@inproceedings{c19ba530e13845e692177d1e5209dad3,
title = "Predicting temporal lobe volume on MRI from genotypes using L 1-L2 regularized regression",
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 L1-L2-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.",
keywords = "Elastic net, Imaging Genetics, MRI, Neuroimaging, Prediction",
author = "Omid Kohannim and Hibar, {Derrek P.} and Neda Jahanshad and Stein, {Jason L.} and Xue Hua and Toga, {Arthur W.} and Jack, {Clifford R.} and Weinen, {Michael W.} and Thompson, {Paul M.}",
year = "2012",
doi = "10.1109/ISBI.2012.6235766",
language = "English",
isbn = "9781457718588",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1160--1163",
booktitle = "2012 9th IEEE International Symposium on Biomedical Imaging",
address = "United States",
note = "2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 ; Conference date: 02-05-2012 Through 05-05-2012",
}