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AKT1 and genetic vulnerability to bipolar disorder
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AKT1 and genetic vulnerability to bipolar disorder Vincent Millischera,b, , Granville J. Mathesonc,d, Lina Martinssonc,d, Inger Römer Ekc,d, Martin Schallinga,b, Catharina Lavebratta,b, Lena Backlunda,c,d ⁎
a
Neurogenetics Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden d Center for Psychiatric Research and Education, Stockholm City Council, Sweden b c
ARTICLE INFO
ABSTRACT
Keywords: Bipolar disorder Psychosis, AKT1 Genetic association Haplotype association
AKT1 encodes a serine/threonine kinase that has as one of its best-known substrates glycogen synthase kinase-3 (GSK3), a primary target for lithium. AKT1 has been previously been implicated as a vulnerability gene for bipolar disorder (BD). We aimed to associate genetic variants in the AKT1 gene with subgroups of BD. BD patients from a Swedish cohort (N = 831) were phenotyped in regards to their psychotic episodes according to mood-congruence in depression and manias, and compared to controls (N = 1,496). All participants were genotyped for SNPs in AKT1 previously implicated to have a role: rs3730358, rs1130214 and rs3803300. None of the effects reported in earlier studies were statistically significant, including the association between rs3803300 and BD without any psychotic symptoms, rs3803300 and mood-congruent psychosis, rs3803300 and the combined groups, as well as the association between the haplotypes formed by rs3730358 and rs1130214 and risk for BD. In a Bayesian analysis, all Bayes’ Factors using default priors supported the null hypothesis in the replication set by a factor of between 5 and 1300 times. Analysis of genome wide association data did not reveal any association between BD and the AKT1 region. We conclude AKT1 is less likely to be a vulnerability gene in BD.
1. Introduction Bipolar disorder (BD) is a genetically complex psychiatric disorder characterized by recurrent episodes of depression and mania. It affects approximately 2% of the world population (Merikangas et al., 2011) and has been ranked as a top cause of lifelong disability by the World Health Organization. The heritability of BD is estimated to be as high as 85% based on twin studies (McGuffin et al., 2003). Like other complex disorders, BD is characterized by a polygenic inheritance, based on many common variants each with small individual effect sizes (Maletic and Raison, 2014). So far, genome wide association studies (GWAS) have identified 30 loci associated with bipolar disorder (Stahl et al., 2019). Although BD and schizophrenia (SCZ) are viewed as two distinct entities in both the DSM and the ICD classification, there is convincing evidence for a genetic overlap between these disorders (Anttila et al., 2018). Furthermore, psychosis, a key symptom in SCZ, is also very common in BD, with the lifetime prevalence of psychotic episodes in BD estimated to be around 60% (Goodwin and Jamison, 2007). Psychotic episodes are referred to as mood-congruent when corresponding with the mood polarity, or as mood-incongruent when they are not. Mood⁎
incongruent psychotic episodes are thought to signify a more severe form of the disease and tend to aggregate in families (Goes et al., 2007; Potash et al., 2001). AKT1 encodes for a serine/threonine kinase, that plays an important role in several normal and pathological cell processes. One of its bestknown substrates is glycogen synthase kinase-3 (GSK3), a primary target for lithium, the first line therapy for BD (Beaulieu, 2012). Common genetic variation in AKT1 was first reported to be associated with SCZ by Emamian et al. (Emamian et al., 2004), but AKT1 has also been studied in the context of BD. AKT1 was initially defined as a candidate gene by a moderate linkage signal to a region in chromosome 14q22-32 (Toyota et al., 2003). Furthermore, Karege et al. (2010) reported several haplotypes of AKT1 SNPs to be associated with BD. The TC haplotype formed by rs1130214/rs3730358, two SNPs that had previously been associated with SCZ, was protective against both SCZ and BD; the GC haplotype of the same SNPs was reported as a risk haplotype (Karege et al., 2010). Two years later, a similar study from the same group (234 SCZ + 130 BD + 165 controls) reported association of three other AKT1 SNPs to different sub-phenotypes of BD (mood congruent psychosis and no psychotic history) (Karege et al., 2012). Furthermore, two studies have analyzed AKT1 mRNA expression in BD with conflicting
Corresponding author: Neurogenetics Unit, Center for Molecular Medicine, Karolinska University Hospital, L8:00, 171 76 Stockholm, Sweden. E-mail address: [email protected] (V. Millischer).
https://doi.org/10.1016/j.psychres.2019.112677 Received 27 August 2019; Received in revised form 31 October 2019; Accepted 3 November 2019 0165-1781/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Please cite this article as: Vincent Millischer, et al., Psychiatry Research, https://doi.org/10.1016/j.psychres.2019.112677
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results. While, in the context of first-episode psychosis, AKT1 mRNA expression was found to be higher in peripheral blood from manic patients compared to patients in the schizophrenia spectrum or healthy controls (Gouvea et al., 2016), AKT1 mRNA expression was shown to be decreased in BD depression versus healthy controls (MachadoVieira et al., 2015). Although AKT1 has a potential role in lithium metabolism, no studies have been directed towards genetics of AKT1 in this context. As a first step in elucidating a potential role of AKT1 in BD and in lithium response, we aimed to replicate the findings of the studies published by Karege et al. (Karege et al., 2012, 2010) using a large, wellphenotyped BD cohort (N = 831) followed by a GWAS analysis based on Psychiatric Genomics Consortium (PGC) data sets.
inference (Ly et al., 2016; Morey et al., 2016; Wagenmakers et al., 2016a). BFs compare the predictive adequacy of two competing hypotheses with one another, and the BF itself can be interpreted as the relative probability of having observed the given data under one hypothesis compared to the other. In this way it is a measure of the relative statistical evidence for competing hypotheses in contrast to orthodox frequentist statistics (Morey et al., 2016). In this way, it is ideally suited for replication studies, for which the outcomes of a previous study, including their uncertainty, can be compared to a skeptic's null hypothesis of no effect (Ly et al., 2018; Verhagen and Wagenmakers, 2014). This has been called the replication BF (Ly et al., 2018; Verhagen and Wagenmakers, 2014; Wagenmakers et al., 2016b). Both BFs using default priors and replication BFs are presented: the former compares the null hypothesis with a hypothesis that there may be an effect about whose magnitude we know little to nothing, while the latter compares the null hypothesis with the effect found before as well as its uncertainty. The replication BF can be conceptualized as representing the relative likelihood of the new data originating under each of these two hypotheses, or alternatively, the relative change in the likelihood of the estimated effect being equal to 0 before and after having observed the data from the replication study (Wagenmakers et al., 2016b). BFs were calculated for each association using the summary statistics provided in the original studies (Karege et al., 2012, 2010), and using the new data, and are presented for each association. BFs using default priors were calculated for the original data, the replication data, as well as the combined data using Bayesian contingency tables in JASP using independent multinomial sampling. Replication BFs were calculated as described by Ly et al. (Ly et al., 2018). Replication BFs were only calculated for those comparisons for which the original study reported statistically significant differences, and therefore rejected the null hypothesis.
2. Method 2.1. Cohort description Patients (n = 831) with the clinical diagnosis of BD were consecutively recruited between 2003 and 2010, mostly from the Unit of Affective disorders at Huddinge University Hospital, Stockholm, Sweden. After informed consent, the bipolar diagnosis was validated and phenotypes such as psychotic symptoms during episodes were assessed according to Schedules for Clinical Assessment in Neuropsychiatry (SCAN; based on DSM IV). Psychotic episodes were sub-grouped according to mood congruence as previously described (Backlund et al., 2012). Controls matched for age and gender were recruited from the anonymous blood donor controls from the Stockholm area (ABD controls; N = 1496). The study was approved in accordance with the Helsinki Declaration of 1975 by the Regional Ethical Review Board in Stockholm. The informed consent process was both verbal and written, during a visit to a special trained psychiatric nurse. All bipolar participants were in euthymic phase and had full capacity to consent.
2.5. Analysis of data from genome-wide association studies from the psychiatric genomics consortium (PGC)
2.2. Genotyping Peripheral blood samples were drawn and genomic DNA was extracted by standard procedures. The samples were genotyped for the SNPs rs3730358, rs1130214 and rs3803300 in the AKT1 using TaqMan SNP genotyping assays on QuantStudio 7 Flex instrument (Applied Biosystems, Foster City, CA, USA). The genotyping efficiency was 93%.
Known SNPs in a 20 kb window upstream and downstream of AKT1 were obtained using the biomaRt package (Durinck et al., 2005). The summary data for these SNPs was obtained for the genome-wide association studies (GWAS) on BD (Stahl et al., 2019) and schizophrenia (Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2018) from the website of the PGC and plotted using the gviz package (Hahne and Ivanek, 2016).
2.3. Statistics All statistical analysis was performed using R Statistical Software (R Core Team, 2017) and JASP (JASP Team, 2018) for Bayesian tests. The measure D’ of linkage disequilibrium (LD) was calculated between SNPs using LD Link, using data from populations of European descent (Machiela and Chanock, 2015). When not reported in the publications, odds ratios (OR) and 95% confidence intervals (95% CI) were calculated from reported minor allele frequencies (MAF) and sample sizes using a logistic regression with an allelic model. The genotype associations were calculated for the SNPs which were concluded to be significantly associated with groups, as well as for three other reported SNPs using logistic regression. The haplotype frequencies were estimated using the haplo.stats R package (Schaid et al., 2002). Haplotype associations were calculated using weighted logistic regression, using the posterior probabilities for each haplotype as weights. Summary OR and 95% CI were calculated using the estimates from the publications pooled with our own data.
3. Results The demographic description of the cohort can be found in Table 1. 67% of all patients where the information was available had at least one psychotic episode in their lifetime, corresponding to previously published cohorts (Goodwin and Jamison, 2007). We first studied five previously reported statistically significant associations (Karege et al., 2012, 2010), namely the association between rs3803300 and BD without any psychotic symptoms (“Group 1”, Nreplication = 268), rs3803300 and mood-congruent psychosis (“Group 2”, Nreplication = 436), rs3803300 and the combined groups (“Group 1 + 2”, Nreplication = 704), as well as the association between the core haplotype TC – formed by rs1130214/rs3730358 – and reduced risk for BD, and the haplotype GC – formed by the same SNPs – and increased risk for BD (Nreplication = 831). Furthermore, we also tested for direct association between the genotyped SNPs and BD that had been reported by (Karege et al., 2010) (Table 2). The power in the Swedish replication cohort to replicate the main effects was above 99% for all results that had been reported as statistically significant. The power to replicate an effect size equal to the half of the reported effect size of the original studies was above 94% for all of these comparisons (data not shown). Following, the “small
2.4. Bayesian statistics We also performed Bayesian hypothesis testing to evaluate the success of the replication attempt, making use of Bayes Factors (BFs) for 2
3
98.6% 98.5% 100% 100% 2.1 2.3 0.41–0.911 1.09–2.151 0.611 1.531 yes yes TC (protective) GC (risk)
Haplotype
Calculated OR and 95%CI using an allelic model, based on frequency tables from Karege (2010). Gender and age corrected OR and 95%CI, reported in Karege (2012). 3 Gender corrected allelic model, logistic regression MAF: Minor allele frequency, OR: Odd's ratio, 95%CI: 95% Confidence interval, BF: Bayes factor. Group 1: BD without any psychotic symptoms. Group 2: BD with mood-congruent psychosis. 2
0.069 0.484 0.79–1.05 1.02–1.30 0.91 1.15 0.037 0.209 0.82–1.11 0.99–1.29 0.95 1.13 % Patients 19.4 69.1 % Controls 20.1 66.4 % Patients 15.7 66.9 % Controls 22.4 56.9 Controls / BD Controls / BD
1
0.032 0.21
– – – – – – 0.028 1.03 0.09 0.88–1.36 0.74–0.96 0.72–1.03 1.09 0.84 0.86 0.022 0.472 0.13 0.79–1.26 0.74–0.98 0.69–1.02 1.00 0.85 0.84 7.9 27.8 11.5 8.0 31.2 13.4 – – – – – – 0.96–3.64 1 0.53–1.071 0.59–1.461 no no no rs3803300 rs1130214 rs3730358
4.76 34.62 15.84
8.55 28.45 14.92
1.871 0.751 0.931
0.242 0.332 0.077
1235.5 0.00081 0.124 0.99–1.52 1.23 0.023 0.77–1.26 0.99 7.9 8.0 94.1% 100% 1.7–5.82 yes rs3803300
4.7
2.25 10.4 yes rs3803300
4.7
13.5
3.142
153.2
30.6 0.033 0.032 0.84–1.43 1.1 0.028 0.72–1.32 0.98 7.8 8.0 94.9% 100% 2 2
1.0–4.8
1.9–7.22 yes rs3803300
4.7
15.9
3.782
0.98
1327.0 0.00075 0.634 1.09–1.87 1.43 0.033 0.7–1.42 1 8.0 8.0 76.7% 100%
MAF (%) Controls Power d33% Power main effect 95% CI OR MAF (%) Patients
Controls / BD group 1 Controls / BD group 2 Controls / BD group 1+2 Controls / BD Controls / BD Controls / BD
The importance of identifying molecules of relevance to lithium response and BD pathophysiology is critical, as both are still poorly understood. AKT1 is an attractive candidate in this context given its role in lithium signaling, being supported by animal and cell-based studies. Two human genetic studies on BD (Karege et al., 2012, 2010) also point to an involvement of AKT1 in BD. However, given the fact that these studies were rather small, we performed a combined replication study and GWAS based analysis of the potential role of AKT1 in BD. Using the ‘small telescopes’ method (Simonsohn, 2015), we estimated that our study had between 70% and 95% power to detect effect
841.3
Replication BF10 95% CI
BF10
4. Discussion
MAF (%) Controls
Table 2 Summary statistics of the original studies, the replication set and the combined data.
