BACKGROUND: Polygenic scores (PGSs) hold the potential to identify patients who respond favorably to specific psychiatric treatments. However, their biological interpretation remains unclear. In this study, we developed pathway-specific PGSs (PSPGSs) for lithium response and assessed their association with clinical lithium response in patients with bipolar disorder. METHODS: Using sets of genes involved in pathways affected by lithium, we developed 9 PSPGSs and evaluated their associations with lithium response in the International Consortium on Lithium Genetics (ConLi+Gen) (N = 2367), with validation in combined PsyCourse (Pathomechanisms and Signatures in the Longitudinal Course of Psychosis) (N = 105) and BipoLife (N = 102) cohorts. The association between each PSPGS and lithium response-defined both as a continuous ALDA score and a categorical outcome (good vs. poor responses)-was evaluated using regression models, with adjustment for confounders. The cutoff for a significant association was p < .05 after multiple testing correction. RESULTS: The PGSs for acetylcholine, GABA (gamma-aminobutyric acid), and mitochondria were associated with response to lithium in both categorical and continuous outcomes. However, the PGSs for calcium channel, circadian rhythm, and GSK (glycogen synthase kinase) were associated only with the continuous outcome. Each score explained 0.29% to 1.91% of the variance in the categorical and 0.30% to 1.54% of the variance in the continuous outcomes. A multivariate model combining PSPGSs that showed significant associations in the univariate analysis (combined PSPGS) increased the percentage of variance explained (R 2) to 3.71% and 3.18% for the categorical and continuous outcomes, respectively. Associations for PGSs for GABA and circadian rhythm were replicated. Patients with the highest genetic loading (10th decile) for acetylcholine variants were 3.03 times more likely (95% CI, 1.95 to 4.69) to show a good lithium response (categorical outcome) than patients with the lowest genetic loading (1st decile). CONCLUSIONS: PSPGSs achieved predictive performance comparable to the conventional genome-wide PGSs, with the added advantage of biological interpretability using a smaller list of genetic variants.

Polygenic scores (PGSs) have the potential to identify patients likely to respond to specific psychiatric treatments, but their biological interpretation remains unclear. In this study, we developed 9 pathway-specific PGSs (PSPGSs) for lithium response by aggregating genetic variants involved in pathways affected by lithium. We assessed their associations with lithium response in the International Consortium on Lithium Genetics (ConLi+Gen) (N = 2367) cohort and validated the findings in the PsyCourse (N = 105) and BipoLife (N = 102) cohorts. Clinical response to lithium treatment was significantly associated with PSPGSs for acetylcholine, GABA (gamma-aminobutyric acid), calcium channel signaling, mitochondria, circadian rhythm, and GSK pathways, with explained variance (R 2) ranging from 0.29% to 1.91%. The combined PSPGS explained up to 3.71% of the variability. Associations for GABA and circadian rhythm PGSs were successfully replicated. In a decile-based analysis, patients with the highest genetic load (10th decile) for acetylcholine pathway variants were 3.03 times more likely to respond well to lithium compared with those in the lowest decile (1st decile). PSPGSs achieved predictive performance comparable to conventional genome-wide PGSs, with better biological interpretability and a more focused set of genetic variants.

• Keywords
• Bipolar disorder, Lithium, Pharmacogenomics, Polygenic score, Psychiatry,
• Publication type
• Journal Article MeSH

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

• MeSH
• Antimanic Agents pharmacology   therapeutic use   MeSH
• Bipolar Disorder * drug therapy   genetics   MeSH
• Genome-Wide Association Study * methods   MeSH
• Pharmacogenetics methods   MeSH
• Focal Adhesions * drug effects   genetics   MeSH
• Genomics methods   MeSH
• Gene Regulatory Networks * drug effects   genetics   MeSH
• Induced Pluripotent Stem Cells drug effects   metabolism   MeSH
• Humans MeSH
• Lithium * pharmacology   therapeutic use   MeSH
• Multiomics MeSH
• Neurons metabolism   drug effects   MeSH
• Lithium Compounds pharmacology   therapeutic use   MeSH
• Gene Expression Profiling methods   MeSH
• Transcriptome * genetics   drug effects   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH
• Names of Substances
• Antimanic Agents MeSH
• Lithium * MeSH
• Lithium Compounds MeSH

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P < 0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P = 9.8 × 10-12, R2 = 1.9%) and continuous (P = 6.4 × 10-9, R2 = 2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22-5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P = 3.9 × 10-4, R2 = 0.9%), but not for the continuous outcome (P = 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment.

