Author
Anna R. Docherty
Other affiliations: Virginia Commonwealth University, Veterans Health Administration, University of Missouri ...read more
Bio: Anna R. Docherty is an academic researcher from University of Utah. The author has contributed to research in topics: Genome-wide association study & Medicine. The author has an hindex of 25, co-authored 104 publications receiving 2471 citations. Previous affiliations of Anna R. Docherty include Virginia Commonwealth University & Veterans Health Administration.
Papers
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TL;DR: Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes.
781 citations
Raymond K. Walters1, Raymond K. Walters2, Renato Polimanti3, Emma C. Johnson4 +168 more•Institutions (48)
TL;DR: The largest genome-wide association study to date of DSM-IV-diagnosed AD found loci associated with AD and characterized the relationship between AD and other psychiatric and behavioral outcomes, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.
Abstract: Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest genome-wide association study to date of DSM-IV-diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case-control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, n = 46,568; African, n = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; P = 9.8 × 10-13) and African ancestries (rs2066702; P = 2.2 × 10-9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit-hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.
434 citations
University of Minnesota1, Stony Brook University2, University of Notre Dame3, Macquarie University4, University of North Texas5, University at Buffalo6, University of Kentucky7, University of Vermont8, University of Toronto9, University of South Florida10, University of Maryland, Baltimore11, Southern Methodist University12, University of Hawaii13, College of William & Mary14, Ghent University15, University of Utah16, University of Michigan17, Columbia University18, University of Kansas19, Pennsylvania State University20, University of California, Davis21, Georgia State University22, University of Iowa23, University of Georgia24, Texas A&M University25, Oklahoma State University–Stillwater26, University of Groningen27, University of Liverpool28, Florida State University29, Uniformed Services University of the Health Sciences30, Maastricht University31, Bryn Mawr College32, Purdue University33, University of Otago34, University of Maryland, College Park35, University of Arizona36, University of New South Wales37, Northwestern University38, Emory University39, Oak Ridge National Laboratory40, University of Pittsburgh41, Vanderbilt University42
TL;DR: The aims and current foci of the HiTOP Consortium, a group of 70 investigators working together to study empirical classification of psychopathology, are described, which pertain to continued research on the empirical organization of psychopathological constructs; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic.
308 citations
College of William & Mary1, Macquarie University2, University of Kansas3, University of Amsterdam4, Pennsylvania State University5, University at Albany, SUNY6, Oklahoma State University–Stillwater7, University of Maryland, College Park8, University of Arizona9, Purdue University10, University of New South Wales11, Vanderbilt University12, Université de Montréal13, University of South Florida14, University of Utah15, University of Minnesota16, University of Liverpool17, Northwestern University18, King's College London19, Maastricht University20, Emory University21, University of Pittsburgh22, University of Kassel23, University of Toronto24, Southern Methodist University25, University of Hawaii at Manoa26, University of Notre Dame27, Medical Research Council28, University of California, Davis29, University of Vermont30, Georgia State University31, Florida State University32, University of North Texas33, Stony Brook University34
TL;DR: The Hierarchical Taxonomy of Psychopathology (HiTOP) as discussed by the authors is based on empirical patterns of co-occurrence among psychological symptoms, and it 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.
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 diverse 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.
