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Variants near TERT and TERC influencing telomere length are associated with high-grade glioma risk

Nature Genetics volume 46, pages 731–735 (2014)Cite this article

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Abstract

Glioma, the most common central nervous system cancer in adults, has poor prognosis. Here we identify a new SNP associated with glioma risk, rs1920116 (near TERC), that reached genome-wide significance (Pcombined = 8.3 × 10−9) in a meta-analysis of genome-wide association studies (GWAS) of high-grade glioma and replication data (1,644 cases and 7,736 controls). This region has previously been associated with mean leukocyte telomere length (LTL). We therefore examined the relationship between LTL and both this new risk locus and other previously established risk loci for glioma using data from a recent GWAS of LTL (n = 37,684 individuals)1. Alleles associated with glioma risk near TERC and TERT were strongly associated with longer LTL (P = 5.5 × 10−20 and 4.4 × 10−19, respectively). In contrast, risk-associated alleles near RTEL1 were inconsistently associated with LTL, suggesting the presence of distinct causal alleles. No other risk loci for glioma were associated with LTL. The identification of risk alleles for glioma near TERC and TERT that also associate with telomere length implicates telomerase in gliomagenesis.

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Figure 1: SNP association plots for high-grade glioma risk and mean LTL.
Figure 2: SNP association plots for high-grade glioma risk and mean LTL.

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Acknowledgements

Work at UCSF was supported by the US National Institutes of Health (grants R25CA112355, R01CA52689, P50CA097257, R01CA126831 and R01CA139020), as well as by the National Brain Tumor Foundation, the UCSF Lewis Chair in Brain Tumor Research, the UCSF Robert Magnin Newman chair in Neuro-Oncology and donations from the families and friends of John Berardi, Helen Glaser, Elvera Olsen, Raymond E. Cooper and William Martinusen. Work at the Mayo Clinic was supported by the US National Institutes of Health (grants P50CA108961 and P30CA15083), the National Institute of Neurological Disorders and Stroke (grant RC1NS068222Z), the Bernie and Edith Waterman Foundation, and the Ting Tsung and Wei Fong Chao Family Foundation. Work at the University of Leicester was undertaken under the European Union Framework Programme 7 ENGAGE Project (HEALTH-F4-2007-201413). V.C. and N.J.S. are supported by the British Heart Foundation.

This project was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, US National Institutes of Health, through UCSF Clinical and Translational Science Institute grant UL1RR024131. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the US National Institutes of Health.

The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; by the National Cancer Institute's Surveillance, Epidemiology and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California and contract HHSN261201000034C awarded to the Public Health Institute; and by the Centers for Disease Control and Prevention National Program of Cancer Registries, under agreement U58DP003862-01 awarded to the California Department of Public Health. The ideas and opinions expressed herein are those of the authors, and endorsement by the State of California Department of Public Health, the National Cancer Institute and the Centers for Disease Control and Prevention or their contractors and subcontractors is not intended nor should be inferred.

The results published here are in part based on data generated by TCGA managed by the National Cancer Institute and the National Human Genome Research Institute. Information about TCGA can be found at http://cancergenome.nih.gov/. This study makes use of data generated by WTCCC. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk/. Funding for the project was provided by the Wellcome Trust under awards 076113 and 085475.

Author information

Authors and Affiliations

  1. Division of Neuroepidemiology, Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA

    Kyle M Walsh, Terri Rice, Helen M Hansen, Lucie S McCoy, Belinda S Cabriga, Shichun Zheng, Joseph L Wiemels, Alexander R Pico, John K Wiencke & Margaret R Wrensch

  2. Program in Cancer Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA

    Kyle M Walsh

  3. Department of Cardiovascular Sciences, University of Leicester, Leicester, UK

    Veryan Codd & Nilesh J Samani

  4. National Institute for Health Research Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK

    Veryan Codd & Nilesh J Samani

  5. Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA

    Ivan V Smirnov, Annette M Molinaro, Mitchell S Berger, Susan M Chang & Michael D Prados

  6. Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, Minnesota, USA

    Paul A Decker, Matthew L Kosel, Hugues Sicotte & Jeanette E Eckel-Passow

  7. Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA

    Thomas Kollmeyer & Robert B Jenkins

  8. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA

    Paige M Bracci & Joseph L Wiemels

  9. Department of Pathology, University of California, San Francisco, San Francisco, California, USA

    Melike Pekmezci & Tarik Tihan

  10. Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA

    Joseph L Wiemels, John K Wiencke & Margaret R Wrensch

  11. Department of Bioinformatics, Gladstone Institutes, San Francisco, California, USA

    Alexander R Pico

  12. Department of Neurology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA

    Daniel H Lachance & Brian Patrick O'Neill

  13. Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    Pim van der Harst

  14. Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    Pim van der Harst

Authors
  1. Kyle M Walsh
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  2. Veryan Codd
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  3. Ivan V Smirnov
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  6. Helen M Hansen
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Consortia

