2026 ECNP Congress

PS04-4122

Sleep-related traits and lithium response in bipolar disorder: an approach using polygenic scores in the ConLiGen sample

Marina Zafrilla López — S. Papiol, M. Acosta-Díez, E. Jiménez, T.G. Schulze, E. Vieta, A. Benabarre, C. ConLiGen Authors, M. Mitjans, B. Arias · University of Barcelona, Evolutive biology- ecology and environmental sciences, Barcelona, Spain

2026 ECNP Congress

Background

Bipolar disorder (BD) is a chronic psychiatric condition with a heritability estimated between 60-85%. One of its most consistent features is the disruption of circadian rhythms and sleep patterns, including reduced rhythm amplitude and a tendency towards eveningness [1].

Lithium (Li), the first-line mood-stabilizing therapy for BD, stabilizes sleep patterns, lengthens the circadian period, and promotes morningness, particularly in excellent responders [1]. Candidate gene studies have identified variants at this intersection between circadian regulation and Li response [2].

We aimed to evaluate the association between polygenic burden of sleep-related traits and Li response using the Consortium on Lithium Genetics (ConLiGen) sample [3], and to identify latent sleep/circadian genetic dimensions associated with treatment outcome using GenomicSEM.

Methods

European-ancestry BD patients from ConLiGen were assessed for Li response using the Alda scale. The dichotomous phenotype comprised 607 Li responders and 1,589 non-responders, while the continuous phenotype included 1,913 individuals. Genotyping, quality control, and imputation were conducted independently across 15 analytical groups from more than 21 centres genotyped on 6 different arrays. Polygenic risk scores (PRS) were generated using PRS-CS [4] from 18 sleep- and circadian-related GWAS, including both self-reported measures, and accelerometer-derived measures from the UK Biobank. PRS were tested within cohorts using regression models adjusted for age, sex, and ancestry, followed by fixed- and random-effects meta-analyses.

To capture the shared genetic architecture across traits and reduce multiple testing burden, Genomic Structural Equation Modeling (GenomicSEM) [5] was applied to GWAS summary statistics. Genetic correlations were computed via LD score regression, and exploratory factor analyses supported a three-factor structure: F1: circadian preference (eveningness, sleep midpoint, M10/L5 timing, difficulty waking up); F2: sleep continuity and insomnia (frequent insomnia, short sleep, sleep non-efficiency); and F3: sleepiness (napping, daytime sleepiness, long sleep, diurnal inactivity). Factor-level GWAS were generated, from which latent-factor PRS were derived and meta-analyzed across cohorts.

Results

In random-effects meta-analyses of individual PRS, higher polygenic risk for frequent insomnia was associated with poorer Li response in the dichotomous outcome (OR=0.87, 95%CI 0.78–0.98; p=0.028), and binary morning chronotype showed a similar direction of effect with the continuous outcome (β=−0.14; p=0.027). No individual PRS associations survived FDR correction.

Among the factor-based PRS, higher genetic liability to the insomnia/sleep continuity factor (F2) was significantly associated with poorer dichotomous Li response in the fixed-effects meta-analysis (OR=0.87, 95%CI 0.77–0.97; p=0.017), and this association survived FDR correction (adj-p=0.032). By contrast, factors capturing circadian timing (F1) and sleepiness (F3) were not significantly associated with Li response. Notably, F1 was negatively correlated with both F2 and F3, suggesting that circadian timing and sleep disturbance represent genetically distinct dimensions.

Conclusions

These findings support a multidimensional genetic architecture linking sleep traits to lithium response in BD. The convergence of results from individual insomnia PRS and the latent insomnia factor (F2) highlights a prominent role for sleep continuity and insomnia genetics, suggesting that patients with higher genetic liability for sleep fragmentation may have more fundamentally disrupted sleep systems, making them less responsive to lithium’s chronobiotic effects [1].

References

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