Clinical and genetic contributions to medical comorbidity in bipolar disorder: a study using electronic health records-linked biobank data.

Bipolar disorder is a chronic and complex polygenic disease with high rates of comorbidity. However, the independent contribution of either diagnosis or genetic risk of bipolar disorder to the medical comorbidity profile of individuals with the disease remains unresolved. Here, we conducted a multi-step phenome-wide association study (PheWAS) of bipolar disorder using phenomes derived from the electronic health records of participants enrolled in the Mayo Clinic Biobank and the Mayo Clinic Bipolar Disorder Biobank. First, we explored the conditions associated with a diagnosis of bipolar disorder by conducting a phenotype-based PheWAS followed by LASSO-penalized regression to account for correlations within the phenome. Then, we explored the conditions associated with bipolar disorder polygenic risk score (BD-PRS) using a PRS-based PheWAS with a sequential exclusion approach to account for the possibility that diagnosis, instead of genetic risk, may drive such associations. 53,386 participants (58.7% women) with a mean age at analysis of 67.8 years (SD = 15.6) were included. A bipolar disorder diagnosis (n = 1479) was associated with higher rates of psychiatric conditions, injuries and poisonings, endocrine/metabolic and neurological conditions, viral hepatitis C, and asthma. BD-PRS was associated with psychiatric comorbidities but, in contrast, had no positive associations with general medical conditions. While our findings warrant confirmation with longitudinal-prospective studies, the limited associations between bipolar disorder genetics and medical conditions suggest that shared environmental effects or environmental consequences of diagnosis may have a greater impact on the general medical comorbidity profile of individuals with bipolar disorder than its genetic risk.

IL17RB genetic variants are associated with acamprosate treatment response in patients with alcohol use disorder: A proteomics-informed genomics study.

Acamprosate is a Food and Drug Administration (FDA) approved medication for the treatment of alcohol use disorder (AUD). However, only a subset of patients achieves optimal treatment outcomes. Currently, no biological measures are utilized to predict response to acamprosate treatment. We applied our established pharmaco-omics informed genomics strategy to identify potential biomarkers associated with acamprosate treatment response. Specifically, our previous open-label acamprosate clinical trial recruited 442 patients with AUD who were treated with acamprosate for three months. We first performed proteomics using baseline plasma samples to identify potential biomarkers associated with acamprosate treatment outcomes. Next, we applied our established "proteomics-informed genome-wide association study (GWAS)" research strategy, and identified 12 proteins, including interleukin-17 receptor B (IL17RB), associated with acamprosate treatment response.​ A GWAS for IL17RB concentrations identified several genome-wide significant signals. Specifically, the top hit single nucleotide polymorphism (SNP) rs6801605 with a minor allele frequency of 38% in the European American population mapped 4 kilobase (Kb) upstream of IL17RB, and intron 1 of the choline dehydrogenase (CHDH) gene on chromosome 3 (p: 4.8E-20). The variant genotype (AA) for the SNP rs6801605 was associated with lower IL17RB protein expression. In addition, we identified a series of genetic variants in IL17RB that were associated with acamprosate treatment outcomes. Furthermore, the variantgenotypes for all of those IL17RB SNPs were protective for alcohol relapse. Finally, we demonstrated that the basal level of mRNA expression of IL17RB was inversely correlated with those of nuclear factor-κB (NF-κB) subunits, and a significantly higher expression of NF-κB subunits was observed in AUD patients who relapsed to alcohol use. In summary, this study illustrates that IL17RB genetic variants might contribute to acamprosate treatment outcomes. This series of studies represents an important step toward generating functional hypotheses that could be tested to gain insight into mechanisms underlying acamprosate treatment response phenotypes. (The ClinicalTrials.gov Identifier: NCT00662571).

Antidepressants that increase mitochondrial energetics may elevate risk of treatment-emergent mania.

