Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling.
Kochunov P., Jahanshad N., Sprooten E., Nichols TE., Mandl RC., Almasy L., Booth T., Brouwer RM., Curran JE., de Zubicaray GI., Dimitrova R., Duggirala R., Fox PT., Hong LE., Landman BA., Lemaitre H., Lopez LM., Martin NG., McMahon KL., Mitchell BD., Olvera RL., Peterson CP., Starr JM., Sussmann JE., Toga AW., Wardlaw JM., Wright MJ., Wright SN., Bastin ME., McIntosh AM., Boomsma DI., Kahn RS., den Braber A., de Geus EJ., Deary IJ., Hulshoff Pol HE., Williamson DE., Blangero J., van 't Ent D., Thompson PM., Glahn DC.
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.