• MeSH
• Acetylcholine metabolism   MeSH
• Antimanic Agents therapeutic use   pharmacology   MeSH
• Bayes Theorem MeSH
• Bipolar Disorder * drug therapy   genetics   MeSH
• Genome-Wide Association Study methods   MeSH
• Adult MeSH
• Polymorphism, Single Nucleotide genetics   MeSH
• Cohort Studies MeSH
• Glutamic Acid metabolism   MeSH
• Middle Aged MeSH
• Humans MeSH
• Lithium * therapeutic use   pharmacology   MeSH
• Multifactorial Inheritance * genetics   MeSH
• Lithium Compounds therapeutic use   pharmacology   MeSH
• Treatment Outcome MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Acetylcholine MeSH
• Antimanic Agents MeSH
• Glutamic Acid MeSH
• Lithium * MeSH
• Lithium Compounds MeSH

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.

• Keywords
• Adherence, Attention-deficit/hyperactivity disorder, Bipolar disorder, Lithium response, Polygenic risk scores,
• Publication type
• Journal Article MeSH

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.

• MeSH
• Bipolar Disorder * drug therapy   genetics   MeSH
• Depression MeSH
• Depressive Disorder, Major * drug therapy   genetics   MeSH
• Genetic Predisposition to Disease MeSH
• Humans MeSH
• Lithium therapeutic use   MeSH
• Multifactorial Inheritance MeSH
• Risk Factors MeSH
• Schizophrenia * drug therapy   genetics   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Meta-Analysis MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Lithium MeSH

Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10-3; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.

• MeSH
• Alleles MeSH
• Bipolar Disorder drug therapy   genetics   MeSH
• Adult MeSH
• Pharmacogenetics MeSH
• Gene Frequency MeSH
• Genetic Predisposition to Disease * MeSH
• Genetic Variation MeSH
• Genotype MeSH
• Haplotypes MeSH
• HLA-DQ beta-Chains genetics   MeSH
• HLA-DRB1 Chains genetics   MeSH
• Middle Aged MeSH
• Humans MeSH
• Lithium therapeutic use   MeSH
• Treatment Outcome MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• HLA-DQ beta-Chains MeSH
• HLA-DQB1 antigen MeSH Browser
• HLA-DRB1 Chains MeSH
• Lithium MeSH

Predicting lithium response (LiR) in bipolar disorder (BD) may inform treatment planning, but phenotypic heterogeneity complicates discovery of genomic markers. We hypothesized that patients with "exemplary phenotypes"-those whose clinical features are reliably associated with LiR and non-response (LiNR)-are more genetically separable than those with less exemplary phenotypes. Using clinical data collected from people with BD (n = 1266 across 7 centers; 34.7% responders), we computed a "clinical exemplar score," which measures the degree to which a subject's clinical phenotype is reliably predictive of LiR/LiNR. For patients whose genotypes were available (n = 321), we evaluated whether a subgroup of responders/non-responders with the top 25% of clinical exemplar scores (the "best clinical exemplars") were more accurately classified based on genetic data, compared to a subgroup with the lowest 25% of clinical exemplar scores (the "poor clinical exemplars"). On average, the best clinical exemplars of LiR had a later illness onset, completely episodic clinical course, absence of rapid cycling and psychosis, and few psychiatric comorbidities. The best clinical exemplars of LiR and LiNR were genetically separable with an area under the receiver operating characteristic curve of 0.88 (IQR [0.83, 0.98]), compared to 0.66 [0.61, 0.80] (p = 0.0032) among poor clinical exemplars. Variants in the Alzheimer's amyloid-secretase pathway, along with G-protein-coupled receptor, muscarinic acetylcholine, and histamine H1R signaling pathways were informative predictors. This study must be replicated on larger samples and extended to predict response to other mood stabilizers.

• MeSH
• Antimanic Agents therapeutic use   MeSH
• Bipolar Disorder * drug therapy   genetics   MeSH
• Phenotype MeSH
• Humans MeSH
• Lithium therapeutic use   MeSH
• Lithium Compounds therapeutic use   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Antimanic Agents MeSH
• Lithium MeSH
• Lithium Compounds MeSH