English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 &lt; 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.

EPID-19. SHARED GENOMIC ARCHITECTURE OF GLIOMA AND NEURO-COGNITIVE AND NEURO-PSYCHIATRIC TRAITS REVEALED BY LD-SCORE REGRESSION