Kohannim et al. Discovering genes that affect brain images Table 1 | Genes showing signiﬁcant associations with an MRI-derived measure of temporal lobe volume. Gene name Gene description Chr. 20 4 12 11 14 8 7 14 16 4 8 7 17 10 10 15 20 3 16 4 7 21 Enr. 2.08 4.60 1.55 2.75 1.08 3.76 3.10 4.64 1.65 3.76 2.03 2.92 0.30 3.00 3.21 0.91 2.40 2.08 1.50 2.04 N SNPs 29 29 4 12 5 20 16 7 3 11 13 10 9 13 17 26 22 7 7 5 16 17 p-value 7.94 10 12 4.87 10 9 7.95 10 9 2.39 10 8 2.84 10 8 3.83 10 8 8.44 10 8 9.06 10 8 3.24 10 7 3.97 10 7 6.93 10 7 7.97 10 7 9.60 10 7 9.65 10 7 1.01 10 6 1.01 10 6 1.12 10 6 1.37 10 6 1.39 10 6 1.80 10 6 1.81 10 6 2.27 10 6 MACROD2 SORCS2 GRIN2B GALNTL4 NRXN3 AK130123 MAGI2 NPAS3 RBFOX1 AY229892 ZMAT4 STAG3L2 GAS7 KIAA1217 ADARB2 GABRG3 CDH4 CLSTN2 CDH13 GALNTL6 PRKAG2 CHODL MACRO domain containing 2, isoform 2 Sortilin-related VPS10 domain containing receptor 2 Glutamate receptor, ionotropic, N-methyl-d-aspartate 2B UDP-N-acetyl-alpha-d-galactosamine:polypeptide N-acetylgalactosaminyltransferase-like 4 Neurexin 3 cDNA FLJ26613 ﬁs, highly similar to serine/threonine protein phosphatase 2A, 55 kDa regulatory subunit B, alpha isoform Membrane associated guanylate kinase, WW and PDZ domain containing 2 Neuronal PAS domain protein 3 Ataxin-2-binding protein 1 FIP1L1/PDGFRA fusion protein Zinc ﬁnger, matrin-type 4 Stromal antigen 3-like 2 Growth arrest-speciﬁc 7 KIAA1217 Adenosine deaminase, RNA-speciﬁc, B2 Gamma-aminobutyric acid (GABA) A receptor, gamma 3 Cadherin 4 Calsyntenin 2 Cadherin 13 UDP-N-acetyl-alpha-d-galactosamine:polypeptide N-acetylgalactosaminyltransferase-like 6 Protein kinase, AMP-activated, gamma 2 non-catalytic subunit Chondrolectin Here we show the ofﬁcial names, gene descriptions, corresponding chromosomes (Chr.), enrichment scores for expression of the genes in the brain (Enr.), number of selected SNPs (NSNPs), and p-values for genes with signiﬁcant associations with temporal lobe volumes. The genes are ranked in order of signiﬁcance. Here, the p-value threshold for signiﬁcance is 2.73 10 6, which is the Bonferroni-corrected for the total number of genes, which is less strict than that for the total number of SNPs. p-Values correspond to F-tests, which assess the joint effect of a sparse subset of the genotyped single nucleotide polymorphisms (SNPs) in the respective gene, selected using the LASSO algorithm, on temporal lobe structure. indicates genes are not found in this particular gene expression database, but we ensured there is brain expression for these genes using another database (see text). NSNPs refers to the number of variants retained in the optimized LASSO models for each gene, which were then fed into partial F-tests. Only GRIN2B and NRXN3 possessed SNPs (2 SNPs in GRIN2B and 1 in NRXN3) that passed a liberal genome-wide signiﬁcance threshold of 1 10 5 in standard GWAS. Penalized regression techniques such as LASSO (Tibshirani, 1996), ridge regression (Hoerl, 1962), and the elastic net (Zou and Hastie, 2005) have recently been highly effective when used in GWAS. They all deal with (1) multicollinearity due to LD, (2) the large dimensionality of the genome, and (3) the problem of multi- ple comparisons (Malo et al., 2008; Cho et al., 2009, 2010; Lin et al., 2009; Shi et al., 2011). LASSO’s emphasis on sparsity is particularly useful in our study, as it helps point to a small set of independent variants in a given gene, which we can then incorporate into a mul- tiple regression framework. This is similar to the approach taken by Chen et al. (2011), in the context of jointly considering rare and common variants. Here, we tested this algorithm in the con- text of ﬁnding genetic inﬂuences on an imaging-derived measure of temporal lobe volume. This allowed us to discover and repli- cate a great number of genes relative to our earlier imaging GWAS study (Stein et al., 2010a). We were also able to implicate several genes with previously identiﬁed relevance to brain disorders (see below). In a recent study by our group, Hibar et al. (2011a) also consid- ered gene-based associations with brain images with a new method based on principal component regression, which associates genes with images by capturing most of the variation among intra- genic SNPs. Our approach complements this method, as it instead emphasizes sparsity of the model based on the available SNP data for each gene. In the case of principal components regression, a rather different line of analysis is taken in which the covariance in a set of N genotyped SNPs is analyzed to produce a reduced set of k (<N ) predictors, that encode some of the genetic variance, but are more efﬁcient than the original set. That method can also produce overall p-values for a speciﬁc gene to quantify their effects on brain structure. However, the results of principal components regression are less readily ascribed to any speciﬁc sets of SNPs on the genome. Although the p-values we obtained for the two genes discovered in univariate GWAS were more signiﬁcant than the univariate p- values of their top SNPs, this need not be the case for every gene. As Frontiers in Neuroscience | Neurogenomics August 2012 | Volume 6 | Article 115 | 6