Predicting temporal lobe volume on MRI from genotypes using L1-L2 regularized regression
Year: 2012
Type: conference-paper
Abstract: Penalized or sparse regression methods are gaining 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 multivariate approach, based on L(1)-L(2)-regularized 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 crossvalidation 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 univariate genomewide search.
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: G104586243, Assessment of Gene Effects on the Brain and Disease Risk through Machine Learning, UC Davis Alzheimer's Disease Core Center, Algorithms to Map Disease &Genetic Effects on the Brain, Novel PDEs for Cortical Mapping and Analysis in Disease, UCLA Alzheimer's Disease Research Center, "MR Morphometrics and Cognitive Decline Rate in Large-Scale Aging Studies", Alzheimer's disease risk analyzed using population imaging genomics, UCLA-Caltech Medical Scientist Training Program, Alzheimer's Disease Neuroimaging Initiative, Alzheimer's Disease Cooperative Study
OpenAlex ID: W2033267690