M
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.
Papers
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Journal ArticleDOI
Interleukin-1 region meta-analysis with osteoarthritis phenotypes
George Moxley,Ingrid Meulenbelt,Kay Chapman,C.M. van Diujn,P. Eline Slagboom,Michael C. Neale,Anthony J. Smith,Andrew Carr,John Loughlin +8 more
TL;DR: The meta-analysis data suggest that some hand and hip OA risk could be associated with the IL-1 region, particularly centered in IL1B and possibly also IL1RN.
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Genetic and environmental influences of white and gray matter signal contrast: a new phenotype for imaging genetics?
Matthew S. Panizzon,Christine Fennema-Notestine,Thomas S. Kubarych,Chi-Hua Chen,Lisa T. Eyler,Bruce Fischl,Bruce Fischl,Carol E. Franz,Michael D. Grant,Samar Hamza,Amy J. Jak,Terry L. Jernigan,Michael J. Lyons,Michael C. Neale,Elizabeth Prom-Wormley,Larry J. Seidman,Ming T. Tsuang,Hao Wu,Hong Xian,Anders M. Dale,William S. Kremen +20 more
TL;DR: It is demonstrated that like cortical thickness, WM/GM contrast is a genetically influenced brain structure phenotype, and the lack of significant genetic correlations with cortical thickness suggests that this measure potentially represents a unique source of genetic variance, one that has yet to be explored by the field of imaging genetics.
Journal Article
Analyzing the relationship between age at onset and risk to relatives.
TL;DR: Age at onset is correlated between twins, but this correlation appears to be associated with factors that are separate from those which affect liability to disease, and even this relatively large sample of twins is too small to draw firm conclusions about any causal relationship between disease liability and onset.
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Discrete latent Markov models for normally distributed response data
TL;DR: In addition to presenting various normal-based Markov models, it is demonstrated how these models, formulated as multinormal finite mixtures, may be fitted using the freely available program Mx (Neale, Boker, Xie, & Maes, 2002).
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Problems with using sum scores for estimating variance components: contamination and measurement noninvariance.
TL;DR: It is shown that absence of measurement invariance across zygosity can bias estimates of genetic and environmental components of variance, and that the analysis of sum scores typically biases both MZ and DZ correlations compared to the true latent trait correlation.