improvement over the course of treatment (A score; range, 0-10), which is then weighted against 5 criteria (B score) that assess confounding factors, each scored 0, 1, or 2. The total score is calculated by subtracting the total B score from the A score, with negative scores set to zero.22 We employed a categorical and a continuous outcome for response to lithium. The categorical (ie, good vs poor) response to lithium was defined based on the total score as a cutoff score of 7, in which patients with a total score of 7 or higher were categorized as responders. The ALDA score on subscale A was used as a continuous outcome after excluding individuals with a total B score greater than 4 or who had missing data on the totals of ALDA subscale A or B.22
Quality-controlled SNPs were clumped for linkage disequilibrium based on GWAS association P value–informed clumping using r2 = 0.1 within a 250-kilobase (kb) window to create an SNP set in linkage equilibrium using PLINK software40 run on Linux (plink–clump-p1 1–clump-p2 1–clump-r2 0.1–clump-kb 250). Then, the SNPs up to 10 P value thresholds (<1 × 10−4, <1 × 10−3, <.01, <.05, <.1, <.20, <.30, <.40, <.50, and <1.0) were selected to compute the SCZ PGSs in the ConLi+Gen sample. A genome-wide weighted SCZ PGS for each participant was calculated at each P value threshold as the sum of independent SNPs genotype dosage (from 0 to 2) of the reference allele in the ConLi+Gen genotype data, multiplied by effect sizes on the SCZ GWAS for the reference allele, estimated as log (OR) divided by the total number of SNPs in each threshold.
Statistical analysis was performed from September 2016 to February 2017. We applied PGS association analyses, cross-trait meta-GWAS, and IPA of the cross-trait findings.
Once the PGSs were constructed, the association of the PGSs at each threshold P value with treatment response to lithium was evaluated using regression models. While a binary logistic regression was implemented for the categorical outcome (response vs nonresponse), a linear regression was applied to treatment response to lithium on the continuous scale. Using the PGS at the most significant threshold (P < 5 × 10−2), we divided the study samples into 10 deciles, ranging from the lowest polygenic load (first decile) to the highest polygenic load (10th decile). We then compared patients with BPAD with a lower polygenic load (first to ninth deciles) for SCZ with patients with the highest polygenic load (10th decile) to quantify the association of SCZ polygenic load with lithium treatment outcomes.
To control for confounding factors, the PGS association analyses were adjusted for the covariates of age, sex, genotyping platforms, and 7 principal components. The analyses were performed using R (R Foundation for Statistical Computing) and PLINK, version 1.9, for Linux.40 The accuracy of determining factors and the percentage of variance in lithium response accounted for by the PGS at each P value threshold were estimated as the variance explained by the full model including each PGS and covariates minus the variance explained by the model including only covariates. Statistical significance was determined at P < .05 after adjusting for covariates.
Biologically, a significantly associated PGS implies that genetic factors influencing the 2 traits are overlapping. Thus, further analyses were performed to identify genetic polymorphisms that are likely to increase the susceptibility to SCZ and also influence treatment response to lithium in patients with BPAD. We performed cross-trait meta-analyses by combining the summary statistics for GWAS on lithium response from the ConLi+Gen22 and GWAS on SCZ from the PGC.36 We applied both the O’Brien method and the direct linear combination of dependent test statistics approach,41,42 which are implemented in the C2+ eLX package (https://sites.google.com/site/multivariateyihsianghsu/). In brief, the O’Brien method and the direct linear combination of dependent test statistics approach combine univariate meta-GWAS summary statistics (β coefficients or z scores) at each SNP.41,42 Further details are available elsewhere.41,42
To characterize the potential biological significance of the SNPs discovered from the cross-trait meta-analyses, we performed analyses using IPA (eAppendix in the Supplement).
A total of 3193 patients with BPAD who had undergone lithium treatment and had available genotype and clinical data participated in the study. After quality control, 2586 patients remained for analysis, of whom 2366 were of European ancestry and the rest Asian. The mean (SD) age of all the patients combined was 47.2 (13.9) years and 1478 (57.2%) were female. A total of 704 patients (27.2%) had a good response to lithium treatment (ALDA scale score ≥7). The mean (SD) ALDA scale score for all participants was 4.1 (3.2) (Table 1).
