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Michael C. Neale

Researcher at Virginia Commonwealth University

Publications -  647
Citations -  72612

Michael C. Neale is an academic researcher from Virginia Commonwealth University. The author has contributed to research in topics: Twin study & Population. The author has an hindex of 121, co-authored 620 publications receiving 66343 citations. Previous affiliations of Michael C. Neale include VU University Amsterdam & University of East London.

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Comparing Factor, Class, and Mixture Models of Cannabis Initiation and DSM Cannabis Use Disorder Criteria, Including Craving, in the Brisbane Longitudinal Twin Study

TL;DR: A parsimonious, best-fitting cannabis use disorder (CUD) phenotype was derived based on DSM-IV criteria and whether DSM-5 craving loads onto a general factor was determined, which concluded that a second clinically relevant factor defined by features of social and occupational impairment was found for frequent cannabis use.
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Extending Causality Tests With Genetic Instruments: An Integration Of Mendelian Randomization And The Classical Twin Design

TL;DR: This work developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model, which allows us to test pleiotropy directly and will enhance and extend MR’s range of applications, and increase the value of the large cohorts collected at twin registries as they correctly detect causation and estimate effect sizes even in the presence of Pleiotropy.
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Genetic association between RGS1 and internalizing disorders.

TL;DR: The data suggest that markers in the RGS1 gene might be in linkage disequilibrium with a protective allele that reduces the risk of anxiety and depressive disorders.
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Genetically Informed Regression Analysis: Application to Aggression Prediction by Inattention and Hyperactivity in Children and Adults

TL;DR: A procedure to simultaneously fit a genetic covariance structure model and a regression model to multivariate data from mono- and dizygotic twin pairs to test for the prediction of a dependent trait by multiple correlated predictors is presented and this ADCE model is identified in trivariate data.