ENGAGE Consortium Telomere Group

Contributions

K.M.W., M.R.W. and J.K.W. led the study at UCSF, R.B.J. led the study at the Mayo Clinic, and N.J.S. led the study at the University of Leicester. K.M.W., V.C., R.B.J., M.R.W., M.P. and T.R. contributed to manuscript preparation. Study coordination was the responsibility of T.K. at the Mayo Clinic and T.R. and L.S.M. at UCSF. K.M.W. and V.C. codirected and conducted biostatistics and bioinformatics analyses with additional support from P.A.D., J.E.E.-P., M.L.K., A.M.M., P.M.B., T.R., H.S., A.R.P., I.V.S., P.v.d.H. and the ENGAGE Consortium Telomere Group. Laboratory work was performed by T.K. under the direction of R.B.J. at the Mayo Clinic and by H.M.H., S.Z. and B.S.C. under the direction of J.K.W. and J.L.W. at UCSF. Pathology support was provided by T.T. Subject enrollment or clinical record review was performed or facilitated by M.D.P., S.M.C., M.S.B., B.P.O., D.H.L. and P.v.d.H.

Corresponding author

Correspondence to Kyle M Walsh.

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The authors declare no competing financial interests.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Summary of study design and analysis.

The flowchart details the flow of analyses through the four stages of the study.

Supplementary Figure 2 Cumulative effect of three telomere-associated glioma risk SNPs (rs1920116, TERC; rs2736100, TERT; rs6010620, RTEL1).

(a) Plot of the increasing ORs for high-grade glioma with increasing numbers of risk alleles (range of 0–6). The ORs are relative to the median number of 4 risk alleles. Vertical bars correspond to 95% confidence intervals. Odds ratios increase in a montonic fashion. (b) Distribution of telomere-associated glioma risk alleles in high-grade glioma cases (dark gray bars) and controls (light gray bars).

Supplementary Figure 3 Changes in the magnitude of high-grade glioma risk associated with telomere-associated SNPs across subject age strata in analyses of UCSF AGS cases and controls.

Odds ratios for glioma were calculated in case-control analyses, adjusted for sex. The y axis is represented on a log scale (base 2), ranging from 0.50 to 2.0. Vertical bars correspond to 95% confidence intervals.

Supplementary Figure 4 SNP association plots for high-grade glioma risk (top) and mean leukocyte telomere length (bottom) at 3q26.2, 5p15.33 and 20q13.33.

The strength of linkage disequilibrium between each SNP and the high-grade glioma top hit (purple circle) is indicated by color. Recombination rates, plotted in light blue, are based on 1000 Genomes CEU samples. Black vertical bars in Supplementary Figure 4c mark the location of the RTEL1-PCNA interaction motif (PIP box).

Supplementary Figure 5 Odds ratios near TERC for high-grade glioma risk, plotted with –log10 (P values) for glioma risk and mean leukocyte telomere length.

Glioma odds ratios for SNPs with P values of <0.05 are plotted in black and correspond to the risk associated with each additional copy of the glioma risk allele. Odds ratios range from 1.11 to 1.26. –log10 (P values) for SNPs associated with high-grade glioma in the discovery stage (P < 0.05) appear in green, and –log10 (P values) for SNPs associated with LTL appear in pink. A horizontal black line indicates a –log10 (P value) of 2, corresponding to a P value of 0.01.

Supplementary Figure 6 SNP association plots for high-grade glioma risk at 3q26.2, conditioned on lead glioma SNP rs1920116 (top) and lead LTL SNP rs10936599 (bottom).

The strength of linkage disequilibrium between each SNP and rs1920116 is indicated by color. Recombination rates, plotted in light blue, are based on 1000 Genomes CEU samples.

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Supplementary Note, Supplementary Tables 1–4 and Supplementary Figures 1–6 (PDF 3029 kb)

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Walsh, K., Codd, V., Smirnov, I. et al. Variants near TERT and TERC influencing telomere length are associated with high-grade glioma risk. Nat Genet 46, 731–735 (2014). https://doi.org/10.1038/ng.3004

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