Data synthesis and method evaluation for brain imaging genetics

Jinhua Sheng, Sungeun Kim, Jingwen Yan, Jason Moore, Andrew Saykin, Li Shen

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

5 Citations (Scopus)

Abstract

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. We present initial efforts on evaluating a few SCCA methods for brain imaging genetics. This includes a data synthesis method to create realistic imaging genetics data with known SNP-QT associations, application of three SCCA algorithms to the synthetic data, and comparative study of their performances. Our empirical results suggest, approximating covariance structure using an identity or diagonal matrix, an approach used in these SCCA algorithms, could limit the SCCA capability in identifying the underlying imaging genetics associations. An interesting future direction is to develop enhanced SCCA methods that effectively take into account the covariance structures in the imaging genetics data.

Original languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1202-1205
Number of pages4
ISBN (Print)9781467319591
StatePublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period4/29/145/2/14

Fingerprint

Neuroimaging
Brain
Imaging techniques
Nucleotides
Polymorphism
Single Nucleotide Polymorphism
Multivariate Analysis
Genetics
Association reactions
Research

Keywords

  • Data synthesis
  • Genetics
  • Neuroimaging
  • Sparse canonical correlation analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Sheng, J., Kim, S., Yan, J., Moore, J., Saykin, A., & Shen, L. (2014). Data synthesis and method evaluation for brain imaging genetics. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 1202-1205). [6868091] Institute of Electrical and Electronics Engineers Inc..

Data synthesis and method evaluation for brain imaging genetics. / Sheng, Jinhua; Kim, Sungeun; Yan, Jingwen; Moore, Jason; Saykin, Andrew; Shen, Li.

2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1202-1205 6868091.

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

Sheng, J, Kim, S, Yan, J, Moore, J, Saykin, A & Shen, L 2014, Data synthesis and method evaluation for brain imaging genetics. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6868091, Institute of Electrical and Electronics Engineers Inc., pp. 1202-1205, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 4/29/14.
Sheng J, Kim S, Yan J, Moore J, Saykin A, Shen L. Data synthesis and method evaluation for brain imaging genetics. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1202-1205. 6868091
Sheng, Jinhua ; Kim, Sungeun ; Yan, Jingwen ; Moore, Jason ; Saykin, Andrew ; Shen, Li. / Data synthesis and method evaluation for brain imaging genetics. 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1202-1205
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