학술논문
Combining meta- and mega- analytic approaches for multi-site diffusion imaging based genetic studies: From the ENIGMA-DTI working group
Document Type
Conference
Author
Jahanshad, Neda; Kochunov, Peter; Nichols, Thomas E; Sprooten, Emma; Mandl, Rene C; Almasy, Laura; Brouwer, Rachel M; Curran, Joanne E; de Zubicaray, Greig I; Dimitrova, Rali; Fox, Peter T; Hong, L Elliot; Landman, Bennett A; Lemaitre, Herve; Lopez, Lorna; Martin, Nicholas G; McMahon, Katie L; Mitchell, Braxton D; Olvera, Rene L; Peterson, Charles P; 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, Rene S; den Braber, Anouk; Deary, Ian J; Pol, Hilleke E Hulshoff; Williamson, Douglas; Blangero, John; van't Ent, Dennis; Glahn, David C; Thompson, Paul M
Source
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on. :1234-1238 Apr, 2014
Subject
Language
ISSN
1945-7928
1945-8452
1945-8452
Abstract
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.