BF10
Swedish replication study
MAF (%) Patients
OR3
95% CI
BF10
telescopes” technique suggested by Simonsohn et al. (Simonsohn, 2015), we calculated the power for our replication sample to detect the effect size which the original studies had 33% power to detect (d33%). For three out of the four associations, our sample had a power of greater than 94%, and for the association between rs3803300 and group 1, the power was 77%. We therefore conclude that our sample was adequately powered to replicate the results of the original studies (Table 2, columns Power main effect and Power d33%). None of the statistically significant effects reported in the original studies were statistically significant in our replication study. In the Bayesian analysis, all BFs using default priors supported the null hypothesis in the replication set, with moderate to strong evidence, for comparisons which were significant in the original investigation by Karege et al. (Karege et al., 2012, 2010). Replication BFs also favored the null hypothesis relative to the original reported results, i.e. an unsuccessful replication, in all cases with moderate to strong evidence. This means that the data was more likely to have occurred under the null hypothesis than under the originally reported conclusions by a factor of between 5 and 1327 times (Table 2, column Replication BF01). Although the 95% confidence interval for the control-to-BD GC risk haplotype did not overlap with zero in the combined data, after taking multiple comparisons into account, this result would no longer be considered significant (Table 2, last row). Regarding the original findings in Karege et al. which were not significant (Karege et al., 2012, 2010) (Table 2, row 4–6), we observed similar results: all BFs supported the null hypothesis in the replication set, and in the combined data, BFs either supported the null or provided no evidence for either hypothesis. We then compared our findings to the summary statistics of the latest genome-wide association studies for BD (Stahl et al., 2019) and schizophrenia (Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2018). None of the SNPs within a 20 kb window of AKT1 reached genome-wide significance for either of the disorders, nor the less stringent significance threshold of 5.10−5 (Fig. 1).
OR
% over all patients.
Significance reported
1
78% 12% 3% 6% 67% 45% 22% 23% 4% 4%
SNP
651 102 24 54 533 358 175 186 35 30
Comparision groups
Diagnosis Bipolar Type I Bipolar Type II Schizoaffective disorder NOS Psychosis Mood congruent in mania Mood incongruent in mania Mood congruent in depression Mood incongruent in depression Information not available1
Combined studies
57.5 46.6
Original studies
Sex (% women) Age (mean ± SD)
Replication BF01
Table 1 Subject demographics.
30.9 4.69
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Fig. 1. GWAS summary data for bipolar disorder and schizophrenia. Summary statistics from the latest BD and schizophreniaGWAS presented as –log10(p-value) of SNPs in a 20 kb window upstream and downstream of AKT1. The red vertical lines indicate the three SNPs genotyped in this study: 1, rs3730358; 2, rs1130214; 3, rs3803300. The black horizontal lines indicate the genome wide significance threshold (5.10−8), as well as an indicative threshold of 5.10−5.
sizes that the authors of the original articles would have considered too small to be of interest (i.e. the effect sizes for which the original studies had only 33% power), and for this reason, we can consider the Swedish replication sample sufficiently powered statistically. We were not able to replicate any of the five associations at a significance level of α = 0.05, and our Bayesian analysis suggested that the null hypothesis was more likely than the originally reported conclusions in all cases. For the three associations reported in the later study (Karege et al., 2012), the replication OR did not fall within the predicted 95%CI, nor did the OR of the combined data reach statistical significance. The replication BFs for these three associations show that the replication data are much better predicted by the null hypothesis than by the alternative hypothesis – an association between the SNPs and subphenotypes of BD – described by the original conclusions. Regarding the associations reported in the first study (Karege et al., 2010), we did not have enough statistical power to detect the lowest effect sizes predicted by Karege et al. Indeed, although the replication OR fell within the predicted 95%CI, they did not reach significance with α = 0.05. However, the evidence against the alternative hypothesis informed by the original data set is very strong for the “protective” (TC) haplotype (replication BF01 = 30.9). The patients were representative for patients with BD, due to the fact that all participants were recruited from clinics with responsibility for bipolar patients in the catchment area. The controls were also recruited from Stockholm. Both samples were therefore obtained from an ethnically homogeneous population (Lappalainen et al., 2009). The sample size in our study was comparatively large and the frequency of psychosis in episodes was 67% in the bipolar sample, close to the estimated prevalence, which strengthens the representativity of our sample (Keck et al., 2003; Kessing, 2006; McGuffin et al., 2003). Finally, we strengthened our findings using data from the latest GWAS for BD and SCZ: no associations between these disorders and AKT1 were found in these samples. However, there are also a number of limitations in this study. For example, the assessment of phenotypes was retrospective, even if we tried to minimize variability by letting only two investigators (one senior psychiatrist and one special trained nurse)
perform all personal interviews and reading of the medical records. Another weakness of the study was the lack of detailed information about healthy controls recruited from blood donors. Judging from our findings, the data is more likely to have occurred under the null hypothesis than under the hypothesis corresponding to the originally reported conclusions by a factor 5–1300. Replication failure is a common problem in the biomedical and behavioral sciences, with replication rates much lower than what would be desirable (Begley and Ellis, 2012; Open Science Collaboration, 2015), resulting in a great deal of wasted time and resources (Macleod et al., 2014). There are threats to the veracity of scientific findings at every stage of the scientific process, as for example the overestimation of true genetic effect sizes, commonly known as “winner's curse” (Zollner and Pritchard, 2007). Replication is the ultimate standard by which published claims can be judged, replication studies are therefore of critical importance for the progress of these fields (Munafò et al., 2017). Declaration of Competing Interest This project was supported by grants from Karolinska Institutet, the Swedish Research Council, The Söderström-Königska Foundation, and Psychiatry Southwest, Stockholm. Financial support was also provided through the regional agreement on medical training and clinical research (ALF) between the Stockholm County Council and Karolinska Institutet. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgement This project was supported by grants from Karolinska Institutet, the Swedish Research Council, the Söderström-Königska Foundation, the Swedish Brain Foundation, and Psychiatry Southwest, Stockholm. Financial support was also provided through the regional agreement on medical training and clinical research (ALF) between the Stockholm County Council and Karolinska Institutet. The funders had no role in study design, data collection and analysis, decision to publish, or 4
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preparation of the manuscript. We want to thank all the patients who participated in this study. The authors would also like to thank Ninni Mu, MSc for skillful assistance.
McLaughlin, N., Nestadt, P.S., Grabe, H.J., Depienne, C., Konkashbaev, A., Lanzagorta, N., Valencia-Duarte, A., Bramon, E., Buccola, N., Cahn, W., Cairns, M., Chong, S.A., Cohen, D., Crespo-Facorro, B., Crowley, J., Davidson, M., DeLisi, L., Dinan, T., Donohoe, G., Drapeau, E., Duan, J., Haan, L., Hougaard, D., KarachanakYankova, S., Khrunin, A., Klovins, J., Kucinskas, V., Lee Chee Keong, J., Limborska, S., Loughland, C., Lonnqvist, J., Maher, B., Mattheisen, M., McDonald, C., Murphy, K.C., Nenadic, I., van Os, J., Pantelis, C., Pato, M., Petryshen, T., Quested, D., Roussos, P., Sanders, A.R., Schall, U., Schwab, S.G., Sim, K., So, H.C., Stogmann, E., Subramaniam, M., Toncheva, D., Waddington, J., Walters, J., Weiser, M., Cheng, W., Cloninger, R., Curtis, D., Gejman, P.V., Henskens, F., Mattingsdal, M., Oh, S.Y., Scott, R., Webb, B., Breen, G., Churchhouse, C., Bulik, C.M., Daly, M., Dichgans, M., Faraone, S.V., Guerreiro, R., Holmans, P., Kendler, K.S., Koeleman, B., Mathews, C.A., Price, A., Scharf, J., Sklar, P., Williams, J., Wood, N.W., Cotsapas, C., Palotie, A., Smoller, J.W., Sullivan, P., Rosand, J., Corvin, A., Neale, B.M., Schott, J.M., Anney, R., Elia, J., Grigoroiu-Serbanescu, M., Edenberg, H.J., Murray, R., 2018. Analysis of shared heritability in common disorders of the brain. Science 360 (6395). https:// doi.org/10.1126/science.aap8757. Backlund, L., Lavebratt, C., Frisen, L., Nikamo, P., Hukic Sudic, D., Traskman-Bendz, L., Landen, M., Edman, G., Vawter, M.P., Osby, U., Schalling, M., 2012. P2RX7: expression responds to sleep deprivation and associates with rapid cycling in bipolar disorder type 1. PLoS ONE 7 (8), e43057. https://doi.org/10.1371/journal.pone. 0043057. Beaulieu, J.M., 2012. A role for Akt and glycogen synthase kinase-3 as integrators of dopamine and serotonin neurotransmission in mental health. J Psychiatry Neurosci. 