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Bipolar disorder (BD) has high clinical heterogeneity, frequent psychiatric comorbidities, and elevated suicide risk. To determine genetic differences between common clinical sub-phenotypes of BD, we performed a systematic polygenic risk score (PRS) analysis using multiple PRSs from a range of psychiatric, personality, and lifestyle traits to dissect differences in BD sub-phenotypes in two BD cohorts: the Mayo Clinic BD Biobank (N = 968) and Genetic Association Information Network (N = 1001). Participants were assessed for history of psychosis, early-onset BD, rapid cycling (defined as four or more episodes in a year), and suicide attempts using questionnaires and the Structured Clinical Interview for DSM-IV. In a combined sample of 1969 bipolar cases (45.5% male), those with psychosis had higher PRS for SCZ (OR = 1.3 per S.D.; p = 3e-5) but lower PRSs for anhedonia (OR = 0.87; p = 0.003) and BMI (OR = 0.87; p = 0.003). Rapid cycling cases had higher PRS for ADHD (OR = 1.23; p = 7e-5) and MDD (OR = 1.23; p = 4e-5) and lower BD PRS (OR = 0.8; p = 0.004). Cases with a suicide attempt had higher PRS for MDD (OR = 1.26; p = 1e-6) and anhedonia (OR = 1.22; p = 2e-5) as well as lower PRS for educational attainment (OR = 0.87; p = 0.003). The observed novel PRS associations with sub-phenotypes align with clinical observations such as rapid cycling BD patients having a greater lifetime prevalence of ADHD. Our findings confirm that genetic heterogeneity contributes to clinical heterogeneity of BD and consideration of genetic contribution to psychopathologic components of psychiatric disorders may improve genetic prediction of complex psychiatric disorders.

Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics., Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.

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.

Bipolar disorder (BD) is associated with binge eating behavior (BE), and both conditions are heritable. Previously, using data from the Genetic Association Information Network (GAIN) study of BD, we performed genome-wide association (GWA) analyses of BD with BE comorbidity. Here, utilizing data from the Mayo Clinic BD Biobank (969 BD cases, 777 controls), we performed a GWA analysis of a BD subtype defined by BE, and case-only analysis comparing BD subjects with and without BE. We then performed a meta-analysis of the Mayo and GAIN results. The meta-analysis provided genome-wide significant evidence of association between single nucleotide polymorphisms (SNPs) in PRR5-ARHGAP8 and BE in BD cases (rs726170 OR = 1.91, P = 3.05E-08). In the meta-analysis comparing cases with BD with comorbid BE vs. non-BD controls, a genome-wide significant association was observed at SNP rs111940429 in an intergenic region near PPP1R2P5 (p = 1.21E-08). PRR5-ARHGAP8 is a read-through transcript resulting in a fusion protein of PRR5 and ARHGAP8. PRR5 encodes a subunit of mTORC2, a serine/threonine kinase that participates in food intake regulation, while ARHGAP8 encodes a member of the RhoGAP family of proteins that mediate cross-talk between Rho GTPases and other signaling pathways. Without BE information in controls, it is not possible to determine whether the observed association reflects a risk factor for BE in general, risk for BE in individuals with BD, or risk of a subtype of BD with BE. The effect of PRR5-ARHGAP8 on BE risk thus warrants further investigation.

The serotonin transporter-linked polymorphic region (5-HTTLPR) of the serotonin transporter gene (SLC6A4) S allele is linked to pathogenesis of depression and slower response to selective serotonin reuptake inhibitors (SSRIs); depression and SSRIs are independently associated with bone loss. We aimed to determine whether 5-HTTLPR was associated with bone loss. This cross-sectional study included psychiatric patients with both 5-HTTLPR analysis and bone mineral density (BMD) assessment (hip and spine Z-scores if age T-scores if ⩾50 years). BMD association with 5-HTTLPR was evaluated under models with additive allele effects and dominant S allele effects using linear regression models. Patients were stratified by age (Z-scores at the hip (P=0.002, dominant S allele effects; P=0.004, additive allele effects) and spine (P=0.0006, dominant S allele effects; P=0.01, additive allele effects). In sex-stratified analyses, the association of the S allele with lower BMD in the younger patients was also significant in the subset of women (P⩽0.003 for both hip and spine BMD under the additive allele effect model). In the small group of men younger than 50 years, the S allele was marginally associated with higher spine BMD (P=0.05). BMD T-scores were not associated with 5-HTTLPR genotypes in patients 50 years or older. The 5-HTTLPR variants may modify serotonin effects on bone with sex-specific effects.

