Genotype-based gene signature of glioma risk.
BACKGROUND
Glioma accounts for 80% of malignant brain tumors, but its etiologic determinants remain elusive. Despite genetic susceptibility loci identified by genome-wide association study (GWAS), the agnostic approach leaves open the possibility that other susceptibility genes remain to be discovered. Here we conduct a gene-centric integrative GWAS (iGWAS) of glioma risk that combines transcriptomics and genetics.


METHODS
We synthesized a brain transcriptomics dataset (n = 354), a GWAS dataset (n = 4203), and an advanced glioma tumor transcriptomic dataset (n = 483) to conduct an iGWAS. Using the expression quantitative trait loci (eQTL) dataset, we built models to predict gene expression for the GWAS data, based on eQTL genotypes. With the predicted gene expression, iGWAS analyses were performed using a novel statistical method. Gene signature risk score was constructed using a penalized logistic regression model.


RESULTS
A total of 30527 transcripts were analyzed using the iGWAS approach. Four novel glioma susceptibility genes were identified with internal and external validation, including DRD5 (P = 3.0 × 10-79), WDR1 (P = 8.4 × 10-77), NOMO1 (P = 1.3 × 10-25), and PDXDC1 (P = 8.3 × 10-24). The genotype-predicted transcription pattern between cases and controls is consistent with that between tumor and its matched normal tissue. The genotype-based 4-gene signature improved the classification between glioma cases and controls based on age, gender, and population stratification, with area under the receiver operating characteristic curve increasing from 0.77 to 0.85 (P = 8.1 × 10-23).


CONCLUSIONS
A new genotype-based gene signature of glioma was identified using a novel iGWAS approach, which integrates multiplatform genomic data as well as different genetic association studies.
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan; Department of Epidemiology; Department of Biostatistics, Brown University, Providence, Rhode Island; Department of Public Health and Community Medicine, Tufts University, Boston, Massachusetts.
Yen-Tsung
YT
Huang
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan; Department of Epidemiology; Department of Biostatistics, Brown University, Providence, Rhode Island; Department of Public Health and Community Medicine, Tufts University, Boston, Massachusetts.
Yi
Y
Zhang
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan; Department of Epidemiology; Department of Biostatistics, Brown University, Providence, Rhode Island; Department of Public Health and Community Medicine, Tufts University, Boston, Massachusetts.
Zhijin
Z
Wu
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan; Department of Epidemiology; Department of Biostatistics, Brown University, Providence, Rhode Island; Department of Public Health and Community Medicine, Tufts University, Boston, Massachusetts.
Dominique S
DS
Michaud
D001932:Brain Neoplasms / Q000378:metabolism*; D016208:Databases, Factual; D005260:Female; D015870:Gene Expression; D020869:Gene Expression Profiling; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics; D005838:Genotype; D005910:Glioma / Q000235:genetics* / Q000378:metabolism*; D006801:Humans; D008297:Male; D040641:Quantitative Trait Loci; D012372:ROC Curve; D012307:Risk Factors
expression quantitative trait loci; gene-signature; genome-wide association study; glioma; integrative genomics