RNAseq TRANSCRIPTOME STUDY OF SCHIZOPHRENIA IN THE MGS AFRICAN AMERICAN SAMPLE

GWAS on predominantly European Ancestry (EA) samples have implicated over 100 loci as being associated at genome-wide significant levels with risk for Schizophrenia (SZ). Some of these risk loci are shared across ancestral groups, including within variably admixed African American (AA) samples. Regulation of mRNA expression may be involved in the etiology for some of these loci, thus suggesting utility of a transcriptomic profiling approach. In both of two previous large transcriptomic profiling studies on lymphoblastoid cell lines (LCLs) from non-overlapping EA SZ case-control samples, using arrays (N=714; Sanders et al., 2013) and RNAseq (N=1,189; Sanders et al., 2017), we found genes differentially expressed by affection status to be enriched for immune-related genes, consistent with hypothesized immune contributions to SZ risk. The single largest reported SZ GWAS on an AA sample is on the Molecular Genetics of SZ (MGS) dataset (Shi et al., 2009), from which we derived the current transcriptomic (RNAseq) sample. After various sample quality control steps including identity verification ensuring concordance between known sex (i.e., dosages of X and Y chromosomes) and RNAseq expression levels of sex-dimorphic expressed genes on chromosomes X and Y, and RNAseq-derived genotype concordance versus previous GWAS genotypes, we moved samples with at least 8 M read depth from 378 SZ cases and 386 controls to analyses. To adjust for known or potentially confounding effects, we collected data on measured (EBV load, growth rate, and energy status), RNAseq batch, ancestry (top two genotypic principal components from GWAS), and other demographic (sex, age) covariates to include in the regression analysis to identify genes differentially expressed by affection status. We will present and discuss results in several main areas: (1) degree of replication of AA transcriptomic results for previous EA findings, (2) pathway and network analyses of differentially expressed genes, (3) gene set enrichment analyses, especially for immune related genes, (4) differences compared to findings for previous EA transcriptomic work, and (5) combined analyses of the current AA and previous EA transcriptomic datasets. This work was supported NIH grant R01MH098059 using samples from the MGS collaboration.