BACKGROUND: About 7 million people, 2.8% of US adults, have bipolar disorder (BD). While second-generation antipsychotics (SGA) are indicated as acute and maintenance treatments for BD, therapeutic success requires medication adherence and reported nonadherence estimates to range as high as 60%. Identifying patient risk factors for nonadherence is important for reducing it. OBJECTIVE: The objective of this study was to quantify the associations of risk factors, including social determinants of health, with SGA nonadherence among patients with BD. METHODS: In this cross-sectional study of 2015-2017 MassHealth Medicaid data, we examined several definitions of adherence and used logistic regression to identify risk factors for nonadherence (medication possession ratio < 0.8) among all adults aged 18-64 diagnosed with BD who could be followed for 12 months following SGA initiation. RESULTS: Among 5197 patients, the mean (+/-SD) age was 37.7 (+/-11.4) years, and 42.3% were men. Almost half (47.7%) of patients were nonadherent to SGAs when measured by medication possession ratio. The prevalence of nonadherence peaked at middle age for men and younger for women. Nonadherence was less common among Massachusetts' Department of Mental Health clients (odds ratio=0.60, 95% confidence limit: 0.48-0.74) and among those who used other psychotropic medications (odds ratios between 0.45 and 0.81); in contrast, increase in neighborhood socioeconomic stress was associated with increased odds of nonadherence. CONCLUSIONS/IMPLICATIONS: Adherence to SGA treatment is suboptimal among people with BD. Recognizing risk factors, including those related to social determinants of health, can help target interventions to improve adherence for people at high risk and has implications for adherence-based quality measures.

Over the last decade there has been a great effort to annotate noncoding regions of the genome, particularly those that regulate gene expression. These regulatory elements contain binding sites for transcription factors (TF), which interact with one another and transcriptional machinery to initiate, enhance, or repress gene expression. The Encyclopedia of DNA Elements (ENCODE) consortium has generated thousands of epigenomic datasets, such as DNase-seq and ChIP-seq experiments, with the goal of defining such regions. By integrating these assays, we developed the Registry of candidate Regulatory Elements (cREs), a collection of putative regulatory regions across human and mouse. In total, we identified over 1.3M human and 400k mouse cREs each annotated with cell-type specific signatures (e.g. promoter-like, enhancer-like) in over 400 human and 100 mouse biosamples. We then demonstrated the biological utility of these regions by analyzing cell type enrichments for genetic variants reported by genome wide association studies (GWAS). To search and visualize these cREs, we developed the online database SCREEN (search candidate regulatory elements by ENCODE). After defining cREs, we next sought to determine their potential gene targets. To compare target gene prediction methods, we developed a comprehensive benchmark of enhancer-gene links by curating ChIA-PET, Hi-C and eQTL datasets. We then used this benchmark to evaluate unsupervised linking approaches such as the correlation of epigenomic signal. We determined that these methods have low overall performance and do not outperform simply selecting the closest gene. We then developed a supervised Random Forest model which had notably better performance than unsupervised methods. We demonstrated that this model can be applied across cell types and can be used to predict target genes for GWAS associated variants. Finally, we used the registry of cREs to annotate variants associated with psychiatric disorders. We found that these "psych SNPs" are enriched in cREs active in brain tissue and likely target genes involved in neural development pathways. We also demonstrated that psych SNPs overlap binding sites for TFs involved in neural and immune pathways. Finally, by identifying psych SNPs with allele imbalance in chromatin accessibility, we highlighted specific cases of psych SNPs altering TF binding motifs resulting in the disruption of TF binding. Overall, we demonstrated our collection of putative regulatory regions, the Registry of cREs, can be used to understand the potential biological function of noncoding variation and develop hypotheses for future testing.

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies to determine if commonly varying single nucleotide polymorphisms are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn's disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations among the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.