At the most significantly associated P value threshold (P < 5 × 10−2), the PGS for SCZ was strongly associated with lithium treatment response in BPAD for the categorical outcome on the ALDA scale (Figure, A), explaining 0.8% of the variance. For the continuous outcome (total score on the ALDA subscale A), the direction of association was congruent with the finding on the categorical outcome but was not statistically significant. As shown in eFigure 1 in the Supplement, the relationship between the PGS for SCZ and the total score on the ALDA subscale A deviates from linearity; thus, the continuous scale might be a less powerful and less suitable measure to represent treatment response to lithium in a linear model. The association results of the categorical and continuous outcomes at each threshold level are detailed in the Figure, A. At each threshold, a lower polygenic load for SCZ was associated with a favorable treatment response to lithium in patients with BPAD (Figure, B).
Table 2 shows the ORs for the association between treatment response to lithium in BPAD and SCZ PGS in deciles, comparing the response status of patients in the low polygenic load categories (first to ninth deciles) with the response status of patients in the highest polygenic load category for SCZ (10th decile). Patients with BPAD who carry a lower polygenic load for SCZ have higher odds of favorable treatment response to lithium compared with patients carrying a high polygenic load; the OR of favorable treatment response decreased as the genetic load for SCZ increased, ranging from an OR of 3.46 (95% CI, 1.42-8.41) at the first decile to an OR of 2.03 (95% CI, 0.86-4.81) at the ninth decile, compared with the reference SCZ PGS at the 10th decile (Table 2). There was a significant linear trend in the odds of treatment response to lithium across the deciles (Figure, B).
Subsequent to the PGS analysis, we performed an SNP-based cross-trait meta-analysis by combining the summary statistics for the GWASs on SCZ and treatment response to lithium in the categorical outcome and on SCZ and treatment response to lithium in the continuous outcome. This meta-analysis yielded 15 loci with P values below the genome-wide significance level (P < 5 × 10−8). The top 6 loci and closest genes were rs1611255 (HCG4 [HUGO Gene Nomenclature Committee 21241]), rs66486766 (ADAMTSL3 [OMIM 609199]), rs7405404 (ERCC4 [OMIM 133520), rs1611259 (HCG4), rs3919583 (CCNH [OMIM 601953]), and rs59724122 (EPHX2 [OMIM 132811) (Table 3 and eFigure 2A and B in the Supplement).
To characterize the functional implications of these loci, we undertook IPA using query gene inputs generated from the results of the cross-trait and expression quantitative trait loci analyses (http://www.genenetwork.org/webqtl/main.py; http://www.braineac.org/; eTable 1 in the Supplement). The IPA found significantly represented canonical pathways, with the top 5 being antigen presentation pathway, OX40 signaling pathway, autoimmune thyroid disease signaling, Cdc42 signaling, and B-cell development (eTable 2 in the Supplement). These pathways were predominantly identified on the basis of several HLA antigen genes: HLA-A (OMIM 142800), HLA-DMA (OMIM 142855), HLA-DMB (OMIM 142856), HLA-DOB (OMIM 600629), HLA-DPB1 (OMIM 142858), HLA-F (OMIM 143110), HLA-G (OMIM 142871), PSMB9 (OMIM 177045), and TAP2 (OMIM 170261).
The IPA revealed 2 relevant functional networks (eTable 3 in the Supplement). As shown in eFigure 3A and B in the Supplement, the top 2 networks indicate that tumor necrosis factor (TNF), interleukin 4 (IL-4), and interferon-gamma (IFNγ) might represent important functional molecular nodes in the interaction between response to lithium and SCZ.
The present study reports 2 main findings. First, using PGS, we demonstrate that there is an inverse association between genetic loading for SCZ risk variants and long-term therapeutic response to lithium in patients with BPAD on the categorical outcome of the ALDA scale. Second, we show in the cross-trait meta-GWAS and IPA that genetic variants in the HLA antigen region, the antigen presentation pathway, and inflammatory cytokines such as TNF, IL-4 and IFNγ could play a biological role in treatment response to lithium in BPAD.
