Notes:
Multi-site study of additive genetic effects on fractional
anisotropy of cerebral white matter: Comparing meta and
megaanalytical approaches for data pooling.
Neuroimage. 2014 Jul 15;95:136-50.
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.
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