37 (1), 7–16. https://doi.org/10.1503/jpn.110011. Begley, C.G., Ellis, L.M., 2012. Drug development: raise standards for preclinical cancer research. Nature 483 (7391), 531–533. https://doi.org/10.1038/483531a. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2018. Genomic dissection of bipolar disorder and schizophrenia, including 28 Subphenotypes. Cell 173 (7) 1705-1715.e1716. https://doi.org/10.1016/ j.cell.2018.05.046. Durinck, S., Moreau, Y., Kasprzyk, A., Davis, S., De Moor, B., Brazma, A., Huber, W., 2005. BioMart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21 (16), 3439–3440. https://doi.org/10. 1093/bioinformatics/bti525. Emamian, E.S., Hall, D., Birnbaum, M.J., Karayiorgou, M., Gogos, J.A., 2004. Convergent evidence for impaired AKT1-GSK3beta signaling in schizophrenia. Nat. Genet. 36 (2), 131–137. https://doi.org/10.1038/ng1296. Goes, F.S., Zandi, P.P., Miao, K., McMahon, F.J., Steele, J., Willour, V.L., Mackinnon, D.F., Mondimore, F.M., Schweizer, B., Nurnberger Jr., J.I., Rice, J.P., Scheftner, W., Coryell, W., Berrettini, W.H., Kelsoe, J.R., Byerley, W., Murphy, D.L., Gershon, E.S., Bipolar Disorder Phenome, G., Depaulo Jr., J.R., McInnis, M.G., Potash, J.B., 2007. Mood-incongruent psychotic features in bipolar disorder: familial aggregation and suggestive linkage to 2p11-q14 and 13q21-33. Am. J. Psychiatry 164 (2), 236–247. https://doi.org/10.1176/ajp.2007.164.2.236. Goodwin, F.K., Jamison, K.R., 2007. Manic-Depressive Illness: Bipolar Disorders and Recurrent Depression, Band 1, 2 ed. Oxford University Press ISBN: 9780195135794. Gouvea, E.S., Ota, V.K., Noto, C., Santoro, M.L., Spindola, L.M., Moretti, P.N., Carvalho, C.M., Xavier, G., Rios, A.C., Sato, J.R., Hayashi, M.A., Brietzke, E., Gadelha, A., Bressan, R.A., Cordeiro, Q., Belangero, S.I., 2016. Gene expression alterations related to mania and psychosis in peripheral blood of patients with a first episode of psychosis. Transl. Psychiatry 6 (10), e908. https://doi.org/10.1038/tp.2016.159. Hahne, F., Ivanek, R., 2016. Visualizing genomic data using gviz and bioconductor. In: Mathé, E., Davis, S. (Eds.), Statistical Genomics: Methods and Protocols. Springer New York, New York, NY, pp. 335–351 ISBN: 978-1-4939-3578-9. JASP Team, 2018, JASP (Version 0.9).https://jasp-stats.org/. Karege, F., Meary, A., Perroud, N., Jamain, S., Leboyer, M., Ballmann, E., Fernandez, R., Malafosse, A., Schurhoff, F., 2012. Genetic overlap between schizophrenia and bipolar disorder: a study with AKT1 gene variants and clinical phenotypes. Schizophr. Res. 135 (1–3), 8–14. https://doi.org/10.1016/j.schres.2011.12.015. Karege, F., Perroud, N., Schurhoff, F., Meary, A., Marillier, G., Burkhardt, S., Ballmann, E., Fernandez, R., Jamain, S., Leboyer, M., La Harpe, R., Malafosse, A., 2010. Association of AKT1 gene variants and protein expression in both schizophrenia and bipolar disorder. Genes. Brain Behav. 9 (5), 503–511. https://doi.org/10.1111/j. 1601-183X.2010.00578.x. Keck Jr., P.E., McElroy, S.L., Havens, J.R., Altshuler, L.L., Nolen, W.A., Frye, M.A., Suppes, T., Denicoff, K.D., Kupka, R., Leverich, G.S., Rush, A.J., Post, R.M., 2003. Psychosis in bipolar disorder: phenomenology and impact on morbidity and course of illness. Compr. Psychiatry 44 (4), 263–269. https://doi.org/10.1016/S0010-440X (03)00089-0. Kessing, L.V., 2006. Diagnostic subtypes of bipolar disorder in older versus younger adults. Bipolar Disord. 8 (1), 56–64. https://doi.org/10.1111/j.1399-5618.2006. 00278.x. Lappalainen, T., Hannelius, U., Salmela, E., von Dobeln, U., Lindgren, C.M., Huoponen, K., Savontaus, M.L., Kere, J., Lahermo, P., 2009. Population structure in contemporary Sweden–a Y-chromosomal and mitochondrial DNA analysis. Ann. Hum. Genet. 73 (1), 61–73. https://doi.org/10.1111/j.1469-1809.2008.00487.x. Ly, A., Etz, A., Marsman, M., Wagenmakers, E.J., 2018. Replication bayes factors from evidence updating. Behav. Res. Methods. https://doi.org/10.3758/s13428-0181092-x. Ly, A., Verhagen, J., Wagenmakers, E.-J., 2016. Harold Jeffreys’s default bayes factor hypothesis tests: explanation, extension, and application in psychology. J. Math. Psychol. 72, 19–32. https://doi.org/10.1016/j.jmp.2015.06.004. Machado-Vieira, R., Zanetti, M.V., Teixeira, A.L., Uno, M., Valiengo, L.L., Soeiro-deSouza, M.G., Oba-Shinjo, S.M., de Sousa, R.T., Zarate Jr., C.A., Gattaz, W.F., Marie, S.K., 2015. Decreased Akt1/mTOR pathway mRNA expression in short-term bipolar
References Anttila, V., Bulik-Sullivan, B., Finucane, H.K., Walters, R.K., Bras, J., Duncan, L., EscottPrice, V., Falcone, G.J., Gormley, P., Malik, R., Patsopoulos, N.A., Ripke, S., Wei, Z., Yu, D., Lee, P.H., Turley, P., Grenier-Boley, B., Chouraki, V., Kamatani, Y., Berr, C., Letenneur, L., Hannequin, D., Amouyel, P., Boland, A., Deleuze, J.F., Duron, E., Vardarajan, B.N., Reitz, C., Goate, A.M., Huentelman, M.J., Kamboh, M.I., Larson, E.B., Rogaeva, E., St George-Hyslop, P., Hakonarson, H., Kukull, W.A., Farrer, L.A., Barnes, L.L., Beach, T.G., Demirci, F.Y., Head, E., Hulette, C.M., Jicha, G.A., Kauwe, J.S.K., Kaye, J.A., Leverenz, J.B., Levey, A.I., Lieberman, A.P., Pankratz, V.S., Poon, W.W., Quinn, J.F., Saykin, A.J., Schneider, L.S., Smith, A.G., Sonnen, J.A., Stern, R.A., Van Deerlin, V.M., Van Eldik, L.J., Harold, D., Russo, G., Rubinsztein, D.C., Bayer, A., Tsolaki, M., Proitsi, P., Fox, N.C., Hampel, H., Owen, M.J., Mead, S., Passmore, P., Morgan, K., Nothen, M.M., Rossor, M., Lupton, M.K., Hoffmann, P., Kornhuber, J., Lawlor, B., McQuillin, A., Al-Chalabi, A., Bis, J.C., Ruiz, A., Boada, M., Seshadri, S., Beiser, A., Rice, K., van der Lee, S.J., De Jager, P.L., Geschwind, D.H., Riemenschneider, M., Riedel-Heller, S., Rotter, J.I., Ransmayr, G., Hyman, B.T., Cruchaga, C., Alegret, M., Winsvold, B., Palta, P., Farh, K.H., Cuenca-Leon, E., Furlotte, N., Kurth, T., Ligthart, L., Terwindt, G.M., Freilinger, T., Ran, C., Gordon, S.D., Borck, G., Adams, H.H.H., Lehtimaki, T., Wedenoja, J., Buring, J.E., Schurks, M., Hrafnsdottir, M., Hottenga, J.J., Penninx, B., Artto, V., Kaunisto, M., Vepsalainen, S., Martin, N.G., Montgomery, G.W., Kurki, M.I., Hamalainen, E., Huang, H., Huang, J., Sandor, C., Webber, C., Muller-Myhsok, B., Schreiber, S., Salomaa, V., Loehrer, E., Gobel, H., Macaya, A., Pozo-Rosich, P., Hansen, T., Werge, T., Kaprio, J., Metspalu, A., Kubisch, C., Ferrari, M.D., Belin, A.C., van den Maagdenberg, A., Zwart, J.A., Boomsma, D., Eriksson, N., Olesen, J., Chasman, D.I., Nyholt, D.R., Avbersek, A., Baum, L., Berkovic, S., Bradfield, J., Buono, R., Catarino, C.B., Cossette, P., De Jonghe, P., Depondt, C., Dlugos, D., Ferraro, T.N., French, J., Hjalgrim, H., JamnadasKhoda, J., Kalviainen, R., Kunz, W.S., Lerche, H., Leu, C., Lindhout, D., Lo, W., Lowenstein, D., McCormack, M., Moller, R.S., Molloy, A., Ng, P.W., Oliver, K., Privitera, M., Radtke, R., Ruppert, A.K., Sander, T., Schachter, S., Schankin, C., Scheffer, I., Schoch, S., Sisodiya, S.M., Smith, P., Sperling, M., Striano, P., Surges, R., Thomas, G.N., Visscher, F., Whelan, C.D., Zara, F., Heinzen, E.L., Marson, A., Becker, F., Stroink, H., Zimprich, F., Gasser, T., Gibbs, R., Heutink, P., Martinez, M., Morris, H.R., Sharma, M., Ryten, M., Mok, K.Y., Pulit, S., Bevan, S., Holliday, E., Attia, J., Battey, T., Boncoraglio, G., Thijs, V., Chen, W.M., Mitchell, B., Rothwell, P., Sharma, P., Sudlow, C., Vicente, A., Markus, H., Kourkoulis, C., Pera, J., Raffeld, M., Silliman, S., Boraska Perica, V., Thornton, L.M., Huckins, L.M., William Rayner, N., Lewis, C.M., Gratacos, M., Rybakowski, F., Keski-Rahkonen, A., Raevuori, A., Hudson, J.I., Reichborn-Kjennerud, T., Monteleone, P., Karwautz, A., Mannik, K., Baker, J.H., O’Toole, J.K., Trace, S.E., Davis, O.S.P., Helder, S.G., Ehrlich, S., Herpertz-Dahlmann, B., Danner, U.N., van Elburg, A.A., Clementi, M., Forzan, M., Docampo, E., Lissowska, J., Hauser, J., Tortorella, A., Maj, M., Gonidakis, F., Tziouvas, K., Papezova, H., Yilmaz, Z., Wagner, G., Cohen-Woods, S., Herms, S., Julia, A., Rabionet, R., Dick, D.M., Ripatti, S., Andreassen, O.A., Espeseth, T., Lundervold, A.J., Steen, V.M., Pinto, D., Scherer, S.W., Aschauer, H., Schosser, A., Alfredsson, L., Padyukov, L., Halmi, K.A., Mitchell, J., Strober, M., Bergen, A.W., Kaye, W., Szatkiewicz, J.P., Cormand, B., Ramos-Quiroga, J.A., Sanchez-Mora, C., Ribases, M., Casas, M., Hervas, A., Arranz, M.J., Haavik, J., Zayats, T., Johansson, S., Williams, N., Dempfle, A., Rothenberger, A., Kuntsi, J., Oades, R.D., Banaschewski, T., Franke, B., Buitelaar, J.K., Arias Vasquez, A., Doyle, A.E., Reif, A., Lesch, K.P., Freitag, C., Rivero, O., Palmason, H., Romanos, M., Langley, K., Rietschel, M., Witt, S.H., Dalsgaard, S., Borglum, A.D., Waldman, I., Wilmot, B., Molly, N., Bau, C.H.D., Crosbie, J., Schachar, R., Loo, S.K., McGough, J.J., Grevet, E.H., Medland, S.E., Robinson, E., Weiss, L.A., Bacchelli, E., Bailey, A., Bal, V., Battaglia, A., Betancur, C., Bolton, P., Cantor, R., Celestino-Soper, P., Dawson, G., De