Many early studies in imaging genetics explored univariate associations between genotypes and imaging measures, assuming each gene acted independently. One disadvantage of such studies is their limited statistical power to detect gene effects on the brain. Meta-analyses such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) project [

1] have boosted statistical power, by analyzing MRI and genome-wide genotype data from over 20,000 subjects, gaining power from very large sample sizes. Multivariate approaches, which simultaneously consider entire sets of genotypes, sets of voxels in an image, or both, have also become more popular [

2], as they also handle potential problems in high-dimensional data, such as highly correlated predictors, where almost all have no detectable effects.

In [

2], we reviewed several recent multivariate, imaging genetics studies that applied principal component regression [

3], sparse reduced rank regression [

4], or independent components analysis [

5] to discover genetic influences on the brain that would have been missed by using only univariate techniques. Regularized, sparse regression methods, in particular, use penalty terms to tackle the problems of high dimensionality (e.g., having more predictors than samples), multiple highly correlated measures, and multiple comparisons across an image, the genome, or both. The “elastic net” combines L

^{1}- and L

^{2}- norm regularization and benefits from the advantages of both methods, to handle high-dimensional, highly correlated data. The algorithm takes advantage of the sparsity properties of L

^{1} (Least Absolute Shrinkage and Selection Operator, or LASSO), along with the stability of L

^{2} (ridge) regression [

6]. Here, we introduce an elastic net approach to predict an imaging measure from top genotypes. We aim to incorporate top genetic variants (i.e., single nucleotide polymorphisms or SNPs), screened based on univariate genome-wide search (as in a genome-wide association analysis or GWAS), into an elastic net model, to predict temporal lobe volume on MRI. Recently, the elastic net has been applied to genomics [

7,

8], for jointly considering genetic polymorphisms as well as imaging [

9], to integrate large numbers of imaging and clinical predictors. More recently, the algorithm has also been used to detect multi- SNP associations with hippocampal surface morphometry [

10], and to integrate imaging and proteomic data in Alzheimer’s disease [

11].

We hypothesize that this doubly regularized, multivariate regression method would allow us to make significant predictions of MRI-derived temporal lobe volume from genotypes. This predictive approach, we propose, may have implications for early, personalized risk assessment of brain disorders such as Alzheimer’s disease, where the temporal lobes undergo significant atrophy.