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Genetic variants associated with mosaic Y chromosome loss highlight cell cycle genes and overlap with cancer susceptibility

Nature Genetics volume 49, pages 674–679 (2017)Cite this article

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Abstract

The Y chromosome is frequently lost in hematopoietic cells, which represents the most common somatic alteration in men. However, the mechanisms that regulate mosaic loss of chromosome Y (mLOY), and its clinical relevance, are unknown. We used genotype-array-intensity data and sequence reads from 85,542 men to identify 19 genomic regions (P < 5 × 10−8) that are associated with mLOY. Cumulatively, these loci also predicted X chromosome loss in women (n = 96,123; P = 4 × 10−6). Additional epigenome-wide methylation analyses using whole blood highlighted 36 differentially methylated sites associated with mLOY. The genes identified converge on aspects of cell proliferation and cell cycle regulation, including DNA synthesis (NPAT), DNA damage response (ATM), mitosis (PMF1, CENPN and MAD1L1) and apoptosis (TP53). We highlight the shared genetic architecture between mLOY and cancer susceptibility, in addition to inferring a causal effect of smoking on mLOY. Collectively, our results demonstrate that genotype-array-intensity data enables a measure of cell cycle efficiency at population scale and identifies genes implicated in aneuploidy, genome instability and cancer susceptibility.

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Figure 1: Estimated X chromosome and Y chromosome loss with age in members of the Icelandic deCODE study.
Figure 2: Association of the 19-SNP mLOY genetic risk score with X chromosome loss in women.
Figure 3: Overview of the involvement of mLOY-associated genes in the cell cycle.

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Acknowledgements

We thank members of the MRC Epidemiology Unit genetics group for useful Friday morning discussions. This research was conducted using the UK Biobank Resource under application number 9905. This work was supported by the UK Medical Research Council (Unit Programme no. MC_UU_12015/1 (N.J.W.) and MC_UU_12015/2 (K.K.O.). Research in S.P.J.'s laboratory is funded by Cancer Research UK (CRUK; programme grant C6/A18796), with Institute core funding provided by CRUK (C6946/A14492) and the Wellcome Trust (WT092096). S.P.J. receives a salary from the University of Cambridge, which is supplemented by CRUK.

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Author notes

  1. Daniel J Wright, Felix R Day, Nicola D Kerrison, Florian Zink, Kari Stefansson and John R B Perry: These authors contributed equally to this work.

Authors and Affiliations

  1. MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK

    Daniel J Wright, Felix R Day, Nicola D Kerrison, Alexia Cardona, Claudia Langenberg, Nicholas J Wareham, Robert A Scott, Ken K Ong & John R B Perry

  2. deCODE Genetics–Amgen, Inc., Reykjavik, Iceland

    Florian Zink, Patrick Sulem, Svanhvit Sigurjonsdottir, Daniel F Gudbjartsson, Agnar Helgason, Unnur Thorsteindottir & Kari Stefansson

  3. Department of Public Health and Primary Care, Center for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK

    Deborah J Thompson

  4. Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK

    J Ross Chapman

  5. Wellcome Trust and Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, UK

    Steve P Jackson

  6. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Steve P Jackson

  7. Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    Unnur Thorsteindottir & Kari Stefansson

  8. Department of Pediatrics, University of Cambridge, Cambridge, UK

    Ken K Ong

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Contributions

D.J.W., F.R.D., N.D.K., F.Z., A.C., P.S., R.A.S. and J.R.B.P. performed the statistical analysis; S.S., D.F.G., A.H., N.D.K., A.C. and F.Z. collected samples, and performed genotyping and phenotyping; C.L., N.J.W., U.T., K.K.O., K.S. and J.R.B.P. were the principal investigators of the individual studies; and D.J.W., F.R.D., N.D.K., P.S., D.J.T., J.R.C., S.P.J., C.L., N.J.W., U.T., K.K.O., K.S. and J.R.B.P. designed the project and interpreted the results. All authors reviewed the original and revised manuscripts.

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Correspondence to John R B Perry.

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Integrated supplementary information

Supplementary Figure 1 Distribution of mean LRR-Y values in male participants from the UK Biobank study.

Supplementary Figure 2 Manhattan plot of genome-wide test statistics for mLRR-Y association.

SNPs were filtered based on a MAF < 1%, and info score < 0.4. Regions that demonstrated possible technical artefacts were removed as detailed in the methods. Signals which retained genome-wide significance after replication are highlighted in blue.

Supplementary Figure 3 QQ plot of genome-wide test statistics for mLRR-Y.

Line of null effect is shown in black

Supplementary Figure 4 Effect of SNPs associated with prostate cancer, as compared to their effect on mLOY.

Each of the SNPs previously identified for prostate cancer is plotted with regard to its effect on mean LRR-Y, including 95% confidence intervals of effect. The colored lines represent estimates from MR based analyses: Red - inverse weighted variance (p=0.349), Blue – Egger’s (p=0.263), Yellow – weighted median (p=0.782), Green (aligned with yellow) – penalised weighted median (p=0.772).

Supplementary Figure 5 Effect of SNPs associated with mLOY as compared to their effect on cancer.

Each of the SNPs identified for mean LRR-Y is plotted with regard to its effect on the any cancer variable, including 95% confidence intervals of effect. The colored lines represent estimates from MR based analyses: Yellow – weighted median (p=0.196), Green (aligned with yellow) – penalised weighted median (p=0.205), Red (aligned with green) - inverse weighted variance (p=0.0576), Blue – Egger’s (p=0.938).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 (PDF 661 kb)

Supplementary Table 1

Effect of the 19 identified mLOY variants on dichotomised low mLRR-Y ranked individuals (bottom 5% vs median 25%) and high ranked mLRR-Y individuals (top 5% vs median 25%). (XLSX 13 kb)

Supplementary Table 2

MAGENTA pathway results from analysis across full genome-wide summary statistics (XLSX 415 kb)

Supplementary Table 3

Gene expression imputation results using the SMR method (XLSX 1288 kb)

Supplementary Table 4

Gene expression imputation results using the TWAS method (XLSX 471 kb)

Supplementary Table 5

Gene expression imputation results using the MetaXcan method (XLSX 1042 kb)

Supplementary Table 6

Methylation CpGs associated with mLRR-Y (XLSX 14 kb)

Supplementary Table 7

meQTL SNPs associated with probes from Supplementary Table 6, with corresponding association statistics for mLRR-Y (XLSX 16 kb)

Supplementary Table 8

Lookup of known prostate cancer associated variants for mLRR-Y (XLSX 21 kb)

Supplementary Table 9

Lookup of identified mLOY variants on all-cause cancer in UK Biobank (XLSX 13 kb)

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Wright, D., Day, F., Kerrison, N. et al. Genetic variants associated with mosaic Y chromosome loss highlight cell cycle genes and overlap with cancer susceptibility. Nat Genet 49, 674–679 (2017). https://doi.org/10.1038/ng.3821

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