BACKGROUND: Male pattern baldness (androgenetic alopecia, AGA) is a highly heritable trait and the most common form of hair loss in humans. Eight genome-wide significant risk loci for AGA have been identified. OBJECTIVES: To determine whether a polygenic component contributes to the genetic risk for AGA. METHODS: This study used a German case-control sample for AGA, which comprised 581 severely affected patients and 617 controls, to determine the contribution of polygenic variance to AGA risk. The sample was divided evenly into discovery and test samples. An additive polygenic risk score was calculated from risk alleles with increasingly liberal P-values in the discovery dataset, which was then used to test for the enrichment of AGA risk score alleles in the independent test samples. RESULTS: The polygenic score analysis provided significant evidence for a polygenic contribution to AGA where the amount of variance explained was 1·4-4·5%. CONCLUSION: This study provides evidence for the specific contribution of a polygenic component to the overall heritable risk for AGA. To some degree, the polygenic architecture of AGA might reflect the complexity of the biological pathways involved. Further analyses and strategies that complement conventional genome-wide association studies are needed to identify these factors. These may include pathway-based analyses, the analysis of functional candidate genes and tests for epistatic effects with known loci.
BACKGROUND: Male pattern baldness (androgenetic alopecia, AGA) is a highly heritable trait and the most common form of hair loss in humans. Eight genome-wide significant risk loci for AGA have been identified. OBJECTIVES: To determine whether a polygenic component contributes to the genetic risk for AGA. METHODS: This study used a German case-control sample for AGA, which comprised 581 severely affected patients and 617 controls, to determine the contribution of polygenic variance to AGA risk. The sample was divided evenly into discovery and test samples. An additive polygenic risk score was calculated from risk alleles with increasingly liberal P-values in the discovery dataset, which was then used to test for the enrichment of AGA risk score alleles in the independent test samples. RESULTS: The polygenic score analysis provided significant evidence for a polygenic contribution to AGA where the amount of variance explained was 1·4-4·5%. CONCLUSION: This study provides evidence for the specific contribution of a polygenic component to the overall heritable risk for AGA. To some degree, the polygenic architecture of AGA might reflect the complexity of the biological pathways involved. Further analyses and strategies that complement conventional genome-wide association studies are needed to identify these factors. These may include pathway-based analyses, the analysis of functional candidate genes and tests for epistatic effects with known loci.
Authors: Lizeth Martinez-Jacobo; Claudia I Ancer-Arellano; Rocio Ortiz-Lopez; Mauricio Salinas-Santander; Cesar Daniel Villarreal-Villarreal; Jesus Ancer-Rodriguez; Bianka Camacho-Zamora; Viviana Zomosa-Signoret; Carlos E Medina-De la Garza; Jorge Ocampo-Candiani; Augusto Rojas-Martinez Journal: Skin Appendage Disord Date: 2017-12-07
Authors: Valentina Escott-Price; Rebecca Sims; Christian Bannister; Denise Harold; Maria Vronskaya; Elisa Majounie; Nandini Badarinarayan; Kevin Morgan; Peter Passmore; Clive Holmes; John Powell; Carol Brayne; Michael Gill; Simon Mead; Alison Goate; Carlos Cruchaga; Jean-Charles Lambert; Cornelia van Duijn; Wolfgang Maier; Alfredo Ramirez; Peter Holmans; Lesley Jones; John Hardy; Sudha Seshadri; Gerard D Schellenberg; Philippe Amouyel; Julie Williams Journal: Brain Date: 2015-10-21 Impact factor: 13.501
Authors: Valentina Escott-Price; Mike A Nalls; Huw R Morris; Steven Lubbe; Alexis Brice; Thomas Gasser; Peter Heutink; Nicholas W Wood; John Hardy; Andrew B Singleton; Nigel M Williams Journal: Ann Neurol Date: 2015-03-13 Impact factor: 10.422
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 Journal: Nat Commun Date: 2017-03-08 Impact factor: 14.919
Authors: Saskia P Hagenaars; W David Hill; Sarah E Harris; Stuart J Ritchie; Gail Davies; David C Liewald; Catharine R Gale; David J Porteous; Ian J Deary; Riccardo E Marioni Journal: PLoS Genet Date: 2017-02-14 Impact factor: 5.917