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    Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

    Kochunov, Peter and Jahanshad, Neda and Sprooten, Emma and Nichols, Thomas E. and Mandl, René C. and Almasy, Laura and Booth, Tom and Brouwer, Rachel M. and Curran, Joanne E. and de Zubicaray, Greig I. and Dimitrova, Rali and Duggirala, Ravi and Fox, Peter T. and Elliot Hong, L. and Landman, Bennett A. and Lemaitre, Hervé and Lopez, Lorna M. and Martin, Nicholas G. and McMahon, Katie L. and Mitchell, Braxton D. and Olvera, Rene L. and Peterson, Charles P. and Starr, John M. and Sussmann, Jessika E. and Toga, Arthur W. and Wardlaw, Joanna M. and Wright, Margaret J. and Wright, Susan N. and Bastin, Mark E. and McIntosh, Andrew M. and Boomsma, Dorret I. and Kahn, René S. and den Braber, Anouk and de Geus, Eco J.C. and Deary, Ian J. and Hulshoff Pol, Hilleke E. and Williamson, Douglas E. and Blangero, John and van 't Ent, Dennis and Thompson, Paul M. and Glahn, David C. (2014) Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling. NeuroImage, 95. pp. 136-150. ISSN 1053-8119

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    Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9–85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large “mega-family”. We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

    Item Type: Article
    Keywords: Diffusion tensor imaging; DTI; Imaging genetics; Heritability; Meta-analysis; Multi-site; Reliability;
    Academic Unit: Faculty of Science and Engineering > Biology
    Faculty of Science and Engineering > Research Institutes > Human Health Institute
    Item ID: 17244
    Identification Number:
    Depositing User: Lorna Lopez
    Date Deposited: 29 May 2023 11:55
    Journal or Publication Title: NeuroImage
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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