Meta-analysis identifies novel risk loci and yields systematic insights into the biology of male-pattern baldness View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-03-08

AUTHORS

Stefanie Heilmann-Heimbach, Christine Herold, Lara M. Hochfeld, Axel M. Hillmer, Dale R. Nyholt, Julian Hecker, Asif Javed, Elaine G. Y. Chew, Sonali Pechlivanis, Dmitriy Drichel, Xiu Ting Heng, Ricardo C. -H. del Rosario, Heide L. Fier, Ralf Paus, Rico Rueedi, Tessel E. Galesloot, Susanne Moebus, Thomas Anhalt, Shyam Prabhakar, Rui Li, Stavroula Kanoni, George Papanikolaou, Zoltán Kutalik, Panos Deloukas, Michael P. Philpott, Gérard Waeber, Tim D. Spector, Peter Vollenweider, Lambertus A. L. M. Kiemeney, George Dedoussis, J. Brent Richards, Michael Nothnagel, Nicholas G. Martin, Tim Becker, David A. Hinds, Markus M. Nöthen

ABSTRACT

Male-pattern baldness (MPB) is a common and highly heritable trait characterized by androgen-dependent, progressive hair loss from the scalp. Here, we carry out the largest GWAS meta-analysis of MPB to date, comprising 10,846 early-onset cases and 11,672 controls from eight independent cohorts. We identify 63 MPB-associated loci (P<5 × 10-8, METAL) of which 23 have not been reported previously. The 63 loci explain ∼39% of the phenotypic variance in MPB and highlight several plausible candidate genes (FGF5, IRF4, DKK2) and pathways (melatonin signalling, adipogenesis) that are likely to be implicated in the key-pathophysiological features of MPB and may represent promising targets for the development of novel therapeutic options. The data provide molecular evidence that rather than being an isolated trait, MPB shares a substantial biological basis with numerous other human phenotypes and may deserve evaluation as an early prognostic marker, for example, for prostate cancer, sudden cardiac arrest and neurodegenerative disorders. More... »

PAGES

14694

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ncomms14694

    DOI

    http://dx.doi.org/10.1038/ncomms14694

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1084128922

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28272467


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