GN-SCCA: Graphnet based sparse canonical correlation analysis for brain imaging genetics

Lei Du, Jingwen Yan, Sungeun Kim, Shannon L. Risacher, Heng Huang, Mark Inlow, Jason H. Moore, Andrew Saykin, Li Shen, Disease Neuroimaging Initiative Alzheimer’s Disease Neuroimaging Initiative

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Identifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages275-284
Number of pages10
Volume9250
ISBN (Print)9783319233437
DOIs
StatePublished - 2015
Event8th International Conference on Brain Informatics and Health, BIH 2015 - London, United Kingdom
Duration: Aug 30 2015Sep 2 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9250
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th International Conference on Brain Informatics and Health, BIH 2015
CountryUnited Kingdom
CityLondon
Period8/30/159/2/15

Fingerprint

Canonical Correlation Analysis
Brain
Imaging
Neuroimaging
Imaging techniques
Prior Knowledge
Elastic Net
Experiments
Covariance Structure
Graph in graph theory
Genetics
Smoothness
Adjacent
Coefficient
Experiment

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Du, L., Yan, J., Kim, S., Risacher, S. L., Huang, H., Inlow, M., ... Alzheimer’s Disease Neuroimaging Initiative, D. N. I. (2015). GN-SCCA: Graphnet based sparse canonical correlation analysis for brain imaging genetics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9250, pp. 275-284). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9250). Springer Verlag. https://doi.org/10.1007/978-3-319-23344-4_27

GN-SCCA : Graphnet based sparse canonical correlation analysis for brain imaging genetics. / Du, Lei; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L.; Huang, Heng; Inlow, Mark; Moore, Jason H.; Saykin, Andrew; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative, Disease Neuroimaging Initiative.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9250 Springer Verlag, 2015. p. 275-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9250).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Du, L, Yan, J, Kim, S, Risacher, SL, Huang, H, Inlow, M, Moore, JH, Saykin, A, Shen, L & Alzheimer’s Disease Neuroimaging Initiative, DNI 2015, GN-SCCA: Graphnet based sparse canonical correlation analysis for brain imaging genetics. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9250, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9250, Springer Verlag, pp. 275-284, 8th International Conference on Brain Informatics and Health, BIH 2015, London, United Kingdom, 8/30/15. https://doi.org/10.1007/978-3-319-23344-4_27
Du L, Yan J, Kim S, Risacher SL, Huang H, Inlow M et al. GN-SCCA: Graphnet based sparse canonical correlation analysis for brain imaging genetics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9250. Springer Verlag. 2015. p. 275-284. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-23344-4_27
Du, Lei ; Yan, Jingwen ; Kim, Sungeun ; Risacher, Shannon L. ; Huang, Heng ; Inlow, Mark ; Moore, Jason H. ; Saykin, Andrew ; Shen, Li ; Alzheimer’s Disease Neuroimaging Initiative, Disease Neuroimaging Initiative. / GN-SCCA : Graphnet based sparse canonical correlation analysis for brain imaging genetics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9250 Springer Verlag, 2015. pp. 275-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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