Prediction of male-pattern baldness from genotypes.
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.
Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.|Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
Fan
0000-0001-9241-8161
F
Liu
Department of Dermatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Merel A
MA
Hamer
Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany.|Institute of Human Genetics, University of Bonn, Bonn, Germany.
Stefanie
S
Heilmann
German Center for Neurodegenerative Disease (DZNE), Bonn, Germany.
Christine
C
Herold
Institute of Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany.
Susanne
S
Moebus
Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Albert
A
Hofman
Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.|Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
André G
AG
Uitterlinden
Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany.|Institute of Human Genetics, University of Bonn, Bonn, Germany.
Markus M
MM
Nöthen
Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Cornelia M
CM
van Duijn
Department of Dermatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Tamar Ec
TE
Nijsten
Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Manfred
M
Kayser
D000328:Adult; D000368:Aged; D000505:Alopecia / Q000235:genetics*; D016022:Case-Control Studies; D056426:Genetic Loci; D005838:Genotype; D006801:Humans; D008297:Male; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide