A fast computational framework for genome-wide association studies with neuroimaging data

Benoit da Mota 1, 2, 3 Vincent Frouin 1 Edouard Duchesnay 1 Soizic Laguitton 1 Gaël Varoquaux 1, 2 Jean-Baptiste Poline 1, 2 Bertrand Thirion 1, 2
1 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
3 MSR - INRIA - Microsoft Research - Inria Joint Centre
Abstract : In the last few years, it has become possible to acquire high-dimensional neuroimaging and genetic data on relatively large cohorts of subjects, which provides novel means to understand the large between-subject variability observed in brain organization. Genetic association studies aim at unveiling correlations between the genetic variants and the numerous phenotypes extracted from brain images and thus face a dire multiple comparisons issue. While these statistics can be accumulated across the brain volume for the sake of sensitivity, the significance of the resulting summary statistics can only be assessed through permutations. Fortunately, the increase of computational power can be exploited, but this requires designing new parallel algorithms. The MapReduce framework coupled with efficient algorithms permits to deliver a scalable analysis tool that deals with high-dimensional data and thousands of permutations in a few hours. On a real functional MRI dataset, this tool shows promising results with a genetic variant that survives the very strict correction for multiple testing.
Document type :
Conference papers
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download
https://hal.inria.fr/hal-00720265
Contributor : Benoit da Mota <>
Submitted on : Tuesday, July 24, 2012 - 11:21:36 AM
Last modification on : Tuesday, January 7, 2020 - 4:26:06 PM
Long-term archiving on: Thursday, October 25, 2012 - 2:40:15 AM

File

article.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00720265, version 1

Collections

INRIA | CEA | INSMI | CEA-DRF | JOLIOT | NEUROSPIN

Citation

Benoit da Mota, Vincent Frouin, Edouard Duchesnay, Soizic Laguitton, Gaël Varoquaux, et al.. A fast computational framework for genome-wide association studies with neuroimaging data. 20th International Conference on Computational Statistics (COMPSTAT 2012), Aug 2012, Limassol, Cyprus. ⟨hal-00720265⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

902

Files downloads

611

 

 

Contact

archive-ouverte@inria.fr