Prediction of male-pattern baldness from genotypes

Fan Liu, Merel Hamer, S Heilmann, C Herold, S Moebus, Bert Hofman, André Uitterlinden, MM Nothen, Cornelia Duijn, Tamar Nijsten, Manfred Kayser

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)

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.
Original languageUndefined/Unknown
Pages (from-to)895-902
Number of pages8
JournalEuropean Journal of Human Genetics
Volume24
Issue number6
DOIs
Publication statusPublished - 2016

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