ORCID Profile
0000-0001-7139-7007
Current Organisations
University of Utah School of Medicine
,
Huntsman Mental Health Institute
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Publisher: SAGE Publications
Date: 07-03-2019
Abstract: For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system—the Hierarchical Taxonomy of Psychopathology (HiTOP)—that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across erse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
Publisher: American Psychological Association (APA)
Date: 10-2019
DOI: 10.1037/ABN0000434
Abstract: Structural models of psychopathology provide dimensional alternatives to traditional categorical classification systems. Competing models, such as the bifactor and correlated factors models, are typically compared via statistical indices to assess how well each model fits the same data. However, simulation studies have found evidence for probifactor fit index bias in several psychological research domains. The present study sought to extend this research to models of psychopathology, wherein the bifactor model has received much attention, but its susceptibility to bias is not well characterized. We used Monte Carlo simulations to examine how various model misspecifications produced fit index bias for 2 commonly used estimators, WLSMV and MLR. We simulated binary indicators to represent psychiatric diagnoses and positively skewed continuous indicators to represent symptom counts. Across combinations of estimators, indicator distributions, and misspecifications, complex patterns of bias emerged, with fit indices more often than not failing to correctly identify the correlated factors model as the data-generating model. No fit index emerged as reliably unbiased across all misspecification scenarios. Although, tests of model equivalence indicated that in one instance fit indices were not biased-they favored the bifactor model, albeit not unfairly. Overall, results suggest that comparisons of bifactor models to alternatives using fit indices may be misleading and call into question the evidentiary meaning of previous studies that identified the bifactor model as superior based on fit. We highlight the importance of comparing models based on substantive interpretability and their utility for addressing study aims, the methodological significance of model equivalence, as well as the need for implementation of statistical metrics that evaluate model quality. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Publisher: Elsevier BV
Date: 12-2019
DOI: 10.1016/J.DRUGALCDEP.2019.107704
Abstract: Substance use, substance use disorders (SUDs), and psychiatric disorders commonly co-occur. Genetic risk common to these complex traits is an important explanation however, little is known about how polygenic risk for tobacco or alcohol use overlaps the genetic risk for the comorbid SUDs and psychiatric disorders. We constructed polygenic risk scores (PRSs) using GWAS meta-analysis summary statistics from a large discovery s le, GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN), for smoking initiation (SI N = 631,564), age of initiating regular smoking (AI N = 258,251), cigarettes per day (CPD N = 258,999), smoking cessation (SC N = 312,273), and drinks per week (DPW N = 527,402). We then estimated the fixed effect of these PRSs on the liability to 15 phenotypes related to tobacco and alcohol use, substance use disorders, and psychiatric disorders in an independent target s le of Australian adults. After adjusting for multiple testing, 10 of 75 combinations of discovery and target phenotypes remained significant. PRS-SI (R Polygenic risks associated with tobacco use are also associated with liability to alcohol consumption, nicotine dependence, and conduct disorder.
Publisher: Elsevier BV
Date: 12-2020
Publisher: American Psychological Association (APA)
Date: 02-2020
DOI: 10.1037/ABN0000486
Publisher: Cold Spring Harbor Laboratory
Date: 23-08-2019
DOI: 10.1101/741512
Abstract: Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa (BN) and problem alcohol use (genetic correlation [ r g ], twin-based=0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge-eating, AN without binge-eating, and a BN factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder (MDD). Total s le sizes per phenotype ranged from ~2,400 to ~537,000 in iduals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN ( r g =0.18 false discovery rate q =0.0006), cannabis initiation and AN ( r g =0.23 q .0001), and cannabis initiation and AN with binge-eating ( r g =0.27 q =0.0016). Conversely, significant negative genetic correlations were observed between three non-diagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge-eating ( r gs =-0.19 to −0.23 qs .04). The genetic correlation between AUD and AN was no longer significant after co-varying for MDD loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships between these behaviors.
Publisher: Wiley
Date: 16-02-2020
DOI: 10.1111/ADB.12880
Publisher: Springer Science and Business Media LLC
Date: 30-03-2021
DOI: 10.1038/S41467-021-21226-Z
Abstract: Genome-wide association meta-analysis (GWAMA) is an effective approach to enlarge s le sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA (Meta-Analysis Model-based Assessment of replicability), for assessing the “posterior-probability-of-replicability” for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million in iduals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants.
Publisher: Springer Science and Business Media LLC
Date: 26-11-2018
Publisher: Oxford University Press (OUP)
Date: 27-08-2015
Publisher: Elsevier BV
Date: 12-2019
Publisher: Cold Spring Harbor Laboratory
Date: 05-07-2022
Publisher: Springer Science and Business Media LLC
Date: 14-01-2019
Publisher: Wiley
Date: 18-05-2021
DOI: 10.1002/WPS.20844
Publisher: Cold Spring Harbor Laboratory
Date: 25-04-2022
DOI: 10.1101/2022.04.20.22273895
Abstract: Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. Here we dramatically relax GWAS stringency by orders of magnitude and apply GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks from erse lines of evidence. From multiple GWAS summary statistics of suicide attempt, a complex psychiatric phenotype, GRIN identified additional genes that replicated across independent cohorts and retained genes that were more biologically interrelated despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways to influence suicidal behavior and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate that GRIN is a useful community resource for improving the signal to noise ratio of GWAS results.
Publisher: Oxford University Press (OUP)
Date: 16-05-2018
Publisher: Wiley
Date: 07-09-2018
DOI: 10.1002/WPS.20566
Publisher: Wiley
Date: 11-05-2020
DOI: 10.1002/WPS.20730
Publisher: Wiley
Date: 11-12-2017
DOI: 10.1002/PMH.1408
Publisher: American Psychological Association (APA)
Date: 12-2019
DOI: 10.1037/CCP0000452
Publisher: Guilford Publications
Date: 25-02-2019
Location: United States of America
Location: United States of America
Location: United States of America
No related grants have been discovered for Anna Docherty.