Rubeis, S., Duque, F., Green, A., Klauck, S.M., Leboyer, M., Levitt, P., Maestrini, E., Mane, S., De-Luca, D.M., Parr, J., Regan, R., Reichenberg, A., Sandin, S., Vorstman, J., Wassink, T., Wijsman, E., Cook, E., Santangelo, S., Delorme, R., Roge, B., Magalhaes, T., Arking, D., Schulze, T.G., Thompson, R.C., Strohmaier, J., Matthews, K., Melle, I., Morris, D., Blackwood, D., McIntosh, A., Bergen, S.E., Schalling, M., Jamain, S., Maaser, A., Fischer, S.B., Reinbold, C.S., Fullerton, J.M., Guzman-Parra, J., Mayoral, F., Schofield, P.R., Cichon, S., Muhleisen, T.W., Degenhardt, F., Schumacher, J., Bauer, M., Mitchell, P.B., Gershon, E.S., Rice, J., Potash, J.B., Zandi, P.P., Craddock, N., Ferrier, I.N., Alda, M., Rouleau, G.A., Turecki, G., Ophoff, R., Pato, C., Anjorin, A., Stahl, E., Leber, M., Czerski, P.M., Cruceanu, C., Jones, I.R., Posthuma, D., Andlauer, T.F.M., Forstner, A.J., Streit, F., Baune, B.T., Air, T., Sinnamon, G., Wray, N.R., MacIntyre, D.J., Porteous, D., Homuth, G., Rivera, M., Grove, J., Middeldorp, C.M., Hickie, I., Pergadia, M., Mehta, D., Smit, J.H., Jansen, R., de Geus, E., Dunn, E., Li, Q.S., Nauck, M., Schoevers, R.A., Beekman, A.T., Knowles, J.A., Viktorin, A., Arnold, P., Barr, C.L., Bedoya-Berrio, G., Bienvenu, O.J., Brentani, H., Burton, C., Camarena, B., Cappi, C., Cath, D., Cavallini, M., Cusi, D., Darrow, S., Denys, D., Derks, E.M., Dietrich, A., Fernandez, T., Figee, M., Freimer, N., Gerber, G., Grados, M., Greenberg, E., Hanna, G.L., Hartmann, A., Hirschtritt, M.E., Hoekstra, P.J., Huang, A., Huyser, C., Illmann, C., Jenike, M., Kuperman, S., Leventhal, B., Lochner, C., Lyon, G.J., Macciardi, F., Madruga-Garrido, M., Malaty, I.A., Maras, A., McGrath, L., Miguel, E.C., Mir, P., Nestadt, G., Nicolini, H., Okun, M.S., Pakstis, A., Paschou, P., Piacentini, J., Pittenger, C., Plessen, K., Ramensky, V., Ramos, E.M., Reus, V., Richter, M.A., Riddle, M.A., Robertson, M.M., Roessner, V., Rosario, M., Samuels, J.F., Sandor, P., Stein, D.J., Tsetsos, F., Van Nieuwerburgh, F., Weatherall, S., Wendland, J.R., Wolanczyk, T., Worbe, Y., Zai, G., Goes, F.S.,
5
Psychiatry Research xxx (xxxx) xxxx
V. Millischer, et al. disorder. Eur. Neuropsychopharmacol. 25 (4), 468–473. https://doi.org/10.1016/j. euroneuro.2015.02.002. Machiela, M.J., Chanock, S.J., 2015. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31 (21), 3555–3557. https://doi.org/10.1093/ bioinformatics/btv402. Macleod, M.R., Michie, S., Roberts, I., Dirnagl, U., Chalmers, I., Ioannidis, J.P., Al-Shahi Salman, R., Chan, A.W., Glasziou, P., 2014. Biomedical research: increasing value, reducing waste. Lancet 383 (9912), 101–104. https://doi.org/10.1016/s01406736(13)62329-6. Maletic, V., Raison, C., 2014. Integrated neurobiology of bipolar disorder. Front Psychiatry 5, 98. https://doi.org/10.3389/fpsyt.2014.00098. McGuffin, P., Rijsdijk, F., Andrew, M., Sham, P., Katz, R., Cardno, A., 2003. The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Arch. Gen. Psychiatry 60 (5), 497–502. https://doi.org/10.1001/archpsyc.60.5. 497. Merikangas, K.R., Jin, R., He, J.P., Kessler, R.C., Lee, S., Sampson, N.A., Viana, M.C., Andrade, L.H., Hu, C., Karam, E.G., Ladea, M., Medina-Mora, M.E., Ono, Y., PosadaVilla, J., Sagar, R., Wells, J.E., Zarkov, Z., 2011. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch. Gen. Psychiatry 68 (3), 241–251. https://doi.org/10.1001/archgenpsychiatry.2011.12. Morey, R.D., Romeijn, J.-W., Rouder, J.N., 2016. The philosophy of bayes factors and the quantification of statistical evidence. J. Math. Psychol. 72, 6–18. https://doi.org/10. 1016/j.jmp.2015.11.001. Munafò, M.R., Nosek, B.A., Bishop, D.V.M., Button, K.S., Chambers, C.D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-.J., Ware, J.J., Ioannidis, J.P.A., 2017. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021. https://doi.org/10.1038/ s41562-016-0021. Open Science Collaboration, 2015. PSYCHOLOGY. Estimating the reproducibility of psychological science. Science 349 (6251) aac4716. https://doi.org/10.1126/ science.aac4716. Potash, J.B., Willour, V.L., Chiu, Y.F., Simpson, S.G., MacKinnon, D.F., Pearlson, G.D., DePaulo Jr., J.R., McInnis, M.G., 2001. The familial aggregation of psychotic symptoms in bipolar disorder pedigrees. Am. J. Psychiatry 158 (8), 1258–1264. https:// doi.org/10.1176/appi.ajp.158.8.1258. R. Core Team, 2017, A language and environment for statistical computing. https://www. R-project.org/. Schaid, D.J., Rowland, C.M., Tines, D.E., Jacobson, R.M., Poland, G.A., 2002. Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am. J. Hum. Genet. 70 (2), 425–434. https://doi.org/10.1086/338688. Simonsohn, U., 2015. Small telescopes: detectability and the evaluation of replication results. Psychol. Sci. 26 (5), 559–569. https://doi.org/10.1177/0956797614567341. Stahl, E.A., Breen, G., Forstner, A.J., McQuillin, A., Ripke, S., Trubetskoy, V., Mattheisen, M., Wang, Y., Coleman, J.R.I., Gaspar, H.A., de Leeuw, C.A., Steinberg, S., Pavlides, J.M.W., Trzaskowski, M., Byrne, E.M., Pers, T.H., Holmans, P.A., Richards, A.L., Abbott, L., Agerbo, E., Akil, H., Albani, D., Alliey-Rodriguez, N., Als, T.D., Anjorin, A., Antilla, V., Awasthi, S., Badner, J.A., Bækvad-Hansen, M., Barchas, J.D., Bass, N., Bauer, M., Belliveau, R., Bergen, S.E., Pedersen, C.B., Bøen, E., Boks, M.P., Boocock, J., Budde, M., Bunney, W., Burmeister, M., Bybjerg-Grauholm, J., Byerley, W., Casas, M., Cerrato, F., Cervantes, P., Chambert, K., Charney, A.W., Chen, D., Churchhouse, C., Clarke, T.-.K., Coryell, W., Craig, D.W., Cruceanu, C., Curtis, D., Czerski, P.M., Dale, A.M., de Jong, S., Degenhardt, F., Del-Favero, J., DePaulo, J.R., Djurovic, S., Dobbyn, A.L., Dumont, A., Elvsåshagen, T., Escott-Price, V., Fan, C.C., Fischer, S.B., Flickinger, M., Foroud, T.M., Forty, L., Frank, J., Fraser, C., Freimer, N.B., Frisén, L.,
Gade, K., Gage, D., Garnham, J., Giambartolomei, C., Pedersen, M.G., Goldstein, J., Gordon, S.D., Gordon-Smith, K., Green, E.K., Green, M.J., Greenwood, T.A., Grove, J., Guan, W., Guzman-Parra, J., Hamshere, M.L., Hautzinger, M., Heilbronner, U., Herms, S., Hipolito, M., Hoffmann, P., Holland, D., Huckins, L., Jamain, S., Johnson, J.S., Juréus, A., Kandaswamy, R., Karlsson, R., Kennedy, J.L., Kittel-Schneider, S., Knowles, J.A., Kogevinas, M., Koller, A.C., Kupka, R., Lavebratt, C., Lawrence, J., Lawson, W.B., Leber, M., Lee, P.H., Levy, S.E., Li, J.Z., Liu, C., Lucae, S., Maaser, A., MacIntyre, D.J., Mahon, P.B., Maier, W., Martinsson, L., McCarroll, S., McGuffin, P., McInnis, M.G., McKay, J.D., Medeiros, H., Medland, S.E., Meng, F., Milani, L., Montgomery, G.W., Morris, D.W., Mühleisen, T.W., Mullins, N., Nguyen, H., Nievergelt, C.M., Adolfsson, A.N., Nwulia, E.A., O'Donovan, C., Loohuis, L.M.O., Ori, A.P.S., Oruc, L., Ösby, U., Perlis, R.H., Perry, A., Pfennig, A., Potash, J.B., Purcell, S.M., Regeer, E.J., Reif, A., Reinbold, C.S., Rice, J.P., Rivas, F., Rivera, M., Roussos, P., Ruderfer, D.M., Ryu, E., Sánchez-Mora, C., Schatzberg, A.F., Scheftner, W.A., Schork, N.J., Shannon Weickert, C., Shehktman, T., Shilling, P.D., Sigurdsson, E., Slaney, C., Smeland, O.B., Sobell, J.L., Søholm Hansen, C., Spijker, A.T., St Clair, D., Steffens, M., Strauss, J.S., Streit, F., Strohmaier, J., Szelinger, S., Thompson, R.C., Thorgeirsson, T.E., Treutlein, J., Vedder, H., Wang, W., Watson, S.J., Weickert, T.W., Witt, S.H., Xi, S., Xu, W., Young, A.H., Zandi, P., Zhang, P., Zöllner, S., Adolfsson, R., Agartz, I., Alda, M., Backlund, L., Baune, B.T., Bellivier, F., Berrettini, W.H., Biernacka, J.M., Blackwood, D.H.R., Boehnke, M., Børglum, A.D., Corvin, A., Craddock, N., Daly, M.J., Dannlowski, U., Esko, T., Etain, B., Frye, M., Fullerton, J.M., Gershon, E.S., Gill, M., Goes, F., Grigoroiu-Serbanescu, M., Hauser, J., Hougaard, D.M., Hultman, C.M., Jones, I., Jones, L.A., Kahn, R.S., Kirov, G., Landén, M., Leboyer, M., Lewis, C.M., Li, Q.S., Lissowska, J., Martin, N.G., Mayoral, F., McElroy, S.L., McIntosh, A.M., McMahon, F.J., Melle, I., Metspalu, A., Mitchell, P.B., Morken, G., Mors, O., Mortensen, P.B., Müller-Myhsok, B., Myers, R.M., Neale, B.M., Nimgaonkar, V., Nordentoft, M., Nöthen, M.M., O'Donovan, M.C., Oedegaard, K.J., Owen, M.J., Paciga, S.A., Pato, C., Pato, M.T., Posthuma, D., Ramos-Quiroga, J.A., Ribasés, M., Rietschel, M., Rouleau, G.A., Schalling, M., Schofield, P.R., Schulze, T.G., Serretti, A., Smoller, J.W., Stefansson, H., Stefansson, K., Stordal, E., Sullivan, P.F., Turecki, G., Vaaler, A.E., Vieta, E., Vincent, J.B., Werge, T., Nurnberger, J.I., Wray, N.R., Di Florio, A., Edenberg, H.J., Cichon, S., Ophoff, R.A., Scott, L.J., Andreassen, O.A., Kelsoe, J., Sklar, P.e.Q.C., Consortium, B., the Bipolar Disorder Working Group of the Psychiatric Genomics, C., 2019. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51 (5), 793–803. https://doi.org/ 10.1038/s41588-019-0397-8. Toyota, T., Yamada, K., Detera-Wadleigh, S.D., Yoshikawa, T., 2003. Analysis of a cluster of polymorphisms in AKT1 gene in bipolar pedigrees: a family-based association study. Neurosci. Lett. 339 (1), 5–8. https://doi.org/10.1016/s0304-3940(02) 01428-3. Verhagen, J., Wagenmakers, E.J., 2014. Bayesian tests to quantify the result of a replication attempt. J. Exp. Psychol. Gen. 143 (4), 1457–1475. https://doi.org/10. 1037/a0036731. Wagenmakers, E.-J., Morey, R.D., Lee, M.D., 2016a. Bayesian benefits for the pragmatic researcher. Curr. Dir. Psychol. Sci. 25 (3), 169–176. https://doi.org/10.1177/ 0963721416643289. Wagenmakers, E.J., Verhagen, J., Ly, A., 2016b. How to quantify the evidence for the absence of a correlation. Behav. Res. Methods 48 (2), 413–426. https://doi.org/10. 3758/s13428-015-0593-0. Zollner, S., Pritchard, J.K., 2007. Overcoming the winner's curse: estimating penetrance parameters from case-control data. Am. J. Hum. Genet. 80 (4), 605–615. https://doi. org/10.1086/512821.
