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Proceedings Paper

Secure multivariate large-scale multi-centric analysis through on-line learning: an imaging genetics case study
Author(s): Marco Lorenzi; Boris Gutman; Paul M. Thompson; Daniel C. Alexander; Sebastien Ourselin; Andre Altmann
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Paper Abstract

State-of-the-art data analysis methods in genetics and related fields have advanced beyond massively univariate analyses. However, these methods suffer from the limited amount of data available at a single research site. Recent large-scale multi-centric imaging-genetic studies, such as ENIGMA, have to rely on meta-analysis of mass univariate models to achieve critical sample sizes for uncovering statistically significant associations. Indeed, model parameters, but not data, can be securely and anonymously shared between partners. We propose here partial least squares (PLS) as a multivariate imaging-genetics model in meta-studies. In particular, we propose an online estimation approach to partial least squares for the sequential estimation of the model parameters in data batches, based on an approximation of the singular value decomposition (SVD) of partitioned covariance matrices. We applied the proposed approach to the challenging problem of modeling the association between 1,167,117 genetic markers (SNPs, single nucleotide polymorphisms) and the brain cortical and sub-cortical atrophy (354,804 anatomical surface features) in a cohort of 639 individuals from the Alzheimer's Disease Neuroimaging Initiative. We compared two different modeling strategies (sequential- and meta-PLS) to the classic non-distributed PLS. Both strategies exhibited only minimal approximation errors of model parameters. The proposed approaches pave the way to the application of multivariate models in large scale imaging-genetics meta-studies, and may lead to novel understandings of the complex brain phenotype-genotype interactions.

Paper Details

Date Published: 26 January 2017
PDF: 7 pages
Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 1016016 (26 January 2017); doi: 10.1117/12.2256799
Show Author Affiliations
Marco Lorenzi, Univ. College London (United Kingdom)
Boris Gutman, The Univ. of Southern California (United States)
Paul M. Thompson, The Univ. of Southern California (United States)
Daniel C. Alexander, Univ. College London (United Kingdom)
Sebastien Ourselin, Univ. College London (United Kingdom)
Andre Altmann, Univ. College London (United Kingdom)


Published in SPIE Proceedings Vol. 10160:
12th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Jorge Brieva; Ignacio Larrabide; , Editor(s)

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