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

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.

GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk

“…Compared with the recent GWA study by Heilmann et al 9 describing 63 loci, we were unable to replicate 17 at genome-wide significance, while the remaining 46 can be summarised into 41 of ours due to the different locus definition. We were thus able to discover and replicate 30 novel loci, almost doubling the number known.…”

“…SNPs were then divided into 71 independent loci (as defined in “Methods” and Table 1 ), hereafter referred to using numbers 1–71. Of the 71 loci, 30 were newly identified in this study and 41 loci had previously been described and replicated in association studies 9 , 14 . (Fig.…”

“…Despite MPB’s high heritability (~80%), after the first identification of the AR/EDA2R locus on the X chromosome 4 , 5 it took several years to identify additional loci, bringing the overall number to 12 6 – 8 . Recently, a larger study including 20,000 people has greatly increased the number of loci although independent replication was not demonstrated 9 . Studies investigating the genetic correlations of MBP with other traits/diseases have been inconclusive.…”

Update on the predictability of tall stature from DNA markers in Europeans

“…So far, the most successful FDP examples are restricted to human pigmentation traits, which is explained by the presence of major gene effects, so that statistical models consisting of a limited number of SNPs can provide highly accurate prediction results [2][3][4]. Because SNPs with larger effects are easier to identify in gene mapping studies, and prediction tools based on a limited number of SNPs can easily be developed, FDP systems for eye, hair and skin colour prediction from trace DNA have already been developed and forensically validated [5,6] Widening appearance DNA prediction beyond pigmentation traits is generally troubled by the absence of major gene effects in non-pigmentation appearance traits, thus requiring a much larger number of DNA predictors due to their small phenotypic effects [7][8][9]. Recent progress illustrated, as is theoretically expected, that when genome-wide association studies (GWASs) based on increased sample size are applied for appearance traits, larger numbers of genome-wide significant SNPs with small effects are found, such as shown for hair structure [7], hairloss in men [8,10], hair colour [11], and skin colour [12].…”

“…Because SNPs with larger effects are easier to identify in gene mapping studies, and prediction tools based on a limited number of SNPs can easily be developed, FDP systems for eye, hair and skin colour prediction from trace DNA have already been developed and forensically validated [5,6] Widening appearance DNA prediction beyond pigmentation traits is generally troubled by the absence of major gene effects in non-pigmentation appearance traits, thus requiring a much larger number of DNA predictors due to their small phenotypic effects [7][8][9]. Recent progress illustrated, as is theoretically expected, that when genome-wide association studies (GWASs) based on increased sample size are applied for appearance traits, larger numbers of genome-wide significant SNPs with small effects are found, such as shown for hair structure [7], hairloss in men [8,10], hair colour [11], and skin colour [12]. Moreover, it has been demonstrated recently for hair structure [13], hairloss in men [10], and hair colour [11] that when the enlarged numbers of identified SNPs are applied in prediction studies, they provide increased prediction accuracies compared to earlier models based on fewer numbers of SNPs [14,15].…”