Genetic clustering on the hippocampal surface for genome-wide association studies

Derrek P. Hibar; Sarah E. Medland; Jason L. Stein; Sungeun Kim; Li Shen; Andrew J. Saykin; Greig I. De Zubicaray; Katie L. McMahon; Grant W. Montgomery; Nicholas G. Martin; Margaret J. Wright; Srdjan Djurovic; Ingrid A. Agartz; Ole A. Andreassen; Paul M. Thompson

doi:10.1007/978-3-642-40763-5_85

Genetic clustering on the hippocampal surface for genome-wide association studies

Derrek P. Hibar, Sarah E. Medland, Jason L. Stein, Sungeun Kim, Li Shen, Andrew J. Saykin, Greig I. De Zubicaray, Katie L. McMahon, Grant W. Montgomery, Nicholas G. Martin, Margaret J. Wright, Srdjan Djurovic, Ingrid A. Agartz, Ole A. Andreassen, Paul M. Thompson

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

10 Scopus citations

Abstract

Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (r_g) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages	690-697
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-40763-5_85
State	Published - 2013
Externally published	Yes
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: 22 Sep 2013 → 26 Sep 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	8150 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	22/09/13 → 26/09/13

Keywords

3D surfaces
GWAS
clustering
heritability
hippocampus
imaging genetics

Access to Document

10.1007/978-3-642-40763-5_85

Cite this

Hibar, D. P., Medland, S. E., Stein, J. L., Kim, S., Shen, L., Saykin, A. J., De Zubicaray, G. I., McMahon, K. L., Montgomery, G. W., Martin, N. G., Wright, M. J., Djurovic, S., Agartz, I. A., Andreassen, O. A., & Thompson, P. M. (2013). Genetic clustering on the hippocampal surface for genome-wide association studies. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 2 ed., pp. 690-697). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-40763-5_85

Hibar, Derrek P. ; Medland, Sarah E. ; Stein, Jason L. et al. / Genetic clustering on the hippocampal surface for genome-wide association studies. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2. ed. 2013. pp. 690-697 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{55a95e6b77424355a3e214137347f18e,

title = "Genetic clustering on the hippocampal surface for genome-wide association studies",

abstract = "Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r.",

keywords = "3D surfaces, GWAS, clustering, heritability, hippocampus, imaging genetics",

author = "Hibar, {Derrek P.} and Medland, {Sarah E.} and Stein, {Jason L.} and Sungeun Kim and Li Shen and Saykin, {Andrew J.} and {De Zubicaray}, {Greig I.} and McMahon, {Katie L.} and Montgomery, {Grant W.} and Martin, {Nicholas G.} and Wright, {Margaret J.} and Srdjan Djurovic and Agartz, {Ingrid A.} and Andreassen, {Ole A.} and Thompson, {Paul M.}",

year = "2013",

doi = "10.1007/978-3-642-40763-5_85",

language = "English",

isbn = "9783642407628",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "690--697",

booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings",

edition = "PART 2",

note = "16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 26-09-2013",

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Hibar, DP, Medland, SE, Stein, JL, Kim, S, Shen, L, Saykin, AJ, De Zubicaray, GI, McMahon, KL, Montgomery, GW, Martin, NG, Wright, MJ, Djurovic, S, Agartz, IA, Andreassen, OA & Thompson, PM 2013, Genetic clustering on the hippocampal surface for genome-wide association studies. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8150 LNCS, pp. 690-697, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 22/09/13. https://doi.org/10.1007/978-3-642-40763-5_85

Genetic clustering on the hippocampal surface for genome-wide association studies. / Hibar, Derrek P.; Medland, Sarah E.; Stein, Jason L. et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2. ed. 2013. p. 690-697 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8150 LNCS, No. PART 2).

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

TY - GEN

T1 - Genetic clustering on the hippocampal surface for genome-wide association studies

AU - Hibar, Derrek P.

AU - Medland, Sarah E.

AU - Stein, Jason L.

AU - Kim, Sungeun

AU - Shen, Li

AU - Saykin, Andrew J.

AU - De Zubicaray, Greig I.

AU - McMahon, Katie L.

AU - Montgomery, Grant W.

AU - Martin, Nicholas G.

AU - Wright, Margaret J.

AU - Djurovic, Srdjan

AU - Agartz, Ingrid A.

AU - Andreassen, Ole A.

AU - Thompson, Paul M.

PY - 2013

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N2 - Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r.

AB - Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson's r.

KW - 3D surfaces

KW - GWAS

KW - clustering

KW - heritability

KW - hippocampus

KW - imaging genetics

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U2 - 10.1007/978-3-642-40763-5_85

DO - 10.1007/978-3-642-40763-5_85

M3 - Conference contribution

C2 - 24579201

AN - SCOPUS:84897570305

SN - 9783642407628

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 690

EP - 697

BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings

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

Y2 - 22 September 2013 through 26 September 2013

ER -

Hibar DP, Medland SE, Stein JL, Kim S, Shen L, Saykin AJ et al. Genetic clustering on the hippocampal surface for genome-wide association studies. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 2 ed. 2013. p. 690-697. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-40763-5_85

Genetic clustering on the hippocampal surface for genome-wide association studies

Abstract

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Conference

Keywords

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