We developed Stratified Genomic SEM to allow the basic principles of Genomic SEM to be applied to genetic covariance matrices estimated within different gene sets and categories (Methods). These gene sets and categories, collectively referred to as annotations, can be constructed based on a variety of sources, such as collateral gene expression data obtained from single-cell RNA sequencing. Such an analysis goes beyond methods such as Stratified LDSC (S-LDSC)32 that estimate enrichment of heritability for particular traits within functional annotations. Rather, Stratified Genomic SEM utilizes a multivariate framework to ask whether shared and unique genetic signal across a set of traits is enriched within particular annotations. Enrichment is defined as the ratio of the proportion of genome-wide risk sharing indexed by the annotation to that annotation’s size as a proportion of the genome (Methods). The null, corresponding to no enrichment, is a ratio of 1.0, with values above 1.0 indicating enriched signal within a functional annotation.