Patients with bipolar disorder (BD) have a high prevalence of comorbid medical illness. However, the mechanisms underlying these comorbidities with BD are not well known. Certain genetic variants may have pleiotropic effects, increasing the risk of BD and other medical illnesses simultaneously. In this study, we evaluated the association of BD-susceptibility genetic variants with various medical conditions that tend to co-exist with BD, using electronic health records (EHR) data linked to genome-wide single-nucleotide polymorphism (SNP) data. Data from 7316 Caucasian subjects were used to test the association of 19 EHR-derived phenotypes with 34 SNPs that were previously reported to be associated with BD. After Bonferroni multiple testing correction, P−5 was considered statistically significant. The top association findings suggested that the BD risk alleles at SNP rs4765913 in CACNA1C gene and rs7042161 in SVEP1 may be associated with increased risk of ‘cardiac dysrhythmias’ (odds ratio (OR)=1.1, P=3.4 × 10−3) and ‘essential hypertension’ (OR=1.1, P=3.5 × 10−3), respectively. Although these associations are not statistically significant after multiple testing correction, both genes have been previously implicated with cardiovascular phenotypes. Moreover, we present additional evidence supporting these associations, particularly the association of the SVEP1 SNP with hypertension. This study shows the potential for EHR-based analyses of large cohorts to discover pleiotropic effects contributing to complex psychiatric traits and commonly co-occurring medical conditions.

The last decade of human genetic research witnessed the completion of hundreds of genome-wide association studies (GWASs). However, the genetic variants discovered through these efforts account for only a small proportion of the heritability of complex traits. One explanation for the missing heritability is that the common analysis approach, assessing the effect of each single-nucleotide polymorphism (SNP) individually, is not well suited to the detection of small effects of multiple SNPs. Gene set analysis (GSA) is one of several approaches that may contribute to the discovery of additional genetic risk factors for complex traits. Complex phenotypes are thought to be controlled by networks of interacting biochemical and physiological pathways influenced by the products of sets of genes. By assessing the overall evidence of association of a phenotype with all measured variation in a set of genes, GSA may identify functionally relevant sets of genes corresponding to relevant biomolecular pathways, which will enable more focused studies of genetic risk factors. This approach may thus contribute to the discovery of genetic variants responsible for some of the missing heritability. With the increased use of these approaches for the secondary analysis of data from GWAS, it is important to understand the different GSA methods and their strengths and weaknesses, and consider challenges inherent in these types of analyses. This paper provides an overview of GSA, highlighting the key challenges, potential solutions, and directions for ongoing research.

Acamprosate has been widely used since the Food and Drug Administration approved the medication for treatment of alcohol use disorders (AUDs) in 2004. Although the detailed molecular mechanism of acamprosate remains unclear, it has been largely known that acamprosate inhibits glutamate action in the brain. However, AUD is a complex and heterogeneous disorder. Thus, biomarkers are required to prescribe this medication to patients who will have the highest likelihood of responding positively. To identify pharmacometabolomic biomarkers of acamprosate response, we utilized serum samples from 120 alcohol-dependent subjects, including 71 responders (maintained continuous abstinence) and 49 non-responders (any alcohol use) during 12 weeks of acamprosate treatment. Notably, baseline serum glutamate levels were significantly higher in responders compared with non-responders. Importantly, serum glutamate levels of responders are normalized after acamprosate treatment, whereas there was no significant glutamate change in non-responders. Subsequent functional studies in animal models revealed that, in the absence of alcohol, acamprosate activates glutamine synthetase, which synthesizes glutamine from glutamate and ammonia. These results suggest that acamprosate reduces serum glutamate levels for those who have elevated baseline serum glutamate levels among responders. Taken together, our findings demonstrate that elevated baseline serum glutamate levels are a potential biomarker associated with positive acamprosate response, which is an important step towards development of a personalized approach to treatment for AUD.