Preclinical evidence suggests that antidepressants (ADs) may differentially influence mitochondrial energetics. This study was conducted to investigate the relationship between mitochondrial function and illness vulnerability in bipolar disorder (BD), specifically risk of treatment-emergent mania (TEM). Participants with BD already clinically phenotyped as TEM+ (n = 176) or TEM- (n = 516) were further classified whether the TEM associated AD, based on preclinical studies, increased (Mito+, n = 600) or decreased (Mito-, n = 289) mitochondrial electron transport chain (ETC) activity. Comparison of TEM+ rates between Mito+ and Mito- ADs was performed using generalized estimating equations to account for participants exposed to multiple ADs while adjusting for sex, age at time of enrollment into the biobank and BD type (BD-I/schizoaffective vs. BD-II). A total of 692 subjects (62.7% female, 91.4% White, mean age 43.0 ± 14.0 years) including 176 cases (25.3%) of TEM+ and 516 cases (74.7%) of TEM- with previous exposure to Mito+ and/or Mito- antidepressants were identified. Adjusting for age, sex and BD subtype, TEM+ was more frequent with antidepressants that increased (24.7%), versus decreased (13.5%) mitochondrial energetics (OR = 2.21; p = 0.000009). Our preliminary retrospective data suggests there may be merit in reconceptualizing AD classification, not solely based on monoaminergic conventional drug mechanism of action, but additionally based on mitochondrial energetics. Future prospective clinical studies on specific antidepressants and mitochondrial activity are encouraged. Recognizing pharmacogenomic investigation of drug response may extend or overlap to genomics of disease risk, future studies should investigate potential interactions between mitochondrial mechanisms of disease risk and drug response.

Patterns of pharmacotherapy for bipolar disorder: A GBC survey.

OBJECTIVES: To understand treatment practices for bipolar disorders (BD), this study leveraged the Global Bipolar Cohort collaborative network to investigate pharmacotherapeutic treatment patterns in multiple cohorts of well-characterized individuals with BD in North America, Europe, and Australia. METHODS: Data on pharmacotherapy, demographics, diagnostic subtypes, and comorbidities were provided from each participating cohort. Individual site and regional pooled proportional meta-analyses with generalized linear mixed methods were conducted to identify prescription patterns. RESULTS: This study included 10,351 individuals from North America (n = 3985), Europe (n = 3822), and Australia (n = 2544). Overall, participants were predominantly female (60%) with BD-I (60%; vs. BD-II = 33%). Cross-sectionally, mood-stabilizing anticonvulsants (44%), second-generation antipsychotics (42%), and antidepressants (38%) were the most prescribed medications. Lithium was prescribed in 29% of patients, primarily in the Australian (31%) and European (36%) cohorts. First-generation antipsychotics were prescribed in 24% of the European versus 1% in the North American cohort. Antidepressant prescription rates were higher in BD-II (47%) compared to BD-I (35%). Major limitations were significant differences among cohorts based on inclusion/exclusion criteria, data source, and time/year of enrollment into cohort. CONCLUSIONS: Mood-stabilizing anticonvulsants, second-generation antipsychotics, and antidepressants were the most prescribed medications suggesting prescription patterns that are not necessarily guideline concordant. Significant differences exist in the prescription practices across different geographic regions, especially the underutilization of lithium in the North American cohorts and the higher utilization of first-generation antipsychotics in the European cohorts. There is a need to conduct future longitudinal studies to further explore these differences and their impact on outcomes, and to inform and implement evidence-based guidelines to help improve treatment practices in BD.

Phenotypic distinctions in depression and anxiety: a comparative analysis of comorbid and isolated cases.

BACKGROUND: Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety. METHODS: Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses. RESULTS: Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10-27), sleep apnea (OR 1.71; p = 1 × 10-22), and type II diabetes (OR 1.74; p = 9 × 10-18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10-25), benign skin neoplasms (OR 1.61; p = 2 × 10-17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10-12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression. CONCLUSIONS: While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.

The importance of social activity to risk of major depression in older adults.

BACKGROUND: Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults. METHODS: We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis. RESULTS: Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level]. CONCLUSION: Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people.