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AKT1 and genetic vulnerability to bipolar disorder Vincent Millischera,b, , Granville J. Mathesonc,d, Lina Martinssonc,d, Inger Römer Ekc,d, Martin Schallinga,b, Catharina Lavebratta,b, Lena Backlunda,c,d ⁎
a
Neurogenetics Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden d Center for Psychiatric Research and Education, Stockholm City Council, Sweden b c
ARTICLE INFO
ABSTRACT
Keywords: Bipolar disorder Psychosis, AKT1 Genetic association Haplotype association
AKT1 encodes a serine/threonine kinase that has as one of its best-known substrates glycogen synthase kinase-3 (GSK3), a primary target for lithium. AKT1 has been previously been implicated as a vulnerability gene for bipolar disorder (BD). We aimed to associate genetic variants in the AKT1 gene with subgroups of BD. BD patients from a Swedish cohort (N = 831) were phenotyped in regards to their psychotic episodes according to mood-congruence in depression and manias, and compared to controls (N = 1,496). All participants were genotyped for SNPs in AKT1 previously implicated to have a role: rs3730358, rs1130214 and rs3803300. None of the effects reported in earlier studies were statistically significant, including the association between rs3803300 and BD without any psychotic symptoms, rs3803300 and mood-congruent psychosis, rs3803300 and the combined groups, as well as the association between the haplotypes formed by rs3730358 and rs1130214 and risk for BD. In a Bayesian analysis, all Bayes’ Factors using default priors supported the null hypothesis in the replication set by a factor of between 5 and 1300 times. Analysis of genome wide association data did not reveal any association between BD and the AKT1 region. We conclude AKT1 is less likely to be a vulnerability gene in BD.
1. Introduction Bipolar disorder (BD) is a genetically complex psychiatric disorder characterized by recurrent episodes of depression and mania. It affects approximately 2% of the world population (Merikangas et al., 2011) and has been ranked as a top cause of lifelong disability by the World Health Organization. The heritability of BD is estimated to be as high as 85% based on twin studies (McGuffin et al., 2003). Like other complex disorders, BD is characterized by a polygenic inheritance, based on many common variants each with small individual effect sizes (Maletic and Raison, 2014). So far, genome wide association studies (GWAS) have identified 30 loci associated with bipolar disorder (Stahl et al., 2019). Although BD and schizophrenia (SCZ) are viewed as two distinct entities in both the DSM and the ICD classification, there is convincing evidence for a genetic overlap between these disorders (Anttila et al., 2018). Furthermore, psychosis, a key symptom in SCZ, is also very common in BD, with the lifetime prevalence of psychotic episodes in BD estimated to be around 60% (Goodwin and Jamison, 2007). Psychotic episodes are referred to as mood-congruent when corresponding with the mood polarity, or as mood-incongruent when they are not. Mood⁎
incongruent psychotic episodes are thought to signify a more severe form of the disease and tend to aggregate in families (Goes et al., 2007; Potash et al., 2001). AKT1 encodes for a serine/threonine kinase, that plays an important role in several normal and pathological cell processes. One of its bestknown substrates is glycogen synthase kinase-3 (GSK3), a primary target for lithium, the first line therapy for BD (Beaulieu, 2012). Common genetic variation in AKT1 was first reported to be associated with SCZ by Emamian et al. (Emamian et al., 2004), but AKT1 has also been studied in the context of BD. AKT1 was initially defined as a candidate gene by a moderate linkage signal to a region in chromosome 14q22-32 (Toyota et al., 2003). Furthermore, Karege et al. (2010) reported several haplotypes of AKT1 SNPs to be associated with BD. The TC haplotype formed by rs1130214/rs3730358, two SNPs that had previously been associated with SCZ, was protective against both SCZ and BD; the GC haplotype of the same SNPs was reported as a risk haplotype (Karege et al., 2010). Two years later, a similar study from the same group (234 SCZ + 130 BD + 165 controls) reported association of three other AKT1 SNPs to different sub-phenotypes of BD (mood congruent psychosis and no psychotic history) (Karege et al., 2012). Furthermore, two studies have analyzed AKT1 mRNA expression in BD with conflicting
Corresponding author: Neurogenetics Unit, Center for Molecular Medicine, Karolinska University Hospital, L8:00, 171 76 Stockholm, Sweden. E-mail address: [email protected] (V. Millischer).
https://doi.org/10.1016/j.psychres.2019.112677 Received 27 August 2019; Received in revised form 31 October 2019; Accepted 3 November 2019 0165-1781/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Please cite this article as: Vincent Millischer, et al., Psychiatry Research, https://doi.org/10.1016/j.psychres.2019.112677
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results. While, in the context of first-episode psychosis, AKT1 mRNA expression was found to be higher in peripheral blood from manic patients compared to patients in the schizophrenia spectrum or healthy controls (Gouvea et al., 2016), AKT1 mRNA expression was shown to be decreased in BD depression versus healthy controls (MachadoVieira et al., 2015). Although AKT1 has a potential role in lithium metabolism, no studies have been directed towards genetics of AKT1 in this context. As a first step in elucidating a potential role of AKT1 in BD and in lithium response, we aimed to replicate the findings of the studies published by Karege et al. (Karege et al., 2012, 2010) using a large, wellphenotyped BD cohort (N = 831) followed by a GWAS analysis based on Psychiatric Genomics Consortium (PGC) data sets.
inference (Ly et al., 2016; Morey et al., 2016; Wagenmakers et al., 2016a). BFs compare the predictive adequacy of two competing hypotheses with one another, and the BF itself can be interpreted as the relative probability of having observed the given data under one hypothesis compared to the other. In this way it is a measure of the relative statistical evidence for competing hypotheses in contrast to orthodox frequentist statistics (Morey et al., 2016). In this way, it is ideally suited for replication studies, for which the outcomes of a previous study, including their uncertainty, can be compared to a skeptic's null hypothesis of no effect (Ly et al., 2018; Verhagen and Wagenmakers, 2014). This has been called the replication BF (Ly et al., 2018; Verhagen and Wagenmakers, 2014; Wagenmakers et al., 2016b). Both BFs using default priors and replication BFs are presented: the former compares the null hypothesis with a hypothesis that there may be an effect about whose magnitude we know little to nothing, while the latter compares the null hypothesis with the effect found before as well as its uncertainty. The replication BF can be conceptualized as representing the relative likelihood of the new data originating under each of these two hypotheses, or alternatively, the relative change in the likelihood of the estimated effect being equal to 0 before and after having observed the data from the replication study (Wagenmakers et al., 2016b). BFs were calculated for each association using the summary statistics provided in the original studies (Karege et al., 2012, 2010), and using the new data, and are presented for each association. BFs using default priors were calculated for the original data, the replication data, as well as the combined data using Bayesian contingency tables in JASP using independent multinomial sampling. Replication BFs were calculated as described by Ly et al. (Ly et al., 2018). Replication BFs were only calculated for those comparisons for which the original study reported statistically significant differences, and therefore rejected the null hypothesis.
2. Method 2.1. Cohort description Patients (n = 831) with the clinical diagnosis of BD were consecutively recruited between 2003 and 2010, mostly from the Unit of Affective disorders at Huddinge University Hospital, Stockholm, Sweden. After informed consent, the bipolar diagnosis was validated and phenotypes such as psychotic symptoms during episodes were assessed according to Schedules for Clinical Assessment in Neuropsychiatry (SCAN; based on DSM IV). Psychotic episodes were sub-grouped according to mood congruence as previously described (Backlund et al., 2012). Controls matched for age and gender were recruited from the anonymous blood donor controls from the Stockholm area (ABD controls; N = 1496). The study was approved in accordance with the Helsinki Declaration of 1975 by the Regional Ethical Review Board in Stockholm. The informed consent process was both verbal and written, during a visit to a special trained psychiatric nurse. All bipolar participants were in euthymic phase and had full capacity to consent.
2.5. Analysis of data from genome-wide association studies from the psychiatric genomics consortium (PGC)
2.2. Genotyping Peripheral blood samples were drawn and genomic DNA was extracted by standard procedures. The samples were genotyped for the SNPs rs3730358, rs1130214 and rs3803300 in the AKT1 using TaqMan SNP genotyping assays on QuantStudio 7 Flex instrument (Applied Biosystems, Foster City, CA, USA). The genotyping efficiency was 93%.
Known SNPs in a 20 kb window upstream and downstream of AKT1 were obtained using the biomaRt package (Durinck et al., 2005). The summary data for these SNPs was obtained for the genome-wide association studies (GWAS) on BD (Stahl et al., 2019) and schizophrenia (Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2018) from the website of the PGC and plotted using the gviz package (Hahne and Ivanek, 2016).
2.3. Statistics All statistical analysis was performed using R Statistical Software (R Core Team, 2017) and JASP (JASP Team, 2018) for Bayesian tests. The measure D’ of linkage disequilibrium (LD) was calculated between SNPs using LD Link, using data from populations of European descent (Machiela and Chanock, 2015). When not reported in the publications, odds ratios (OR) and 95% confidence intervals (95% CI) were calculated from reported minor allele frequencies (MAF) and sample sizes using a logistic regression with an allelic model. The genotype associations were calculated for the SNPs which were concluded to be significantly associated with groups, as well as for three other reported SNPs using logistic regression. The haplotype frequencies were estimated using the haplo.stats R package (Schaid et al., 2002). Haplotype associations were calculated using weighted logistic regression, using the posterior probabilities for each haplotype as weights. Summary OR and 95% CI were calculated using the estimates from the publications pooled with our own data.
3. Results The demographic description of the cohort can be found in Table 1. 67% of all patients where the information was available had at least one psychotic episode in their lifetime, corresponding to previously published cohorts (Goodwin and Jamison, 2007). We first studied five previously reported statistically significant associations (Karege et al., 2012, 2010), namely the association between rs3803300 and BD without any psychotic symptoms (“Group 1”, Nreplication = 268), rs3803300 and mood-congruent psychosis (“Group 2”, Nreplication = 436), rs3803300 and the combined groups (“Group 1 + 2”, Nreplication = 704), as well as the association between the core haplotype TC – formed by rs1130214/rs3730358 – and reduced risk for BD, and the haplotype GC – formed by the same SNPs – and increased risk for BD (Nreplication = 831). Furthermore, we also tested for direct association between the genotyped SNPs and BD that had been reported by (Karege et al., 2010) (Table 2). The power in the Swedish replication cohort to replicate the main effects was above 99% for all results that had been reported as statistically significant. The power to replicate an effect size equal to the half of the reported effect size of the original studies was above 94% for all of these comparisons (data not shown). Following, the “small
2.4. Bayesian statistics We also performed Bayesian hypothesis testing to evaluate the success of the replication attempt, making use of Bayes Factors (BFs) for 2
3
98.6% 98.5% 100% 100% 2.1 2.3 0.41–0.911 1.09–2.151 0.611 1.531 yes yes TC (protective) GC (risk)
Haplotype
Calculated OR and 95%CI using an allelic model, based on frequency tables from Karege (2010). Gender and age corrected OR and 95%CI, reported in Karege (2012). 3 Gender corrected allelic model, logistic regression MAF: Minor allele frequency, OR: Odd's ratio, 95%CI: 95% Confidence interval, BF: Bayes factor. Group 1: BD without any psychotic symptoms. Group 2: BD with mood-congruent psychosis. 2
0.069 0.484 0.79–1.05 1.02–1.30 0.91 1.15 0.037 0.209 0.82–1.11 0.99–1.29 0.95 1.13 % Patients 19.4 69.1 % Controls 20.1 66.4 % Patients 15.7 66.9 % Controls 22.4 56.9 Controls / BD Controls / BD
1
0.032 0.21
– – – – – – 0.028 1.03 0.09 0.88–1.36 0.74–0.96 0.72–1.03 1.09 0.84 0.86 0.022 0.472 0.13 0.79–1.26 0.74–0.98 0.69–1.02 1.00 0.85 0.84 7.9 27.8 11.5 8.0 31.2 13.4 – – – – – – 0.96–3.64 1 0.53–1.071 0.59–1.461 no no no rs3803300 rs1130214 rs3730358
4.76 34.62 15.84
8.55 28.45 14.92
1.871 0.751 0.931
0.242 0.332 0.077
1235.5 0.00081 0.124 0.99–1.52 1.23 0.023 0.77–1.26 0.99 7.9 8.0 94.1% 100% 1.7–5.82 yes rs3803300
4.7
2.25 10.4 yes rs3803300
4.7
13.5
3.142
153.2
30.6 0.033 0.032 0.84–1.43 1.1 0.028 0.72–1.32 0.98 7.8 8.0 94.9% 100% 2 2
1.0–4.8
1.9–7.22 yes rs3803300
4.7
15.9
3.782
0.98
1327.0 0.00075 0.634 1.09–1.87 1.43 0.033 0.7–1.42 1 8.0 8.0 76.7% 100%
MAF (%) Controls Power d33% Power main effect 95% CI OR MAF (%) Patients
Controls / BD group 1 Controls / BD group 2 Controls / BD group 1+2 Controls / BD Controls / BD Controls / BD
The importance of identifying molecules of relevance to lithium response and BD pathophysiology is critical, as both are still poorly understood. AKT1 is an attractive candidate in this context given its role in lithium signaling, being supported by animal and cell-based studies. Two human genetic studies on BD (Karege et al., 2012, 2010) also point to an involvement of AKT1 in BD. However, given the fact that these studies were rather small, we performed a combined replication study and GWAS based analysis of the potential role of AKT1 in BD. Using the ‘small telescopes’ method (Simonsohn, 2015), we estimated that our study had between 70% and 95% power to detect effect
841.3
Replication BF10 95% CI
BF10
4. Discussion
MAF (%) Controls
Table 2 Summary statistics of the original studies, the replication set and the combined data.
