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Aneuploidy syndromes impact multiple organ systems but understanding of tissue-specific aneuploidy effects remains limited—especially for the comparison between peripheral tissues and relatively inaccessible tissues like brain. Here, we address this gap in knowledge by studying the transcriptomic effects of chromosome X, Y, and 21 aneuploidies in lymphoblastoid cell lines, fibroblasts and iPSC-derived neuronal cells (LCLs, FCL, and iNs, respectively). We root our analyses in sex chromosome aneuploidies, which offer a uniquely wide karyotype range for dosage effect analysis. We first harness a large LCL RNA-seq dataset from 197 individuals with one of 6 sex chromosome dosages (SCDs: XX, XXX, XY, XXY, XYY, and XXYY) to i) validate theoretical models of SCD sensitivity and ii) define an expanded set of 41 genes that show obligate dosage sensitivity to SCD and are all in cis (i.e., reside on the X or Y chromosome). We then use multiple complementary analyses to show that cis effects of SCD in LCLs are preserved in both FCLs (n = 32) and iNs (n = 24), whereas trans effects (i.e., those on autosomal gene expression) are mostly not preserved. Analysis of additional datasets confirms that the greater cross-cell type reproducibility of cis vs. trans effects is also seen in trisomy 21 cell lines. These findings i) expand our understanding of X, Y, and 21 chromosome dosage effects on human gene expression and ii) suggest that LCLs may provide a good model system for understanding cis effects of aneuploidy in harder-to-access cell types.

Regional cellular heterogeneity is a fundamental feature of the human neocortex; however, details of this heterogeneity are still undefined. We used single-nucleus RNA-sequencing to examine cell-specific transcriptional features in the dorsolateral PFC (DLPFC) and the subgenual anterior cingulate cortex (sgACC), regions implicated in major psychiatric disorders. Droplet-based nuclei-capture and library preparation were performed on replicate samples from 8 male donors without history of psychiatric or neurologic disorder. Unsupervised clustering identified major neural cell classes. Subsequent iterative clustering of neurons further revealed 20 excitatory and 22 inhibitory subclasses. Inhibitory cells were consistently more abundant in the sgACC and excitatory neuron subclusters exhibited considerable variability across brain regions. Excitatory cell subclasses also exhibited greater within-class transcriptional differences between the two regions. We used these molecular definitions to determine which cell classes might be enriched in loci carrying a genetic signal in genome-wide association studies or for differentially expressed genes in mental illness. We found that the heritable signals of psychiatric disorders were enriched in neurons and that, while the gene expression changes detected in bulk-RNA-sequencing studies were dominated by glial cells, some alterations could be identified in specific classes of excitatory and inhibitory neurons. Intriguingly, only two excitatory cell classes exhibited concomitant region-specific enrichment for both genome-wide association study loci and transcriptional dysregulation. In sum, by detailing the molecular and cellular diversity of the DLPFC and sgACC, we were able to generate hypotheses on regional and cell-specific dysfunctions that may contribute to the development of mental illness., Click the following link to launch an "iSEE" interactive session via shinyapps.io https://hbcc-nimh.shinyapps.io/shinyApp_JNS2023/

Recent postmortem transcriptomic studies of schizophrenia (SCZ) have shown hundreds of differentially expressed genes. However, the extent to which these gene expression changes reflect antipsychotic drug (APD) exposure remains uncertain. We compared differential gene expression in the prefrontal cortex of SCZ patients who tested positive for APDs at the time of death with SCZ patients who did not. APD exposure was associated with numerous changes in the brain transcriptome, especially among SCZ patients on atypical APDs. Brain transcriptome data from macaques chronically treated with APDs showed that APDs affect the expression of many functionally relevant genes, some of which show expression changes in the same directions as those observed in SCZ. Co-expression modules enriched for synaptic function showed convergent patterns between SCZ and some of the APD effects, while those associated with inflammation and glucose metabolism exhibited predominantly divergent patterns between SCZ and APD effects. In contrast, major cell-type shifts inferred in SCZ were primarily unaffected by APD use. These results show that APDs may confound SCZ-associated gene expression changes in postmortem brain tissue. Disentangling these effects will help identify causal genes and improve our neurobiological understanding of SCZ.

Despite strong evidence of heritability and growing discovery of genetic markers for major mental illness, little is known about how gene expression in the brain differs across psychiatric diagnoses, or how known genetic risk factors shape these differences. Here we investigate expressed genes and gene transcripts in postmortem subgenual anterior cingulate cortex (sgACC), a key component of limbic circuits linked to mental illness. RNA obtained postmortem from 200 donors diagnosed with bipolar disorder, schizophrenia, major depression, or no psychiatric disorder was deeply sequenced to quantify expression of over 85,000 gene transcripts, many of which were rare. Case–control comparisons detected modest expression differences that were correlated across disorders. Case–case comparisons revealed greater expression differences, with some transcripts showing opposing patterns of expression between diagnostic groups, relative to controls. The ~250 rare transcripts that were differentially-expressed in one or more disorder groups were enriched for genes involved in synapse formation, cell junctions, and heterotrimeric G-protein complexes. Common genetic variants were associated with transcript expression (eQTL) or relative abundance of alternatively spliced transcripts (sQTL). Common genetic variants previously associated with disease risk were especially enriched for sQTLs, which together accounted for disproportionate fractions of diagnosis-specific heritability. Genetic risk factors that shape the brain transcriptome may contribute to diagnostic differences between broad classes of mental illness.

Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium’s therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.

Growing evidence suggests that the glutamatergic modulator ketamine has rapid antidepressant effects in treatment-resistant depressed subjects. The anticholinergic agent scopolamine has also shown promise as a rapid-acting antidepressant. This study applied genome-wide markers to investigate the role of genetic variants in predicting acute antidepressant response to both agents. The ketamine-treated sample included 157 unrelated European subjects with major depressive disorder (MDD) or bipolar disorder (BD). The scopolamine-treated sample comprised 37 unrelated European subjects diagnosed with either MDD or BD who had a current Major Depressive Episode (MDE), and had failed at least two adequate treatment trials for depression. Change in Montgomery–Asberg Depression Rating Scale (MADRS) or the 17-item Hamilton Depression Rating Scale (HAM-D) scale scores at day 1 (24 h post-treatment) was considered the primary outcome. Here, we conduct pilot genome-wide association study (GWAS) analyses to identify potential markers of ketamine response and dissociative side effects. Polygenic risk score analysis of SNPs ranked by the strength of their association with ketamine response was then calculated in order to assess whether common genetic markers from the ketamine study could predict response to scopolamine. Findings require replication in larger samples in light of low power of analyses of these small samples. Neverthless, these data provide a promising illustration of our future potential to identify genetic variants underlying rapid treatment response in mood disorders and may ultimately guide individual patient treatment selection in the future.

Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients (N = 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SCZ) were computed using lassosum and in a model including all three PRSs and other covariates, and the PRS of ADHD (β = −0.14; 95% confidence interval [CI]: −0.24 to −0.03; p value = 0.010) and MDD (β = −0.16; 95% CI: −0.27 to −0.04; p value = 0.005) predicted worse quantitative lithium response. A higher SCZ PRS was associated with higher rates of medication nonadherence (OR = 1.61; 95% CI: 1.34–1.93; p value = 2e−7). This study indicates that genetic risk for ADHD and depression may influence lithium treatment response. Interestingly, a higher SCZ PRS was associated with poor adherence, which can negatively impact treatment response. Incorporating genetic risk of ADHD, depression, and SCZ in combination with clinical risk may lead to better clinical care for patients with BD.

Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures.

Bipolar disorder is often comorbid with anxiety, which is itself associated with poorer clinical outcomes, including suicide. A better etiologic understanding of this comorbidity could inform diagnosis and treatment. The present study aims to test whether comorbid anxiety in bipolar disorder reflects shared genetic risk factors. We also sought to assess the contribution of genetic risk for anxiety to suicide attempts in bipolar disorder. Polygenic risk scores (PRS) were calculated from published genome-wide association studies of samples of controls and cases with anxiety (n = 83,566) or bipolar disorder (n = 51,710), then scored in independent target samples (total n = 3369) of individuals with bipolar disorder who reported or denied lifetime anxiety disorders or suicidal attempts in research interviews. Participants were recruited from clinical and nonclinical settings and genotyped for common genetic variants. The results show that polygenic risk for anxiety was associated with comorbid anxiety disorders and suicide attempts in bipolar disorder, while polygenic risk for bipolar disorder was not associated with any of these variables. Our findings point out that comorbid anxiety disorders in bipolar disorder reflect a dual burden of bipolar and anxiety-related genes; the latter may also contribute to suicide attempts. Clinical care that recognizes and addresses this dual burden may help improve outcomes in people living with comorbid bipolar and anxiety disorders.

OBJECTIVES: Bipolar disorder (BD) with early disease onset is associated with an unfavorable clinical outcome and constitutes a clinically and biologically homogenous subgroup within the heterogeneous BD spectrum. Previous studies have found an accumulation of early age at onset (AAO) in BD families and have therefore hypothesized that there is a larger genetic contribution to the early-onset cases than to late onset BD. To investigate the genetic background of this subphenotype, we evaluated whether an increased polygenic burden of BD- and schizophrenia (SCZ)-associated risk variants is associated with an earlier AAO in BD patients. METHODS: A total of 1995 BD type 1 patients from the Consortium of Lithium Genetics (ConLiGen), PsyCourse and Bonn-Mannheim samples were genotyped and their BD and SCZ polygenic risk scores (PRSs) were calculated using the summary statistics of the Psychiatric Genomics Consortium as a training data set. AAO was either separated into onset groups of clinical interest (childhood and adolescence [≤18 years] vs adulthood [>18 years]) or considered as a continuous measure. The associations between BD- and SCZ-PRSs and AAO were evaluated with regression models. RESULTS: BD- and SCZ-PRSs were not significantly associated with age at disease onset. Results remained the same when analyses were stratified by site of recruitment. CONCLUSIONS: The current study is the largest conducted so far to investigate the association between the cumulative BD and SCZ polygenic risk and AAO in BD patients. The reported negative results suggest that such a polygenic influence, if there is any, is not large, and highlight the importance of conducting further, larger scale studies to obtain more information on the genetic architecture of this clinically relevant phenotype. peerReviewed