These findings are consistent with previous clinical and epidemiologic studies of response to lithium. Lithium is not an effective medication for people with SCZ spectrum disorders.32,43 Moreover, lithium may be deleterious for patients with SCZ because of their greater liability to developing lithium-induced neurotoxic effects even at modest doses and blood levels.43,44 The severity of psychotic symptoms in patients with BPAD was found to be inversely associated with treatment response to lithium.45 Similarly, slow resolution of psychosis in response to lithium treatment during acute manic episodes has been shown to be associated with poorer overall response to the drug.46 Among patients with BPAD, those with a family history of SCZ show poorer response to lithium compared with those with a family history of BPAD.47 Our findings may provide insight into the genetic architecture underlying these clinical observations.
In the SCZ to lithium response cross-trait GWAS meta-analyses, 15 genetic loci located within protein-coding genes that appear to have overlapping outcomes on SCZ risk and treatment response to lithium in BPAD were identified. Only 1 of these genes, type 1 adenylyl cyclase (ADCY1 [OMIM 103072]), had previously been directly implicated in genetic studies of both SCZ48 and treatment response to lithium.26
Both the most significant finding of the cross-trait GWAS and the SNPs from the post-GWAS functional analyses suggest that the HLA antigen system could be implicated in genetic susceptibility to SCZ and treatment response to lithium. The HLA antigen region is the most robust genetic finding in SCZ49 and could be marking a SCZ-type pathogenesis that is associated with nonresponse to lithium. Although the extensive linkage disequilibrium in the HLA antigen region, and the fact that non-HLA antigen genes are embedded within it, could compromise the biological precision of our pathway analysis, some previous studies have linked HLA antigen surface protein composition to responsiveness to lithium in patients with BPAD.50-52 Lithium exposure of human monocytes and mouse microglia in vitro resulted in an increased expression of complement component 3, an HLA antigen protein, which in turn was driven by the inhibition of glycogen synthase kinase-3.53 Inhibition of glycogen synthase kinase-3 is, to date, the most comprehensively documented molecular effect of lithium in neurons, glia, and peripheral immune cells.54,55 Whether these outcomes are in some way compromised by the decreased neuronal complement component 3 expression that is associated with SCZ risk variants in the HLA antigen region,49 and whether such mechanisms play a role in the clinical efficacy of lithium, needs to be explored in future studies.
Furthermore, network analyses of genes from our meta-GWAS findings implicated TNF, IL-4, and IFNγ as central functional nodes, suggesting that the negative interaction between response to lithium and genetic predisposition for SCZ could be mediated by mechanisms implicating these inflammatory cytokines; this finding is also supported by a growing body of evidence describing aberrant inflammatory processes in patients with a first episode of psychosis56 and SCZ.57 Previous studies have reported modulatory outcomes of lithium treatment on these cytokines and underscore the possibility that mechanisms involving inflammatory cytokines might play a role in mediating the therapeutic outcomes of lithium in patients with BPAD.58-65
Our findings have important implications for the treatment of BPAD and for future research. We show for the first time, to our knowledge, that genetic characterization has the potential to aid the stratification of patients with BPAD into those who respond and those who do not respond to lithium, prior to initiation of treatment. Our study also supports the idea that responsiveness to lithium could represent a true psychiatric endophenotype beyond current nosologic descriptions.66 The findings underscore the importance of careful assessments of patients’ family psychiatric histories in the context of treatment selection. In schizoaffective disorder, which remains challenging clinically owing to a lack of specific effective treatments,67 determination of SCZ PGS might aid the choice of mood-stabilizing agents. To achieve full clinical translation, PGS analyses could be combined with other biological and clinical factors in prognostic algorithms.
This study has some limitations. First, the polygenic load for SCZ accounted for only a modest percentage (approximately 1%) of the observed variation in lithium treatment response in patients with BPAD. Although this finding is in line with previous reports on the outcomes of PGSs on complex clinical phenotypes such as SCZ and BPAD,68 the significance of this finding at clinical and population levels needs to be further explored. Second, response to lithium in our study was assessed using the ALDA scale, which is a retrospective measure. To substantiate our findings further, prospective studies are required that can prospectively measure clinical responses to lithium. Third, while our strategy for exploring the biological context of our genetic findings can point toward avenues for future research, it is not designed to provide definitive mechanistic answers. Hypothesis-driven experiments are required to follow up on these leads.
