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Genetic Biomarkers for Endometriosis

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Biomarkers for Endometriosis

Abstract

GWAS studies identified seven genomic regions with robust evidence for genome-wide significant association with endometriosis risk. One important question that arises is whether these genetic markers can be used to predict risk of developing endometriosis for individual women. As with most complex diseases, the effect sizes for genetic markers linked to endometriosis risk are small with odds ratios less than 1.3. If we combine information from all seven markers, we explain only 1.85% of the total phenotypic variance on the liability scale (assuming a population prevalence of endometriosis of 8%) with no predictive power for individual risk.

To explore the ability of all common genetic markers to predict endometriosis risk in individuals, we conducted simulations to quantify how useful endometriosis risk prediction is given current parameters. Applying our estimate of heritability (h 2 = 0.26) from all common SNPs and assuming data were available from ~30,000 endometriosis cases, the proportion of variance explained by the risk predictor is still only ~0.08. To improve this prediction would require a far greater sample size. Current data may be useful for population-based stratification into risk categories. This can have applications in some cases such as improved efficiency of screening in breast cancer. In the future, risk prediction for endometriosis might be improved through combining genetic risk scores with clinical data, estimates of environmental effects such as DNA methylation signals, and/or better understanding of disease subtypes.

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Acknowledgments

GWM is an NHMRC Principal Research Fellow (1078399), SHL is an ARC Future Fellow (FT160100229), and the work was supported by NHMRC project grants (1026033, 1049472, 1080157).

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Authors and Affiliations

  1. School of Environmental and Rural Science, The University of New England, Armidale, NSW, 2351, Australia

    Sang Hong Lee

  2. Centre for Neurogenetics and Statistical Genomics, Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia

    Sang Hong Lee

  3. Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA

    Yadav Sapkota

  4. The Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, 4072, Australia

    Jenny Fung & Grant W. Montgomery

Authors
  1. Sang Hong Lee
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  2. Yadav Sapkota
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  3. Jenny Fung
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  4. Grant W. Montgomery
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Corresponding author

Correspondence to Grant W. Montgomery .

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Editors and Affiliations

  1. Department of Development and Regeneration Organ Systems, Group Biomedical Sciences, KU Leuven (University of Leuven), Leuven, Belgium

    Thomas D'Hooghe

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Lee, S.H., Sapkota, Y., Fung, J., Montgomery, G.W. (2017). Genetic Biomarkers for Endometriosis. In: D'Hooghe, T. (eds) Biomarkers for Endometriosis. Springer, Cham. https://doi.org/10.1007/978-3-319-59856-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-59856-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59854-3

  • Online ISBN: 978-3-319-59856-7

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