This study sought to reproduce, in a larger sample, previous findings of a correlation between smaller raw 3-Tesla (3T) hippocampal volumes and improved antidepressant efficacy of ketamine in individuals with major depressive disorder (MDD). A secondary analysis stratified subjects according to functional BDNF rs6265 (val66met) genotype.Unmedicated subjects with treatment-resistant MDD ( n=55) underwent baseline structural 3T MRI. Data processing was conducted with FSL/FIRST and Freesurfer software. The amygdala, hippocampus, and thalamus were selected a priori for analysis. All subjects received a single 0.5mg/kg × 40-minute ketamine infusion. Pearson correlations were performed with subcortical volumes and percent change in MADRS score (from baseline to 230 minutes, 1 day, and 1 week post-infusion).Raw and corrected subcortical volumes did not correlate with antidepressant response at any timepoint. In val/val subjects ( n=23), corrected left and right thalamic volume positively correlated with antidepressant response to ketamine at 230 minutes post-infusion but did not reach statistical significance. In met carriers ( n=14), corrected left and right thalamic volume negatively correlated with antidepressant response to ketamine.Baseline subcortical volumes implicated in MDD did not correlate with ketamine's antidepressant efficacy. Baseline thalamic volume and BDNF genotype may be a combinatorial rapid antidepressant response biomarker.

How do differences in onset, symptoms, and treatment response arise between various mental illnesses despite substantial overlap of genetic risk factors? To address this question, we carried out deep RNA sequencing of human postmortem subgenual anterior cingulate cortex, a key component of limbic circuits linked to mental illness. Samples were obtained from 200 individuals diagnosed with bipolar disorder, schizophrenia, or major depression, and controls. Differential expression analysis in cases versus controls detected modest differences that were similar across disorders, although transcript-level differences were more pronounced. Case-case comparisons revealed greater expression differences between disorders, including many genes and transcripts that were expressed in opposite directions in each diagnostic group, compared to controls. Relative transcript abundances were associated with common genetic variants that accounted for disproportionate fractions of diagnosis-specific heritability. Inherited genetic risk factors shape the brain transcriptome and contribute to diagnostic differences between broad classes of mental illness.

Growing evidence suggests that the glutamatergic modulator ketamine has rapid antidepressant effects in treatment-resistant depressed subjects. The anticholinergic agent scopolamine has also shown promise as a rapid-acting antidepressant. This study applied genome-wide markers to investigate the role of genetic variants in predicting acute antidepressant response to both agents. The ketamine-treated sample included 157 unrelated European subjects with major depressive disorder (MDD) or bipolar disorder (BD). The scopolamine-treated sample comprised 37 unrelated European subjects diagnosed with either MDD or BD who had a current Major Depressive Episode (MDE), and had failed at least two adequate treatment trials for depression. Change in Montgomery-Asberg Depression Rating Scale (MADRS) or the 17-item Hamilton Depression Rating Scale (HAM-D) scale scores at day 1 (24 h post-treatment) was considered the primary outcome. Here, we conduct pilot genome-wide association study (GWAS) analyses to identify potential markers of ketamine response and dissociative side effects. Polygenic risk score analysis of SNPs ranked by the strength of their association with ketamine response was then calculated in order to assess whether common genetic markers from the ketamine study could predict response to scopolamine. Findings require replication in larger samples in light of low power of analyses of these small samples. Neverthless, these data provide a promising illustration of our future potential to identify genetic variants underlying rapid treatment response in mood disorders and may ultimately guide individual patient treatment selection in the future.

e16552 Background: Esophageal cancer (EC) is one of the most lethal cancers with 90% mortality rate. Uneven Worldwide geographical distribution and varying etiologic factors contribute to the heterogenic nature of EC worldwide. In the US, 75% esophageal squamous cell carcinoma (ESCC) is diagnosed in African Americans (AA). However, despite comprehensive and multi-geographical efforts to understand the genomic basis of ESCC, the knowledge of molecular and genetic mechanisms leading to genomic subtyping of AA has been limited due to the underrepresentation of AAs in these studies. We hypothesized that the differences in mutational events in AA ESCC might, in part, cause the disparity in outcome and the mutational events may determine treatment options. Methods: Whole exome sequencing (WES) of matched- tumor and normal-cell DNA from late-stage ESCC of ten AA patients were performed by using Agilent SureSelect XT Human All Exon V6+UTR. Somatic variants per tumor samples called by two or more methods (Mutect2, VarScan2, and Strelka2) were combined and used for downstream population filtering. Somatic copy number changes were determined by using CNVKit in SevenBridges Genomics. Results: Somatic variants called by two or more algorithms filtered and sorted for rare mutations (equal to or less than 1% African originated population frequency) demonstrated an average of 23 nonsynonymous mutations per MB in high mutation rate AA ESCC. The remaining four samples had two or fewer nonsynonymous mutations per MB. TP53 was one of the most frequently mutated gene with 50% of mutation frequency. ARID2 mutated in 30%, EP300 in 20%, and RB1 in 10% of our cohort. Multiple amplified and deleted regions ranging from 94 to 46 were observed in seven samples in contrast to three samples that were silent in terms of copy number variations ranging from 29 to 9. Significant CNV were mostly seen in proliferation, cell cycle and checkpoint genes, squamous cell homeostasis, epithelial to mesenchymal transition, invasion and metastasis, receptor tyrosine kinase and signaling pathways, chromatin remodeling, detoxification, RNA/DNA editing and angiogenesis. Conclusions: WES of ten AA ESCC samples demonstrated a higher mutation rate in a group of patients with many passenger mutations and complex and recurrent copy number changes that affect oncogenic driver genes, which might suggest enhanced subtyping of ESCC in AA.

