NeuroSCORE: A Genome-wide Omics-Based Model to Identify Candidate Disease Genes of the Central Nervous System
Davis, K. W.; Bilancia, C. G.; Martin, M.; Vanzo, R.; Rimmasch, M.; Hom, Y.; Uddin, M.; Serrano, M.
Show abstract
To identify and prioritize candidate disease genes of the central nervous system (CNS) we created the Neurogenetic Systematic Correlation of Omics-Related Evidence (NeuroSCORE). We used five genome-wide metrics highly associated with neurological phenotypes to score 19,598 protein-coding genes. Genes scored one point per metric, resulting in a range of scores from 0-5. Approximately 13,000 genes were then paired with phenotype data from the Online Mendelian Inheritance in Man (OMIM) database. We used logistic regression to determine the odds ratio of each metric and compared genes scoring 1+ to cause a known CNS-related phenotype compared to genes that scored zero. We tested NeuroSCORE using microarray copy number variants (CNVs) in case-control cohorts, mouse model phenotype data, and gene ontology (GO) and pathway analyses. NeuroSCORE identified 8,296 genes scored [≥]1, of which 1,580 are "high scoring" genes (scores [≥]3). High scoring genes are significantly associated with CNS phenotypes (OR=5.5, p<2x10-16), enriched in case CNVs, and enriched in mouse ortholog genes associated with behavioral and nervous system abnormalities. GO and pathway analyses showed high scoring genes were enriched in chromatin remodeling, mRNA splicing, dendrite development, and neuron projection. OMIM has no phenotype for 1,062 high scoring genes (67%). Top scoring genes include ANKRD17, CCAR1, CLASP1, DOCK9, EIF4G2, G3BP2, GRIA1, MAP4K4, MARK2, PCBP2, RNF145, SF1, SYNCRIP, TNPO2, and ZSWIM8. NeuroSCORE identifies and prioritizes CNS-disease candidate genes, many not yet associated with any phenotype in OMIM. These findings can help direct future research and improve molecular diagnostics for individuals with neurological conditions.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Chromosome X-Wide Common Variant Association Study (XWAS) in Autism Spectrum Disorder 96%
- Loss-of-function of the Zinc Finger Homeobox 4 ( ZFHX4 ) gene underlies a neurodevelopmental disorder 95%
- Exome copy number variant detection, analysis and classification in a large cohort of families with undiagnosed rare genetic disease 95%
Similar papers in this journal
- Convergent and distributed effects of the schizophrenia-associated 3q29 deletion on the human neural transcriptome 94%
- Autism spectrum disorder common variants associated with regional lobe volume variations at birth: cross-sectional study in 273 European term neonates in developing Human Connectome Project 94%
- Meta-analysis of the brain transcriptomes of multiple genetic mouse models of schizophrenia highlights dysregulation in striatum and thalamus 93%
Similar papers in this journal
- Association between genes regulating neural pathways for quantitative traits of speech and language disorders 96%
- Comprehensive reanalysis for CNVs in ES data from unsolved rare disease cases results in new diagnoses 95%
- Biallelic truncation variants in ATP9A are associated with a novel autosomal recessive neurodevelopmental disorder 94%
Similar papers in this journal
- Altered gene expression profiles impair the nervous system development in individuals with 15q13.3 microdeletion 96%
- Identification of ultra-rare genetic variants in Pediatric Acute Onset Neuropsychiatric Syndrome (PANS) by exome and whole genome sequencing 95%
- Exome Sequencing in Individuals with Isolated Biliary Atresia 94%
Similar papers in this journal
- Poison exon annotations improve the yield of clinically relevant variants in genomic diagnostic testing 96%
- Rare variants found in clinical gene panels illuminate the genetic and allelic architecture of orofacial clefting 94%
- The Importance of Automation in Genetic Diagnosis: Lessons from Analyzing an Inherited Retinal Degeneration Cohort with the Mendelian Analysis Toolkit (MATK) 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.