EPID-19. SHARED GENOMIC ARCHITECTURE OF GLIOMA AND NEURO-COGNITIVE AND NEURO-PSYCHIATRIC TRAITS REVEALED BY LD-SCORE REGRESSION. (11th November 2019)

• Record Type: Journal Article
• Authors: Walsh, Kyle; Ostrom, Quinn; Zhang, Chenan; Edelson, Jacob; Shen, Erica; Byun, Jinyoung; Han, Younghun; Amos, Christopher; Bondy, Melissa
• Is Part Of: Neuro-oncology. Volume 21(2019)Supplement 6
• Page Start: vi78
• Page End: vi78
• Publication Date: 2019-11-11
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noz175.319
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: BACKGROUND: Genome-wide analyses estimate glioma heritability at 25%, yet known risk loci account for just one-third of this risk and suggest that sporadic glioma is a highly polygenic disease with hitherto unaccounted for genomic architecture. LD-score regression leverages results from genome-wide association studies (GWAS) and known patterns of linkage disequilibrium (LD) to estimate correlation between the genetic architecture of multiple phenotypes. We applied LD-score regression to identify associations with neuro-cognitive and neuro-psychiatric traits not amenable to study in prior glioma case-control analyses. METHODS: GWAS summary statistics were obtained from the Glioma International Case-Control Consortium (GICC) meta-analysis (Melin, et al. 2017) and for 64 neuro-cognitive and neuro-psychiatric traits primarily from the UK Biobank. Included SNPs had quality scores ≥0.70 and minor allele frequency ≥0.01. Pairwise genetic correlations between traits were estimated using the LDSC package. P-values < 7.8x10 -4 ( i.e. 0.05/64) were considered significant. RESULTS: Significant negative correlations were identified between the genetic architectures of glioma and bipolar disorder (Rg =-0.41, P=1.4x10 -9 ) and schizophrenia (Rg =-0.29, P=7.1x10 -9 ), consistent in both GBM and LGG. Significant positive correlations were identified with measures of educational attainment, including age at educational completion (Rg =0.11, P=2.0x10 -4 ), obtaining a college degree (Rg =0.086, P=4.9x10 -4 ), college attendance (Rg =0.086, P=5.9x10 -4 ), and years of education (Rg =0.081, P=7.7x10 -4 ). These associations were notably stronger with LGG. A number of additional nominal associations were observed, including with anorexia (anti-correlated) and performance on a pair-matching cognitive test (positively correlated). Importantly, LD-score regression did not identify an association between glioma risk in GICC data and Townsend deprivation index in UK Biobank data. CONCLUSIONS: These results implicate a shared genetic basis for glioma and several psychiatric and cognitive traits. Further research is needed to understand these associations and to explore underlying mechanisms, including the mediating effects of neuro-inflammation, developmental differences in neural‒glial cell circuitry, and inter-individual variation in myelination.