BackgroundLithium is a first-line medication for bipolar disorder (BD), but only ~30% of patients respond optimally to the drug. Since genetic factors are known to mediate lithium treatment response, we hypothesized whether polygenic susceptibility to the spectrum of depression traits is associated with treatment outcomes in patients with BD. In addition, we explored the potential molecular underpinnings of this relationship.MethodsWeighted polygenic scores (PGSs) were computed for major depressive disorder (MDD) and depressive symptoms (DS) in BD patients from the Consortium on Lithium Genetics (ConLi+Gen; n=2,586) who received lithium treatment. Lithium treatment outcome was assessed using the ALDA scale. Summary statistics from genome-wide association studies (GWAS) in MDD (130,664 cases and 330,470 controls) and DS (n=161,460) were used for PGS weighting. Associations between PGSs of depression traits and lithium treatment response were assessed by binary logistic regression. We also performed a cross-trait meta-GWAS, followed by Ingenuity® Pathway Analysis.OutcomesBD patients with a low polygenic load for depressive traits were more likely to respond well to lithium, compared to patients with high polygenic load (MDD: OR =1.64 [95%CI: 1.26-2.15], lowest vs highest PGS quartiles; DS: OR=1.53 [95%CI: 1.18-2.00]). Associations were significant for type 1, but not type 2 BD. Cross-trait GWAS and functional characterization implicated voltage-gated potassium channels, insulin-related pathways, mitogen-activated protein-kinase (MAPK) signaling, and miRNA expression.InterpretationGenetic loading to depression traits in BD patients lower their odds of responding optimally to lithium. Our findings support the emerging concept of a lithium-responsive biotype in BD.FundingSee attached details

Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable. Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen). Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD-associated miRNAs. However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGenGWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene-based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genomewide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs in larger GWAS samples of BD and lithium response is warranted. COL0029147

Progress in genetics in the last decades has been spurred largely by the Human Genome Project. Successful sequencing of the human genome and genomes of organisms across the evolutionary scale has been achieved. Scientific discoveries and innovations steered the refinement and annotation of sequences, cataloging of variation including SNPs and CNVs, development of fast, precise, high throughput methods and improved bioinformatics and statistical tools. These advances boosted GWAS on large samples identifying multiple susceptibility loci in diverse complex diseases and traits. Exome, whole genome and RNA sequencing has uncovered intricate elements of coding, noncoding and regulatory regions and the richness of the transcriptome. Evolution in genome editing has heightened the ability to alter any nucleotide and discern its biological effect. Exploiting these innovations in studies involving cells derived from patients through the iPSC technology offers manifold possibilities for functional studies that could enhance disease modeling and yield new therapeutic targets.

IMPORTANCE 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). OBJECTIVES 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 lithiumwas 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. RESULTS 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.

Background Approximately 30% of patients with Major Depressive Disorder (MDD) meet criteria for Treatment-Resistant Depression (TRD), defined as non-response to two or more different classes of antidepressants. Pharmacogenomics research has shown that genetic factors can impact medication effectiveness and tolerability by modulating drug levels or target affinity. Antidepressant response is heritable, but genome-wide association studies of common variants have failed to find consistent signals. Since patients with TRD occupy the extreme end of the treatment response distribution, they may also carry alleles that occupy an extreme of frequency and functional impact. Methods Study participants included 146 unrelated patients, 130 with MDD or bipolar TRD and 16 with good response to antidepressants, drawn from several sources. All participants had experienced at least two failed antidepressant trials not explained by non-adherence or severe comorbidities. Exomes were captured with various arrays and sequenced by use of Illumina or SOLiD platforms. Variants were called and assessed for quality using the GATK best practices pipeline. Results After quality control, 348,077 variants were identified, of which 99,711 were exonic and had allele frequencies 1 TRD patient, but LIST GENES each carried the same qualifying variant in >2 individuals. A total of 820 genes carried at least 2 qualifying variants. This set of genes was tested for overlap with genes implicated in MDD (Hyde et al., 2016) or antidepressant treatment response (GENDEP, MARS, and STAR⁎ D Investigators, 2013). Significant excess overlap of genes was detected for both MDD (OR=1.5, p=0.035) and response to 2 wks of antidepressant treatment (OR=2.16; P=1×10-5). The latter gene set was enriched for “actin cytoskeleton” by ENRICHR (q=0.01). Discussion Some of the same genes implicated in MDD or antidepressant treatment response through common variants may also harbor rare, functional variants in patients with TRD. Further study of these genes could yield new insights into the pathophysiology of treatment-resistant depression.

