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

BACKGROUND: Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N = 2064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. RESULTS: We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. CONCLUSIONS: Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.

• Keywords
• Bipolar disorder, Comorbidity, Genetics, Lithium treatment, Psychiatric symptoms,
• Publication type
• Journal Article 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