BF10
Swedish replication study
MAF (%) Patients
OR3
95% CI
BF10
telescopes” technique suggested by Simonsohn et al. (Simonsohn, 2015), we calculated the power for our replication sample to detect the effect size which the original studies had 33% power to detect (d33%). For three out of the four associations, our sample had a power of greater than 94%, and for the association between rs3803300 and group 1, the power was 77%. We therefore conclude that our sample was adequately powered to replicate the results of the original studies (Table 2, columns Power main effect and Power d33%). None of the statistically significant effects reported in the original studies were statistically significant in our replication study. In the Bayesian analysis, all BFs using default priors supported the null hypothesis in the replication set, with moderate to strong evidence, for comparisons which were significant in the original investigation by Karege et al. (Karege et al., 2012, 2010). Replication BFs also favored the null hypothesis relative to the original reported results, i.e. an unsuccessful replication, in all cases with moderate to strong evidence. This means that the data was more likely to have occurred under the null hypothesis than under the originally reported conclusions by a factor of between 5 and 1327 times (Table 2, column Replication BF01). Although the 95% confidence interval for the control-to-BD GC risk haplotype did not overlap with zero in the combined data, after taking multiple comparisons into account, this result would no longer be considered significant (Table 2, last row). Regarding the original findings in Karege et al. which were not significant (Karege et al., 2012, 2010) (Table 2, row 4–6), we observed similar results: all BFs supported the null hypothesis in the replication set, and in the combined data, BFs either supported the null or provided no evidence for either hypothesis. We then compared our findings to the summary statistics of the latest genome-wide association studies for BD (Stahl et al., 2019) and schizophrenia (Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2018). None of the SNPs within a 20 kb window of AKT1 reached genome-wide significance for either of the disorders, nor the less stringent significance threshold of 5.10−5 (Fig. 1).
OR
% over all patients.
Significance reported
1
78% 12% 3% 6% 67% 45% 22% 23% 4% 4%
SNP
651 102 24 54 533 358 175 186 35 30
Comparision groups
Diagnosis Bipolar Type I Bipolar Type II Schizoaffective disorder NOS Psychosis Mood congruent in mania Mood incongruent in mania Mood congruent in depression Mood incongruent in depression Information not available1
Combined studies
57.5 46.6
Original studies
Sex (% women) Age (mean ± SD)
Replication BF01
Table 1 Subject demographics.
30.9 4.69
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Fig. 1. GWAS summary data for bipolar disorder and schizophrenia. Summary statistics from the latest BD and schizophreniaGWAS presented as –log10(p-value) of SNPs in a 20 kb window upstream and downstream of AKT1. The red vertical lines indicate the three SNPs genotyped in this study: 1, rs3730358; 2, rs1130214; 3, rs3803300. The black horizontal lines indicate the genome wide significance threshold (5.10−8), as well as an indicative threshold of 5.10−5.
sizes that the authors of the original articles would have considered too small to be of interest (i.e. the effect sizes for which the original studies had only 33% power), and for this reason, we can consider the Swedish replication sample sufficiently powered statistically. We were not able to replicate any of the five associations at a significance level of α = 0.05, and our Bayesian analysis suggested that the null hypothesis was more likely than the originally reported conclusions in all cases. For the three associations reported in the later study (Karege et al., 2012), the replication OR did not fall within the predicted 95%CI, nor did the OR of the combined data reach statistical significance. The replication BFs for these three associations show that the replication data are much better predicted by the null hypothesis than by the alternative hypothesis – an association between the SNPs and subphenotypes of BD – described by the original conclusions. Regarding the associations reported in the first study (Karege et al., 2010), we did not have enough statistical power to detect the lowest effect sizes predicted by Karege et al. Indeed, although the replication OR fell within the predicted 95%CI, they did not reach significance with α = 0.05. However, the evidence against the alternative hypothesis informed by the original data set is very strong for the “protective” (TC) haplotype (replication BF01 = 30.9). The patients were representative for patients with BD, due to the fact that all participants were recruited from clinics with responsibility for bipolar patients in the catchment area. The controls were also recruited from Stockholm. Both samples were therefore obtained from an ethnically homogeneous population (Lappalainen et al., 2009). The sample size in our study was comparatively large and the frequency of psychosis in episodes was 67% in the bipolar sample, close to the estimated prevalence, which strengthens the representativity of our sample (Keck et al., 2003; Kessing, 2006; McGuffin et al., 2003). Finally, we strengthened our findings using data from the latest GWAS for BD and SCZ: no associations between these disorders and AKT1 were found in these samples. However, there are also a number of limitations in this study. For example, the assessment of phenotypes was retrospective, even if we tried to minimize variability by letting only two investigators (one senior psychiatrist and one special trained nurse)
perform all personal interviews and reading of the medical records. Another weakness of the study was the lack of detailed information about healthy controls recruited from blood donors. Judging from our findings, the data is more likely to have occurred under the null hypothesis than under the hypothesis corresponding to the originally reported conclusions by a factor 5–1300. Replication failure is a common problem in the biomedical and behavioral sciences, with replication rates much lower than what would be desirable (Begley and Ellis, 2012; Open Science Collaboration, 2015), resulting in a great deal of wasted time and resources (Macleod et al., 2014). There are threats to the veracity of scientific findings at every stage of the scientific process, as for example the overestimation of true genetic effect sizes, commonly known as “winner's curse” (Zollner and Pritchard, 2007). Replication is the ultimate standard by which published claims can be judged, replication studies are therefore of critical importance for the progress of these fields (Munafò et al., 2017). Declaration of Competing Interest This project was supported by grants from Karolinska Institutet, the Swedish Research Council, The Söderström-Königska Foundation, and Psychiatry Southwest, Stockholm. Financial support was also provided through the regional agreement on medical training and clinical research (ALF) between the Stockholm County Council and Karolinska Institutet. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgement This project was supported by grants from Karolinska Institutet, the Swedish Research Council, the Söderström-Königska Foundation, the Swedish Brain Foundation, and Psychiatry Southwest, Stockholm. Financial support was also provided through the regional agreement on medical training and clinical research (ALF) between the Stockholm County Council and Karolinska Institutet. The funders had no role in study design, data collection and analysis, decision to publish, or 4
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preparation of the manuscript. We want to thank all the patients who participated in this study. The authors would also like to thank Ninni Mu, MSc for skillful assistance.
McLaughlin, N., Nestadt, P.S., Grabe, H.J., Depienne, C., Konkashbaev, A., Lanzagorta, N., Valencia-Duarte, A., Bramon, E., Buccola, N., Cahn, W., Cairns, M., Chong, S.A., Cohen, D., Crespo-Facorro, B., Crowley, J., Davidson, M., DeLisi, L., Dinan, T., Donohoe, G., Drapeau, E., Duan, J., Haan, L., Hougaard, D., KarachanakYankova, S., Khrunin, A., Klovins, J., Kucinskas, V., Lee Chee Keong, J., Limborska, S., Loughland, C., Lonnqvist, J., Maher, B., Mattheisen, M., McDonald, C., Murphy, K.C., Nenadic, I., van Os, J., Pantelis, C., Pato, M., Petryshen, T., Quested, D., Roussos, P., Sanders, A.R., Schall, U., Schwab, S.G., Sim, K., So, H.C., Stogmann, E., Subramaniam, M., Toncheva, D., Waddington, J., Walters, J., Weiser, M., Cheng, W., Cloninger, R., Curtis, D., Gejman, P.V., Henskens, F., Mattingsdal, M., Oh, S.Y., Scott, R., Webb, B., Breen, G., Churchhouse, C., Bulik, C.M., Daly, M., Dichgans, M., Faraone, S.V., Guerreiro, R., Holmans, P., Kendler, K.S., Koeleman, B., Mathews, C.A., Price, A., Scharf, J., Sklar, P., Williams, J., Wood, N.W., Cotsapas, C., Palotie, A., Smoller, J.W., Sullivan, P., Rosand, J., Corvin, A., Neale, B.M., Schott, J.M., Anney, R., Elia, J., Grigoroiu-Serbanescu, M., Edenberg, H.J., Murray, R., 2018. Analysis of shared heritability in common disorders of the brain. Science 360 (6395). https:// doi.org/10.1126/science.aap8757. Backlund, L., Lavebratt, C., Frisen, L., Nikamo, P., Hukic Sudic, D., Traskman-Bendz, L., Landen, M., Edman, G., Vawter, M.P., Osby, U., Schalling, M., 2012. P2RX7: expression responds to sleep deprivation and associates with rapid cycling in bipolar disorder type 1. PLoS ONE 7 (8), e43057. https://doi.org/10.1371/journal.pone. 0043057. Beaulieu, J.M., 2012. A role for Akt and glycogen synthase kinase-3 as integrators of dopamine and serotonin neurotransmission in mental health. J Psychiatry Neurosci. 