We sequenced the genomes of 200 individuals from 41 families multiply affected with bipolar disorder (BD) to identify contributions of rare variants to genetic risk. We initially focused on 3,087 candidate genes with known synaptic functions or prior evidence from genome-wide association studies. BD pedigrees had an increased burden of rare variants in genes encoding neuronal ion channels, including subunits of GABAA receptors and voltage-gated calcium channels. Four uncommon coding and regulatory variants also showed significant association, including a missense variant in GABRA6. Targeted sequencing of 26 of these candidate genes in an additional 3,014 cases and 1,717 controls confirmed rare variant associations in ANK3, CACNA1B, CACNA1C, CACNA1D, CACNG2, CAMK2A, and NGF. Variants in promoters and 5' and 3' UTRs contributed more strongly than coding variants to risk for BD, both in pedigrees and in the case-control cohort. The genes and pathways identified in this study regulate diverse aspects of neuronal excitability. We conclude that rare variants in neuronal excitability genes contribute to risk for BD.

ImportanceLithium is a first-line mood stabilizer for the maintenance treatment of Bipolar Disorder (BPD). However, the efficacy of lithium varies widely, with a non-response rate of up to 30%. Biological response markers and predictors are lacking.ObjectiveGenetic factors are thought to mediate lithium treatment response, and the previously reported genetic overlap between BPD and schizophrenia (SCZ) led us to test whether a polygenic score (PGS) for SCZ could predict lithium treatment response in BPD. Further, we explored the potential molecular underpinnings of this association.DesignWeighted SCZ PGSs were computed at ten p-value thresholds (PT) using summary statistics from a genome-wide association study (GWAS) of 36,989 SCZ cases, and genotype data for BPD patients from the Consortium on Lithium Genetics (ConLi+Gen). For functional exploration, we performed a cross-trait meta-GWAS and pathway analysis, combining GWAS summary statistics on SCZ and lithium treatment response.SettingInternational multicenter GWAS.ParticipantsPatients with BPD who had undergone lithium treatment were genotyped and retrospectively assessed for long-term treatment response (n=2,586).Main outcome measuresClinical treatment response to lithium was defined on both the categorical and continuous scales using the ALDA score. The effect measures include odds ratios (ORs) and the proportion of variance explained (R2), and a significant association was determined at pResultsThe PGS for SCZ was inversely associated with lithium treatment response in the categorical outcome (p=8×10−5), at PT−2. Patients with BPD who had low polygenic load for SCZ responded better to lithium, with ORs for lithium response ranging from 3.46 [95%CI: 1.42-8.41 at 1stdecile] to 2.03 [95%CI: 0.86-4.81 at the 9th decile], compared to the patients in the 10thdecile 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 complex and inflammatory cytokines (TNFα, IL-4, IFNγ) as molecular contributors to lithium treatment response in BPD.Conclusions and RelevanceThe study provides, for the first-time, evidence for a negative association between high genetic loading for SCZ and poor response to lithium in patients with BPD. These results suggest the potential for translational research aimed at personalized prescribing of lithium.Key PointsQuestionDoes a polygenic score for Schizophrenia (SCZ) predict response to lithium in patients with Bipolar Disorder (BPD)? What are the molecular drivers of the association between SCZ and lithium treatment response?FindingsWe found an inverse association between genetic loading for SCZ risk variants and response to lithium in patients with BPD. Genetic variants in the HLA region on chromosome 6, the antigen presentation pathway and markers of inflammation (TNFα, IL-4, IFNγ) point to molecular underpinnings of lithium treatment response in BPD.MeaningIn patients with BPD, an assessment of a polygenic load for SCZ risk variants may assist in conjunction with clinical data to predict whether they would respond to lithium treatment.

RNA-sequencing (RNA-seq) is a powerful technique to investigate the complexity of gene expression in the human brain. We used RNA-seq to survey the brain transcriptome in high-quality postmortem dorsolateral prefrontal cortex from 11 individuals diagnosed with bipolar disorder (BD) and from 11 age- and gender-matched controls. Deep sequencing was performed, with over 350 million reads per specimen. At a false discovery rate of

Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behavior. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the X-chromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, p = 5.87×10−9; odds ratio = 1.12) and markers within ERBB2 (rs2517959, p = 4.53×10−9; odds ratio = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.

