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

Kochunov, Peter, Jahanshad, Neda, Sprooten, Emma, Nichols, Thomas E., Mandl, René C., Almasy, Laura, Booth, Tom, Brouwer, Rachel M., Curran, Joanne E., de Zubicaray, Greig I., Dimitrova, Rali, Duggirala, Ravi, Fox, Peter T., Hong, L. Elliot, Landman, Bennett A., Lemaitre, Hervé, Lopez, Lorna M., Martin, Nicholas G., McMahon, Katie L., Mitchell, Braxton D., Olvera, Rene L., Peterson, Charles P., Starr, John M., 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, René S., den Braber, Anouk, de Geus, Eco J. C., Deary, Ian J., Hulshoff Pol, Hilleke E., Williamson, Douglas E., Blangero, John, van 't Ent, Dennis, Thompson, Paul M. and Glahn, David C. (2014) Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: comparing meta and megaanalytical approaches for data pooling. Neuroimage, 95 136-150. doi:10.1016/j.neuroimage.2014.03.033

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Kochunov, Peter
Jahanshad, Neda
Sprooten, Emma
Nichols, Thomas E.
Mandl, René C.
Almasy, Laura
Booth, Tom
Brouwer, Rachel M.
Curran, Joanne E.
de Zubicaray, Greig I.
Dimitrova, Rali
Duggirala, Ravi
Fox, Peter T.
Hong, L. Elliot
Landman, Bennett A.
Lemaitre, Hervé
Lopez, Lorna M.
Martin, Nicholas G.
McMahon, Katie L.
Mitchell, Braxton D.
Olvera, Rene L.
Peterson, Charles P.
Starr, John M.
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, René S.
den Braber, Anouk
de Geus, Eco J. C.
Deary, Ian J.
Hulshoff Pol, Hilleke E.
Williamson, Douglas E.
Blangero, John
van 't Ent, Dennis
Thompson, Paul M.
Glahn, David C.
Title Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: comparing meta and megaanalytical approaches for data pooling
Journal name Neuroimage   Check publisher's open access policy
ISSN 1053-8119
1095-9572
Publication date 2014-07-15
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2014.03.033
Open Access Status DOI
Volume 95
Start page 136
End page 150
Total pages 15
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
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.
Formatted abstract
Highlights
• Data pooling using analytical approaches leads to improved power of genetic analyses.
• In the largest DTI study to date we evaluated three metaanalytical methods.
• Data from 2248 subjects were normalized using ENIGMA-DTI protocol.
• Heritability estimates varied substantially for five cohort that contributed subjects.
• Meta-analyses boosted power and improved stability of heritability estimates.

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.
Keyword Diffusion tensor imaging (DTI)
Heritability
Imaging genetics
Meta-analysis
Multi-site
Reliability
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID P41 EB015922
EB007813
R01 EB007813
087727/Z/08/Z
R01 EB015611
R01AA016274
MH083824
G1001245
MR/K026992/1
R01 MH078111
EB008432
100309
R01 HD050735
R01 EB008432
G0701120
MH59490
R01 MH059490
EB008281
R37 MH059490
G0700704
BB/F019394/1
R01 MH083824
R01 EB008281
R01 AA016274
MH0708143
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Medicine Publications
School of Psychology Publications
Centre for Advanced Imaging Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 29 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 35 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 22 May 2014, 23:55:49 EST by Sandrine Ducrot on behalf of School of Psychology