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Comparative Study
. 2014 Jul 15:95:136-50.
doi: 10.1016/j.neuroimage.2014.03.033. Epub 2014 Mar 18.

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

Peter Kochunov  1 , Neda Jahanshad  2 , Emma Sprooten  3 , Thomas E Nichols  4 , René C Mandl  5 , Laura Almasy  6 , Tom Booth  7 , Rachel M Brouwer  5 , Joanne E Curran  6 , Greig I de Zubicaray  8 , Rali Dimitrova  9 , Ravi Duggirala  6 , Peter T Fox  10 , L Elliot Hong  11 , Bennett A Landman  12 , Hervé Lemaitre  13 , Lorna M Lopez  7 , Nicholas G Martin  14 , Katie L McMahon  15 , Braxton D Mitchell  16 , Rene L Olvera  17 , Charles P Peterson  6 , John M Starr  18 , Jessika E Sussmann  9 , Arthur W Toga  2 , Joanna M Wardlaw  19 , Margaret J Wright  14 , Susan N Wright  11 , Mark E Bastin  19 , Andrew M McIntosh  9 , Dorret I Boomsma  20 , René S Kahn  5 , Anouk den Braber  20 , Eco J C de Geus  20 , Ian J Deary  7 , Hilleke E Hulshoff Pol  5 , Douglas E Williamson  17 , John Blangero  6 , Dennis van 't Ent  20 , Paul M Thompson  21 , David C Glahn  3
Affiliations
Comparative Study

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

Peter Kochunov et al. Neuroimage. .

Abstract

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.

Keywords: Diffusion tensor imaging (DTI); Heritability; Imaging genetics; Meta-analysis; Multi-site; Reliability.

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Figures

FIGURE 1
FIGURE 1
mega workflow
Figure 2
Figure 2
Individual site heritability plot
Figure 3
Figure 3
joint analyses heritability plot + forest plot
Figure 4
Figure 4
voxelwise maps of meta and mega slices
Figure 5
Figure 5
3 scatter correlation plots
Figure 6
Figure 6
leave one out for average FA values all joint analyses
Figure 7
Figure 7
3 plots -- leave one out for all ROIS all joint analyses
Figure 7
Figure 7
3 plots -- leave one out for all ROIS all joint analyses
Figure 7
Figure 7
3 plots -- leave one out for all ROIS all joint analyses
See this image and copyright information in PMC

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