37 (1), 7–16. https://doi.org/10.1503/jpn.110011. Begley, C.G., Ellis, L.M., 2012. Drug development: raise standards for preclinical cancer research. Nature 483 (7391), 531–533. https://doi.org/10.1038/483531a. Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2018. Genomic dissection of bipolar disorder and schizophrenia, including 28 Subphenotypes. Cell 173 (7) 1705-1715.e1716. https://doi.org/10.1016/ j.cell.2018.05.046. Durinck, S., Moreau, Y., Kasprzyk, A., Davis, S., De Moor, B., Brazma, A., Huber, W., 2005. BioMart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21 (16), 3439–3440. https://doi.org/10. 1093/bioinformatics/bti525. Emamian, E.S., Hall, D., Birnbaum, M.J., Karayiorgou, M., Gogos, J.A., 2004. Convergent evidence for impaired AKT1-GSK3beta signaling in schizophrenia. Nat. Genet. 36 (2), 131–137. https://doi.org/10.1038/ng1296. Goes, F.S., Zandi, P.P., Miao, K., McMahon, F.J., Steele, J., Willour, V.L., Mackinnon, D.F., Mondimore, F.M., Schweizer, B., Nurnberger Jr., J.I., Rice, J.P., Scheftner, W., Coryell, W., Berrettini, W.H., Kelsoe, J.R., Byerley, W., Murphy, D.L., Gershon, E.S., Bipolar Disorder Phenome, G., Depaulo Jr., J.R., McInnis, M.G., Potash, J.B., 2007. Mood-incongruent psychotic features in bipolar disorder: familial aggregation and suggestive linkage to 2p11-q14 and 13q21-33. Am. J. Psychiatry 164 (2), 236–247. https://doi.org/10.1176/ajp.2007.164.2.236. Goodwin, F.K., Jamison, K.R., 2007. Manic-Depressive Illness: Bipolar Disorders and Recurrent Depression, Band 1, 2 ed. Oxford University Press ISBN: 9780195135794. Gouvea, E.S., Ota, V.K., Noto, C., Santoro, M.L., Spindola, L.M., Moretti, P.N., Carvalho, C.M., Xavier, G., Rios, A.C., Sato, J.R., Hayashi, M.A., Brietzke, E., Gadelha, A., Bressan, R.A., Cordeiro, Q., Belangero, S.I., 2016. Gene expression alterations related to mania and psychosis in peripheral blood of patients with a first episode of psychosis. Transl. Psychiatry 6 (10), e908. https://doi.org/10.1038/tp.2016.159. Hahne, F., Ivanek, R., 2016. Visualizing genomic data using gviz and bioconductor. In: Mathé, E., Davis, S. (Eds.), Statistical Genomics: Methods and Protocols. Springer New York, New York, NY, pp. 335–351 ISBN: 978-1-4939-3578-9. JASP Team, 2018, JASP (Version 0.9).https://jasp-stats.org/. Karege, F., Meary, A., Perroud, N., Jamain, S., Leboyer, M., Ballmann, E., Fernandez, R., Malafosse, A., Schurhoff, F., 2012. Genetic overlap between schizophrenia and bipolar disorder: a study with AKT1 gene variants and clinical phenotypes. Schizophr. Res. 135 (1–3), 8–14. https://doi.org/10.1016/j.schres.2011.12.015. Karege, F., Perroud, N., Schurhoff, F., Meary, A., Marillier, G., Burkhardt, S., Ballmann, E., Fernandez, R., Jamain, S., Leboyer, M., La Harpe, R., Malafosse, A., 2010. Association of AKT1 gene variants and protein expression in both schizophrenia and bipolar disorder. Genes. Brain Behav. 9 (5), 503–511. https://doi.org/10.1111/j. 1601-183X.2010.00578.x. Keck Jr., P.E., McElroy, S.L., Havens, J.R., Altshuler, L.L., Nolen, W.A., Frye, M.A., Suppes, T., Denicoff, K.D., Kupka, R., Leverich, G.S., Rush, A.J., Post, R.M., 2003. Psychosis in bipolar disorder: phenomenology and impact on morbidity and course of illness. Compr. Psychiatry 44 (4), 263–269. https://doi.org/10.1016/S0010-440X (03)00089-0. Kessing, L.V., 2006. Diagnostic subtypes of bipolar disorder in older versus younger adults. Bipolar Disord. 8 (1), 56–64. https://doi.org/10.1111/j.1399-5618.2006. 00278.x. Lappalainen, T., Hannelius, U., Salmela, E., von Dobeln, U., Lindgren, C.M., Huoponen, K., Savontaus, M.L., Kere, J., Lahermo, P., 2009. Population structure in contemporary Sweden–a Y-chromosomal and mitochondrial DNA analysis. Ann. Hum. Genet. 73 (1), 61–73. https://doi.org/10.1111/j.1469-1809.2008.00487.x. Ly, A., Etz, A., Marsman, M., Wagenmakers, E.J., 2018. Replication bayes factors from evidence updating. Behav. Res. Methods. https://doi.org/10.3758/s13428-0181092-x. Ly, A., Verhagen, J., Wagenmakers, E.-J., 2016. Harold Jeffreys’s default bayes factor hypothesis tests: explanation, extension, and application in psychology. J. Math. Psychol. 72, 19–32. https://doi.org/10.1016/j.jmp.2015.06.004. Machado-Vieira, R., Zanetti, M.V., Teixeira, A.L., Uno, M., Valiengo, L.L., Soeiro-deSouza, M.G., Oba-Shinjo, S.M., de Sousa, R.T., Zarate Jr., C.A., Gattaz, W.F., Marie, S.K., 2015. Decreased Akt1/mTOR pathway mRNA expression in short-term bipolar
References Anttila, V., Bulik-Sullivan, B., Finucane, H.K., Walters, R.K., Bras, J., Duncan, L., EscottPrice, V., Falcone, G.J., Gormley, P., Malik, R., Patsopoulos, N.A., Ripke, S., Wei, Z., Yu, D., Lee, P.H., Turley, P., Grenier-Boley, B., Chouraki, V., Kamatani, Y., Berr, C., Letenneur, L., Hannequin, D., Amouyel, P., Boland, A., Deleuze, J.F., Duron, E., Vardarajan, B.N., Reitz, C., Goate, A.M., Huentelman, M.J., Kamboh, M.I., Larson, E.B., Rogaeva, E., St George-Hyslop, P., Hakonarson, H., Kukull, W.A., Farrer, L.A., Barnes, L.L., Beach, T.G., Demirci, F.Y., Head, E., Hulette, C.M., Jicha, G.A., Kauwe, J.S.K., Kaye, J.A., Leverenz, J.B., Levey, A.I., Lieberman, A.P., Pankratz, V.S., Poon, W.W., Quinn, J.F., Saykin, A.J., Schneider, L.S., Smith, A.G., Sonnen, J.A., Stern, R.A., Van Deerlin, V.M., Van Eldik, L.J., Harold, D., Russo, G., Rubinsztein, D.C., Bayer, A., Tsolaki, M., Proitsi, P., Fox, N.C., Hampel, H., Owen, M.J., Mead, S., Passmore, P., Morgan, K., Nothen, M.M., Rossor, M., Lupton, M.K., Hoffmann, P., Kornhuber, J., Lawlor, B., McQuillin, A., Al-Chalabi, A., Bis, J.C., Ruiz, A., Boada, M., Seshadri, S., Beiser, A., Rice, K., van der Lee, S.J., De Jager, P.L., Geschwind, D.H., Riemenschneider, M., Riedel-Heller, S., Rotter, J.I., Ransmayr, G., Hyman, B.T., Cruchaga, C., Alegret, M., Winsvold, B., Palta, P., Farh, K.H., Cuenca-Leon, E., Furlotte, N., Kurth, T., Ligthart, L., Terwindt, G.M., Freilinger, T., Ran, C., Gordon, S.D., Borck, G., Adams, H.H.H., Lehtimaki, T., Wedenoja, J., Buring, J.E., Schurks, M., Hrafnsdottir, M., Hottenga, J.J., Penninx, B., Artto, V., Kaunisto, M., Vepsalainen, S., Martin, N.G., Montgomery, G.W., Kurki, M.I., Hamalainen, E., Huang, H., Huang, J., Sandor, C., Webber, C., Muller-Myhsok, B., Schreiber, S., Salomaa, V., Loehrer, E., Gobel, H., Macaya, A., Pozo-Rosich, P., Hansen, T., Werge, T., Kaprio, J., Metspalu, A., Kubisch, C., Ferrari, M.D., Belin, A.C., van den Maagdenberg, A., Zwart, J.A., Boomsma, D., Eriksson, N., Olesen, J., Chasman, D.I., Nyholt, D.R., Avbersek, A., Baum, L., Berkovic, S., Bradfield, J., Buono, R., Catarino, C.B., Cossette, P., De Jonghe, P., Depondt, C., Dlugos, D., Ferraro, T.N., French, J., Hjalgrim, H., JamnadasKhoda, J., Kalviainen, R., Kunz, W.S., Lerche, H., Leu, C., Lindhout, D., Lo, W., Lowenstein, D., McCormack, M., Moller, R.S., Molloy, A., Ng, P.W., Oliver, K., Privitera, M., Radtke, R., Ruppert, A.K., Sander, T., Schachter, S., Schankin, C., Scheffer, I., Schoch, S., Sisodiya, S.M., Smith, P., Sperling, M., Striano, P., Surges, R., Thomas, G.N., Visscher, F., Whelan, C.D., Zara, F., Heinzen, E.L., Marson, A., Becker, F., Stroink, H., Zimprich, F., Gasser, T., Gibbs, R., Heutink, P., Martinez, M., Morris, H.R., Sharma, M., Ryten, M., Mok, K.Y., Pulit, S., Bevan, S., Holliday, E., Attia, J., Battey, T., Boncoraglio, G., Thijs, V., Chen, W.M., Mitchell, B., Rothwell, P., Sharma, P., Sudlow, C., Vicente, A., Markus, H., Kourkoulis, C., Pera, J., Raffeld, M., Silliman, S., Boraska Perica, V., Thornton, L.M., Huckins, L.M., William Rayner, N., Lewis, C.M., Gratacos, M., Rybakowski, F., Keski-Rahkonen, A., Raevuori, A., Hudson, J.I., Reichborn-Kjennerud, T., Monteleone, P., Karwautz, A., Mannik, K., Baker, J.H., O’Toole, J.K., Trace, S.E., Davis, O.S.P., Helder, S.G., Ehrlich, S., Herpertz-Dahlmann, B., Danner, U.N., van Elburg, A.A., Clementi, M., Forzan, M., Docampo, E., Lissowska, J., Hauser, J., Tortorella, A., Maj, M., Gonidakis, F., Tziouvas, K., Papezova, H., Yilmaz, Z., Wagner, G., Cohen-Woods, S., Herms, S., Julia, A., Rabionet, R., Dick, D.M., Ripatti, S., Andreassen, O.A., Espeseth, T., Lundervold, A.J., Steen, V.M., Pinto, D., Scherer, S.W., Aschauer, H., Schosser, A., Alfredsson, L., Padyukov, L., Halmi, K.A., Mitchell, J., Strober, M., Bergen, A.W., Kaye, W., Szatkiewicz, J.P., Cormand, B., Ramos-Quiroga, J.A., Sanchez-Mora, C., Ribases, M., Casas, M., Hervas, A., Arranz, M.J., Haavik, J., Zayats, T., Johansson, S., Williams, N., Dempfle, A., Rothenberger, A., Kuntsi, J., Oades, R.D., Banaschewski, T., Franke, B., Buitelaar, J.K., Arias Vasquez, A., Doyle, A.E., Reif, A., Lesch, K.P., Freitag, C., Rivero, O., Palmason, H., Romanos, M., Langley, K., Rietschel, M., Witt, S.H., Dalsgaard, S., Borglum, A.D., Waldman, I., Wilmot, B., Molly, N., Bau, C.H.D., Crosbie, J., Schachar, R., Loo, S.K., McGough, J.J., Grevet, E.H., Medland, S.E., Robinson, E., Weiss, L.A., Bacchelli, E., Bailey, A., Bal, V., Battaglia, A., Betancur, C., Bolton, P., Cantor, R., Celestino-Soper, P., Dawson, G., De