BACKGROUND: Lithium is a first-line treatment in bipolar disorder, but individual response is variable. Previous studies have suggested that lithium response is a heritable trait. However, no genetic markers of treatment response have been reproducibly identified.METHODS: Here, we report the results of a genome-wide association study of lithium response in 2563 patients collected by 22 participating sites from the International Consortium on Lithium Genetics (ConLiGen). Data from common single nucleotide polymorphisms (SNPs) were tested for association with categorical and continuous ratings of lithium response. Lithium response was measured using a well established scale (Alda scale). Genotyped SNPs were used to generate data at more than 6 million sites, using standard genomic imputation methods. Traits were regressed against genotype dosage. Results were combined across two batches by meta-analysis.FINDINGS: A single locus of four linked SNPs on chromosome 21 met genome-wide significance criteria for association with lithium response (rs79663003, p=1·37 × 10(-8); rs78015114, p=1·31 × 10(-8); rs74795342, p=3·31 × 10(-9); and rs75222709, p=3·50 × 10(-9)). In an independent, prospective study of 73 patients treated with lithium monotherapy for a period of up to 2 years, carriers of the response-associated alleles had a significantly lower rate of relapse than carriers of the alternate alleles (p=0·03268, hazard ratio 3·8, 95% CI 1·1-13·0).INTERPRETATION: The response-associated region contains two genes for long, non-coding RNAs (lncRNAs), AL157359.3 and AL157359.4. LncRNAs are increasingly appreciated as important regulators of gene expression, particularly in the CNS. Confirmed biomarkers of lithium response would constitute an important step forward in the clinical management of bipolar disorder. Further studies are needed to establish the biological context and potential clinical utility of these findings.FUNDING: Deutsche Forschungsgemeinschaft, National Institute of Mental Health Intramural Research Program.

Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behaviour. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ∼2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the X-chromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, P = 5.87 × 10 (-) (9); odds ratio (OR) = 1.12) and markers within ERBB2 (rs2517959, P = 4.53 × 10 (-) (9); OR = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.

Genome-wide association studies (GWAS) in complex phenotypes, including psychiatric disorders, have yielded many replicated findings, yet individual markers account for only a small fraction of the inherited differences in risk. We tested the performance of polygenic models in discriminating between cases and healthy controls and among cases with distinct psychiatric diagnoses.GWAS results in bipolar disorder (BD), major depressive disorder (MDD), schizophrenia (SZ), and Parkinson's disease (PD) were used to assign weights to individual alleles, based on odds ratios. These weights were used to calculate allele scores for individual cases and controls in independent samples, summing across many single nucleotide polymorphisms (SNPs). How well allele scores discriminated between cases and controls and between cases with different disorders was tested by logistic regression.Large sets of SNPs were needed to achieve even modest discrimination between cases and controls. The most informative SNPs were overlapping in BD, SZ, and MDD, with correlated effect sizes. Little or no overlap was seen between allele scores for psychiatric disorders and those for PD.BD, SZ, and MDD all share a similar polygenic component, but the polygenic models tested lack discriminative accuracy and are unlikely to be useful for clinical diagnosis.

The overall neurobiological mechanisms by which lithium and valproate stabilize mood in bipolar disorder patients have yet to be fully defined. The therapeutic efficacy and dissimilar chemical structures of these medications suggest that they perturb both shared and disparate cellular processes. To investigate key pathways and functional clusters involved in the global action of lithium and valproate, we generated interaction networks formed by well-supported drug targets. Striking functional similarities emerged. Intersecting nodes in lithium and valproate networks highlighted a strong enrichment of apoptosis clusters and neurotrophin signaling. Other enriched pathways included MAPK, ErbB, insulin, VEGF, Wnt and long-term potentiation indicating a widespread effect of both drugs on diverse signaling systems. MAPK1/3 and AKT1/2 were the most preponderant nodes across pathways suggesting a central role in mediating pathway interactions. The convergence of biological responses unveils a functional signature for lithium and valproate that could be key modulators of their therapeutic efficacy.

Functional magnetic resonance imaging is commonly used to characterize brain activity underlying a variety of psychiatric disorders. A previous functional magnetic resonance imaging study found that amygdala activation during a face-processing task differed between pediatric patients with bipolar disorder (BD) and healthy controls. We undertook a genome-wide association study to explore the genetic architecture of this neuroimaging phenotype.Thirty-nine patients with BD and 29 healthy controls who had previously undergone functional magnetic resonance imaging when viewing a neutral face were genotyped using a genome-wide single-nucleotide polymorphism (SNP) array. After quality control, 104,043 SNPs were tested against normalized amygdala activation scores obtained from the right and left hemispheres. Genetic association was tested with covariates to control for race and ethnicity. Patients and controls were grouped together in the primary analyses.Right amygdala activation under the hostility contrast was most strongly associated with an SNP in the gene DOK5 (rs2023454, p = 4.88 x 10(-7), false discovery rate = 0.05). DOK5 encodes a substrate of tropomyosin-related kinase B/C receptors involved in neurotrophin signaling. This SNP accounted for about 33% of the variance in youths with BD and 12% of the variance in healthy youths. Other results (false discovery rate50%) were also observed at SNPs near several other genes.To our knowledge, this is the first genome-wide association study of amygdala activation in adolescents with BD. Although preliminary, these data suggest that DOK5 and perhaps several other genes influence the magnitude of amygdala activation during face processing, particularly in those with BD. Further studies are needed to replicate these findings and characterize the mechanisms involved.

