Quantifying the relative importance of genetics and environment on the comorbidity between mental and cardiometabolic disorders using 17 million Scandinavians.

Mental disorders are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders. Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and near-complete genealogies of Denmark and Sweden (n = 17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six mental disorders and 15 cardiometabolic disorders. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with cardiometabolic disorders, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with cardiometabolic disorders was mainly or fully driven by environmental factors. In this work we provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

ADuLT: An efficient and robust time-to-event GWAS.

Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Polygenic Risk and Progression to Bipolar or Psychotic Disorders Among Individuals Diagnosed With Unipolar Depression in Early Life.

OBJECTIVE: The authors investigated the associations between polygenic liability and progression to bipolar disorder or psychotic disorders among individuals diagnosed with unipolar depression in early life. METHODS: A cohort comprising 16,949 individuals (69% female, 10-35 years old at the first depression diagnosis) from the iPSYCH Danish case-cohort study (iPSYCH2012) who were diagnosed with depression in Danish psychiatric hospitals from 1994 to 2016 was examined. Polygenic risk scores (PRSs) for major depression, bipolar disorder, and schizophrenia were generated using the most recent results from the Psychiatric Genomics Consortium. Hazard ratios for each disorder-specific PRS were estimated using Cox regressions with adjustment for the other two PRSs. Absolute risk of progression was estimated using the cumulative hazard. RESULTS: Patients were followed for up to 21 years (median=7 years, interquartile range, 5-10 years). The absolute risks of progression to bipolar disorder and psychotic disorders were 7.3% and 13.8%, respectively. After mutual adjustment for the other PRSs, only the PRS for bipolar disorder predicted progression to bipolar disorder (adjusted hazard ratio for a one-standard-deviation increase in PRS=1.11, 95% CI=1.03, 1.21), and only the PRS for schizophrenia predicted progression to psychotic disorders (adjusted hazard ratio=1.10, 95% CI=1.04, 1.16). After adjusting for PRSs, parental history still strongly predicted progression to bipolar disorder (adjusted hazard ratio=5.02, 95% CI=3.53, 7.14) and psychotic disorders (adjusted hazard ratio=1.63, 95% CI=1.30, 2.06). CONCLUSIONS: PRSs for bipolar disorder and schizophrenia are associated with risk for progression to bipolar disorder or psychotic disorders, respectively, among individuals diagnosed with depression; however, the effects are small compared with parental history, particularly for bipolar disorder.