We demonstrated for the first time that lower SCZ loading is associated with better response to lithium in patients with BPAD. Follow-up functional analyses implicate genes that code for the immune system, including the HLA antigen complex and inflammatory cytokines. For future clinical translation, a high genetic loading for SCZ risk variants could be used in conjunction with clinical parameters to determine the likelihood of nonresponse to lithium treatment in BPAD.
Corresponding Author: Bernhard T. Baune, PhD, MD, MPH, FRANZCP, Discipline of Psychiatry, School of Medicine, University of Adelaide, 57 N Terrace, Adelaide, South Australia 5000, Australia (email@example.com).
Accepted for Publication: August 26, 2017.
Published Online: November 9, 2017. doi:10.1001/jamapsychiatry.2017.3433
International Consortium on Lithium Genetics (ConLi+Gen) Authors: The following investigators take authorship responsibility for the study results: Azmeraw T. Amare, MPH, MSc; Klaus Oliver Schubert, MD, PhD; Liping Hou, PhD; Scott R. Clark, MD, PhD; Sergi Papiol, PhD; Urs Heilbronner, PhD; Franziska Degenhardt, MD; Fasil Tekola-Ayele, PhD; Yi-Hsiang Hsu, PhD; Tatyana Shekhtman, MSc; Mazda Adli, MD; Nirmala Akula, PhD; Kazufumi Akiyama, MD; Raffaella Ardau, MD; Bárbara Arias, PhD; Jean-Michel Aubry, MD; Lena Backlund, MD; Abesh Kumar Bhattacharjee, MD; Frank Bellivier, MD, PhD; Antonio Benabarre, MD, PhD; Susanne Bengesser, MD; Joanna M. Biernacka, PhD; Armin Birner, MD; Clara Brichant-Petitjean, MD; Pablo Cervantes, MD; Hsi-Chung Chen, MD, PhD; Caterina Chillotti, MD; Sven Cichon, PhD; Cristiana Cruceanu, BSc; Piotr M. Czerski, PhD; Nina Dalkner, MSc; Alexandre Dayer, MD; Maria Del Zompo, MD; J. Raymond DePaulo, MD; Bruno Étain, MD, PhD; Peter Falkai, MD; Andreas J. Forstner, MD; Louise Frisen, MD; Mark A. Frye, MD; Janice M. Fullerton, PhD; Sébastien Gard, MD; Julie S. Garnham, BN; Fernando S. Goes, MD; Maria Grigoroiu-Serbanescu, PhD; Paul Grof, MD, PhD; Ryota Hashimoto, MD, PhD; Joanna Hauser, MD; Stefan Herms, Dipl-Biol; Per Hoffmann, PhD; Andrea Hofmann, PhD; Stephane Jamain, PhD; Esther Jiménez, PhD; Jean-Pierre Kahn, MD, PhD; Layla Kassem, PhD; Po-Hsiu Kuo, PhD; Tadafumi Kato, MD, PhD; John Kelsoe, MD; Sarah Kittel-Schneider, MD; Sebastian Kliwicki, MD; Barbara König, MSc; Ichiro Kusumi, MD; Gonzalo Laje, MD; Mikael Landén, MD; Catharina Lavebratt, PhD; Marion Leboyer, MD, PhD; Susan G. Leckband, BSc; Alfonso Tortorella, MD; Mirko Manchia, MD, PhD; Lina Martinsson, MD; Michael J. McCarthy, MD, PhD; Susan McElroy, MD; Francesc Colom, PhD; Marina Mitjans, PhD; Francis M. Mondimore, MD; Palmiero Monteleone, MD; Caroline M. Nievergelt, PhD; Markus M. Nöthen, MD; Tomas Novák, MD; Claire O’Donovan, MB; Norio Ozaki, MD; Urban Ösby, MD, PhD; Andrea Pfennig, MD; James B. Potash, MD, MPH; Andreas Reif, MD; Eva Reininghaus, MD; Guy A. Rouleau, MD; Janusz K. Rybakowski, MD; Martin Schalling, MD; Peter R. Schofield, PhD, DSc; Barbara W. Schweizer, RN; Giovanni Severino, MD; Paul D. Shilling, PhD; Katzutaka Shimoda, MD; Christian Simhandl, MD; Claire M. Slaney, RN; Alessio Squassina, PhD; Thomas Stamm, MD; Pavla Stopkova, MD; Mario Maj, MD; Gustavo Turecki, MD; Eduard Vieta, MD, PhD; Julia Volkert, PhD; Stephanie Witt, PhD; Adam Wright, MCP; Peter P. Zandi, PhD; Philip B. Mitchell, MD; Michael Bauer, MD, PhD; Martin Alda, MD; Marcella Rietschel, MD; Francis J. McMahon, MD; Thomas G. Schulze, MD; Bernhard T. Baune, MD, PhD, MPH, FRANZCP.