BACKGROUND: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation. METHODS: Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry. RESULTS: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity. CONCLUSIONS: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Antidepressant-Associated Treatment Emergent Mania: A Meta-Analysis to Guide Risk Modeling Pharmacogenomic Targets of Potential Clinical Value.

BACKGROUND: The purpose of this study was to review the association between the SLC6A4 5-HTTLPR polymorphism and antidepressant (AD)-associated treatment emergent mania (TEM) in bipolar disorder alongside starting a discussion on the merits of developing risk stratification models to guide when not to provide AD treatment for bipolar depression. METHODS: Studies that examined the association between clinical and genetic risk factors, specifically monoaminergic transporter genetic variation, and TEM were identified. A meta-analysis was performed using the odds ratio to estimate the effect size under the Der-Simonian and Laird model. RESULTS: Seven studies, referencing the SLC6A4 5-HTTLPR polymorphism and TEM (total N = 1578; TEM+ =594, TEM- = 984), of 142 identified articles were included. The time duration between the start of the AD to emergence of TEM ranged from 4 to 12 weeks. There was a nominally significant association between the s allele of the 5-HTTLPR polymorphism and TEM (odds ratio, 1.434; 95% confidence interval, 1.001-2.055; P = 0.0493; I2 = 52%). No studies have investigated norepinephrine or dopamine transporters. CONCLUSION: Although the serotonin transporter genetic variation is commercially available in pharmacogenomic decision support tools, greater efforts, more broadly, should focus on complete genome-wide approaches to determine genetic variants that may contribute to TEM. Moreover, these data are exemplary to the merits of developing risk stratification models, which include both clinical and biological risk factors, to guide when not to use ADs in bipolar disorder. Future studies will need to validate new risk models that best inform the development of personalized medicine best practices treating bipolar depression.

Single cell transcriptomics reveals distinct transcriptional responses to oxycodone and buprenorphine by iPSC-derived brain organoids from patients with opioid use disorder.

The opioid epidemic represents a national crisis. Oxycodone is one of the most prescribed opioid medications in the United States, whereas buprenorphine is currently the most prescribed medication for opioid use disorder (OUD) pharmacotherapy. Given the extensive use of prescription opioids and the global opioid epidemic, it is essential to understand how opioids modulate brain cell type function at the single-cell level. We performed single nucleus RNA-seq (snRNA-seq) using iPSC-derived forebrain organoids from three male OUD subjects in response to oxycodone, buprenorphine, or vehicle for seven days. We utilized the snRNA-seq data to identify differentially expressed genes following drug treatment using the Seurat integrative analysis pipeline. We utilized iPSC-derived forebrain organoids and single-cell sequencing technology as an unbiased tool to study cell-type-specific and drug-specific transcriptional responses. After quality control filtering, we analyzed 25787 cells and identified sixteen clusters using unsupervised clustering analysis. Our results reveal distinct transcriptional responses to oxycodone and buprenorphine by iPSC-derived brain organoids from patients with OUD. Specifically, buprenorphine displayed a significant influence on transcription regulation in glial cells. However, oxycodone induced type I interferon signaling in many cell types, including neural cells in brain organoids. Finally, we demonstrate that oxycodone, but not buprenorphine activated STAT1 and induced the type I interferon signaling in patients with OUD. These data suggest that elevation of STAT1 expression associated with OUD might play a role in transcriptional regulation in response to oxycodone. In summary, our results provide novel mechanistic insight into drug action at single-cell resolution.

Polygenic prediction of bipolar disorder in a Latin American sample.