The major mood disorders, which include bipolar disorder (BD) and major depressive disorder (MDD), are substantially heritable, but few risk loci have been identified. We performed a meta-analysis of 5 major mood disorder case-control samples, including over 13,600 unique individuals genotyped with approximately 500,000 to 1 million single nucleotide polymorphism (SNP) markers on high-density arrays. Allele-wise association results were meta-analyzed with a method that weights results by sample size. We found genome-wide significant evidence that SNPs in a region of chromosome 3p21.1were associated with major mood disorders. The SNP rs2251219 returned the smallest meta-analysis p-value, 3.63 × 10−8, with a pooled odds ratio of 0.87. Supportive results were observed in 2 out of 3 independent samples tested in a replication study. These results implicate one or more genes in this region in the etiology of major mood disorders and suggest that BD and MDD share genetic risk factors.

Objective Functional magnetic resonance imaging is commonly used to characterize brain activity underlying a variety of psychiatric disorders. A previous functional magnetic resonance imaging study found that amygdala activation during a face-processing task differed between pediatric patients with bipolar disorder (BD) and healthy controls. We undertook a genome-wide association study to explore the genetic architecture of this neuroimaging phenotype. Method Thirty-nine patients with BD and 29 healthy controls who had previously undergone functional magnetic resonance imaging when viewing a neutral face were genotyped using a genome-wide single-nucleotide polymorphism (SNP) array. After quality control, 104,043 SNPs were tested against normalized amygdala activation scores obtained from the right and left hemispheres. Genetic association was tested with covariates to control for race and ethnicity. Patients and controls were grouped together in the primary analyses. Results Right amygdala activation under the hostility contrast was most strongly associated with an SNP in the gene DOK5 (rs2023454, p = 4.88 × 10 −7 , false discovery rate=0.05). DOK5 encodes a substrate of tropomyosin-related kinase B/C receptors involved in neurotrophin signaling. This SNP accounted for about 33% of the variance in youths with BD and 12% of the variance in healthy youths. Other results (false discovery rate Conclusions To our knowledge, this is the first genome-wide association study of amygdala activation in adolescents with BD. Although preliminary, these data suggest that DOK5 and perhaps several other genes influence the magnitude of amygdala activation during face processing, particularly in those with BD. Further studies are needed to replicate these findings and characterize the mechanisms involved.

Suicidal ideation is an uncommon but worrisome symptom than can emerge during antidepressant treatment. We have described earlier the association between treatment-emergent suicidal ideation (TESI) and markers in genes encoding glutamate receptor subunits GRIK2 and GRIA3. The present genome-wide association study was conducted to identify additional genetic markers associated with TESI that may help identify individuals at high risk who may benefit from closer monitoring, alternative treatments, and/or specialty care.A clinically representative cohort of outpatients with nonpsychotic major depressive disorder enrolled in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial were treated with citalopram under a standard protocol for up to 14 weeks. DNA samples from 90 White participants who developed TESI and a sex-matched and race-matched equal number of treated participants who denied any suicidal ideas were genotyped with 109 365 single nucleotide polymorphisms on the Illumina's Human-1 BeadChip.One marker was found to be associated with TESI in this sample at the experiment-wide adjusted P less than 0.05 level (marker rs11628713, allelic P = 6.2x10, odds ratio = 4.7, permutation P = 0.01). A second marker was associated at the experiment-wide adjusted P = 0.06 level (rs10903034, allelic P = 3.02x10, odds ratio = 2.7, permutation P = 0.06). These markers reside within the genes PAPLN and IL28RA, respectively. PAPLN encodes papilin, a protoglycan-like sulfated glycoprotein. IL28RA encodes an interleukin receptor.Together with our earlier report, these findings may shed light on the biological basis of TESI and may help identify patients at increased risk of this potentially serious adverse event.

Two recent reports have highlighted ANK3 as a susceptibility gene for bipolar disorder (BD). We first reported association between BD and the ANK3 marker rs9804190 in a genome-wide association study (GWAS) of two independent samples (Baum et al., 2008). Subsequently, a meta-analysis of GWAS data based on samples from the US and the UK reported association with a different ANK3 marker, rs10994336 (Ferreira et al., 2008). The markers lie about 340 kb apart in the gene. Here, we test both markers in additional samples and characterize the contribution of each marker to BD risk. Our previously reported findings at rs9804190, which had been based on DNA pooling, were confirmed by individual genotyping in the National Institute of Mental Health (NIMH) waves 1-4 (P=0.05; odds ratio (OR)=1.24) and German (P=0.0006; OR=1.34) samples. This association was replicated in an independent US sample known as NIMH wave 5 (466 cases, 212 controls; P=0.017; OR=1.38). A random-effects meta-analysis of all three samples was significant (P=3 x 10(-6); OR=1.32), with no heterogeneity. Individual genotyping of rs10994336 revealed a significant association in the German sample (P=0.0001; OR=1.70), and similar ORs in the NIMH 1-4 and NIMH 5 samples that were not significant at the P0.05 level. Meta-analysis of all three samples supported an association with rs10994336 (P=1.7 x 10(-5); OR=1.54), again with no heterogeneity. There was little linkage disequilibrium between the two markers. Further analysis suggested that each marker contributed independently to BD, with no significant marker x marker interaction. Our findings strongly support ANK3 as a BD susceptibility gene and suggest true allelic heterogeneity.