Affiliations of International Consortium on Lithium Genetics (ConLi+Gen) Authors: Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia (Amare, Schubert, Clark, Baune); Northern Adelaide Local Health Network, Mental Health Services, Adelaide, South Australia, Australia (Schubert); Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, Maryland (Hou, Akula, Kassem, Laje, McMahon, Schulze); Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig-Maximilian University of Munich, Munich, Germany (Papiol, Heilbronner, Schulze); Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University of Munich, Munich, Germany (Papiol, Falkai); Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University Göttingen, Göttingen, Germany (Heilbronner, Schulze); Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany (Degenhardt, Cichon, Forstner, Herms, Hoffmann, Hofmann, Nöthen); Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland (Tekola-Ayele); Hebrew SeniorLife Institute for Aging Research, Harvard Medical School, Boston, Massachusetts (Hsu); Program for Quantitative Genomics, Harvard School of Public Health, Boston, Massachusetts (Hsu); Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts (Hsu); Department of Psychiatry, University of California San Diego (Shekhtman, Bhattacharjee, Kelsoe, McCarthy, Nievergelt); Department of Psychiatry and Psychotherapy, Charité–Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany (Adli, Stamm); Department of Biological Psychiatry and Neuroscience, Dokkyo Medical University School of Medicine, Mibu, Tochigi, Japan (Akiyama); Unit of Clinical Pharmacology, Hospital University Agency of Cagliari, Cagliari, Italy (Ardau, Chillotti); Unitat de Zoologia i Antropologia Biològica (Dpt Biologia Evolutiva, Ecologia i Ciències Ambientals), Facultat de Biologia and Institut de Biomedicina, University of Barcelona, Centro de Investigación Biomédica en Red de Salud Mental, Barcelona, Spain (Arias); Department of Psychiatry, Mood Disorders Unit, Geneva University Hospitals, Geneva, Switzerland (Aubry, Dayer); Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden (Backlund, Frisen, Lavebratt, Schalling, Shilling); Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique 1144, Université Paris Diderot, Département de Psychiatrie et de Médecine Addictologique, Assistance Publique–Hôpitaux de Paris, Groupe Hospitalier Saint-Louis-Lariboisière-F. Widal, Paris, France (Bellivier, Brichant-Petitjean, Étain); Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, University of Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centro de Investigación Biomédica en Red Salud Mental, Barcelona, Catalonia, Spain (Benabarre, Jiménez, Colom, Vieta); Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria (Bengesser, Birner, Dalkner, Reininghaus); Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota (Biernacka); Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota (Biernacka, Frye); The Neuromodulation Unit, McGill University Health Centre, Montreal, Canada (Cervantes); Department of Psychiatry and Center of Sleep Disorders, National Taiwan University Hospital, Taipei, Taiwan. (Chen); Human Genomics Research Group, Department of Biomedicine, University Hospital Basel, Basel, Switzerland (Cichon, Forstner, Herms, Hoffmann); Douglas Mental Health University Institute, McGill University, Montreal, Canada (Cruceanu, Turecki); Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznan, Poland (Czerski, Hauser); Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy (Del Zompo, Severino, Squassina); Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland (DePaulo, Goes, Mondimore, Potash, Schweizer, Schulze); Department of Psychiatry, University of Basel, Basel, Switzerland (Forstner); Neuroscience Research Australia, Sydney, New South Wales, Australia (Fullerton, Schofield); School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia (Fullerton, Schofield); Service de Psychiatrie, Hôpital Charles Perrens, Bordeaux, France (Gard); Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada (Garnham, O’Donovan, Slaney, Alda); Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania (Grigoroiu-Serbanescu); Mood Disorders Center of Ottawa, Ontario, Canada (Grof); Molecular Research Center for Children’s Mental Development, United Graduate School of Child Development, Osaka University, Osaka, Japan (Hashimoto); Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan (Hashimoto); Institut