Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group
Jahanshad N., Kochunov P., Nichols TE., Sprooten E., Mandl RC., Almasy L., Brouwer RM., Curran JE., De Zubicaray GI., Dimitrova R., Fox PT., Hong LE., Landman BA., Lemaitre H., Lopez L., Martin NG., McMahon KL., Mitchell BD., Olvera RL., Peterson CP., Sussmann JE., Toga AW., Wardlaw JM., Wright MJ., Wright SN., Bastin ME., McIntosh AM., Boomsma DI., Kahn RS., Den Braber A., Deary IJ., Pol HEH., Williamson D., Blangero J., Van't Ent D., Glahn DC., Thompson PM.
© 2014 IEEE. Meta-analyses estimate a statistical effect size for a test or an analysis by combining results from multiple studies without necessarily having access to each individual study's raw data. Multi-site meta-analysis is crucial for imaging genetics, as single sites rarely have a sample size large enough to pick up effects of single genetic variants associated with brain measures. However, if raw data can be shared, combining data in a "mega-analysis" is thought to improve power and precision in estimating global effects. As part of an ENIGMA-DTI investigation, we use fractional anisotropy (FA) maps from 5 studies (total N=2, 203 subjects, aged 9-85) to estimate heritability. We combine the studies through meta-and mega-analyses as well as a mixture of the two - combining some cohorts with mega-analysis and meta-analyzing the results with those of the remaining sites. A combination of mega-and meta-approaches may boost power compared to meta-analysis alone.