To date, bipolar disorder (BD) genetic studies and polygenic risk scores (PRSs) for BD are based primarily on populations of European descent (EUR) and lack representation from other ancestries including Latin American (LAT). Here, we describe a new LAT cohort from the Mayo Clinic Bipolar Biobank (MCBB), a multisite collaboration with recruitment sites in the United States (EUR; 1,443 cases and 777 controls) and Mexico and Chile (LAT; 211 cases and 161 controls) and use the sample to explore the performance of a BD-PRS in a LAT population. Using results from the largest genome-wide association study of BD in EUR individuals, PRSice2 and LDpred2 were used to compute BD-PRSs in the LAT and EUR samples from the MCBB. PRSs explained up to 1.4% (PRSice) and 4% (LDpred2) of the phenotypic variance on the liability scale in the LAT sample compared to 3.8% (PRSice2) and 3.4% (LDpred2) in the EUR samples. Future larger studies should further explore the differential performance of different PRS approaches across ancestries. International multisite studies, such as this one, have the potential to address diversity-related limitations of prior genomic studies and ultimately contribute to the reduction of health disparities.

Clinical Phenotype of Tardive Dyskinesia in Bipolar Disorder.

PURPOSE: Recognizing the negative impact that antipsychotic-induced movement disorders have on the quality of life and treatment outcomes in bipolar disorder (BD), this study aimed to assess clinical correlates and antipsychotic use patterns of tardive dyskinesia (TD+) in BD. MATERIALS AND METHODS: Participants with and without TD were included. Clinical variables were compared using t-test and χ2 test. Antipsychotic use patterns in TD+, including number of trials, mean doses, and estimated cumulative exposure, were assessed in a case-only analysis. RESULTS: The prevalence rate of TD was 5.1%. In comparison to the TD- group (n = 1074), TD+ participants (n = 58) were older, more likely to be female and have type I bipolar illness. There were 60.3% of the TD+ group that continued using antipsychotics at study entry and had a mean cumulative exposure to antipsychotics of 18.2 ± 15.6 years. Average dose, in haloperidol equivalents, was 5.9 ± 3.5 mg and 77.7% of the trials were second-generation antipsychotics. CONCLUSIONS: This study confirms previously identified TD risk factors, such as age, sex, and bipolar subtype in a large BD cohort. Limitations included a cross-sectional design and the lack of tardive illness severity assessment. As atypical antipsychotics continue to be primary mood stabilization treatment, attempting to harmonize large data sets to identify additional biomarkers of tardive risk will optimize individualized care for patients with BD.

TCF7L2 lncRNA: a link between bipolar disorder and body mass index through glucocorticoid signaling.

Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the effect of body mass index (BMI), which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in the BD and BMI interaction remained unclear. In the present study, we demonstrated by studying human induced pluripotent stem cell (hiPSC)-derived astrocytes, cells that highly express TCF7L2 in the CNS, that the BD-BMI GWAS risk SNP is associated with glucocorticoid-dependent repression of the expression of a previously uncharacterized TCF7L2 transcript variant. That transcript is a long non-coding RNA (lncRNA-TCF7L2) that is highly expressed in the CNS but not in peripheral tissues such as the liver and pancreas that are involved in metabolism. In astrocytes, knockdown of the lncRNA-TCF7L2 resulted in decreased expression of the parent gene, TCF7L2, as well as alterations in the expression of a series of genes involved in insulin signaling and diabetes. We also studied the function of TCF7L2 in hiPSC-derived astrocytes by integrating RNA sequencing data after TCF7L2 knockdown with TCF7L2 chromatin-immunoprecipitation sequencing (ChIP-seq) data. Those studies showed that TCF7L2 directly regulated a series of BD risk genes. In summary, these results support the existence of a CNS-based mechanism underlying BD-BMI genetic risk, a mechanism based on a glucocorticoid-dependent expression quantitative trait locus that regulates the expression of a novel TCF7L2 non-coding transcript.

Pain Catastrophizing and Pain Self-Efficacy Mediate Interdisciplinary Pain Rehabilitation Program Outcomes at Posttreatment and Follow-Up.

