Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study

Ming Chang Chiang; Marina Barysheva; Katie L. McMahon; Greig I. De Zubicaray; Kori Johnson; Nicholas G. Martin; Arthur W. Toga; Margaret J. Wright; Paul M. Thompson

doi:10.1109/ISBI.2011.5872533

Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study

Ming Chang Chiang
, Marina Barysheva
, Katie L. McMahon
, Greig I. De Zubicaray
, Kori Johnson
, Nicholas G. Martin
, Arthur W. Toga
, Margaret J. Wright
, Paul M. Thompson

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

4 Scopus citations

Abstract

Genetic correlation (r_g) analysis determines how much of the correlation between two measures is due to common genetic influences. In an analysis of 4 Tesla diffusion tensor images (DTI) from 531 healthy young adult twins and their siblings, we generalized the concept of genetic correlation to determine common genetic influences on white matter integrity, measured by fractional anisotropy (FA), at all points of the brain, yielding an NxN genetic correlation matrix r_g(x,y) between FA values at all pairs of voxels in the brain. With hierarchical clustering, we identified brain regions with relatively homogeneous genetic determinants, to boost the power to identify causal single nucleotide polymorphisms (SNP). We applied genome-wide association (GWA) to assess associations between 529,497 SNPs and FA in clusters defined by hubs of the clustered genetic correlation matrix. We identified a network of genes, with a scale-free topology, that influences white matter integrity over multiple brain regions.

Original language	English
Title of host publication	2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI'11
Publisher	IEEE Computer Society
Pages	832-835
Number of pages	4
ISBN (Print)	9781424441280
DOIs	https://doi.org/10.1109/ISBI.2011.5872533
State	Published - 2011
Externally published	Yes
Event	8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 - Chicago, IL, United States Duration: 30 Mar 2011 → 2 Apr 2011

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011
Country/Territory	United States
City	Chicago, IL
Period	30/03/11 → 2/04/11

Keywords

diffusion tensor imaging
genome-wide association
hierarchical clustering
scale-free topology
twins

Access to Document

10.1109/ISBI.2011.5872533

Cite this

Chiang, M. C., Barysheva, M., McMahon, K. L., De Zubicaray, G. I., Johnson, K., Martin, N. G., Toga, A. W., Wright, M. J., & Thompson, P. M. (2011). Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study. In 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 (pp. 832-835). Article 5872533 (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI.2011.5872533

Chiang, Ming Chang ; Barysheva, Marina ; McMahon, Katie L. et al. / Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure : An N=531 twin study. 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. IEEE Computer Society, 2011. pp. 832-835 (Proceedings - International Symposium on Biomedical Imaging).

@inproceedings{50967e7c198e48a8b14f38bea57fed87,

title = "Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study",

abstract = "Genetic correlation (rg) analysis determines how much of the correlation between two measures is due to common genetic influences. In an analysis of 4 Tesla diffusion tensor images (DTI) from 531 healthy young adult twins and their siblings, we generalized the concept of genetic correlation to determine common genetic influences on white matter integrity, measured by fractional anisotropy (FA), at all points of the brain, yielding an NxN genetic correlation matrix rg(x,y) between FA values at all pairs of voxels in the brain. With hierarchical clustering, we identified brain regions with relatively homogeneous genetic determinants, to boost the power to identify causal single nucleotide polymorphisms (SNP). We applied genome-wide association (GWA) to assess associations between 529,497 SNPs and FA in clusters defined by hubs of the clustered genetic correlation matrix. We identified a network of genes, with a scale-free topology, that influences white matter integrity over multiple brain regions.",

keywords = "diffusion tensor imaging, genome-wide association, hierarchical clustering, scale-free topology, twins",

author = "Chiang, \{Ming Chang\} and Marina Barysheva and McMahon, \{Katie L.\} and \{De Zubicaray\}, \{Greig I.\} and Kori Johnson and Martin, \{Nicholas G.\} and Toga, \{Arthur W.\} and Wright, \{Margaret J.\} and Thompson, \{Paul M.\}",

year = "2011",

doi = "10.1109/ISBI.2011.5872533",

language = "English",

isbn = "9781424441280",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "832--835",

booktitle = "2011 8th IEEE International Symposium on Biomedical Imaging",

address = "United States",

note = "8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011 ; Conference date: 30-03-2011 Through 02-04-2011",

}

Chiang, MC, Barysheva, M, McMahon, KL, De Zubicaray, GI, Johnson, K, Martin, NG, Toga, AW, Wright, MJ & Thompson, PM 2011, Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study. in 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11., 5872533, Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, pp. 832-835, 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, Chicago, IL, United States, 30/03/11. https://doi.org/10.1109/ISBI.2011.5872533

Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study. / Chiang, Ming Chang; Barysheva, Marina; McMahon, Katie L. et al.
2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. IEEE Computer Society, 2011. p. 832-835 5872533 (Proceedings - International Symposium on Biomedical Imaging).

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

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AU - Chiang, Ming Chang

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AU - McMahon, Katie L.

AU - De Zubicaray, Greig I.

AU - Johnson, Kori

AU - Martin, Nicholas G.

AU - Toga, Arthur W.

AU - Wright, Margaret J.

AU - Thompson, Paul M.

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AB - Genetic correlation (rg) analysis determines how much of the correlation between two measures is due to common genetic influences. In an analysis of 4 Tesla diffusion tensor images (DTI) from 531 healthy young adult twins and their siblings, we generalized the concept of genetic correlation to determine common genetic influences on white matter integrity, measured by fractional anisotropy (FA), at all points of the brain, yielding an NxN genetic correlation matrix rg(x,y) between FA values at all pairs of voxels in the brain. With hierarchical clustering, we identified brain regions with relatively homogeneous genetic determinants, to boost the power to identify causal single nucleotide polymorphisms (SNP). We applied genome-wide association (GWA) to assess associations between 529,497 SNPs and FA in clusters defined by hubs of the clustered genetic correlation matrix. We identified a network of genes, with a scale-free topology, that influences white matter integrity over multiple brain regions.

KW - diffusion tensor imaging

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ER -

Chiang MC, Barysheva M, McMahon KL, De Zubicaray GI, Johnson K, Martin NG et al. Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study. In 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. IEEE Computer Society. 2011. p. 832-835. 5872533. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2011.5872533

Hierarchical clustering of the genetic connectivity matrix reveals the network topology of gene action on brain microstructure: An N=531 twin study

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Keywords

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