Rubeis, S., Duque, F., Green, A., Klauck, S.M., Leboyer, M., Levitt, P., Maestrini, E., Mane, S., De-Luca, D.M., Parr, J., Regan, R., Reichenberg, A., Sandin, S., Vorstman, J., Wassink, T., Wijsman, E., Cook, E., Santangelo, S., Delorme, R., Roge, B., Magalhaes, T., Arking, D., Schulze, T.G., Thompson, R.C., Strohmaier, J., Matthews, K., Melle, I., Morris, D., Blackwood, D., McIntosh, A., Bergen, S.E., Schalling, M., Jamain, S., Maaser, A., Fischer, S.B., Reinbold, C.S., Fullerton, J.M., Guzman-Parra, J., Mayoral, F., Schofield, P.R., Cichon, S., Muhleisen, T.W., Degenhardt, F., Schumacher, J., Bauer, M., Mitchell, P.B., Gershon, E.S., Rice, J., Potash, J.B., Zandi, P.P., Craddock, N., Ferrier, I.N., Alda, M., Rouleau, G.A., Turecki, G., Ophoff, R., Pato, C., Anjorin, A., Stahl, E., Leber, M., Czerski, P.M., Cruceanu, C., Jones, I.R., Posthuma, D., Andlauer, T.F.M., Forstner, A.J., Streit, F., Baune, B.T., Air, T., Sinnamon, G., Wray, N.R., MacIntyre, D.J., Porteous, D., Homuth, G., Rivera, M., Grove, J., Middeldorp, C.M., Hickie, I., Pergadia, M., Mehta, D., Smit, J.H., Jansen, R., de Geus, E., Dunn, E., Li, Q.S., Nauck, M., Schoevers, R.A., Beekman, A.T., Knowles, J.A., Viktorin, A., Arnold, P., Barr, C.L., Bedoya-Berrio, G., Bienvenu, O.J., Brentani, H., Burton, C., Camarena, B., Cappi, C., Cath, D., Cavallini, M., Cusi, D., Darrow, S., Denys, D., Derks, E.M., Dietrich, A., Fernandez, T., Figee, M., Freimer, N., Gerber, G., Grados, M., Greenberg, E., Hanna, G.L., Hartmann, A., Hirschtritt, M.E., Hoekstra, P.J., Huang, A., Huyser, C., Illmann, C., Jenike, M., Kuperman, S., Leventhal, B., Lochner, C., Lyon, G.J., Macciardi, F., Madruga-Garrido, M., Malaty, I.A., Maras, A., McGrath, L., Miguel, E.C., Mir, P., Nestadt, G., Nicolini, H., Okun, M.S., Pakstis, A., Paschou, P., Piacentini, J., Pittenger, C., Plessen, K., Ramensky, V., Ramos, E.M., Reus, V., Richter, M.A., Riddle, M.A., Robertson, M.M., Roessner, V., Rosario, M., Samuels, J.F., Sandor, P., Stein, D.J., Tsetsos, F., Van Nieuwerburgh, F., Weatherall, S., Wendland, J.R., Wolanczyk, T., Worbe, Y., Zai, G., Goes, F.S.,
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V. Millischer, et al. disorder. Eur. Neuropsychopharmacol. 25 (4), 468–473. https://doi.org/10.1016/j. euroneuro.2015.02.002. Machiela, M.J., Chanock, S.J., 2015. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31 (21), 3555–3557. https://doi.org/10.1093/ bioinformatics/btv402. Macleod, M.R., Michie, S., Roberts, I., Dirnagl, U., Chalmers, I., Ioannidis, J.P., Al-Shahi Salman, R., Chan, A.W., Glasziou, P., 2014. Biomedical research: increasing value, reducing waste. Lancet 383 (9912), 101–104. https://doi.org/10.1016/s01406736(13)62329-6. Maletic, V., Raison, C., 2014. Integrated neurobiology of bipolar disorder. Front Psychiatry 5, 98. https://doi.org/10.3389/fpsyt.2014.00098. McGuffin, P., Rijsdijk, F., Andrew, M., Sham, P., Katz, R., Cardno, A., 2003. The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Arch. Gen. Psychiatry 60 (5), 497–502. https://doi.org/10.1001/archpsyc.60.5. 497. Merikangas, K.R., Jin, R., He, J.P., Kessler, R.C., Lee, S., Sampson, N.A., Viana, M.C., Andrade, L.H., Hu, C., Karam, E.G., Ladea, M., Medina-Mora, M.E., Ono, Y., PosadaVilla, J., Sagar, R., Wells, J.E., Zarkov, Z., 2011. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch. Gen. Psychiatry 68 (3), 241–251. https://doi.org/10.1001/archgenpsychiatry.2011.12. Morey, R.D., Romeijn, J.-W., Rouder, J.N., 2016. The philosophy of bayes factors and the quantification of statistical evidence. J. Math. Psychol. 72, 6–18. https://doi.org/10. 1016/j.jmp.2015.11.001. Munafò, M.R., Nosek, B.A., Bishop, D.V.M., Button, K.S., Chambers, C.D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-.J., Ware, J.J., Ioannidis, J.P.A., 2017. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021. https://doi.org/10.1038/ s41562-016-0021. Open Science Collaboration, 2015. PSYCHOLOGY. Estimating the reproducibility of psychological science. Science 349 (6251) aac4716. https://doi.org/10.1126/ science.aac4716. Potash, J.B., Willour, V.L., Chiu, Y.F., Simpson, S.G., MacKinnon, D.F., Pearlson, G.D., DePaulo Jr., J.R., McInnis, M.G., 2001. The familial aggregation of psychotic symptoms in bipolar disorder pedigrees. Am. J. Psychiatry 158 (8), 1258–1264. https:// doi.org/10.1176/appi.ajp.158.8.1258. R. Core Team, 2017, A language and environment for statistical computing. https://www. R-project.org/. Schaid, D.J., Rowland, C.M., Tines, D.E., Jacobson, R.M., Poland, G.A., 2002. Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am. J. Hum. Genet. 70 (2), 425–434. https://doi.org/10.1086/338688. Simonsohn, U., 2015. Small telescopes: detectability and the evaluation of replication results. Psychol. Sci. 26 (5), 559–569. https://doi.org/10.1177/0956797614567341. Stahl, E.A., Breen, G., Forstner, A.J., McQuillin, A., Ripke, S., Trubetskoy, V., Mattheisen, M., Wang, Y., Coleman, J.R.I., Gaspar, H.A., de Leeuw, C.A., Steinberg, S., Pavlides, J.M.W., Trzaskowski, M., Byrne, E.M., Pers, T.H., Holmans, P.A., Richards, A.L., Abbott, L., Agerbo, E., Akil, H., Albani, D., Alliey-Rodriguez, N., Als, T.D., Anjorin, A., Antilla, V., Awasthi, S., Badner, J.A., Bækvad-Hansen, M., Barchas, J.D., Bass, N., Bauer, M., Belliveau, R., Bergen, S.E., Pedersen, C.B., Bøen, E., Boks, M.P., Boocock, J., Budde, M., Bunney, W., Burmeister, M., Bybjerg-Grauholm, J., Byerley, W., Casas, M., Cerrato, F., Cervantes, P., Chambert, K., Charney, A.W., Chen, D., Churchhouse, C., Clarke, T.-.K., Coryell, W., Craig, D.W., Cruceanu, C., Curtis, D., Czerski, P.M., Dale, A.M., de Jong, S., Degenhardt, F., Del-Favero, J., DePaulo, J.R., Djurovic, S., Dobbyn, A.L., Dumont, A., Elvsåshagen, T., Escott-Price, V., Fan, C.C., Fischer, S.B., Flickinger, M., Foroud, T.M., Forty, L., Frank, J., Fraser, C., Freimer, N.B., Frisén, L.,
Gade, K., Gage, D., Garnham, J., Giambartolomei, C., Pedersen, M.G., Goldstein, J., Gordon, S.D., Gordon-Smith, K., Green, E.K., Green, M.J., Greenwood, T.A., Grove, J., Guan, W., Guzman-Parra, J., Hamshere, M.L., Hautzinger, M., Heilbronner, U., Herms, S., Hipolito, M., Hoffmann, P., Holland, D., Huckins, L., Jamain, S., Johnson, J.S., Juréus, A., Kandaswamy, R., Karlsson, R., Kennedy, J.L., Kittel-Schneider, S., Knowles, J.A., Kogevinas, M., Koller, A.C., Kupka, R., Lavebratt, C., Lawrence, J., Lawson, W.B., Leber, M., Lee, P.H., Levy, S.E., Li, J.Z., Liu, C., Lucae, S., Maaser, A., MacIntyre, D.J., Mahon, P.B., Maier, W., Martinsson, L., McCarroll, S., McGuffin, P., McInnis, M.G., McKay, J.D., Medeiros, H., Medland, S.E., Meng, F., Milani, L., Montgomery, G.W., Morris, D.W., Mühleisen, T.W., Mullins, N., Nguyen, H., Nievergelt, C.M., Adolfsson, A.N., Nwulia, E.A., O'Donovan, C., Loohuis, L.M.O., Ori, A.P.S., Oruc, L., Ösby, U., Perlis, R.H., Perry, A., Pfennig, A., Potash, J.B., Purcell, S.M., Regeer, E.J., Reif, A., Reinbold, C.S., Rice, J.P., Rivas, F., Rivera, M., Roussos, P., Ruderfer, D.M., Ryu, E., Sánchez-Mora, C., Schatzberg, A.F., Scheftner, W.A., Schork, N.J., Shannon Weickert, C., Shehktman, T., Shilling, P.D., Sigurdsson, E., Slaney, C., Smeland, O.B., Sobell, J.L., Søholm Hansen, C., Spijker, A.T., St Clair, D., Steffens, M., Strauss, J.S., Streit, F., Strohmaier, J., Szelinger, S., Thompson, R.C., Thorgeirsson, T.E., Treutlein, J., Vedder, H., Wang, W., Watson, S.J., Weickert, T.W., Witt, S.H., Xi, S., Xu, W., Young, A.H., Zandi, P., Zhang, P., Zöllner, S., Adolfsson, R., Agartz, I., Alda, M., Backlund, L., Baune, B.T., Bellivier, F., Berrettini, W.H., Biernacka, J.M., Blackwood, D.H.R., Boehnke, M., Børglum, A.D., Corvin, A., Craddock, N., Daly, M.J., Dannlowski, U., Esko, T., Etain, B., Frye, M., Fullerton, J.M., Gershon, E.S., Gill, M., Goes, F., Grigoroiu-Serbanescu, M., Hauser, J., Hougaard, D.M., Hultman, C.M., Jones, I., Jones, L.A., Kahn, R.S., Kirov, G., Landén, M., Leboyer, M., Lewis, C.M., Li, Q.S., Lissowska, J., Martin, N.G., Mayoral, F., McElroy, S.L., McIntosh, A.M., McMahon, F.J., Melle, I., Metspalu, A., Mitchell, P.B., Morken, G., Mors, O., Mortensen, P.B., Müller-Myhsok, B., Myers, R.M., Neale, B.M., Nimgaonkar, V., Nordentoft, M., Nöthen, M.M., O'Donovan, M.C., Oedegaard, K.J., Owen, M.J., Paciga, S.A., Pato, C., Pato, M.T., Posthuma, D., Ramos-Quiroga, J.A., Ribasés, M., Rietschel, M., Rouleau, G.A., Schalling, M., Schofield, P.R., Schulze, T.G., Serretti, A., Smoller, J.W., Stefansson, H., Stefansson, K., Stordal, E., Sullivan, P.F., Turecki, G., Vaaler, A.E., Vieta, E., Vincent, J.B., Werge, T., Nurnberger, J.I., Wray, N.R., Di Florio, A., Edenberg, H.J., Cichon, S., Ophoff, R.A., Scott, L.J., Andreassen, O.A., Kelsoe, J., Sklar, P.e.Q.C., Consortium, B., the Bipolar Disorder Working Group of the Psychiatric Genomics, C., 2019. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51 (5), 793–803. https://doi.org/ 10.1038/s41588-019-0397-8. Toyota, T., Yamada, K., Detera-Wadleigh, S.D., Yoshikawa, T., 2003. Analysis of a cluster of polymorphisms in AKT1 gene in bipolar pedigrees: a family-based association study. Neurosci. Lett. 339 (1), 5–8. https://doi.org/10.1016/s0304-3940(02) 01428-3. Verhagen, J., Wagenmakers, E.J., 2014. Bayesian tests to quantify the result of a replication attempt. J. Exp. Psychol. Gen. 143 (4), 1457–1475. https://doi.org/10. 1037/a0036731. Wagenmakers, E.-J., Morey, R.D., Lee, M.D., 2016a. Bayesian benefits for the pragmatic researcher. Curr. Dir. Psychol. Sci. 25 (3), 169–176. https://doi.org/10.1177/ 0963721416643289. Wagenmakers, E.J., Verhagen, J., Ly, A., 2016b. How to quantify the evidence for the absence of a correlation. Behav. Res. Methods 48 (2), 413–426. https://doi.org/10. 3758/s13428-015-0593-0. Zollner, S., Pritchard, J.K., 2007. Overcoming the winner's curse: estimating penetrance parameters from case-control data. Am. J. Hum. Genet. 80 (4), 605–615. https://doi. org/10.1086/512821.
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