Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach

Lei Du, Tuo Zhang, Kefei Liu, Jingwen Yan, Xiaohui Yao, Shannon L. Risacher, Andrew Saykin, Junwei Han, Lei Guo, Li Shen

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

4 Citations (Scopus)

Abstract

Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
PublisherSpringer Verlag
Pages543-555
Number of pages13
Volume10265 LNCS
ISBN (Print)9783319590493
DOIs
StatePublished - 2017
Event25th International Conference on Information Processing in Medical Imaging, IPMI 2017 - Boone, United States
Duration: Jun 25 2017Jun 30 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10265 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th International Conference on Information Processing in Medical Imaging, IPMI 2017
CountryUnited States
CityBoone
Period6/25/176/30/17

Fingerprint

Canonical Correlation Analysis
Phenotype
Brain
Imaging
Imaging techniques
Lasso
Neuroimaging
Graph in graph theory
Genetic Association
Canonical Correlation
Prior Knowledge
Network Structure
Grouping
Penalty
Genetics
Pairwise
Optimization Algorithm
Efficient Algorithms
Upper bound

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Du, L., Zhang, T., Liu, K., Yan, J., Yao, X., Risacher, S. L., ... Shen, L. (2017). Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach. In Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings (Vol. 10265 LNCS, pp. 543-555). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10265 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59050-9_43

Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach. / Du, Lei; Zhang, Tuo; Liu, Kefei; Yan, Jingwen; Yao, Xiaohui; Risacher, Shannon L.; Saykin, Andrew; Han, Junwei; Guo, Lei; Shen, Li.

Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Vol. 10265 LNCS Springer Verlag, 2017. p. 543-555 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10265 LNCS).

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

Du, L, Zhang, T, Liu, K, Yan, J, Yao, X, Risacher, SL, Saykin, A, Han, J, Guo, L & Shen, L 2017, Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach. in Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. vol. 10265 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10265 LNCS, Springer Verlag, pp. 543-555, 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, Boone, United States, 6/25/17. https://doi.org/10.1007/978-3-319-59050-9_43
Du L, Zhang T, Liu K, Yan J, Yao X, Risacher SL et al. Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach. In Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Vol. 10265 LNCS. Springer Verlag. 2017. p. 543-555. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-59050-9_43
Du, Lei ; Zhang, Tuo ; Liu, Kefei ; Yan, Jingwen ; Yao, Xiaohui ; Risacher, Shannon L. ; Saykin, Andrew ; Han, Junwei ; Guo, Lei ; Shen, Li. / Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach. Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Vol. 10265 LNCS Springer Verlag, 2017. pp. 543-555 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{f475cbcd569840b5a99b78bdd017897b,
title = "Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach",
abstract = "Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations.",
author = "Lei Du and Tuo Zhang and Kefei Liu and Jingwen Yan and Xiaohui Yao and Risacher, {Shannon L.} and Andrew Saykin and Junwei Han and Lei Guo and Li Shen",
year = "2017",
doi = "10.1007/978-3-319-59050-9_43",
language = "English (US)",
isbn = "9783319590493",
volume = "10265 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "543--555",
booktitle = "Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings",

}

TY - GEN

T1 - Identifying associations between brain imaging phenotypes and genetic factors via a novel structured SCCA approach

AU - Du, Lei

AU - Zhang, Tuo

AU - Liu, Kefei

AU - Yan, Jingwen

AU - Yao, Xiaohui

AU - Risacher, Shannon L.

AU - Saykin, Andrew

AU - Han, Junwei

AU - Guo, Lei

AU - Shen, Li

PY - 2017

Y1 - 2017

N2 - Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations.

AB - Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations.

UR - http://www.scopus.com/inward/record.url?scp=85020512099&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020512099&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-59050-9_43

DO - 10.1007/978-3-319-59050-9_43

M3 - Conference contribution

AN - SCOPUS:85020512099

SN - 9783319590493

VL - 10265 LNCS

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

SP - 543

EP - 555

BT - Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings

PB - Springer Verlag

ER -