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

Peter Kochunov, Neda Jahanshad, Emma Sprooten, Thomas E. Nichols, René C. Mandl, Laura Almasy, Tom Booth, Rachel M. Brouwer, Joanne E. Curran, Greig I. de Zubicaray, Rali Dimitrova, Ravi Duggirala, Peter T. Fox, L. Elliot Hong, Bennett A. Landman, Hervé Lemaitre, Lorna M. Lopez, Nicholas G. Martin, Katie L. McMahon, Braxton D. Mitchell, Rene L. Olvera, Charles P. Peterson, John M. Starr, Jessika E. Sussmann, Arthur W. Toga, Joanna M. Wardlaw, Margaret J. Wright, Susan N. Wright, Mark E. Bastin, Andrew M. McIntosh, Dorret I. Boomsma, René S. Kahn, Anouk den Braber, Eco J.C. de Geus, Ian J. Deary, Hilleke E. Hulshoff Pol, Douglas E. Williamson, John Blangero, Dennis van 't Ent, Paul M. Thompson, David C. Glahn
NeuroImage. 2014-07-01; 95: 136-150
DOI: 10.1016/j.neuroimage.2014.03.033


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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.

Auteurs Bordeaux Neurocampus