Our approach differs in several key ways from approaches that require eQTL data3,13. First, our approach can be applied to expression data sets such as the Franke lab data set, the Cahoy data set, the PsychENCODE data set, and the ImmGen data set that do not have genotypes or eQTLs available (Table 1). Second, methods based on eQTLs require gene expression sample sizes that are large enough to detect eQTLs. In an analysis of data from the GTEx project, we determined that we could identify strong enrichments such as brain enrichment for schizophrenia with just one brain sample, though subtler enrichments had decreasing levels of significance as the gene expression data were down-sampled (Figure S11, Supplementary Note). Results from our analysis of ImmGen data, which has 2.8 samples per cell type on average, confirm that LDSC-SEG can identify significant enrichments even when the gene expression data has a small number of samples per tissue/cell type, in contrast to eQTL-based methods. Finally, we note that a recent study86 tested 30 phenotypes for tissue-specific enrichment in 44 tissues from GTEx using the