BACKGROUND: Decreasing pain catastrophizing and improving self-efficacy to self-manage chronic pain symptoms are important treatment targets in the context of interdisciplinary pain rehabilitation. Greater pain catastrophizing has been shown to be associated with greater impact of pain symptoms on functioning; conversely, greater pain self-efficacy has been associated with lower pain intensity and lower levels of disability. OBJECTIVE: To prospectively evaluate interdisciplinary pain rehabilitation outcomes, as well as to evaluate the mediating effects of both pain catastrophizing and pain self-efficacy on outcome. METHODS: Participants were 315 patients with chronic pain between April 2017 and April 2018 who completed a 3-week interdisciplinary pain rehabilitation program. Pain severity, pain interference, pain catastrophizing, pain self-efficacy, quality of life, depressive symptom questionnaires, and measures of physical performance were assessed before and after treatment. Follow-up questionnaires were returned by 163 participants. Effect size and reliable change analyses were conducted from pre- to posttreatment and from pretreatment to 6-month follow-up. Mediation analyses were conducted to determine the mediating effect of pain catastrophizing and pain self-efficacy on pain outcome. RESULTS: Significant improvements from pre- to posttreatment in pain outcomes were observed, and more than 80% evidenced a reliable change in at least one pain-relevant measure. Pain catastrophizing and pain self-efficacy mediated the relationship between changes in pain outcomes. CONCLUSIONS: Interdisciplinary pain rehabilitation is an effective treatment, and decreasing pain catastrophizing and increasing pain self-efficacy can influence maintenance of treatment gains.

Systematic review: Wearable remote monitoring to detect nonalcohol/nonnicotine-related substance use disorder symptoms.

BACKGROUND AND OBJECTIVES: Substance use disorders (SUDs) are chronic relapsing diseases characterized by significant morbidity and mortality. Phenomenologically, patients with SUDs present with a repeating cycle of intoxication, withdrawal, and craving, significantly impacting their diagnosis and treatment. There is a need for better identification and monitoring of these disease states. Remote monitoring chronic illness with wearable devices offers a passive, unobtrusive, constant physiological data assessment. We evaluate the current evidence base for remote monitoring of nonalcohol, nonnicotine SUDs. METHODS: We performed a systematic, comprehensive literature review and screened 1942 papers. RESULTS: We found 15 studies that focused mainly on the intoxication stage of SUD. These studies used wearable sensors measuring several physiological parameters (ECG, HR, O2 , Accelerometer, EDA, temperature) and implemented study-specific algorithms to evaluate the data. DISCUSSION AND CONCLUSIONS: Studies were extracted, organized, and analyzed based on the three SUD disease states. The sample sizes were relatively small, focused primarily on the intoxication stage, had low monitoring compliance, and required significant computational power preventing "real-time" results. Cardiovascular data was the most consistently valuable data in the predictive algorithms. This review demonstrates that there is currently insufficient evidence to support remote monitoring of SUDs through wearable devices. SCIENTIFIC SIGNIFICANCE: This is the first systematic review to show the available data on wearable remote monitoring of SUD symptoms in each stage of the disease cycle. This clinically relevant approach demonstrates what we know and do not know about the remote monitoring of SUDs within disease states.

A principal component approach to improve association testing with polygenic risk scores.

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS-PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS-PCA approach with a permutation test and an a priori selected p-value threshold. The PRS-PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

Increased Patient Safety-Related Incidents Following the Transition into Daylight Savings Time.

BACKGROUND: "Spring forward," the start of daylight savings time (DST), reduces sleep opportunity by an hour. Insufficient sleep in healthcare workers resulting from the spring forward time change could potentially result in an increase in medical errors. OBJECTIVE: We examined the change in reported patient safety-related incidents (SRIs), in the week following the transition into and out of DST over a period of 8 years. DESIGN: Observational study SETTING: A US-based large healthcare organization with sites across multiple states MEASUREMENTS: Voluntarily reported SRIs that occurred 7 days prior to and following the spring and fall time changes for years 2010-2017 were ascertained. SRIs likely resulting from human error were identified separately. The changes in the number of SRIs (either all SRIs or SRIs restricted to those likely resulting from human error) from the week before and after the time change (either spring or fall) were modeled using a negative binomial mixed model with a random effect to correct for non-independent observations in consecutive weeks. RESULTS: Over the 8-year period, we observed 4.2% (95% CI: - 1.1 to 9.7%; p = 0.12) and 8.8% (95% CI: - 2.5 to 21.5%; p = 0.13) increases in overall SRIs in the 7 days following DST when compared with 7 days prior for spring and fall, respectively. By restricting to SRIs likely resulting from human errors, we observed 18.7% (95% CI: 5.6 to 33.6%; p = 0.004) and 4.9% (95% CI: - 1.3 to 11.5%; p = 0.12) increases for spring and fall, respectively. CONCLUSION: Policy makers and healthcare organizations should evaluate delayed start of shifts or other contingency measures to mitigate the increased risk of SRIs during transition to DST in spring.

