Abstract
The global demand for products that effectively prevent the development of male-pattern baldness (MPB) has drastically increased. However, there is currently no established genetic model for the estimation of MPB risk. We conducted a prediction analysis using single-nucleotide polymorphisms (SNPs) identified from previous GWASs of MPB in a total of 2725 German and Dutch males. A logistic regression model considering the genotypes of 25 SNPs from 12 genomic loci demonstrates that early-onset MPB risk is predictable at an accuracy level of 0.74 when 14 SNPs were included in the model, and measured using the area under the receiver-operating characteristic curves (AUC). Considering age as an additional predictor, the model can predict normal MPB status in middle-aged and elderly individuals at a slightly lower accuracy (AUC 0.69–0.71) when 6–11 SNPs were used. A variance partitioning analysis suggests that 55.8% of early-onset MPB genetic liability can be explained by common autosomal SNPs and 23.3% by X-chromosome SNPs. For normal MPB status in elderly individuals, the proportion of explainable variance is lower (42.4% for autosomal and 9.8% for X-chromosome SNPs). The gap between GWAS findings and the variance partitioning results could be explained by a large body of common DNA variants with small effects that will likely be identified in GWAS of increased sample sizes. Although the accuracy obtained here has not reached a clinically desired level, our model was highly informative for up to 19% of Europeans, thus may assist decision making on early MPB intervention actions and in forensic investigations.
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Acknowledgements
We thank Dr David Gunn for his valuable discussions and useful comments on the manuscript. We thank Pascal Arp; Mila Jhamai; Marijn Verkerk; Lizbeth Herrera; Marjolein Peters, MSc; Carolina Medina-Gomez, MSc; and Fernando Rivadeneira, MD PhD, for their help in creating the GWAS database, and Karol Estrada, PhD; Yurii Aulchenko, PhD; and Carolina Medina-Gomez, MSc, for the creation and analysis of imputed data. We thank Sophie Flohil, Emmilia Dowlatshahi, Robert van der Leest, Leonie Jacobs, Joris Verkouteren, Ella van der Voort, and Shmaila Talib for collecting the phenotype data in the RS. This work was supported in part by the Erasmus MC University Medical Center Rotterdam and funds from the Netherlands Genomics Initiative/Netherlands Organization of Scientific Research (NWO) within the framework of the Netherlands Consortium of Healthy Ageing (NCHA).
The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS data sets are supported by the Netherlands Organisation of Scientific Research NWO Investments (no. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO), and the Netherlands Consortium for Healthy Aging (NCHA), project no. 050-060-810. FL is supported by Chinese Thousand Talent Program for Distinguished Young Scholars and MAH is supported by Unilever. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam; Netherlands Organization for the Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science; the Ministry for Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists.
ERF Study as a part of EUROSPAN (European Special Populations Research Network) was supported by the European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and also received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme ‘Quality of Life and Management of the Living Resources’ of 5th Framework Programme (no. QLG2-CT-2002-01254) as well as the FP7 project EUROHEADPAIN (no. 602633). High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043).
The BONN Study is supported by Heinz Nixdorf Foundation, the German Ministry of Education and Science and the German Research Council (D Glass; Project SI 236/8-1, SI236/9-1, ER 155/6-1); German Research Council (D Glass; FOR 423); and the Life and Brain GmbH (Bonn, Germany; project grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Liu, F., Hamer, M., Heilmann, S. et al. Prediction of male-pattern baldness from genotypes. Eur J Hum Genet 24, 895–902 (2016). https://doi.org/10.1038/ejhg.2015.220
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DOI: https://doi.org/10.1038/ejhg.2015.220
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