Predicting temporal lobe volume on MRI from genotypes using L 1-L 2 regularized regression

Omid Kohannim, Derrek P. Hibar, Neda Jahanshad, Jason L. Stein, Xue Hua, Arthur W. Toga, Clifford R. Jack, Michael W. Weinen, Paul M. Thompson

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

22 Scopus citations

Abstract

Penalized or sparse regression methods are gaming increasing attention in imaging genomics, as they can select optimal regressors from a large set of predictors whose individual effects are small or mostly zero. We applied a multivanate approach, based on L 1-L 2-Aregulanzed regression (elastic net) to predict a magnetic resonance imaging (MRI) tensor-based morphometry-derived measure of temporal lobe volume from a genome-wide scan in 740 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects. We tuned the elastic net model's parameters using internal cross-validation and evaluated the model on independent test sets. Compared to 100,000 permutations performed with randomized imaging measures, the predictions were found to be statistically significant (p ∼ 0.001). The rs9933137 variant in the RBFOX1 gene was a highly contributory genotype, along with rs10845840 in GRIN2B and rs2456930, discovered previously in a univanate genome-wide search.

Original languageEnglish (US)
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages1160-1163
Number of pages4
DOIs
StatePublished - Aug 15 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
CountrySpain
CityBarcelona
Period5/2/125/5/12

Keywords

  • Elastic net
  • Imaging Genetics
  • MRI
  • Neuroimaging
  • Prediction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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    Kohannim, O., Hibar, D. P., Jahanshad, N., Stein, J. L., Hua, X., Toga, A. W., Jack, C. R., Weinen, M. W., & Thompson, P. M. (2012). Predicting temporal lobe volume on MRI from genotypes using L 1-L 2 regularized regression. In 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings (pp. 1160-1163). [6235766] (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2012.6235766