Genetic contributions to bipolar disorder: current status and future directions.

Bipolar disorder (BD) is a highly heritable mental disorder and is estimated to affect about 50 million people worldwide. Our understanding of the genetic etiology of BD has greatly increased in recent years with advances in technology and methodology as well as the adoption of international consortiums and large population-based biobanks. It is clear that BD is also highly heterogeneous and polygenic and shows substantial genetic overlap with other psychiatric disorders. Genetic studies of BD suggest that the number of associated loci is expected to substantially increase in larger future studies and with it, improved genetic prediction of the disorder. Still, a number of challenges remain to fully characterize the genetic architecture of BD. First among these is the need to incorporate ancestrally-diverse samples to move research away from a Eurocentric bias that has the potential to exacerbate health disparities already seen in BD. Furthermore, incorporation of population biobanks, registry data, and electronic health records will be required to increase the sample size necessary for continued genetic discovery, while increased deep phenotyping is necessary to elucidate subtypes within BD. Lastly, the role of rare variation in BD remains to be determined. Meeting these challenges will enable improved identification of causal variants for the disorder and also allow for equitable future clinical applications of both genetic risk prediction and therapeutic interventions.

A linear mixed model framework for gene-based gene-environment interaction tests in twin studies.

Interaction between genes and environments (G×E) can be well investigated in families due to the shared genes and environment among family members. However, the majority of the current tests of G×E interaction between a set of variants and an environment are only suitable for studies with unrelated subjects. In this paper, we extend several G×E interaction tests to a linear mixed model framework to study interaction between a set of correlated environments and a candidate gene in families. The correlated environments can either be modeled separately or jointly in one model. We demonstrate theoretically that the tests developed by modeling correlated environments separately are valid and present a computationally fast alternative to detect G×E interaction in families. For either strategy, we propose treating the genetic main effects as a random effect to reduce the number of main-effect parameters and thus improve the power to detect interactions. Additionally, we propose a generalization of a test of interaction that adaptively sums the interactions using a sequential algorithm. This generalized set of tests, referred to as the sequential algorithm for the sum of powered score (Seq-SPU) family of tests, can be expressed as a weighted version of the SPU. We find that the adaptive version of our test, Seq-aSPU, can outperform aSPU in cases where the interactions effects are in opposite directions. We applied these methods to the Minnesota Center for Twin and Family Research data set and found one significant gene in interaction with four psychosocial environmental factors affecting the alcohol consumption among the twins.

A combination test for detection of gene-environment interaction in cohort studies.

Identifying gene-environment (G-E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G-E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome-wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene. We extend recently developed score statistic based genetic association testing approaches to the G-E interaction testing problem. We also propose tests for interaction using gene-based summary measures that pool variants together. Although it has recently been shown that these summary measures can be biased and may lead to inflated type I error, we show that under several realistic scenarios, we can still provide valid tests of interaction. These tests use significantly less degrees of freedom and thus can have much higher power to detect interaction. Additionally, we demonstrate that the iSeq-aSum-min test, which combines a gene-based summary measure test, iSeq-aSum-G, and an interaction-based summary measure test, iSeq-aSum-I, provides a powerful alternative to test G-E interaction. We demonstrate the performance of these approaches using simulation studies and illustrate their performance to study interaction between the SNPs in several candidate genes and family climate environment on alcohol consumption using the Minnesota Center for Twin and Family Research dataset.