[Symposium 56] Current status of psychiatric research using genetic medicine and genomic medicineImportance of collaborative research among East Asians

[SY-56-02] Multimodal Genomic and Mobile Sensing Reveals Genetic and Behavioral Signatures in Mood Disorder Phenotypes

*Po-Hsiu Kuo^1,5,8, Chiao-Erh Chang¹, Ting-Yi Lee¹, Shiau-Shian Huang^2,3, Ying-Ting Chao^1,4, Hsi-Chung Chen⁵, Ming-Chyi Huang⁶, I-Ming Chen⁵, Chuhsing Kate Hsiao^1,7 (1.Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University(Taiwan), 2.Department of Psychiatry, Taipei Veterans General Hospital(Taiwan), 3.College of Medicine, National Yang Ming Chiao Tung University(Taiwan), 4.Department of Medical Research, National Taiwan University(Taiwan), 5.Department of Psychiatry, National Taiwan University Hospital(Taiwan), 6.Department of Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital(Taiwan), 7.Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University(Taiwan), 8.Psychiatric Research Center, Wan Fang Hospital(Taiwan))

Keywords:

antidepressant induce mania,unipolar mania,digital phenotyping

Mood disorders span diverse phenotypes. We integrate genome-wide analyses and digital phenotyping to clarify how inherited risk and real-world mobility inform mood disorder classification and prediction. Among 772 Han Chinese patients with unipolar depression, 145 (19.7%) developed antidepressant-induced mania (AIM) within 28 days of antidepressant exposure or discontinuation. Genome-wide testing identified eight suggestive SNPs, and higher bipolar polygenic risk scores significantly predicted AIM (OR ≈ 1.25, p < .05). Clinical risk factors included female sex, postpartum depression, OCD, severe episodes, substance use, and psychoses. Additionally, bipolar patient with unipolar mania (UM) were compared to 1,041 with depressive-manic (D-M) presentations. A genome-wide locus (rs149251101, THSD7A) differentiated UM from D-M cases (p = 5.3 × 10 ^-8). PRS for bipolar disorder, major depression, and suicide attempt were positively associated with UM, while insomnia liability was inversely linked. Lastly, in two smartphone cohorts (n=107), passive GPS and mood data over six months revealed over 10,000 person-days. Homestay predicted next-day fatigue, depressed mood, and irritability; higher location variance predicted lower depression. Depressive symptoms, in turn, predicted reduced mobility. Spectral and diurnal analyses identified mood-linked movement cycles and evening mobility declines as digital markers of depression. These multimodal approaches reveal overlapping genetic and behavioral markers in mood disorders, enabling future personalized, movement-informed interventions.