Power estimates for voxel-based genetic association studies using diffusion imaging

Neda Jahanshad; Peter Kochunov; David C. Glahn; John Blangero; Thomas E. Nichols; Katie L. McMahon; Greig I. De Zubicaray; Nicholas G. Martin; Margaret J. Wright; Clifford R. Jack; Matt A. Bernstein; Michael W. Weiner; Arthur W. Toga; Paul M. Thompson

doi:10.1007/978-3-319-02475-2_21

Power estimates for voxel-based genetic association studies using diffusion imaging

Neda Jahanshad, Peter Kochunov, David C. Glahn, John Blangero, Thomas E. Nichols, Katie L. McMahon, Greig I. De Zubicaray, Nicholas G. Martin, Margaret J. Wright, Clifford R. Jack, Matt A. Bernstein, Michael W. Weiner, Arthur W. Toga, Paul M. Thompson

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

The quest to discover genetic variants that affect the human brain will be accelerated by screening brain images from large populations. Even so, the wealth of information in medical images is often reduced to a single numeric summary, such as a regional volume or an average signal, which is then analyzed in a genome wide association study (GWAS). The high cost and penalty formultiple comparisons often constrains us from searching over the entire image space. Here, we developed a method to compute and boost power to detect genetic associations in brain images. We computed voxel-wise heritability estimates for fractional anisotropy in over 1,100 DTI scans, and used the results to threshold FA images from new studies. We describe voxel selection criteria to optimally boost power, as a function of the sample size and allele frequency cut-off. We illustrate our methods by analyzing publicly-available data from the ADNI2 project.

Original language	English (US)
Pages (from-to)	229-238
Number of pages	10
Journal	Mathematics and Visualization
DOIs	https://doi.org/10.1007/978-3-319-02475-2_21
State	Published - 2014
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: Sep 22 2013 → Sep 26 2013

Keywords

DTI
GWAS
Heritability
Multiple comparisons correction
Neuroimaging genetics

ASJC Scopus subject areas

Modeling and Simulation
Geometry and Topology
Computer Graphics and Computer-Aided Design
Applied Mathematics

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10.1007/978-3-319-02475-2_21

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Jahanshad, N., Kochunov, P., Glahn, D. C., Blangero, J., Nichols, T. E., McMahon, K. L., De Zubicaray, G. I., Martin, N. G., Wright, M. J., Jack, C. R., Bernstein, M. A., Weiner, M. W., Toga, A. W., & Thompson, P. M. (2014). Power estimates for voxel-based genetic association studies using diffusion imaging. Mathematics and Visualization, 229-238. https://doi.org/10.1007/978-3-319-02475-2_21

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title = "Power estimates for voxel-based genetic association studies using diffusion imaging",

abstract = "The quest to discover genetic variants that affect the human brain will be accelerated by screening brain images from large populations. Even so, the wealth of information in medical images is often reduced to a single numeric summary, such as a regional volume or an average signal, which is then analyzed in a genome wide association study (GWAS). The high cost and penalty formultiple comparisons often constrains us from searching over the entire image space. Here, we developed a method to compute and boost power to detect genetic associations in brain images. We computed voxel-wise heritability estimates for fractional anisotropy in over 1,100 DTI scans, and used the results to threshold FA images from new studies. We describe voxel selection criteria to optimally boost power, as a function of the sample size and allele frequency cut-off. We illustrate our methods by analyzing publicly-available data from the ADNI2 project.",

keywords = "DTI, GWAS, Heritability, Multiple comparisons correction, Neuroimaging genetics",

author = "Neda Jahanshad and Peter Kochunov and Glahn, {David C.} and John Blangero and Nichols, {Thomas E.} and McMahon, {Katie L.} and {De Zubicaray}, {Greig I.} and Martin, {Nicholas G.} and Wright, {Margaret J.} and Jack, {Clifford R.} and Bernstein, {Matt A.} and Weiner, {Michael W.} and Toga, {Arthur W.} and Thompson, {Paul M.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 26-09-2013",

year = "2014",

doi = "10.1007/978-3-319-02475-2_21",

language = "English (US)",

pages = "229--238",

journal = "Mathematics and Visualization",

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AU - Kochunov, Peter

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AU - Nichols, Thomas E.

AU - McMahon, Katie L.

AU - De Zubicaray, Greig I.

AU - Martin, Nicholas G.

AU - Wright, Margaret J.

AU - Jack, Clifford R.

AU - Bernstein, Matt A.

AU - Weiner, Michael W.

AU - Toga, Arthur W.

AU - Thompson, Paul M.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - The quest to discover genetic variants that affect the human brain will be accelerated by screening brain images from large populations. Even so, the wealth of information in medical images is often reduced to a single numeric summary, such as a regional volume or an average signal, which is then analyzed in a genome wide association study (GWAS). The high cost and penalty formultiple comparisons often constrains us from searching over the entire image space. Here, we developed a method to compute and boost power to detect genetic associations in brain images. We computed voxel-wise heritability estimates for fractional anisotropy in over 1,100 DTI scans, and used the results to threshold FA images from new studies. We describe voxel selection criteria to optimally boost power, as a function of the sample size and allele frequency cut-off. We illustrate our methods by analyzing publicly-available data from the ADNI2 project.

AB - The quest to discover genetic variants that affect the human brain will be accelerated by screening brain images from large populations. Even so, the wealth of information in medical images is often reduced to a single numeric summary, such as a regional volume or an average signal, which is then analyzed in a genome wide association study (GWAS). The high cost and penalty formultiple comparisons often constrains us from searching over the entire image space. Here, we developed a method to compute and boost power to detect genetic associations in brain images. We computed voxel-wise heritability estimates for fractional anisotropy in over 1,100 DTI scans, and used the results to threshold FA images from new studies. We describe voxel selection criteria to optimally boost power, as a function of the sample size and allele frequency cut-off. We illustrate our methods by analyzing publicly-available data from the ADNI2 project.

KW - DTI

KW - GWAS

KW - Heritability

KW - Multiple comparisons correction

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EP - 238

JO - Mathematics and Visualization

JF - Mathematics and Visualization

T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013

Y2 - 22 September 2013 through 26 September 2013

ER -

Power estimates for voxel-based genetic association studies using diffusion imaging

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Keywords

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