Genetic basis of male pattern baldness.

Common pattern baldness (androgenetic alopecia) is the most common form of hair loss in humans. In Caucasians, normal male hair loss, commonly known as ‘‘male pattern baldness’’ (MPB; MIM 109200), is noticeable in about 20% of men aged 20, and increases steadily with age, so that a male in his 90s has a 90% chance of having some degree of MPB. In addition to being among the most common natural conditions that make men self-conscious, recent studies indicate associations of MPB with: (1) benign prostatic hyperplasia (MIM 600082; odds ratio (OR)1⁄4 3.23; 95% con¢dence interval (CI): 1.81^5.79) (Hawk et al, 2000); (2) coronary heart disease (relative risk1⁄41.36; 95% CI: 1.11^1.67) (Lotufo et al, 2000); (3) hyperinsulinemia (OR1⁄41.91; 95% CI: 1.02^3.56); and (4) insulin-resistance-associated disorders, such as obesity (MIM 601665; OR1⁄4 2.90; 95% CI: 1.76^4.79), hypertension (MIM 145500; OR1⁄4 2.09; 95% CI: 1.14^3.82), and dyslipidemia (OR1⁄4 4.45; 95% CI: 1.74^11.34) (Matilainen et al, 2000). MBP is also a risk factor for clinical prostate cancer (MIM 176807; relative risk1⁄41.50; 95% CI: 1.12^2.00) (Oh et al, 1998). Although it is a widely accepted opinion that common baldness is an autosomal dominant phenotype in men and an autosomal recessive phenotype in women, or indeed that baldness is genetically in£uenced, it is based on surprisingly little empirical data. Here we grade MBP, in 476 monozygotic (MZ) and 408 dizygotic (DZ) male twin pairs aged between 25 and 36 y and ¢nd a heritability of 0.81 (95% CI: 0.77^0.85), thus con¢rming that genetic e¡ects play a major part in the progression of common hair loss. Measures of hair loss were obtained in the course of an extensive semistructured telephone interview with respondent booklet, designed to assess physical, psychologic, and social manifestations of alcoholism and related disorders, conducted with 6265 twins born 1964 to 1971 from the volunteer-based Australian Twin Registry. All males (45% of the sample) were asked to rate their degree of hair loss, if any, using the Hamilton^Norwood Baldness scale (Norwood, 1975) (a standard classi¢cation scheme shown to have good test^retest reliability) (Hamilton, 1951; Norwood, 1975), which was printed in the respondent booklet (Fig 1).This data collection scheme was validated in a study by Ellis et al (1998), which compared participant self-assessment hair loss against that determined by an independent trained observer in their research clinic. Speci¢cally, the self-assessed rating of score I in nine subjects was concurred by the trained observer in all but one individual who received a score of II (p1⁄4 0.317, Wilcoxon matched pairs signed rank test), whereas no discrepancies with observer’s scores were detected in ¢ve individuals with selfassessed scores ranging from III toVII (Ellis et al, 1998). Data collected from 476 MZ and 408 DZ male pairs, plus 143 MZ and 154 DZ male individual twins (mean ages for the MZ and DZ twins were 30.3 and 30.5 y, respectively) were analyzed using structural equation modeling, to estimate parameters of a model that include additive genetic e¡ects (A), nonadditive genetic e¡ects (i.e., dominance or epistasis) (D), shared or family environment (C), and random or unique environment (E) (Neale and Cardon, 1992). In addition to the 12 Hamilton^Norwood categories, scoring individuals who answered ‘‘no’’ to the question ‘‘have you experienced hair loss?’’, as zero, resulted in a 13-point scale. A major goal of the genetic analysis was to test the multiple threshold model (Reich et al, 1972; Kendler, 1993), which posits that di¡erent types of hair loss re£ect di¡erent levels of severity on a single dimension, rather than distinct etiologies. These thresholds can be regarded as the z-value of the normal distribution that divides the area under the curve in such a way that it gives the right proportion of individuals in each (hair loss) group, thus re£ecting the prevalence of each group (Neale and Cardon, 1992). For each of the two zygosity groups, the ¢t of a multiple threshold model was tested by calculating the polychoric correlation for the Hamilton^Norwood hair loss gradings, using POLYCORR (http://ourworld.compuserve.com/homepages/ jsuebersax/xpc.htm) or PRELIS 2.30 ( J ̨reskog and S ̨rbom, 1999). The polychoric correlation, also termed the ‘‘correlation of liability’’, assumes that underlying the observed polychotomous distribution of hair loss status there exists a continuous, normally distributed latent liability (Kendler, 1993). A w goodness-of-¢t test is used to test whether the multiple threshold model provides a good ¢t to the observed data. Calculation of 95% CI for the polychoric correlations, the comparison of threshold values within twin pairs and across zygosity groups, and genetic model ¢tting by maximum likelihood univariate analysis of raw data were performed using the Mx program (Neale et al, 1999). Multiple threshold model tests performed on the 13 categories, assuming equal thresholds for twin 1 and twin 2, indicated no signi¢cant departure from normality in either MZ (w1551⁄4117.94, p1⁄4 0.99) or DZ twins (w1551⁄4118.47, p1⁄4 0.99), supporting a single liability dimension model of hair loss. As contingency tables using all 13 categories may be too sparse to yield a meaningful test of the multiple threshold model, however (e.g., the w statistic may not be asymptotically distributed), the MZ and DZ data were combined and the 13 score categories were collapsed into the following eight groups: group 1 (0, I, II, IIa; representing nonbaldness); group 2 (III); group 3 (IIIa); group 4 (IIIv, IV); group 5 (IVa); group 6 (V); group 7 (Va), and group 8 (VI, VII). Groups 2 to 8 represent signi¢cant cosmetic hair loss (Norwood, 1975), while maximizing counts for vertex and recessive hair loss. Multiple threshold model tests performed on both the full 8 8 table and after combining frequencies in the two o¡-diagonal quadrants, also indicated no signi¢cant departure from normality (w481⁄4 55.47, p1⁄4 0.21 and w 2 181⁄419.58, p1⁄4 0.36, respectively).These results strongly support a single liability dimension model of hair loss, with frontal recession not etiologically distinct from vertex balding. Subsequently, a single liability dimension-threshold model was applied to our hair loss data, using the full distribution of ordered hair loss scores (0^I^II^IIa^III^IIIa^IIIv^IV^IVa^V^Va^VI^VII) as an ordered sequence re£ecting the severity of hair loss (see Address correspondence and reprint requests to: Dr Dale R. Nyholt, Queensland Institute of Medical Research, Post O⁄ce Royal Brisbane Hospital, Brisbane QLD 4029, Australia. Email: [email protected] Electronic Database Information: accession number andURL for data in this article are as follows: Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/(for MPB (MIM 109200), benign prostatic hyperplasia (MIM 600082), obesity (MIM 601665), hypertension (MIM 145500), and prostate cancer (MIM 176807)). Manuscript received July 14, 2003; accepted for publication July 28, 2003