Browsing by Subject "psychiatric disorders"

Now showing 1 - 3 of 3
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    Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls
    (Cambridge University Press, 2018-09) Pettersson, E.; Lichtenstein, P.; Larsson, H.; Song, J.; Attention Deficit/Hyperactivity Disorder Working Group of the iPSYCH-Broad-PGC Consortium; Agrawal, A.; Børglum, A. D.; Bulik, C. M.; Daly, M. J.; Davis, L. K.; Demontis, D.; Edenberg, H. J.; Grove, J.; Gelernter, J.; Neale, B. M.; Pardiñas, A. F.; Stahl, E.; Walters, J. T. R.; Walters, R.; Sullivan, P. F.; Posthuma, D.; Polderman, T. J. C.; Biochemistry and Molecular Biology, School of Medicine
    BackgroundMost studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders.MethodsWe assessed heritability based on data of Swedish siblings (N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes (N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia.ResultsHeritability estimates from sibling data varied from 0.30 for Major Depression to 0.80 for ADHD. The estimates based on the measured genotypes were lower, ranging from 0.10 for AD to 0.28 for OCD, but were significant, and correlated positively (0.19) with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.50.ConclusionsGiven the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research.
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    The Intersectionality of Factors Predicting Co-occurring Disorders: A Decision Tree Model
    (Springer Nature, 2024-07) Hong, Saahoon; Kim, Hea-Won; Walton, Betty; Kaboi, Maryanne; School of Social Work
    Individuals with co-occurring psychiatric and substance use disorders (COD) face challenges, including accessing treatment, accurate diagnoses, and effective treatment for both disorders. This study aimed to develop a COD prediction model by examining the intersectionality of COD with race/ethnicity, age, gender identity, pandemic year, and behavioral health needs and strengths. Individuals aged 18 or older who participated in publicly funded behavioral health services (N = 22,629) were selected. Participants completed at least two Adult Needs and Strengths Assessments during 2019 and 2020, respectively. A chi-squared automatic interaction detection (CHAID) decision tree analysis was conducted to identify patterns that increased the likelihood of having COD. Among the decision tree analysis predictors, Involvement in Recovery emerged as the most critical factor influencing COD, with a predictor importance value (PIV) of 0.46. Other factors like Legal Involvement (PIV = 0.12), Decision-Making (PIV = 0.12), Parental/Caregiver Role (PIV = 0.11), Other Self-Harm (PIV = 0.10), and Criminal Behavior (PIV = 0.09) had progressively lower PIVs. Age, gender, race/ethnicity, and pandemic year did not show statistically significant associations with COD. The CHAID decision tree analysis provided insights into the dynamics of COD. It revealed that legal involvement played a crucial role in treatment engagement. Individuals with legal challenges were less likely to be involved in treatment. Individuals with COD displayed more complex behavioral health needs that significantly impaired their functioning compared to individuals with psychiatric disorders to inform the development of targeted interventions.
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    Translating genome-wide association findings into new therapeutics for psychiatry
    (Nature, 2016-11) Breen, Gerome; Li, Qingqin; Roth, Bryan L.; O'Donnell, Patricio; Didriksen, Michael; Dolmetsch, Ricardo; O'Reilly, Paul; Gaspar, Helena; Manji, Husseini; Huebel, Christopher; Kelsoe, John R.; Malhotra, Dheeraj; Bertolino, Alessandro; Posthuma, Danielle; Sklar, Pamela; Kapur, Shitij; Sullivan, Patrick F.; Collier, David A.; Edenberg, Howard J.; Department of Biochemistry & Molecular Biology, IU School of Medicine
    Genome-wide association studies (GWAS) in psychiatry, once they reach sufficient sample size and power, have been enormously successful. The Psychiatric Genomics Consortium (PGC) aims for mega-analyses with sample sizes that will grow to >1 million individuals in the next 5 years. This should lead to hundreds of new findings for common genetic variants across nine psychiatric disorders studied by the PGC. The new targets discovered by GWAS have the potential to restart largely stalled psychiatric drug development pipelines, and the translation of GWAS findings into the clinic is a key aim of the recently funded phase 3 of the PGC. This is not without considerable technical challenges. These approaches complement the other main aim of GWAS studies, risk prediction approaches for improving detection, differential diagnosis, and clinical trial design. This paper outlines the motivations, technical and analytical issues, and the plans for translating PGC phase 3 findings into new therapeutics.