Large-scale collaborative imaging genetics studies of risk genetic factors for Alzheimer’s disease across multiple institutions

Qingyang Li, Tao Yang, Liang Zhan, Derrek Paul Hibar, Neda Jahanshad, Yalin Wang, Jieping Ye, Paul M. Thompson, Jie Wang

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

1 Citation (Scopus)

Abstract

Genome-wide association studies (GWAS) offer new opportunities to identify genetic risk factors for Alzheimer’s disease (AD). Recently,collaborative efforts across different institutions emerged that enhance the power of many existing techniques on individual institution data. However,a major barrier to collaborative studies of GWAS is that many institutions need to preserve individual data privacy. To address this challenge,we propose a novel distributed framework,termed Local Query Model (LQM) to detect risk SNPs for AD across multiple research institutions. To accelerate the learning process,we propose a Distributed Enhanced Dual Polytope Projection (D-EDPP) screening rule to identify irrelevant features and remove them from the optimization. To the best of our knowledge,this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD. Empirical studies are conducted on 809 subjects with 5.9 million SNP features which are distributed across three individual institutions. D-EDPP achieved a 66-fold speed-up by effectively identifying irrelevant features.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages335-343
Number of pages9
Volume9900 LNCS
ISBN (Print)9783319467191
DOIs
StatePublished - 2016
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: Oct 21 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period10/21/1610/21/16

Fingerprint

Alzheimer's Disease
Imaging
Imaging techniques
Polytope
Privacy
Genome
Projection
Selection Procedures
Risk Factors
Genes
Learning Process
Model Selection
Empirical Study
Accelerate
Screening
Ranking
Data privacy
Speedup
Fold
Query

Keywords

  • Alzheimer’s disease
  • Data privacy
  • GWAS
  • Lasso screening

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Li, Q., Yang, T., Zhan, L., Hibar, D. P., Jahanshad, N., Wang, Y., ... Wang, J. (2016). Large-scale collaborative imaging genetics studies of risk genetic factors for Alzheimer’s disease across multiple institutions. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9900 LNCS, pp. 335-343). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_39

Large-scale collaborative imaging genetics studies of risk genetic factors for Alzheimer’s disease across multiple institutions. / Li, Qingyang; Yang, Tao; Zhan, Liang; Hibar, Derrek Paul; Jahanshad, Neda; Wang, Yalin; Ye, Jieping; Thompson, Paul M.; Wang, Jie.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. p. 335-343 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

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

Li, Q, Yang, T, Zhan, L, Hibar, DP, Jahanshad, N, Wang, Y, Ye, J, Thompson, PM & Wang, J 2016, Large-scale collaborative imaging genetics studies of risk genetic factors for Alzheimer’s disease across multiple institutions. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. vol. 9900 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 335-343, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 10/21/16. https://doi.org/10.1007/978-3-319-46720-7_39
Li Q, Yang T, Zhan L, Hibar DP, Jahanshad N, Wang Y et al. Large-scale collaborative imaging genetics studies of risk genetic factors for Alzheimer’s disease across multiple institutions. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS. Springer Verlag. 2016. p. 335-343. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46720-7_39
Li, Qingyang ; Yang, Tao ; Zhan, Liang ; Hibar, Derrek Paul ; Jahanshad, Neda ; Wang, Yalin ; Ye, Jieping ; Thompson, Paul M. ; Wang, Jie. / Large-scale collaborative imaging genetics studies of risk genetic factors for Alzheimer’s disease across multiple institutions. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. pp. 335-343 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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