Genetic linkage studies in both bipolar affective disorder (BPAD) and schizophrenia have implicated overlapping regions of chromosome 22q. We previously reported that BPAD pedigrees containing multiple members with psychotic symptoms showed suggestive linkage to chromosome 22q12.3. Now we have tested 189 single nucleotide polymorphisms (SNPs) spanning a 3 Mb region around the linkage peak for association with BPAD in 305 families, unrelated cases, and controls. SNPs were selected in or near genes, resulting in coverage at a density of 1 SNP per 6.7 kb across the 22 annotated genes in the region. The strongest signal emerged from family-based association analysis of an 11-SNP, 54 kb haplotype straddling the gene HMG2L1 and part of TOM1. A 3-marker haplotype of SNPs within TOM1 was associated with BPAD (allele-wise P = 0.0011) and with psychotic BPAD (allele-wise P = 0.00049). As hypothesized, the mean odds ratio for the risk alleles across the region was 1.39 in the psychotic but only 0.96 in the non-psychotic subset. Genotype-wise analyses yielded similar results, but the psychotic/non-psychotic distinction was more pronounced with mean odds ratios of 1.91 versus 0.8. Permutation of genotype-wise results for rs2413338 in HMG2L1 showed an empirical P = 0.037 for the difference between subsets. HMG2L1 is a negative regulator of Wnt signaling, a pathway of interest in psychotic BPAD as it is activated by both mood stabilizer and anti-psychotic medications. Further work is needed to confirm these results and uncover the functional variation underlying the association signal. © 2007 Wiley-Liss, Inc.

Genome-wide association studies (GWAS) have identified several common variants associated with bipolar disorder (BD), but the biological meaning of these findings remains unclear. Integrative genomics—the integration of GWAS signals with gene expression data—may illuminate genes and gene networks that have key roles in the pathogenesis of BD. We applied weighted gene co-expression network analysis (WGCNA), which exploits patterns of co-expression among genes, to brain transcriptome data obtained by sequencing of poly-A RNA derived from postmortem dorsolateral prefrontal cortex from people with BD, along with age- and sex-matched controls. WGCNA identified 33 gene modules. Many of the modules corresponded closely to those previously reported in human cortex. Three modules were associated with BD, enriched for genes differentially expressed in BD, and also enriched for signals in prior GWAS of BD. Functional analysis of genes within these modules revealed significant enrichment of several functionally related sets of genes, especially those involved in the postsynaptic density (PSD). These results provide convergent support for the hypothesis that dysregulation of genes involved in the PSD is a key factor in the pathogenesis of BD. If replicated in larger samples, these findings could point toward new therapeutic targets for BD.

There are little independent data available about how well single nucleotide polymorphism (SNP) genotyping technologies perform in the typical molecular genetics laboratory. We evaluated the utility and accuracy of a widely used technology, template-directed dye-terminator incorporation with fluorescence-polarization detection (FP-TDI), in a sample of 177 SNPs selected solely on the basis of map location. Genotypes were generated without optimization using standard protocols. Overall, 81% of the SNPs we studied generated readable genotypes by FP-TDI. Thirty-two SNPs were genotyped in duplicate by PCR-RFLP or fluorescent dye-terminator sequencing. Out of a total of 631 duplicate genotypes, no true discrepancies were detected. The true error rate has a 95% chance of lying between 0 and 6 out of 1000 genotypes. We also tested for deviations from Hardy-Weinberg Equilibrium in 33 SNPs genotyped in 50 unrelated individuals, and no significant deviations were detected. Our FP-TDI data were readily adaptable to automated genotype calling using our own method of cluster analysis, which assigns a probability score to each genotype call. We conclude that FP-TDI is both efficient and accurate. The method can easily fill the needs of SNP genotyping projects at the scale typically used for regional or candidate-gene association studies.

The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study revealed poorer antidepressant treatment response among black compared with white participants. This racial disparity persisted even after socioeconomic and baseline clinical factors were taken into account. Some studies have suggested genetic contributions to this disparity, but none have attempted to disentangle race and genetic ancestry. Here we used genome-wide single-nucleotide polymorphism (SNP) data to examine independent contributions of race and genetic ancestry to citalopram response. Secondary data analyses included 1877 STAR*D participants who completed an average of 10 weeks of citalopram treatment and provided DNA samples. Participants reported their race as White (n=1464), black (n=299) or other/mixed (n=114). Genetic ancestry was estimated by multidimensional scaling (MDS) analyses of about 500 000 SNPs. Ancestry proportions were estimated by STRUCTURE. Structural equation modeling was used to examine the direct and indirect effects of observed and latent predictors of response, defined as change in the Quick Inventory of Depressive Symptomatology (QIDS) score from baseline to exit. Socioeconomic and baseline clinical factors, race, and anxiety significantly predicted response, as previously reported. However, direct effects of race disappeared in all models that included genetic ancestry. Genetic African ancestry predicted lower treatment response in all models. Although socioeconomic and baseline clinical factors drive racial differences in antidepressant response, genetic ancestry, rather than self-reported race, explains a significant fraction of the residual differences. Larger samples would be needed to identify the specific genetic mechanisms that may be involved, but these findings underscore the importance of including more African-American patients in drug trials.