National de la Santé et de la Recherche Médicale Unité 955, Psychiatrie Translationnelle, Créteil, France (Jamain); Service de Psychiatrie et Psychologie Clinique, Centre Psychothérapique de Nancy, Université de Lorraine, Nancy, France (Kahn); Department of Public Health and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan (Kuo); Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Brain Science Institute, Saitama, Japan (Kato, Reif); Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany (Kittel-Schneider, Rybakowski, Volkert); Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland (Kliwicki); Department of Psychiatry and Psychotherapeutic Medicine, Landesklinikum Neunkirchen, Neunkirchen, Austria (König); Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan (Kusumi); Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the Gothenburg University, Gothenburg, Sweden (Landén); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Landén); 50Inserm U955, Translational Psychiatry Laboratory, Université Paris-Est-Créteil, Department of Psychiatry and Addictology of Mondor University Hospital, Assistance Publique–Hôpitaux de Paris, Hôpital Albert Chenevier–Henri Mondor, Pôle de Psychiatrie, Créteil, France (Leboyer); Department of Pharmacy, Veterans Affairs San Diego Healthcare System, San Diego, California (Leckband); Department of Psychiatry, University of Perugia, Perugia, Italy (Tortorella); Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy (Manchia); Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada (Manchia); Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden (Martinsson); Department of Psychiatry, Veterans Affairs San Diego Healthcare System, San Diego, California (McCarthy); Department of Psychiatry, Lindner Center of Hope and University of Cincinnati, Mason, Ohio (McElroy); Mental Health Research Group, IMIM–Hospital del Mar, Barcelona, Catalonia, Spain (Colom); Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain (Mitjans); Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany (Mitjans); Neurosciences Section, Department of Medicine and Surgery, University of Salerno, Salerno, Italy (Monteleone); Department of Psychiatry, Second University of Naples, Naples, Italy (Monteleone, Maj); National Institute of Mental Health, Klecany, Czech Republic (Novák, Stopkova); Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan (Ozaki); Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden (Ösby); Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Germany (Pfennig, Bauer); Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada (Rouleau); Department of Psychiatry, Dokkyo University School of Medicine, Mibu, Tochigi, Japan (Shimoda); Bipolar Center Wiener Neustadt, Sigmund Freud University, Medical Faculty, Vienna, Austria (Simhandl); Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany (Witt, Rietschel, Schulze); School of Psychiatry, University of New South Wales, and Black Dog Institute, Sydney, New South Wales, Australia (Wright, Mitchell); Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Zandi).
Author Contributions: Mr Amare and Dr Schubert contributed equally to this study and are co–first authors. Mr Amare and Dr Baune had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Amare, Schubert, Hou, Heilbronner, Hsu, Ardau, Bellivier, Chillotti, Del Zompo, Falkai, Laje, Leckband, Martinsson, Colom, Ozaki, Ösby, Reininghaus, Rybakowski, Schofield, Schweizer, Maj, Alda, Schulze, Baune.
Acquisition, analysis, or interpretation of data: Amare, Schubert, Hou, Clark, Papiol, Degenhardt, Tekola-Ayele, Hsu, Shekhtman, Adli, Akula, Akiyama, Arias, Aubry, Backlund, Bhattacharjee, Bellivier, Benabarre, Bengesser, Biernacka, Cervantes, Chen, Cichon, Cruceanu, Czerski, Dalkner, Dayer, Del Zompo, DePaulo, Etain, Forstner, Frisén, Frye, Fullerton, Gard, Garnham, Goes, Grigoroiu-Serbanescu, Grof, Hashimoto, Hauser, Herms, Hoffmann, Hofmann, Jamain, Jiménez, Kahn, Kassem, Kuo, Kato, Kelsoe, Kittel-Schneider, Kliwicki, König, Kusumi, Laje, Landen, Lavebratt, Leboyer, Leckband, Tortorella, Manchia, McCarthy, McElroy, Colom, Mitchell, Mitjans, Mondimore, Monteleone, Nievergelt, Nöthen, Novák, O'Donovan, Ozaki, Ösby, Pfennig, Potash, Reif, Rouleau, Schalling, Schofield, Severino, Shilling, Shimoda, Simhandl, Slaney, Squassina, Stamm, Stopkova, Turecki, Vieta, Volkert, Witt, Wright, Zandi, Bauer, Alda, Rietschel, McMahon, Birner, Brichant-Petitjean, Schulze, Baune.