225 citations
University of California, Davis1, Stony Brook University2, University of Minnesota3, University of Notre Dame4, University of Kentucky5, University of Vermont6, Syracuse University7, Region Zealand8, University of Toronto9, Harvard University10, University of South Florida11, Southern Methodist University12, University of Hawaii at Manoa13, College of William & Mary14, Ghent University15, University of Utah16, Texas A&M University17, University of Kansas18, Zürcher Fachhochschule19, Dresden University of Technology20, University of British Columbia21, Albany Medical College22, Purdue University23, University of Iowa24, University of Georgia25, Oklahoma State University–Stillwater26, University of Groningen27, Florida State University28, Pennsylvania State University29, University of North Texas30, University of Otago31, University of New South Wales32, Northwestern University33, University of Missouri34, McGill University35, Emory University36, University of Tennessee37, University of Pittsburgh38, Marian University39, Vanderbilt University40
TL;DR: Author(s): Hopwood, Christopher J; Kotov, Roman; Krueger, Robert F; Watson, David; Widiger, Thomas A; Widinger,Thomas A; Althoff, Robert R; Ansell, Emily B; Bach, Bo; Michael Bagby, R; Blais, Mark A; Bornovalova, Marina A; Chmielewski, Michael; Cicero, David C; Conway, Christopher; De Clercq, Barbara;
Abstract: Author(s): Hopwood, Christopher J; Kotov, Roman; Krueger, Robert F; Watson, David; Widiger, Thomas A; Althoff, Robert R; Ansell, Emily B; Bach, Bo; Michael Bagby, R; Blais, Mark A; Bornovalova, Marina A; Chmielewski, Michael; Cicero, David C; Conway, Christopher; De Clercq, Barbara; De Fruyt, Filip; Docherty, Anna R; Eaton, Nicholas R; Edens, John F; Forbes, Miriam K; Forbush, Kelsie T; Hengartner, Michael P; Ivanova, Masha Y; Leising, Daniel; John Livesley, W; Lukowitsky, Mark R; Lynam, Donald R; Markon, Kristian E; Miller, Joshua D; Morey, Leslie C; Mullins-Sweatt, Stephanie N; Hans Ormel, J; Patrick, Christopher J; Pincus, Aaron L; Ruggero, Camilo; Samuel, Douglas B; Sellbom, Martin; Slade, Tim; Tackett, Jennifer L; Thomas, Katherine M; Trull, Timothy J; Vachon, David D; Waldman, Irwin D; Waszczuk, Monika A; Waugh, Mark H; Wright, Aidan GC; Yalch, Mathew M; Zald, David H; Zimmermann, Johannes
196 citations
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01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.
4,409 citations
01 Jan 2010
TL;DR: In this paper, the authors show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait, revealing patterns with important implications for genetic studies of common human diseases and traits.
Abstract: Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
1,751 citations
TL;DR: The saimie paper suggests how susceptible individuals could reduce their total intake of aluminium and suggests that although definite proof is still lacking, there is more than enough evidence to fuel further epidemiological investigation.
Abstract: The saimie paper suggests how susceptible individuals could reduce their total intake of aluminium. In presenting the cpidemiological evidence for a link betveen aluminium and Alzheimcr's, Nart'n suggests that although definite proof is still lacking, there is more than enough positixe evidence to fuel further epidemiological investigation. It states that such investigations might specificallx address the issue of the confounding cffect of silicon and an assessment of exposure to spccific
1,353 citations
1,174 citations
TL;DR: A large international community sample was recruited to complete measures of self-perceived risk of contracting COVID-19, fear of the virus, moral foundations, political orientation, and behavior change in response to the pandemic, and the only predictor of positive behavior change was fear of COVID -19, with no effect of politically relevant variables.
Abstract: In the current context of the global pandemic of coronavirus disease-2019 (COVID-19), health professionals are working with social scientists to inform government policy on how to slow the spread of the virus. An increasing amount of social scientific research has looked at the role of public message framing, for instance, but few studies have thus far examined the role of individual differences in emotional and personality-based variables in predicting virus-mitigating behaviors. In this study, we recruited a large international community sample (N = 324) to complete measures of self-perceived risk of contracting COVID-19, fear of the virus, moral foundations, political orientation, and behavior change in response to the pandemic. Consistently, the only predictor of positive behavior change (e.g., social distancing, improved hand hygiene) was fear of COVID-19, with no effect of politically relevant variables. We discuss these data in relation to the potentially functional nature of fear in global health crises.
913 citations