Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group

Neda Jahanshad; Peter Kochunov; Thomas E. Nichols; Emma Sprooten; René C. Mandl; Laura Almasy; Rachel M. Brouwer; Joanne E. Curran; Greig I. De Zubicaray; Rali Dimitrova; Peter T. Fox; L. Elliot Hong; Bennett A. Landman; Hervé Lemaitre; Lorna Lopez; Nicholas G. Martin; Katie L. McMahon; Braxton D. Mitchell; Rene L. Olvera; Charles P. Peterson; Jessika E. Sussmann; Arthur W. Toga; Joanna M. Wardlaw; Margaret J. Wright; Susan N. Wright; Mark E. Bastin; Andrew M. McIntosh; Dorret I. Boomsma; René S. Kahn; Anouk Den Braber; Ian J. Deary; Hilleke E.Hulshoff Pol; Douglas Williamson; John Blangero; Dennis Van't Ent; David C. Glahn; Paul M. Thompson

Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group

Neda Jahanshad, Peter Kochunov, Thomas E. Nichols, Emma Sprooten, René C. Mandl, Laura Almasy, Rachel M. Brouwer, Joanne E. Curran, Greig I. De Zubicaray, Rali Dimitrova, Peter T. Fox, L. Elliot Hong, Bennett A. Landman, Hervé Lemaitre, Lorna Lopez, Nicholas G. Martin, Katie L. McMahon, Braxton D. Mitchell, Rene L. Olvera, Charles P. PetersonJessika E. Sussmann, Arthur W. Toga, Joanna M. Wardlaw, Margaret J. Wright, Susan N. Wright, Mark E. Bastin, Andrew M. McIntosh, Dorret I. Boomsma, René S. Kahn, Anouk Den Braber, Ian J. Deary, Hilleke E.Hulshoff Pol, Douglas Williamson, John Blangero, Dennis Van't Ent, David C. Glahn, Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Publisher	Institute of Electrical and Electronics Engineers
Pages	1234-1238
Number of pages	5
ISBN (Electronic)	9781467319591
Publication status	Published - 1 Jan 2014
Event	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: 29 Apr 2014 → 2 May 2014

Conference

Conference	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Country/Territory	China
City	Beijing
Period	29/04/14 → 2/05/14

Keywords / Materials (for Non-textual outputs)

DTI
ENIGMA
Heritability
Imaging genetics
Mega-analysis
Meta-analysis
Multi-site

Cite this

Jahanshad, N., Kochunov, P., Nichols, T. E., Sprooten, E., Mandl, R. C., Almasy, L., Brouwer, R. M., Curran, J. E., De Zubicaray, G. I., Dimitrova, R., Fox, P. T., Hong, L. E., Landman, B. A., Lemaitre, H., Lopez, L., Martin, N. G., McMahon, K. L., Mitchell, B. D., Olvera, R. L., ... Thompson, P. M. (2014). Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 1234-1238). Article 6868099 Institute of Electrical and Electronics Engineers.

@inproceedings{9817e8a7ef714645ad46fece2f5575db,

title = "Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group",

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.",

keywords = "DTI, ENIGMA, Heritability, Imaging genetics, Mega-analysis, Meta-analysis, Multi-site",

author = "Neda Jahanshad and Peter Kochunov and Nichols, \{Thomas E.\} and Emma Sprooten and Mandl, \{Ren{\'e} C.\} and Laura Almasy and Brouwer, \{Rachel M.\} and Curran, \{Joanne E.\} and \{De Zubicaray\}, \{Greig I.\} and Rali Dimitrova and Fox, \{Peter T.\} and Hong, \{L. Elliot\} and Landman, \{Bennett A.\} and Herv{\'e} Lemaitre and Lorna Lopez and Martin, \{Nicholas G.\} and McMahon, \{Katie L.\} and Mitchell, \{Braxton D.\} and Olvera, \{Rene L.\} and Peterson, \{Charles P.\} and Sussmann, \{Jessika E.\} and Toga, \{Arthur W.\} and Wardlaw, \{Joanna M.\} and Wright, \{Margaret J.\} and Wright, \{Susan N.\} and Bastin, \{Mark E.\} and McIntosh, \{Andrew M.\} and Boomsma, \{Dorret I.\} and Kahn, \{Ren{\'e} S.\} and \{Den Braber\}, Anouk and Deary, \{Ian J.\} and Pol, \{Hilleke E.Hulshoff\} and Douglas Williamson and John Blangero and \{Van't Ent\}, Dennis and Glahn, \{David C.\} and Thompson, \{Paul M.\}",

year = "2014",

month = jan,

day = "1",

language = "English",

pages = "1234--1238",

booktitle = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 ; Conference date: 29-04-2014 Through 02-05-2014",

}

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, Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6868099, Institute of Electrical and Electronics Engineers, pp. 1234-1238, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 29/04/14.

Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group. / Jahanshad, Neda; Kochunov, Peter; Nichols, Thomas E. et al.
2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers, 2014. p. 1234-1238 6868099.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies

T2 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

AU - Jahanshad, Neda

AU - Kochunov, Peter

AU - Nichols, Thomas E.

AU - Sprooten, Emma

AU - Mandl, René C.

AU - Almasy, Laura

AU - Brouwer, Rachel M.

AU - Curran, Joanne E.

AU - De Zubicaray, Greig I.

AU - Dimitrova, Rali

AU - Fox, Peter T.

AU - Hong, L. Elliot

AU - Landman, Bennett A.

AU - Lemaitre, Hervé

AU - Lopez, Lorna

AU - Martin, Nicholas G.

AU - McMahon, Katie L.

AU - Mitchell, Braxton D.

AU - Olvera, Rene L.

AU - Peterson, Charles P.

AU - Sussmann, Jessika E.

AU - Toga, Arthur W.

AU - Wardlaw, Joanna M.

AU - Wright, Margaret J.

AU - Wright, Susan N.

AU - Bastin, Mark E.

AU - McIntosh, Andrew M.

AU - Boomsma, Dorret I.

AU - Kahn, René S.

AU - Den Braber, Anouk

AU - Deary, Ian J.

AU - Pol, Hilleke E.Hulshoff

AU - Williamson, Douglas

AU - Blangero, John

AU - Van't Ent, Dennis

AU - Glahn, David C.

AU - Thompson, Paul M.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

KW - DTI

KW - ENIGMA

KW - Heritability

KW - Imaging genetics

KW - Mega-analysis

KW - Meta-analysis

KW - Multi-site

UR - https://www.scopus.com/pages/publications/84903895685

M3 - Conference contribution

AN - SCOPUS:84903895685

SP - 1234

EP - 1238

BT - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

PB - Institute of Electrical and Electronics Engineers

Y2 - 29 April 2014 through 2 May 2014

ER -

Combining Meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group

Abstract

Conference

Keywords / Materials (for Non-textual outputs)

Other files and links

Fingerprint

Cite this