Drafting of the manuscript: Amare, Schubert, Baune.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Amare, Hou, Papiol, Hsu, Del Zompo, Goes, Hofmann, Nievergelt, Zandi, Baune.
Obtained funding: Amare, Backlund, Bellivier, Dayer, Etain, Frisén, Frye, Fullerton, Grigoroiu-Serbanescu, Jamain, Landen, Leckband, Schalling, Schofield, Alda, Rietschel, Schulze, Baune.
Administrative, technical, or material support: Amare, Papiol, Degenhardt, Shekhtman, Adli, Akula, Akiyama, Aubry, Backlund, Bhattacharjee, Benabarre, Chen, Cichon, Cruceanu, Czerski, Dalkner, DePaulo, Etain, Falkai, Forstner, Garnham, Goes, Hashimoto, Herms, Hoffmann, Jamain, Kahn, Kelsoe, Kittel-Schneider, Kliwicki, Kusumi, Laje, Landen, Lavebratt, Leboyer, Leckband, Manchia, McCarthy, Mitjans, Mondimore, Monteleone, Nöthen, Ozaki, Ösby, Pfennig, Potash, Reif, Reininghaus, Schalling, Schofield, Schweizer, Shilling, Shimoda, Slaney, Stamm, Stopkova, Turecki, Volkert, Bauer, Rietschel, McMahon, Baune.
Study supervision: Amare, Schubert, Hou, Arias, Bellivier, Benabarre, Cervantes, Cruceanu, Del Zompo, Etain, Hauser, Jiménez, Kuo, Tortorella, Manchia, Colom, Nöthen, Ösby, Reininghaus, Rouleau, Schalling, Schofield, Vieta, Rietschel, Baune.
Conflict of Interest Disclosures: None reported.
Funding/Support: Mr Amare received a Postgraduate Research Scholarship support from the University of Adelaide through the Adelaide Scholarship International (ASI) program. The primary sources of funding were grants RI 908/7-1, FOR2107 and RI 908/11-1 from the Deutsche Forschungsgemeinschaft (Dr Rietschel) and grant NO 246/10-1 (Dr Nöthen) and grant ZIA-MH00284311 from the Intramural Research Program of the National Institute of Mental Health (ClinicalTrials.gov identifier: NCT00001174). The genotyping was funded in part by the German Federal Ministry of Education and Research through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders), under the auspices of the e:Med Programme (Drs Schulze, Rietschel, and Nöthen). This study was supported by National Institutes of Health grants P50CA89392 from the National Cancer Institute and 5K02DA021237 from the National Institute of Drug Abuse. The Canadian part of the study was supported by grant 64410 from the Canadian Institutes of Health Research (Dr Alda). Collection and phenotyping of the Australian University of New South Wales sample was funded by program grant 1037196 from the Australian National Health and Medical Research Council (Mr Mitchell, Dr Schofield, Dr Fullerton, and Mr Wright). The collection of the Barcelona sample was supported by grants PI080247, PI1200906, PI12/00018, 2014SGR1636, 2014SGR398, and MSII14/00030 from the Centro de Investigación en Red de Salud Mental, Institut d’Investigacions Biomèdiques August Pi i Sunyer, the Centres de Recerca de Catalunya Programme/Generalitat de Catalunya, and the Miguel Servet II and Instituto de Salud Carlos III. The Swedish Research Council, the Stockholm County Council, Karolinska Institutet, and the Söderström-Königska Foundation supported this research through grants awarded to Drs Backlund, Frisen, Lavebratt, and Schalling. The collection of the Geneva sample was supported by grants Synapsy–The Synaptic Basis of Mental Diseases 51NF40-158776 and 32003B-125469 from the Swiss National Foundation. The work by the French group was supported by INSERM (Institut National de la Santé et de la Recherche Médicale), AP-HP (Assistance Publique des Hôpitaux de Paris), the Fondation FondaMental (RTRS Santé Mentale), and the labex Bio-PSY (Investissements d’Avenir program managed by the ANR under reference ANR-11-IDEX-0004-02). The collection of the Romanian sample was supported by a grant from Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (Dr Grigoroiu-Serbanescu).The collection of the Czech sample was supported by the project Nr. LO1611 with a financial support from the MEYS under the NPU I program and by the Czech Science Foundation, grant Nr. 17-07070S.