Kohannim et al. Discovering genes that affect brain images FIGURE 1 | Comparison of univariate and LASSO association tests with an imaging-derived measure of temporal lobe volume. (A) p-values are shown, in the left panel, as log10 p (i.e., after logarithmic transformation) for all 291 genotyped SNPs in the MACROD2 gene, using univariate association with temporal lobe volume. The most signiﬁcant SNP in this gene has a p-value of 7.3 10 3. A matrix representing the pairwise correlation (i.e., r 2), due to linkage disequilibrium (LD), between the SNPs is displayed in the right panel. (B) LASSO considers all SNPs in a gene jointly and assigns sparse coefﬁcients to the SNPs. Here, the absolute values of the coefﬁcients jbi j are displayed for all SNPs in MACROD2, 29 of which attained non-zero coefﬁcients as part of the sparse regression model ﬁtted by the LASSO method. When considered jointly in a multiple linear regression model, the selected SNPs yield a boosted p-value of 7.94 10 12 for MACROD2 (see Table 1). As in (A), the correlation structure between the selected SNPs is shown, demonstrating reduced multicollinearity. volume, in the LASSO regression model. The purpose was mainly to explore whether the volumetric effect could be also detected diffusely at the voxel level, and if so how widespread the effect was in the brain. The top genes we identiﬁed showed extensive, statistically sig- niﬁcant effects on maps of temporal lobes’ volumetric differences (Figure 2). These are not to be considered as providing inde- pendent evidence of the effects, they simply pick up a pattern of regional effects that is likely to contribute to the aggregate effect of the gene on the temporal lobe volume. REPLICATION OF GENE-IMAGE ASSOCIATIONS To replicate our most signiﬁcant ﬁnding (i.e., MACROD2) from ADNI, we explored the voxelwise effects of this same gene on temporal lobe maps from N D 564 healthy young adult twins and siblings. We jointly considered the same group of SNPs in the gene, www.frontiersin.org selected by the LASSO method in ADNI, and studied their effect on the younger subjects’ temporal lobe maps, adjusting for sex, age, and relatedness. After correcting for multiple comparisons, MACROD2’s association showed partial reproducibility, as it was statistically signiﬁcant across s15% of the temporal lobe voxels (versus s52% of voxels in ADNI), much of which overlapped with those in ADNI (see Figure 3). CROSS-VALIDATION As mentioned in the Section “Materials and Methods,” leave-one- out cross-validation was used to ﬁnd optimal LASSO parameters, but no further loop of cross-validation was used for the F -tests. In separate analyses, we performed a nested cross-validation, where SNP selection with optimized LASSO was performed in a ﬁfth of the dataset and the joint partial F -tests were then conducted in the rest of the data. This was repeated for ﬁvefolds. The average p-values we obtained for our top genes with this approach did not reach genome-wide signiﬁcance, mostly because the selection of SNPs with LASSO was unstable in the relatively small fractions (i.e., ﬁfths) of the data. We took another nested cross-validation approach, where a randomly selected half of the data was used to estimate LASSO coefﬁcients, and the other half for partial F -tests. This, we pre- sumed, might provide more balanced sample sizes for the SNP selection and for the F -test steps. After repeating this scheme across 10 trials, we obtained average p-values for our top genes. Only GRIN2B obtained gene-wide signiﬁcance averaged across all trials (p D 1.47 10 6), and its association was not boosted when compared to that obtained from a univariate approach. DISCUSSION We set out to discover and replicate gene effects on brain structure using a gene-centric LASSO regression approach. The goal of the method was to sift through the vast amount of genomic data and come up with a more efﬁcient set of variants for association test- ing. LASSO allowed us to select sparse subsets of SNPs among all correlated SNPs within each gene and associate them jointly in par- tial F -tests with an MRI-derived temporal lobe volume measure. Using this approach, we identiﬁed over twenty genes with signiﬁ- cant effects on temporal lobe structure in N D 729 elderly subjects from the ADNI cohort – a considerably larger number of genes, when compared to a univariate approach, which considers the association of single SNPs one-by-one. In all 22 genes identiﬁed, multi-SNP p-values (from partial F -tests), using SNPs selected by LASSO, were “more signiﬁcant” (i.e., lower p-values, greater effect sizes) than the top genotyped SNP within each correspond- ing gene, as computed using standard univariate GWAS. GRIN2B and NRXN3, which were identiﬁed with univariate GWAS (Stein et al., 2010a), were boosted from p-values on the order of 10 7 and 10 6 to 10 9 and 10 8, respectively. In addition, new genes with more signiﬁcant p-values were discovered, whose SNPs’ indi- vidual p-values were too weak to pass genome-wide signiﬁcance in a more standard univariate GWAS experimental design. Fur- thermore, post hoc analysis revealed widespread and signiﬁcant, voxelwise inﬂuences for the top genes on TBM maps of the tempo- ral lobes. We also replicated, at least in part, the spatial effects of our most signiﬁcant ﬁnding in the MACROD2 gene in an independent cohort of healthy, young adults. August 2012 | Volume 6 | Article 115 | 5