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. 2019 Jan 1;21(1):71-82.
doi: 10.1093/neuonc/noy135.

Sex-specific gene and pathway modeling of inherited glioma risk

Quinn T Ostrom  1   2   3 , Warren Coleman  4 , William Huang  5 , Joshua B Rubin  6 , Justin D Lathia  7 , Michael E Berens  8 , Gil Speyer  8 , Peter Liao  1 , Margaret R Wrensch  9 , Jeanette E Eckel-Passow  10 , Georgina Armstrong  2 , Terri Rice  9 , John K Wiencke  9 , Lucie S McCoy  9 , Helen M Hansen  9 , Christopher I Amos  11 , Jonine L Bernstein  12 , Elizabeth B Claus  13   14 , Richard S Houlston  15 , Dora Il'yasova  16   17   18 , Robert B Jenkins  19 , Christoffer Johansen  20 , Daniel H Lachance  21 , Rose K Lai  22 , Ryan T Merrell  23 , Sara H Olson  12 , Siegal Sadetzki  24   25   26 , Joellen M Schildkraut  27 , Sanjay Shete, Ulrika Andersson  28 , Preetha Rajaraman  29 , Stephen J Chanock  29   30 , Martha S Linet  29 , Zhaoming Wang  29   30   31 , Meredith Yeager  29   30 ; GliomaScan consortium; Beatrice Melin  28 , Melissa L Bondy  2 , Jill S Barnholtz-Sloan  1
Collaborators, Affiliations

Sex-specific gene and pathway modeling of inherited glioma risk

Quinn T Ostrom et al. Neuro Oncol. .

Abstract

Background: To date, genome-wide association studies (GWAS) have identified 25 risk variants for glioma, explaining 30% of heritable risk. Most histologies occur with significantly higher incidence in males, and this difference is not explained by currently known risk factors. A previous GWAS identified sex-specific glioma risk variants, and this analysis aims to further elucidate risk variation by sex using gene- and pathway-based approaches.

Methods: Results from the Glioma International Case-Control Study were used as a testing set, and results from 3 GWAS were combined via meta-analysis and used as a validation set. Using summary statistics for nominally significant autosomal SNPs (P < 0.01 in a previous meta-analysis) and nominally significant X-chromosome SNPs (P < 0.01), 3 algorithms (Pascal, BimBam, and GATES) were used to generate gene scores, and Pascal was used to generate pathway scores. Results were considered statistically significant in the discovery set when P < 3.3 × 10-6 and in the validation set when P < 0.001 in 2 of 3 algorithms.

Results: Twenty-five genes within 5 regions and 19 genes within 6 regions reached statistical significance in at least 2 of 3 algorithms in males and females, respectively. EGFR was significantly associated with all glioma and glioblastoma in males only and a female-specific association in TERT, all of which remained nominally significant after conditioning on known risk loci. There were nominal associations with the BioCarta telomeres pathway in both males and females.

Conclusions: These results provide additional evidence that there may be differences by sex in genetic risk for glioma. Additional analyses may further elucidate the biological processes through which this risk is conferred.

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Figures

Fig. 1
Fig. 1
Study schematic for (A) generation of discovery and validation summary statistic sets, (B) generation, prioritization, and validation of gene scores, (C) generation, prioritization, and validation of pathway scores.
Fig. 2
Fig. 2
Gene scores for prioritized genes by algorithm, histology, and sex for (A) BPESC1 (3q23), (B) TERT (5p15.33), (C) EGFR (7p11.2), (D) CDKN2B (9p21.3), (E) DNAH2 (17p13.1), and (F) RTEL1-TNFRSF6B (20q13.33).
Fig. 3
Fig. 3
Conditional gene scores for prioritized genes by algorithm, histology, and sex for (A) TERT (5p15.33), (B) EGFR (7p11.2), (C) CDKN2B (9p21.3), (D) DNAH2 (17p13.1), and (E) RTEL1-TNFRSF6B (20q13.33).
Fig. 4
Fig. 4
Gene scores for genes in the BioCarta telomere pathway for all glioma in (A) males and (B) females, and for glioblastoma in (C) males and (D) females.
See this image and copyright information in PMC

References

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