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: We thank all the patients who participated in the study, and we appreciate the contributions of the clinicians, scientists, research assistants, and study staff who helped in the patient recruitment, data collection, and sample preparation of the studies. We are also indebted to the members of the ConLi+Gen Scientific Advisory Board (http://www.conligen.org/) for critical input over the course of the project. The analysis of this study was carried out using the high-performance computational capabilities of the University of Adelaide, the Phoenix supercomputer (https://www.adelaide.edu.au/phoenix/) and the Lisa Computer Cluster within the Dutch national e-infrastructure (https://www.surf.nl/en/). Some data and biomaterials were collected as part of 11 projects (Study 40) that participated in the National Institute of Mental Health (NIMH) Bipolar Disorder Genetics Initiative. From 2003 to 2007, the principal investigators and co-investigators were: John Nurnberger, MD, PhD, Marvin J. Miller, MD, Elizabeth S. Bowman, MD, N. Leela Rau, MD, P. Ryan Moe, MD, Nalini Samavedy, MD, Rif El-Mallakh, MD (University of Louisville), Husseini Manji, MD (Johnson and Johnson), Debra A. Glitz, MD (Wayne State University), Eric T. Meyer, PhD, MS (Oxford University, UK), Carrie Smiley, RN, Tatiana Foroud, PhD, Leah Flury, MS, Danielle M. Dick, PhD (Virginia Commonwealth University), and Howard Edenberg, PhD, Indiana University (grant R01 MH59545); John Rice, PhD, Theodore Reich, MD, Allison Goate, PhD, and Laura Bierut, MD, Washington University in St. Louis (grants R01 MH059534 and MDK02 DA21237); Melvin McInnis, MD, J. Raymond DePaulo, Jr, MD, Dean F. MacKinnon, MD, Francis M. Mondimore, MD, James B. Potash, MD, Peter P. Zandi, PhD, Dimitrios Avramopoulos, MD, PhD, and Jennifer Payne, MD, Johns Hopkins University (grant R01 MH59533); Wade Berrettini, MD, PhD, University of Pennsylvania (grant R01 MH59553); William Byerley, MD, and Sophia Vinogradov, MD, University of California, San Francisco (grant R01 MH60068); William Coryell, MD, and Raymond Crowe, MD, University of Iowa (grant R01 MH059548); Elliot Gershon, MD, Judith Badner, PhD, Francis McMahon, MD, Chunyu Liu, PhD, Alan Sanders, MD, Maria Caserta, MD, PhD, Steven Dinwiddie, MD, Tu Nguyen, and Donna Harakal, RN, BC, University of Chicago (grant R01 MH59535); John Kelsoe, MD, and Rebecca McKinney, BA, University of California, San Diego (grant R01 MH59567); William Scheftner, MD, Howard M. Kravitz, DO, MPH, Diana Marta, BS, Annette Vaughn-Brown, MSN, RN, and Laurie Bederow, MA, Rush University (grant R01 MH059556); Francis J. McMahon, MD, Layla Kassem, PsyD, Sevilla Detera-Wadleigh, PhD, Lisa Austin, PhD, Dennis L. Murphy, MD, NIMH Intramural Research Program (grant 1Z01MH002810-01); and William B. Lawson, MD, PhD, Evarista Nwulia, MD, and Maria Hipolito, MD, Howard University.
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