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

Kochunov, Peter, Jahanshad, Neda, Sprooten, Emma, Nichols, Thomas E., Mandl, René C., Almasy, Laura, Booth, Tom, Brouwer, Rachel M., Curran, Joanne E., de Zubicaray, Greig I., Dimitrova, Rali, Duggirala, Ravi, Fox, Peter T., Hong, L. Elliot, Landman, Bennett A., Lemaitre, Hervé, Lopez, Lorna M., Martin, Nicholas G., McMahon, Katie L., Mitchell, Braxton D., Olvera, Rene L., Peterson, Charles P., Starr, John M., Sussmann, Jessika E., Toga, Arthur W., Wardlaw, Joanna M., Wright, Margaret J., Wright, Susan N., Bastin, Mark E., McIntosh, Andrew M., Boomsma, Dorret I., Kahn, René S., den Braber, Anouk, de Geus, Eco J. C., Deary, Ian J., Hulshoff Pol, Hilleke E., Williamson, Douglas E., Blangero, John, van 't Ent, Dennis, 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, 95 136-150. doi:10.1016/j.neuroimage.2014.03.033

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Author Kochunov, Peter
Jahanshad, Neda
Sprooten, Emma
Nichols, Thomas E.
Mandl, René C.
Almasy, Laura
Booth, Tom
Brouwer, Rachel M.
Curran, Joanne E.
de Zubicaray, Greig I.
Dimitrova, Rali
Duggirala, Ravi
Fox, Peter T.
Hong, L. Elliot
Landman, Bennett A.
Lemaitre, Hervé
Lopez, Lorna M.
Martin, Nicholas G.
McMahon, Katie L.
Mitchell, Braxton D.
Olvera, Rene L.
Peterson, Charles P.
Starr, John M.
Sussmann, Jessika E.
Toga, Arthur W.
Wardlaw, Joanna M.
Wright, Margaret J.
Wright, Susan N.
Bastin, Mark E.
McIntosh, Andrew M.
Boomsma, Dorret I.
Kahn, René S.
den Braber, Anouk
de Geus, Eco J. C.
Deary, Ian J.
Hulshoff Pol, Hilleke E.
Williamson, Douglas E.
Blangero, John
van 't Ent, Dennis
Thompson, Paul M.
Glahn, David C.
Title Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: comparing meta and megaanalytical approaches for data pooling
Journal name Neuroimage   Check publisher's open access policy
ISSN 1053-8119
Publication date 2014-07-15
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2014.03.033
Open Access Status
Volume 95
Start page 136
End page 150
Total pages 15
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2015
Language eng
Formatted abstract
• Data pooling using analytical approaches leads to improved power of genetic analyses.
• In the largest DTI study to date we evaluated three metaanalytical methods.
• Data from 2248 subjects were normalized using ENIGMA-DTI protocol.
• Heritability estimates varied substantially for five cohort that contributed subjects.
• Meta-analyses boosted power and improved stability of heritability estimates.

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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Medicine Publications
School of Psychology Publications
Centre for Advanced Imaging Publications
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Citation counts: TR Web of Science Citation Count  Cited 13 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 18 times in Scopus Article | Citations
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Created: Thu, 22 May 2014, 13:55:49 EST by Sandrine Ducrot on behalf of School of Psychology