NeuroSCORE is a genome-wide omics-based model that identifies candidate disease genes of the central nervous system

Abstract

Abstract To identify candidate disease genes of central nervous system (CNS) phenotypes, we created the Neurogenetic Systematic Correlation of Omics-Related Evidence (NeuroSCORE). We identified five genome-wide metrics highly associated with CNS phenotypes to score 19,601 protein-coding genes. Genes scored one point per metric (range: 0–5), identifying 8298 scored genes (scores ≥ 1) and 1601 “high scoring” genes (scores ≥ 3). Using logistic regression, we determined the odds ratio that genes with a NeuroSCORE from 1 to 5 would be associated with known CNS-related phenotypes compared to genes that scored zero. We tested NeuroSCORE using microarray copy number variants (CNVs) in case–control cohorts and aggregate mouse model data. High scoring genes are associated with CNS phenotypes (OR = 5.5, p < 2e -16), enriched in case CNVs, and mouse ortholog genes that cause behavioral and nervous system abnormalities. We identified 1058 high scoring genes with no disease association in OMIM. Transforming the logistic regression results indicates high scoring genes have an 84–92% chance of being associated with a CNS phenotype. Top scoring genes include GRIA1, MAP4K4, SF1, TNPO2, and ZSWIM8. Finally, we interrogated CNVs in the Clinical Genome Resource, finding the majority of clinically significant CNVs contain high scoring genes. These findings can direct future research and improve molecular diagnostics.